Podcasts about extrapolating

On June 7, 2025, Oar the Rainbow will be one of just five teams losing sight of shore for an unsupported, human-powered, 2800-mile row from Monterey, CA to Kauai, HI. The team, – including the first out transgender person to row across the Pacific – are about two years into their planning and training.  Members Taylan Stulting, Julie Warren, and Courtney Farber have inclusion, joy, and kindness at the heart of the team, with the belief that representation in sports matters, while pushing the limits of endurance. We talk about the love of rowing, having something to prove, what to expect out there, and fundraising for Athlete Ally and Doctors Without Borders. Stick around for a bonus Q&A with ocean rower, author, and adventurer Jordan Hanssen.   QUICK LOOK 00:00 - Podcast Series Introduction 01:45 - Episode Intro: Rachel and Tara's take on taking on an ocean 08:57 - The Huddle: A quick get to know you  10:39 - Rowing week on a scale of 1-10 11:26 - The Hot Seat Q&A 13:58 - Rowing origin stories: For Courtney the stars aligned in her 30s; Julie got handed a flier as a college freshman; Taylan sought out a college LTR program 19:20 - Most recent training/practical session together 21:09 - The dream of rowing an ocean 26:05 - How Oar the Rainbow came to be 28:40 - Commitment and family sacrifices 30:30 - About the World's Toughest Row 31:20 - The boat named Emma 32:30 - Training 33:37 - Extrapolating personal skills to make the teamwork work 38:03 - The “why”  42:42 - Charities and support 44:14 - Intro to Q&A with ocean rower, author, and adventurer Jordan Hanssen 44:55 -  What is so exciting about rowing on the ocean?  45:56 -  What piece of equipment or personal gear do you wish that you had with you?  48:25 -  How did you feel hitting the halfway point and in the days following that milestone?  49:44 -  What was the transition back to life on land like for you?  51:38 -  How to plan for something unexpected? 53:10 -  Lovely nuggets 56:10 - Steady State Network news and notes . To see photos of Oar the Rainbow, and get links to the people, clubs, and events mentioned in this episode, check out the show notes on our website. . This episode was made possible in part by Breakwater Realty, RowSource, and our Patrons. . Steady State Podcast is written, produced, hosted, and edited by Rachel Freedman and Tara Morgan. Tara provides additional audio engineering and is our sponsor and donor coordinator. Rachel manages the website, social media, and e-newsletter. Our theme music is by Jonas Hipper. . SHOP SSN GEAR: www.steadystatenetwork.com/shop SIGN UP FOR THE SSN NEWSLETTER: www.steadystatenetwork.com/newsletter MAKE A DIFFERENCE: www.steadystatenetwork.com/support  Check out more Steady State Network here:  FB - /SteadyStateNetwork IG - @SteadyStateNetwork FB - /AllieswithOars IG - @AllieswithOars BLUESKY - steadystatenetwork.bsky.social Connect on FB and IG with the hosts: Rachel Freedman - @RowSource Tara Morgan - @CmonBarber  

Summary: We propose measuring AI performance in terms of the length of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under five years, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks. The length of tasks (measured by how long they take human professionals) that generalist frontier model agents can complete autonomously with 50% reliability has been doubling approximately every 7 months for the last 6 years. The shaded region represents 95% CI calculated by hierarchical bootstrap over task families, tasks, and task attempts. Full paper | Github repo We think that forecasting the capabilities of future AI systems is important for understanding and preparing for the impact of [...] ---Outline:(08:58) Conclusion(09:59) Want to contribute?--- First published: March 19th, 2025 Source: https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks --- Narrated by TYPE III AUDIO. ---Images from the article:

This is a link post. Summary: We propose measuring AI performance in terms of the length of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks. Full paper | Github repo --- First published: March 19th, 2025 Source: https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks Linkpost URL:https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

✔️ Sources: ► https://x.com/dennis_porter_/status/1887648455604248666?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/cointelegraph/status/1881922238288736493?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/bitcoinmagazine/status/1881470037963518440?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/swan/status/1881486851086938280?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/dennis_porter_/status/1887634364525387944?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://le.utah.gov/~2025/bills/static/HB0230.htmlTimestamps:00:00 - Intro 00:44 - Utah moves forward with strategic digital asset reserve bill 01:18 - What the actual bill says in regards to the digital asset reserve02:52 - Extrapolating hopium 03:26 - Never forget in 2017 the institutions are coming!.... where? #Bitcoin #crypto #cryptocurrency #dailybitcoinnews The information provided by Pleb Underground ("we," "us," or "our") on Youtube.com (the "Site") our show is for general informational purposes only. All information on the show is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the Site. UNDER NO CIRCUMSTANCE SHALL WE HAVE ANY LIABILITY TO YOU FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF THE SHOW OR RELIANCE ON ANY INFORMATION PROVIDED ON THE SHOW. YOUR USE OF THE SHOW AND YOUR RELIANCE ON ANY INFORMATION ON THE SHOW IS SOLELY AT YOUR OWN RISK.

View all cards mentioned in this episode Zach Barash joins Andy and Anthony for the first episode in a series where he shares his game design expertise. In this first episode, the three talk about the first principles of game design, including the iteration loop most game designers follow (and similar processes in other fields!). Starting with ideation, then forming a hypothesis, finally prototyping and playtesting, designers feed the results they learn back into the beginning of the process. They talk about how cube designers can learn from the process, adopting the role and mindset of a game designer. Zach works at Wizards of the Coast on the Arena team, designing many of the events available — his opinions on the show are his own. We're excited to have him join us for four more episodes in the near future. Discussed in this episode: The Power Broker Kingdom Death Drawing Live: Zach's Column for Hipsters of the Coast Spaced Penguin Playthrough on YouTube The Cascade Cube The Turbo Cube Reading Rainbow Cube Andrew Elebogen Tween about Extrapolating from Too Small Sample Sizes The Beginning of Infinity The Modern Darlings Cube The Museum of Modern Cube Linguistic Relativity Playing Oppression Andy, Anthony, Parker, and Zach on BlueSky Check us out on Twitch and YouTube for paper Cube gameplay. You can find the hosts' Cubes on Cube Cobra: Andy's “Bun Magic” Cube Anthony's “Regular” Cube You can find both your hosts in the MTG Cube Talk Discord. Send in questions to the show at mail@luckypaper.co or our p.o. box: Lucky Paper PO Box 4855 Baltimore, MD 21211 If you'd like to show your support for the show, please leave us a review on iTunes or wherever you listen. Musical production by DJ James Nasty. Timestamps 0:00 - Intro 10:02 - Game Design with Training Wheels 12:43 - Anthony's History with Game Design 19:21 - The Basic Principles of Game Design: The Iteration Loop 33:56 - Balance is Overrated 47:32 - How does one go about uncovering truths about their cube? 59:11 - What does a cube designer mean when they say they want ‘balance'?

Market Valuations Deep Dive, Extrapolating the Stats, and Prudence in September This week's show kicks off with a rapid-fire review of last week's numbers from Wall Street so let's jump right in. The Dow was up 0.6%, the S&P 500 was up 0.6%, and the NASDAQ - which has outperformed both the Dow and the S&P this quarter - was up 1.0%. YTD we see the Dow up 12.3%, the S&P 500 up 20.3%, and the NASDAQ up 20.7%. It's been a September to remember, since this time of year doesn't typically give us a positive month, especially for the Dow. The Money Wise guys admit their surprise and discuss which Fed moves may be helping the markets. Will this rally last in the long term? We simply don't know yet. However, we seem to be at very high valuations these days, and the Money Wise guys practiced prudence in the month of September. We're at High Market Valuations, Folks (Or Are We?) Let's discuss the market valuations aspect of what we've been seeing. We've been running above the 5- and 10-year averages from a historic perspective, and being overvalued can make many investors nervous. However, let's dig into current market valuations a bit deeper. Extrapolating the statistics and removing the Magnificent 7 from the market-cap weighted S&P 500, we see that the market is nowhere near overvalued. In fact, market valuations are below the 5- and 10-year averages. So, we continue to see how the Magnificent 7 is skewing the market valuations of the S&P when you look at valuation in totality. In the second hour today, the Money Wise guys discuss 401(k) rollovers. You don't want to miss the details! Tune in for the full discussion on your favorite podcast provider or at davidsoncap.com, where you can also learn more about the Money Wise guys or take advantage of a portfolio review and analysis with Davidson Capital Management.

Interview with Alan Wancier, CFO of Mineros SAOur previous interview: https://www.cruxinvestor.com/posts/mineros-sa-tsxmsa-230k-ozyr-gold-producer-with-12-dividend-yield-5500Recording date: 17th September 2024Mineros S.A. presents a unique investment opportunity in the gold mining sector, combining sustainable practices, strong financial performance, and an attractive dividend yield. With operations in Colombia and Nicaragua, the company produces between 210,000 and 230,000 ounces of gold annually, employing innovative and environmentally friendly mining techniques.In Colombia, Mineros operates one of the largest alluvial gold mines in the world, producing 80,000 to 90,000 ounces of gold per year. The company's approach to mining is notably eco-friendly, using only water and gravity for extraction without chemicals or cyanide. Powered by its own hydroelectric plant, the operation boasts a minimal environmental footprint. As mining progresses, Mineros systematically rehabilitates mined areas, further demonstrating its commitment to sustainability.The Nicaraguan operations showcase a different but equally innovative approach. Here, Mineros produces about 130,000 ounces of gold annually, with 90,000 ounces sourced from artisanal miners. This unique model supports approximately 6,000 local miners, paying them 40-45% of the spot gold price. By integrating artisanal mining into its business model, Mineros has created a socially responsible operation that benefits the local community while maintaining profitability.Financially, Mineros demonstrates robust performance. In the second quarter, the company reported $43 million in net income and $90 million in EBITDA. Extrapolating these figures suggests potential full-year EBITDA of $170-180 million. Despite this strong financial position, Mineros appears undervalued with a market capitalization of around 300 million Canadian dollars.One of Mineros' most attractive features for income-focused investors is its impressive dividend history. The company has paid dividends for 40 out of the last 42 years, currently offering a yield of 12-13%. This high yield is attributed to the company's low valuation rather than an unsustainable payout ratio, suggesting potential for capital appreciation alongside the income stream.Looking ahead, Mineros is actively pursuing growth opportunities, both organically and through acquisitions. The company is exploring an underground mine project in Nicaragua and is open to expanding into new geographies. Management believes their strength in obtaining and maintaining social licenses to operate in challenging jurisdictions provides a competitive advantage in pursuing these growth opportunities.The macroeconomic environment appears favorable for gold producers like Mineros. With geopolitical tensions and expectations of declining interest rates worldwide, the outlook for gold prices remains positive. This backdrop, combined with the perceived undervaluation of gold companies relative to gold prices, creates potential for industry consolidation through mergers and acquisitions.However, investors should consider potential risks, including political and regulatory challenges in Nicaragua and Colombia, gold price volatility, and operational risks associated with artisanal mining. Additionally, while Mineros emphasizes its environmentally friendly practices, ongoing monitoring of its ESG performance is advisable.In conclusion, Mineros S.A. offers a compelling proposition for investors seeking exposure to gold with a focus on sustainability and income. Its unique operational model, strong financials, attractive dividend yield, and growth potential make it an intriguing option for those looking to diversify their portfolio with a socially responsible gold mining stock.View Mineros S.A.: https://www.cruxinvestor.com/companies/mineros-saSign up for Crux Investor: https://cruxinvestor.com

Episode 206 There's no denying that ChatGPT and other GenerativeAI's do  amazing things. Extrapolating how far they've come in 3 years, many can get carried away with thinking GenerativeAI will lead to machines reaching General and even Super Intelligence. We're impressed by how clever they sound, and we're tempted to believe that they'll chew through problems just like the most expert humans do.  But according to many AI experts, this isn't what's going to happen.   The difference between what GenerativeAI can do and what humans can do is actually quite stark. Everything that it gives you has to be proofed and fact-checked.  The reason why is embedded in how they work. It uses a LLM to crawl the vast repository of human writing and multimedia on the web. It gobbles them up and chops them all up until they're word salad. When you give it a prompt, it measures what words it's usually seen accompanying your words, then spits back what usually comes next in those sequences.  The output IS very impressive, so impressive that when one of these was being tested in 2022 by a Google Engineer with a Masters in Computer Science named Blake Lemoine, became convinced that he was talking with an intelligence that he characterized as having sentience. He spoke to Newsweek about it, saying:   “During my conversations with the chatbot, some of which I published on my blog, I came to the conclusion that the AI could be sentient due to the emotions that it expressed reliably and in the right context. It wasn't just spouting words.”  All the same, GenerativeAI shouldn't be confused with what humans do. Take a published scientific article written by a human. How they would have started is not by hammering their keyboard until all the words came out, they likely started by asking a “what if”, building a hypothesis that makes inferences about something,  and they would have chained this together with reasoning by  others, leading to experimentation, which proved/disproved the original thought. The output of all that is what's written in the article. Although GenerativeAI seems smart, you would too if you skipped all the cognitive steps that had happened prior to the finished work. This doesn't mean General Artificial Intelligence is doomed. It means there's more than one branch of AI - each is good at solving different kinds of problems. One branch called Causal AI doesn't just look for patterns, but instead figures out what causes things to happen  by building a model of something in the real world. That  distinguishes it from GenerativeAI, and it's what enables this type of AI to  recommend decisions that rival the smartest humans. The types of decisions extend into business areas like marketing, making things run more efficiently, and delivering more value and ROI. My guest is the Global Head of AI at (EY) Ernst & Young, having also been an analytics executive at Gartner and CSL Behring and graduating from DePaul with an MBA.  He has written five  books. His 2024 book is about the branch of AI technology we don't hear very much about, Causal AI. So let's go to Chicago now to speak with John Thompson.   Chapter Timestamps 0:00:00 Intro 00:04:36 Welcome John 00:09:05 drawbacks with current Generative AI 00:16:09 problems causal AI is a good fit for 00:22:47 Way Generative AI can help with causal 00:26:50 PSA 00:28:08 How DAGs help in modeling 00:38:36 what is Causal Discovery 00:47:52 contacting John; checking out his books   Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.  

Original by Leopold Aschenbrenner, this summary is not commissioned or endorsed by him. Short Summary Extrapolating existing trends in compute, spending, algorithmic progress, and energy needs implies AGI (remote jobs being completely automatable) by ~2027. AGI will greatly accelerate AI research itself, leading to vastly superhuman intelligences being created ~1 year after AGI. Superintelligence will confer a decisive strategic advantage militarily by massively accelerating all spheres of science and technology. Electricity use will be a bigger bottleneck on scaling datacentres than investment, but is still doable domestically in the US by using natural gas. AI safety efforts in the US will be mostly irrelevant if other actors steal the model weights of an AGI. US AGI research must employ vastly better cybersecurity, to protect both model weights and algorithmic secrets. Aligning superhuman AI systems is a difficult technical challenge, but probably doable, and we must devote lots of [...] ---Outline:(00:12) Short Summary(02:16) 1. From GPT-4 to AGI: Counting the OOMs(02:23) Past AI progress(05:37) Training data limitations(06:41) Trend extrapolations(07:57) The modal year of AGI is soon(09:29) 2. From AGI to Superintelligence: the Intelligence Explosion(09:36) The basic intelligence explosion case(10:46) Objections and responses(14:06) The power of superintelligence(16:28) III The Challenges(16:31) IIIa. Racing to the Trillion-Dollar Cluster(20:58) IIIb. Lock Down the Labs: Security for AGI(21:05) The power of espionage(22:09) Securing model weights(23:46) Protecting algorithmic insights(24:41) Necessary steps for improved security(26:35) IIIc. Superalignment(29:15) IIId. The Free World Must Prevail(32:15) 4. The Project(34:47) 5. Parting Thoughts(35:51) Responses to Situational AwarenessThe original text contained 1 footnote which was omitted from this narration. --- First published: June 8th, 2024 Source: https://forum.effectivealtruism.org/posts/zmRTWsYZ4ifQKrX26/summary-of-situational-awareness-the-decade-ahead --- Narrated by TYPE III AUDIO.

Original by Leopold Aschenbrenner, this summary is not commissioned or endorsed by him. Short Summary Extrapolating existing trends in compute, spending, algorithmic progress, and energy needs implies AGI (remote jobs being completely automatable) by ~2027. AGI will greatly accelerate AI research itself, leading to vastly superhuman intelligences being created ~1 year after AGI. Superintelligence will confer a decisive strategic advantage militarily by massively accelerating all spheres of science and technology. Electricity use will be a bigger bottleneck on scaling datacentres than investment, but is still doable domestically in the US by using natural gas. AI safety efforts in the US will be mostly irrelevant if other actors steal the model weights of an AGI. US AGI research must employ vastly better cybersecurity, to protect both model weights and algorithmic secrets. Aligning superhuman AI systems is a difficult technical challenge, but probably doable, and we must devote lots of [...] ---Outline:(00:13) Short Summary(02:16) 1. From GPT-4 to AGI: Counting the OOMs(02:24) Past AI progress(05:38) Training data limitations(06:42) Trend extrapolations(07:58) The modal year of AGI is soon(09:30) 2. From AGI to Superintelligence: the Intelligence Explosion(09:37) The basic intelligence explosion case(10:47) Objections and responses(14:07) The power of superintelligence(16:29) III The Challenges(16:32) IIIa. Racing to the Trillion-Dollar Cluster(21:12) IIIb. Lock Down the Labs: Security for AGI(21:20) The power of espionage(22:24) Securing model weights(24:01) Protecting algorithmic insights(24:56) Necessary steps for improved security(26:50) IIIc. Superalignment(29:41) IIId. The Free World Must Prevail(32:41) 4. The Project(35:12) 5. Parting Thoughts(36:17) Responses to Situational AwarenessThe original text contained 1 footnote which was omitted from this narration. --- First published: June 8th, 2024 Source: https://forum.effectivealtruism.org/posts/zmRTWsYZ4ifQKrX26/summary-of-situational-awareness-the-decade-ahead --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

In this podcast episode, Dennis interviews Steve Schauer about his calcium study in trauma patients. Steve provides an introduction to himself and his background in emergency medicine and research. He explains that the study aims to determine the prevalence of calcium derangement in trauma patients upon arrival at the trauma center. The study is being conducted at three different trauma centers and has completed enrollment. Steve also discusses the challenges of extrapolating research findings from military trauma to civilian trauma. They also touch on the limitations of the Injury Severity Score (ISS) in assessing military trauma. The conversation then delves into the logistics of the study, including the collection of calcium levels and the potential impact of pre-hospital blood transfusions on calcium levels. They discuss the importance of timing and accuracy in collecting calcium levels and the need for better documentation in trauma care. They also explore the administration of calcium and the differences between calcium gluconate and calcium chloride. Steve emphasizes the importance of administering calcium slowly to avoid adverse effects. They also discuss the timing of calcium administration in relation to blood transfusions and the challenges of determining the optimal calcium levels in trauma patients. The episode concludes with a discussion on the need for iStat machines in trauma centers to monitor calcium levels in real-time. In this conversation, Dennis and Steven Schauer discuss the administration of calcium in trauma patients. They explore the role of calcium in the coagulation cascade and its potential benefits in improving hemodynamics. They also discuss the challenges of administering calcium in the field and the need for further research to determine its efficacy. The conversation highlights the importance of prioritizing blood and tranexamic acid (TXA) administration before considering calcium. Overall, the conversation provides valuable insights into the use of calcium in trauma care. Takeaways The study aims to determine the prevalence of calcium derangement in trauma patients upon arrival at the trauma center. Extrapolating research findings from military trauma to civilian trauma poses challenges due to differences in injury mechanisms. The Injury Severity Score (ISS) has limitations in assessing military trauma. Timing and accuracy are crucial in collecting calcium levels in trauma patients. Calcium administration should be done slowly to avoid adverse effects. Determining the optimal calcium levels in trauma patients is challenging. iStat machines can be valuable in monitoring calcium levels in real-time. Calcium is a cofactor in the coagulation cascade and may play a role in improving hemodynamics in trauma patients. The administration of calcium should be prioritized after blood and tranexamic acid (TXA) in trauma care. The optimal method of calcium administration, such as infusion plus drip, is still under investigation. Further research is needed to determine the efficacy of calcium in trauma care. The availability of resources and logistics may influence the choice of calcium formulation for administration. Thank you to Delta Development Team for in part, sponsoring this podcast. ⁠⁠⁠⁠⁠deltadevteam.com⁠⁠⁠⁠⁠ For more content go to ⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠ ⁠⁠⁠⁠ Consider supporting us: ⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠ or ⁠www.lobocoffeeco.com/product-page/prolonged-field-care

Rob is busy trying to get French citizenship, so favorite-guest Chase Taylor from Pinecone Macro joins the podcast to talk U.S. macro, oil and natural gas prices, and how to think about investing in a world of doom & gloom.--Timestamps:(00:00) - Intro(00:45) – U.S. macro(09:26) – Oil(20:15) – NatGas(27:55) – U.S. v. Intl | Hong Kong/China(41:35) – Extrapolating doom: Egypt and Pakistan(49:10) – Bitcoin--CI Site: cognitive.investmentsJacob Site: jacobshapiro.comJacob Twitter: x.com/JacobShapSubscribe to the Newsletter: bit.ly/weekly-sitrep--Cognitive Investments is an investment advisory firm, founded in 2019 that provides clients with a nuanced array of financial planning, investment advisory and wealth management services. We aim to grow both our clients' material wealth (i.e. their existing financial assets) and their human wealth (i.e. their ability to make good strategic decisions for their business, family, and career).--Disclaimer: Cognitive Investments LLC (“Cognitive Investments”) is a registered investment advisor. Advisory services are only offered to clients or prospective clients where Cognitive Investments and its representatives are properly licensed or exempt from licensure.The information provided is for educational and informational purposes only and does not constitute investment advice and it should not be relied on as such. It should not be considered a solicitation to buy or an offer to sell a security. It does not take into account any investor's particular investment objectives, strategies, tax status or investment horizon. You should consult your attorney or tax advisorThis podcast uses the following third-party services for analysis: Chartable - https://chartable.com/privacyPodtrac - https://analytics.podtrac.com/privacy-policy-gdrp

Welcome to this motivating episode of the Dream Huge Podcast, where we get to know Tristan Gardner: an award-winning tech entrepreneur and real estate investor. Delve into his exciting world as he shares about his path to success and gives us a taste of his thrilling entrepreneurial journey in both the tech and real estate sectors. This intimate conversation digs deep into the dynamics of real estate investments, enlightening listeners on the facets of multifamily and commercial buildings, lucrative investments, and the hidden treasures of small-town properties. Tristan's first-hand experiences and future predictions are a treasure trove of information for those interested in the real estate landscape. In the tech world, Tristan offers an inside look at Easy Justice, the innovative software transforming legal proceedings for government bodies. Hear about their diverse product range, subscription models, licensing structures, and robust support, and how Tristan's passion for tech combined with strategic partnerships has propelled the business to new heights. Moving beyond traditional business talks, Tristan shares his inspiring journey and imparts invaluable lessons on adopting a proactive attitude, embracing failures, and the power of partnerships. Featuring an assortment of anecdotes from his early entrepreneurial experiences, this episode offers remarkable insights into a successful entrepreneurial mindset. Extrapolating from the wisdom of bestselling books, "The 10X Rule" and "The Cashflow Quadrant", Tristan serves insightful advice to listeners dreaming of escaping mediocrity, overcoming fears, and embracing grand ambitions. His charismatic persona, commitment to personal growth, and dedication to make a difference resonate vitally in this episode. Infused with humor and enlightening business talk, this episode is a definite listen for those eager to break the barriers of average and reach new heights in their pursuits. Tune in to fuel your journey to success with the inspiring story of Tristan Gardner. Hosted by Pete Peterson and John Pavlansky Produced by Mark Gray

Get your DEMYSTICON 2024 tickets here: https://www.eventbrite.com/e/demysticon-2024-tickets-727054969987 Join us for our first in-person conversation with Dr. Brian Keating, a distinguished UCSD cosmologist, as we navigate the intricate landscape of scientific epistemology and reward circuits. Our discussion unpacks the complexities of scientific truth, exploring its intersections with faith and unveiling the mysteries of cosmic origins. With Dr. Keating we tap into profound insights on the scientific method, reflects on paradigm shifts, and discuss the spiritual dimensions within scientific theories. This exploration challenges beliefs and encourages you to navigate the squishy truths that inscribe our scientific understanding of the universe. Subscribe to Brian Keating's INTO THE IMPOSSIBLE Podcast here: ⁠https://link.chtbl.com/into-the-impossible⁠  Tell us what you think in the comments or on our Discord: https://discord.gg/MJzKT8CQub Support DSPod & Dr. Keating when you pick up his books here: https://amzn.to/4b21ceZ Sign up for a yearly Patreon membership for discounted conference tickets: https://bit.ly/3lcAasB (00:00) Go! (00:02:33) Intro - The history & context of science (00:06:56) Belief, taste, and faith (00:08:12) Science isn't about proof (00:09:11) Scientific truth vs religious truth (00:12:50) What's the scientific method? (00:17:12) Inspiration as a guiding principle (00:22:50) "The Big Bang Never Happened," Vera Rubin, Galactic Rotation, and the Age of the Universe (00:29:08) Extrapolating ages from prior knowledge (00:32:00) Hubble Tension (00:33:21) The possibility of dissent (00:36:17) Spiritual aspects of scientific theories & paradigm shifts (00:42:30) Technological progress as a necessary ingredient for a paradigm shift (00:46:29) Closing thoughts #BrianKeating #Cosmology #ScientificTruth, #ScientificNavigation, #KeatingCosmos, #TruthAndFaith, #CosmicMysteries, #ScientificInsights, #ParadigmReflections, #SpiritualityInScience, #CosmicPhenomena, #BeliefsChallenged, #UnveilingTheUnknown, #CosmicExploration, #ScienceFaithDialogue, #KeatingPerspective, #ScientificUnderstanding, #QuantumReflections, #CosmicConundrums, #DiscoveringTruths, #CosmologistChat, #SpiritualScience, #BeyondBeliefs Check our short-films channel, @DemystifySci: https://www.youtube.com/c/DemystifyingScience AND our material science investigations of atomics, @MaterialAtomics https://www.youtube.com/@MaterialAtomics Join our mailing list https://bit.ly/3v3kz2S PODCAST INFO: Anastasia completed her PhD studying bioelectricity at Columbia University. When not talking to brilliant people or making movies, she spends her time painting, reading, and guiding backcountry excursions. Shilo also did his PhD at Columbia studying the elastic properties of molecular water. When he's not in the film studio, he's exploring sound in music. They are both freelance professors at various universities. - Blog: http://DemystifySci.com/blog - RSS: https://anchor.fm/s/2be66934/podcast/rss - Donate: https://bit.ly/3wkPqaD - Swag: https://bit.ly/2PXdC2y SOCIAL: - Discord: https://discord.gg/MJzKT8CQub - Facebook: https://www.facebook.com/groups/DemystifySci - Instagram: https://www.instagram.com/DemystifySci/ - Twitter: https://twitter.com/DemystifySci MUSIC: -Shilo Delay: https://g.co/kgs/oty671

We are running an end of year listener survey! Please let us know any feedback you have, what episodes resonated with you, and guest requests for 2024! Survey link here.NeurIPS 2023 took place from Dec 10–16 in New Orleans. The Latent Space crew was onsite for as many of the talks and workshops as we could attend (and more importantly, hosted cocktails and parties after hours)!Picking from the 3586 papers accepted to the conference (available online, full schedule here) is an impossible task, but we did our best to present an audio guide with brief commentary on each. We also recommend MLContests.com NeurIPS recap and Seb Ruder's NeurIPS primer. We also found the VizHub guide useful for a t-SNE clustering of papers.We'll start with the NeurIPS Best Paper Awards, and then go to a selection of non-awarded but highly influential papers, and then arbitrary personal picks to round out the selection. Where we were able to do a poster session interview, please scroll to the relevant show notes for images of their poster for discussion. We give Chris Ré the last word due to the Mamba and StripedHyena state space models drawing particular excitement but still being too early to assess impact. Timestamps* [0:01:19] Word2Vec (Jeff Dean, Greg Corrado)* [0:15:28] Emergence Mirage (Rylan Schaeffer)* [0:28:48] DPO (Rafael Rafailov)* [0:41:36] DPO Poster Session (Archit Sharma)* [0:52:03] Datablations (Niklas Muennighoff)* [1:00:50] QLoRA (Tim Dettmers)* [1:12:23] DataComp (Samir Gadre)* [1:25:38] DataComp Poster Session (Samir Gadre, Alex Dimakis)* [1:35:25] LLaVA (Haotian Liu)* [1:47:21] LLaVA Poster Session (Haotian Liu)* [1:59:19] Tree of Thought (Shunyu Yao)* [2:11:27] Tree of Thought Poster Session (Shunyu Yao)* [2:20:09] Toolformer (Jane Dwivedi-Yu)* [2:32:26] Voyager (Guanzhi Wang)* [2:45:14] CogEval (Ida Momennejad)* [2:59:41] State Space Models (Chris Ré)Papers covered* Distributed Representations of Words and Phrases and their Compositionality (Word2Vec) Tomas Mikolov · Ilya Sutskever · Kai Chen · Greg Corrado · Jeff Dean. The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several improvements that make the Skip-gram model more expressive and enable it to learn higher quality vectors more rapidly. We show that by subsampling frequent words we obtain significant speedup, and also learn higher quality representations as measured by our tasks. We also introduce Negative Sampling, a simplified variant of Noise Contrastive Estimation (NCE) that learns more accurate vectors for frequent words compared to the hierarchical softmax. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of Canada'' and "Air'' cannot be easily combined to obtain "Air Canada''. Motivated by this example, we present a simple and efficient method for finding phrases, and show that their vector representations can be accurately learned by the Skip-gram model.* Are Emergent Abilities of Large Language Models a Mirage? (Schaeffer et al.). Emergent abilities are abilities that are present in large-scale models but not in smaller models and are hard to predict. Rather than being a product of models' scaling behavior, this paper argues that emergent abilities are mainly an artifact of the choice of metric used to evaluate them. Specifically, nonlinear and discontinuous metrics can lead to sharp and unpredictable changes in model performance. Indeed, the authors find that when accuracy is changed to a continuous metric for arithmetic tasks where emergent behavior was previously observed, performance improves smoothly instead. So while emergent abilities may still exist, they should be properly controlled and researchers should consider how the chosen metric interacts with the model.* Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al.)* While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. * In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning. * Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF's ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.* Scaling Data-Constrained Language Models (Muennighoff et al.)* The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations.* QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al.). * This paper proposes QLoRA, a more memory-efficient (but slower) version of LoRA that uses several optimization tricks to save memory. They train a new model, Guanaco, that is fine-tuned only on a single GPU for 24h and outperforms previous models on the Vicuna benchmark. Overall, QLoRA enables using much fewer GPU memory for fine-tuning LLMs. Concurrently, other methods such as 4-bit LoRA quantization have been developed that achieve similar results.* DataComp: In search of the next generation of multimodal datasets (Gadre et al.)* Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the machine learning ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl. Participants in our benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running our standardized CLIP training code and testing the resulting model on 38 downstream test sets. * Our benchmark consists of multiple compute scales spanning four orders of magnitude, which enables the study of scaling trends and makes the benchmark accessible to researchers with varying resources. Our baseline experiments show that the DataComp workflow leads to better training sets. Our best baseline, DataComp-1B, enables training a CLIP ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, outperforming OpenAI's CLIP ViT-L/14 by 3.7 percentage points while using the same training procedure and compute. We release datanet and all accompanying code at www.datacomp.ai.* Visual Instruction Tuning (Liu et al)* Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. * By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.* Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.* Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Yao et al)* Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. * To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. * ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.* Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. * Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.* Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al)* LMs exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel. * In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. * We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. * This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. * Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.* Voyager: An Open-Ended Embodied Agent with Large Language Models (Wang et al)* We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: * 1) an automatic curriculum that maximizes exploration, * 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and * 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. * Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.Voyager discovers new Minecraft items and skills continually by self-driven exploration, significantly outperforming the baselines.* Evaluating Cognitive Maps and Planning in Large Language Models with CogEval (Momennejad et al)* Recently an influx of studies claims emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack systematic Evaluation involving multiple tasks, control conditions, multiple iterations, and statistical robustness tests. Here we make two major contributions. * First, we propose CogEval, a cognitive science-inspired protocol for the systematic evaluation of cognitive capacities in LLMs. The CogEval protocol can be followed for the evaluation of various abilities. * * Second, here we follow CogEval to systematically evaluate cognitive maps and planning ability across eight LLMs (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard, Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base our task prompts on human experiments, which offer both established construct validity for evaluating planning, and are absent from LLM training sets.* * We find that, while LLMs show apparent competence in a few planning tasks with simpler structures, systematic evaluation reveals striking failure modes in planning tasks, including hallucinations of invalid trajectories and falling in loops. These findings do not support the idea of emergent out-of-the-box planning ability in LLMs. This could be because LLMs do not understand the latent relational structures underlying planning problems, known as cognitive maps, and fail at unrolling goal-directed trajectories based on the underlying structure. Implications for application and future directions are discussed.* Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Albert Gu, Tri Dao)* Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. * First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. * Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). * Mamba enjoys fast inference (5x higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-1.4B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.* Get full access to Latent Space at www.latent.space/subscribe

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Extrapolating from Five Words, published by Gordon Seidoh Worley on November 16, 2023 on LessWrong. If you only get about five words to convey an idea, what will someone extrapolate from those five words? Rather than guess, you can use LLMs to experimentally discover what people are likely think those five words mean. You can use this to iterate on what five words you want to say in order to best convey your intended meaning. I got this idea because I tried asking Claude to summarize an article at a link. Claude doesn't follow links, so it instead hallucinated a summary from the title, which was included in the URL path. Here's an example of it doing this with one of my LessWrong posts: It hallucinates some wrong details and leaves out lots of details that are actually in the post, but it's not totally off the mark here. If my ~Five Words were "the problem of the criterion matters", this would be a reasonable extrapolation of why I would say that. Rather than using a link, I can also ask Claude to come up what it thinks I would have put in a post with a particular title: Strangely it does worse here in some ways and better in others. Unlike when it hallucinated the summary of the link, this time it came up with things I would absolutely not say or want someone to come away with, like the idea that we could resolve the problem of the criterion enough to have objective criteria for knowledge. But maybe prompting it about LessWrong was the issue, since LessWrong puts off a lot of positivists vibes, Eliezer's claims to the contrary not withstanding. So I tried a different prompt: This is fine? It's not great. It sounds like a summary of the kind of essay a bored philosophy undergrad would write for their epistemology class. Let me try asking it some version of "what do my ~Five Words mean?": This is pretty good, and basically what I would expect someone to take away from me saying "the problem of the criterion matters". Let's see what happens if I tweak the language: Neat! It's picked up on a lot of nuance implied by saying "important" rather than "matters". This would be useful for trying out different variations on a phrase to see what those small variations change about the implied meaning. I could see this being useful for tasks like word smithing company values and missions and other short phrases where each word has to carry a lot of meaning. Now let's see if it can do the task in reverse! Honestly, "uncertainty undermines knowledge" might be better than anything I've ever come up with. Thanks, Claude! As a final check, can Claude extrapolate from its own summary? Clearly it's lost some of the details, particularly about the problem of the criterion, and has made up some things I wasn't trying to have it get at. Seems par for the course in terms of condensing down a nuanced message into about five words and still having the core of the message conveyed. Okay, final test, what can Claude extrapolate from typical statements I might make about my favorite topic, fundamental uncertainty? Hmm, okay, but not great. Maybe I should try to find another phrase to point to my ideas? Let's see what it thinks about "fundamental uncertainty" as a book title: Close enough. I probably don't need to retitle my book, but I might need to work on a good subtitle. Based on the above experiment in prompt engineering, Claude is reasonably helpful at iterating on summaries of short phrases. It was able to pick up on subtle nuance, and that's really useful for finding the right short phrase to convey a big idea. The next time I need to construct a short phrase to convey a complex idea, I will likely iterate the wording using Claude or another LLM. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Show notes and Transcript At long last it has happened. Andrew Bridgen MP (Reclaim Party) secured a debate on excess deaths in the UK Parliament.  Nearly twenty requests were turned down but Andrew simply would not give up.  His courage and determination to find out the truth won in the end.  Andrew gave a 25 minute presentation of all the data and facts which show a shocking rise in excess deaths since the covid jab rollout.  The fact that many people have died after receiving an injection appears to be the very reason every government wants total silence on this issue.  As you watch Andrew speak, be inspired to speak truth in the circles you find yourself in.  Use the information in the speech to arm yourself with the facts.  We now await a much longer 3 hour debate on excess deaths which Andrew is requesting. *This episode contains a background of the debate, the full speech by Andrew Bridgen MP, his message afterwards to the supporters gathered outside in Parliament Square and Peter catches a few words with the man himself. Andrew Bridgen  Member of Parliament for North West Leicestershire since 2010https://www.reclaimparty.co.uk/andrew-bridgen  Some Key Points Made During the Speech... - Ambulance calls for life-threatening emergencies ranged from a steady 2,000 calls per day until the vaccine rollout, from then it rose to 2,500 daily and calls have stayed at this level since.   - The surveillance systems designed to spot a safety problem have all flashed red, but no one's looking. - Payments for Personal Independent Payments (PIP) for people who have developed a disability and cannot work, have rocketed with the vaccine rollout and have continued to rise ever since. - The trial data showed that one in eight hundred injected people had a serious adverse event, meaning the risk of this was twice as high than the chance of preventing a Covid hospitalisation. - There were just over 14,000 excess deaths in the under 65-year-olds, before vaccination, from April 2020 to the end of March 2021. However, since that time there have been over 21,000 excess deaths in this age group alone. - There were nearly two extra deaths a day in the second half of 2021 among 15 – 19-year-old males,  but potentially even more if those referred to the coroner were fully included. Recorded 20.10.23 *Special thanks to Bosch Fawstin for recording our intro/outro on this podcast. Check out his art https://theboschfawstinstore.blogspot.com/ and follow him on GETTR https://gettr.com/user/BoschFawstin and Twitter https://twitter.com/TheBoschFawstin?s=20  To sign up for our weekly email, find our social media, podcasts, video, livestreaming platforms and more... https://heartsofoak.org/connect/ Support Hearts of Oak by purchasing one of our fancy T-Shirts.... https://heartsofoak.org/shop/ Please subscribe, like and share!   Subscribe now Transcript (Hearts of Oak) Hello, Hearts of Oak. Today we are here with Andrew Bridgen at a debate in Parliament, the first debate in this Parliament, on excess deaths. There's been very little debates, very little discussions on vaccine harms here. Of course, this is the issue that Andrew Bridgen MP was thrown out of the Conservative Party, the Tories, for beginning to raise the issue of vaccine harms and now raising the issue of excess deaths was simply is not discussed in this place. I've seen discussion in other parts of the world, especially Germany, with the AFD. But Andrew Bridgen has made this the hill that he will fight and die on. And he has been thrown out of the Conservative Party. He's lost that position he had for many years. Andrew Bridgen, of course, is one of the original Brexiteers, well known to any of us involved in the Brexit movement, in the UKIP movement. And Andrew has been fearless. He's one of those strange beasts in Westminster. He is led by conviction. He is led by courage and led by a desire to do what is right. And he had no desire to climb up the greasy pole. He's traditionally been a backbencher. So has stood his ground, kept his position as a lowly MP and not wanted to rise to the ministerial level, because that gives him the freedom to discuss what he wants. He's not held, he's not restricted by government restrictions, but he can say what he thinks and do what is right for his constituents, for those who vote for him, and realise that he is the servant of the people and he is not the servant of the government. So today there will be a debate led by Andrew Bridgen, I assume he will be one of maybe very few, one of one, who will actually speak on this. I'm really curious to see. I've seen a couple of Conservative, MPs who have touched on this, who have spoken a little bit about this, sometimes on GB News, but they have not gone as far as Andrew Bridgen. And Andrew Bridgen has gone this far. He has lost his job over it, and he doesn't care, because this is the right thing to do when a jab when an experimental vaccine, so-called vaccine, was rolled out and everyone was coerced and more or less forced to take it. Andrew was in that, he also took it, now regrets that and wants to keep raising the alarm on the ongoing effects of this and of course to challenge this government overreach that wants to force this upon everyone. This of course is a conservative government supposedly that stands up for freedom of speech, personal responsibility, rights, and yet all those traditional understandings of a conservative party have been completely upended and is no longer a party of freedom and liberty but is now a party of coercion and control. A number of MPs I assume will come in and speak after Andrew will present his position on excess deaths and ask the question, why is this? It seems to correlate to the rollout of the jab. You and I know that. We've seen the data. Andrew will be careful in how he puts it forward. He will use parliamentary language. He's skilled enough in this chamber to know what to say, what not to say, what connects with those in the chamber, and to win them over. Because ultimately, politics is about the art of persuasion. It is about winning the public over. And today, it is not necessarily the public is winning over, although you will watch the debate in a few moments, but actually is winning over MPs. And that also is crucial. Whatever you think, we still have 650 individuals and many of us mistrust absolutely, many of us detest. Many of us have had a traditional understanding of politics where there was a level of trust with our institutions and that included those in the building behind me. That is gone. I think for all of us, that is completely gone. And to have an individual who is a champion on the issue of curtailing that government overreach, asking questions, following the money, saying, was this just a push by big pharma for profits? Was this something darker? There are a whole load of areas we can go into, but Andrew has, wisely stayed within the areas he can understand. He has read papers, he has, understood them and he has presented those and I think he has been extremely wise on how far he has gone on this because it is a case of winning people over. That's what we have faced, all of us, over the last three years of winning friends, family, colleagues, connections over to persuade them that this is a dangerous experiment on not only the UK population but on the world population. We have a police car. I hope they don't want to arrest Andrew before his debate. I don't think even our government would do that, would they? Anyway, I will let you watch the debate, watch Andrew speaking, and then after I will try and catch up with a number of the people who have been here to support Andrew. I saw, Mike Yeadon earlier heading into the debate and I saw Matt Le Tissier earlier, I saw Fiona Hines earlier, I saw a big group of people who are here to support Andrew as he speaks truth and to let him know that he is not alone because it must feel very alone in that chamber. No one to back you, no one to support you and you feel as though you are a lonely voice crying out in the wilderness and yet. Many people have come to show Andrew that there are many people behind him who are indebted to him for actually speaking truth in this place and are standing with him shoulder-to-shoulder. So we'll hopefully talk to a few of those people after the debate. (Andrew Bridgen MP) Thank you, Mr Deputy Speaker. We've experienced more excess deaths since July 2021, than the whole of 2020. Unlike the pandemic, however, these deaths are not disproportionately of the old. In other words, the excessive deaths are striking down people in the prime of life. But no one seems to care. I fear history will not judge this House kindly. Worse still, in a country supposedly committed to free and frank exchange of views, it appears that no one cares that no one cares. Well, I care, Mr Deputy Speaker, and I credit those members here in attendance today who also care. And I'd also like to thank the Honourable Member for Lincoln for his support, and I'm, sorry that he couldn't attend today's debate. It's taken a lot of effort and more than 20 rejections to be allowed to raise this topic, But at last we're here to discuss the number of people dying. Nothing could be more serious. Numerous countries are currently gripped in a period of unexpected mortality, and no one wants to talk about it. It's quite normal for death numbers to fluctuate up and down by chance alone, but what we're seeing here is a pattern, repeated across countries, and the rise has not let up. I'll give way to my Honourable Gentleman.  (Phillip Davies MP) I'm very grateful and can I commend him for the tenacious way he's battled on this particular, issue. I certainly admire him for that. I just wonder where he found the media was in all of this, because of course during the Covid pandemic, every day, the media, particularly the BBC, couldn't wait to tell us how many people had died in that particular day without any context of those figures whatsoever. But they seem to have gone strangely quiet over these excess deaths now. (Andrew Bridgen MP) I thank the gentleman for his intervention. He's absolutely right. The media have let the British public down badly. There will be a full press pack going out to all media outlets following my speech with all the evidence to back up all the claims I'll make in that speech. But I don't doubt there'll be no mention of it in the mainstream media. You might think that a debate about excess deaths is going to be full of numbers. This speech does not have that many numbers because most of the important numbers have been kept hidden. Other data has been oddly presented in a distorted way, and concerned people seeking to highlight important findings and ask questions have found themselves inexplicably under attack. Before debating excess deaths, it's important to understand how excess death is determined. To understand if there is an excess, by definition you need to estimate how many deaths it would have been expected. The Organisation for Economic Co-operation and Development used 2015-2019 as a baseline, and the Government's Office of Health Disparities and Improvement used its 2015-2019 baseline modelled to allow for ageing, and I've used that data here. Unforgivably, the Office of National Statistics have included deaths in 2021 as part of their baseline calculation for expected deaths, as if there was anything normal about the deaths in 2021. Exaggerating the number of deaths expected, the number of excess can be minimized. Why would the ONS want to do that? There's just too much that we don't know and it's not good enough Mr. Deputy Speaker. The ONS published promptly each week the number of deaths that were registered and while this is commendable it's not the data point that really matters. There's a total failure to collect, never mind publish, data on deaths that are referred for investigation to the coroner. Why does this matter? A referral means that it can be many months and, given the backlog, many years before a death is formally registered. Needing to investigate the cause of a death is fair enough. Failing to record when the death happened is not. Because of this problem, we actually have no idea how many people actually died in 2021. Even now, the problem is greatest for the younger age groups, where there's, a higher proportion of deaths are investigated. This date of failure is unacceptable. It must change. There's nothing in a coroner's report that can bring anyone back from the dead and those deaths should be reported. The youngest age groups are important not only because they should have their whole lives ahead of them. If there is a new cause of excess mortality across the board, it would not be noticed so much in the older cohorts because the extra deaths would be drowned out amongst the expected deaths. However, in the youngest cohorts, that is not the case. There were nearly two extra deaths a day in the second half of 2021 among 15 to 19 year old males, but potentially even more if those referred to the coroner were fully included. In a judicial review of the decision to vaccinate yet younger children, the ONS refused in court to give anonymised details about these deaths. They, admitted that the data they were withholding was statistically significant and I quote they said, the ONS recognises that more work could be undertaken to examine the mortality rates of young people in 2021 and intends to do so once more reliable data are available. How many more extra deaths in 15 to 19 year olds would it take to trigger such work? Surely the ONS should be desperately keen to investigate deaths in young men. Why else have an independent body charged with examining mortality data? Surely the ONS has a responsibility to collect data from the coroners to produce timely information? Let's move on to old people, because most deaths in the old are registered promptly and we do have a better feel for how many older people are dying. Deaths from dementia and Alzheimer's show what we ought to expect. There was a period of high mortality coinciding with COVID and lockdowns, but ever since there have been fewer deaths than expected. After a period of high mortality, we expect, and historically have seen, a period of low mortality because those who have sadly died cannot die again. Those whose deaths were slightly premature because of COVID and lockdowns, died earlier than they otherwise would have. This principle should hold true for every cause of death and every age group, but that's not what we're seeing. Even for the over 85-year-olds, according to the Office of Health Improvement and Disparities, there were 8,000 excess deaths, 4% above the expected levels, for the 12 months starting in July 2020. That includes all of the autumn 2020 wave of COVID, when we had tiering, the second lockdown, and it includes all of the first COVID winter. However, for the year starting July 2022, there have been over 18,000 excess deaths in this age group, 9% above expected levels, more than twice as many in a period when there should have been a deficit. And when deaths from diseases previously associated with old age were actually fewer than expected. Mr Deputy Speaker, I have raised my concerns around NG163 and the use of midazolam and morphine, which may have caused and may still be causing premature deaths in the vulnerable, but that is sadly a debate for another day. There were just over 14,000 excess deaths in the under 65-year-olds before vaccination from April 2020 to the end of March 2021. However, since that time there have been over 21,000 excess deaths, ignoring the registration delay problem, the majority, 58% of these deaths, were not attributed to Covid. We turned society upside down before vaccination for fear of excess deaths from Covid. Today we have substantially more excess deaths, and in younger people, and there's complete and eerie silence, Mr Deputy, Speaker. The evidence is unequivocal. There was a clear stepwise increase in mortality following the vaccine rollout. There was a reprieve in the winter of 2021-22 because there were fewer than expected respiratory deaths, but otherwise the excess has been incessantly at this high level. Ambulance data for England provides another clue. Ambulance calls for life-threatening emergencies were running at a steady 2,000 calls per day until the vaccine rollout. From then it rose to 2,500 daily and calls have stayed at this level since. The surveillance systems designed to spot a safety problem have all flashed red but no one's looking. Claims for personal independence payments for people who've developed a disability and cannot work rocketed with the vaccine rollout and it's, continued to rise ever since. The same was seen in the USA, also started with the vaccine rollout, not with Covid. A study to determine the vaccination status of a sample of such claimants, would be relatively quick and inexpensive to perform, yet nobody seems interested in ascertaining this vital information. Officials have chosen to turn a blind eye to this disturbing, irrefutable and frightening data, much like Nelson did, but for far less honourable reasons. He would be ashamed of us, Mr Deputy Speaker. Furthermore, data that has been used to sing the praises of the vaccines is deeply flawed. Only one COVID-related death was prevented in each of the initial major trials that led to authorisation of the vaccines and that is taking their data entirely at face value, whereas a growing number of inconsistencies and anomalies suggest we ought not to do this. Extrapolating from that means that between 15,000 and 20,000 people had to be injected to prevent a single death from COVID. To prevent a single COVID hospitalisation, over 1,500 people needed to be injected. The trial data showed that 1 in 800 injected people had a serious adverse event, meaning they were hospitalised or had a life-changing or life-threatening condition. The risk of this was twice as high as the chance of preventing a COVID hospitalisation. We're harming 1 in 800 people to supposedly save 1 in 20,000. This is madness. The strongest claims have too often been based on modelling carried out on the basis of flawed assumptions. Where observational studies have been carried out, researchers will correct, for age and comorbidities to make the vaccines look better. However, they never correct for socio-economic or ethnic differences that would make the vaccines look worse. This matters. For example, claims of high mortality in less vaccinated regions in the United States, took no account of the fact that this was the case before the vaccines were rolled out. That is why studies that claim to show the vaccines prevented Covid deaths also showed a marked effect of them preventing non-Covid deaths. The prevention of non-Covid deaths is always a statistical illusion and claims of preventing Covid deaths should not be assumed when that illusion has not been corrected for. And when it is corrected for, the claims of efficacy for the vaccines vanish with it. COVID disproportionately killed people from ethnic minorities and lower socioeconomic groups. During the 2020, during the pandemic, the deaths among the most deprived were up by 23%, compared to 17% for the least deprived. However, since 2022, the pattern has reversed, with 5% excess mortality amongst the most deprived, compared to 7% among the least deprived. These deaths are being caused by something different. In 2020, the excess was highest in the oldest cohorts and there were fewer than expected deaths amongst the younger age groups. But since 2022, the 50 to 64 year old cohort has had the highest excess mortality. Even the youngest age groups are now seeing substantial excess, with a 9% excess in the under 50s since 2022 compared to 5% now in the over 75 group. Despite London being a younger region, the excess in London is only 3%, whereas it is higher in every more heavily vaccinated region of the UK. It should be noted that London is famously the least vaccinated region in the UK by some margin. Studies comparing regions on a larger scale show the same thing. There are studies from the Netherlands, Germany and the whole world each showing that the highest mortality after vaccination was seen in the most heavily vaccinated regions. So we need to ask, what are people dying of? Since 2022, there has been 11% excess in ischemic heart disease deaths and a 16% excess in heart failure deaths. In meantime, cancer deaths, only 1% above expected levels, which is further evidence that it is not simply, some other factor that affects deaths across the board, such as a failing to account for an aging population or a failing NHS. In fact, the excess itself has a seasonality with a peak in the winter months. The fact it returns to baseline levels in summer is a further indication that this is not due to some statistical error or an ageing population alone. Dr Clare Craig from the Heart Group first highlighted a stepwise increase in cardiac arrest calls after the vaccine rollout in May 2021 and Heart have repeatedly raised concerns about the increase in cardiac deaths and they have every reason to be concerned. Four participants in the vaccine group of the Pfizer trial died from cardiac arrest compared to only one in the placebo group. Overall there were 21 deaths in the vaccine group up to March 2021 compared to 17 in the placebo group. And there are serious anomalies about the reporting of the deaths within this trial, with the deaths in the vaccine group taking much longer to report than those in the placebo group. And that's highly suggestive, Mr Deputy Speaker, of a significant bias in what was supposed to be a blinded trial. An Israeli study clearly showed an increase in cardiac hospital attendances, among 18 to 39 year olds that correlated with vaccination, not with COVID. There have now been several postmortem studies demonstrating a causal link between vaccination and coronary artery disease leading to death up to four months after the last dose. And we need to remember that the safety trial was cut short to only two months. So there's no evidence of any vaccine safety beyond that point. The decision to unblind the trials after two months and vaccinate the placebo group is nothing less than a public health scandal. Everyone involved failed in their duty to the truth. But no one cares, Mr Deputy Speaker. The one place that can help us understand exactly what caused this is Australia. Australia had almost no Covid when vaccines were first introduced, making them the perfect control group. The state of South Australia had only a thousand cases of Covid across its whole population by December 2021, before Omicron arrived. What was the impact of vaccination there? For 15 to 44 year olds there was historically 1,300 emergency cardiac presentations a month. With vaccine rollout in the under 50s this rocketed to 2,172 cases in November 2021 in this age group alone, a 67% more than usual. Overall there were 17,900 South Australians who had a cardiac emergency in 2021, compared to only 13,250 in 2018, a 35% increase. It is clearly the vaccine that must be the number one suspect in this and it cannot be dismissed as just a coincidence. Australian mortality overall has increased from early 2021 and the increase is due to cardiac deaths. These excess deaths are not due to an ageing population because there are fewer deaths in the diseases of old age. These deaths are not an effect of COVID because they've happened in places where COVID have not reached and they're not due to low statin prescriptions or under-treated hypertension, as Chris Whitty would suggest, because prescriptions did not change and in any effect would have taken many years and been very small. The prime suspect must be something that was introduced to the population as a whole, something novel. The prime hypothesis must be the experimental COVID-19 vaccines. The ONS published a data set of deaths by vaccinated and unvaccinated. At first glance, it appears to show that the vaccines are safe and effective. However, there were several huge problems with how they presented that data. One was that for the first three-week period after injection, the ONS claimed, there were only a tiny number of deaths. The number the ONS would normally predict to occur in a single week. Where were the deaths from the usual causes? When this was raised, the ONS claimed that the sickest people did not get vaccinated, and therefore people who were taking the vaccination were self-selecting for those least likely to die. Not only is this not the case in the real world, with even hospices heavily vaccinating their residents, but the ONS's own data showed that the proportion of sickest people was equal in the vaccinated and unvaccinated groups. This inevitably raises serious questions about the ONS's data presentation. There were so many problems with the methodology used by the ONS that the Statistics Regulator agreed that the ONS data could not be used to assess vaccine efficacy or safety. That tells you something about the ONS. Consequently, Hart asked the UK Health Security Agency to provide the data they had on people who had died and therefore needed to be removed from their vaccination dataset. This request has been repeatedly refused, with excuses given, including the false claim that anonymising this data will be equivalent to creating it even though there is case law that, anonymization is not considered creation of new data. Mr Deputy Speaker I believe if this data was released it would be damning. That so many lives have been saved by mass vaccination that any amount of harm, suffering and death caused by the vaccines is a price worth paying. They're delusional, Mr Deputy Speaker. The claim of 20 million lives saved is based on now discredited models which assume that Covid waves do not peak without intervention. There have been numerous waves globally that now demonstrate that is not the case, and it was also based on there having been more than half a million lives saved in the UK. That's more than the worst-case scenario predicted at the beginning of the pandemic. For the claim to have been true, the rate at which Covid killed people would have to have taken off dramatically at the beginning of 2021 in the absence of vaccination. This is ludicrous and it bears no relationship to the truth. In the real world, Australia, New Zealand and South Korea had a mortality rate of 400 deaths per million up to the summer of 2022, after they were first hit with Omicron. So how does that compare with the Wuhan strain? France and Europe as a whole had a mortality rate of under 400 deaths per million up to the summer of 2020. Australia, New Zealand and South Korea were all heavily vaccinated before infection. So tell me, where was the benefit? The UK had just over 800 deaths per million up to the summer of 2020. So twice as much. But we know that Omicron is half as deadly as the Wuhan variant. The death rates per million are the same before and after vaccination. So where was the benefits of vaccination? The regulators have failed in their duty to protect the public. They've allowed these novel products to skip crucial safety testing by letting them be described as vaccines. They've failed to insist on safety testing being done in the years since the first temporary emergency authorisation. Even now, no one can tell you how much spike protein is produced on vaccination and for how long. Yet another example of where there is no data for me to share with the House. And when it comes to properly recording deaths due to vaccination, the system's broken. Not a single doctor registered a death from a rare brain clot before doctors in Scandinavia forced the issue and the MHRA acknowledged the problem. Only then did these deaths start to be certified by doctors in the UK. It turns out that doctors were waiting for permission from the regulator and the regulators were waiting to be alerted by the doctors. This is a lethal circularity. Furthermore, coroners have written Regulation 28 reports highlighting deaths from vaccination to prevent further deaths, yet the MHRA said in a response to an FOI that they had not received any of them. The system we have in place is clearly not functioning to protect the public. The regulators also missed the fact that the Pfizer trial, in the Pfizer trial, the vaccine was made for the trial participants in a highly controlled environment, in stark contrast to the manufacturing process used for the public rollout, which was based on a completely different technology. And just over 200 participants were given the same product that was given to the public. But not only was the data from these people never compared to those in the trial for efficacy and safety, But the MHRA have admitted that they dropped the requirement to provide the data. That means there was never a trial on the Pfizer product that was actually rolled out to the public. And that product has never been compared to the product that was actually trialled. The vaccine mass production processes use vats of Escherichia coli and present a risk of contamination with DNA from the bacteria as well as bacterial cell walls which can, cause dangerous reactions. This is not theoretical, Mr Deputy Speaker, this is now sound evidence that has been replicated by several labs across the world, and the mRNA vaccines were contaminated by DNA which far exceeded the usual permissible levels. Given that this DNA is enclosed in the lipid nanoparticle delivery system, it is arguable that even the permissible levels have been far too high. These lipid nanoparticles are known to enter every organ of the body, as well as this potentially causing some of the acute adverse reactions seen, there is a serious risk that this foreign bacterial DNA is inserting itself into human DNA. Will anybody investigate? No, they won't. I'll give way on that point.  (Danny Kruger MP) I am conscious that time is tight. I recognise that the hon. Gentleman is making a very powerful case. Does he agree that the Government should be looking at this properly and should commission of review into the excess deaths, partly so that we can reassure our constituents that the case he's making is not in fact valid and that the vaccines have no cause behind these excess deaths. (Andrew Bridgen MP) I thank the Honourable Gentleman for his support on this topic and of course that is what exactly any responsible government should do. I wrote to the Prime Minister on the 7th August 2023 with all the evidence of this but sadly Mr Deputy Speaker I still await a response. What will it take to stop these products? Their complete failure to stop infection was not enough and we all know plenty of vaccinated people who have caught and spread Covid. The, mutation of the virus to a weaker variant, Omicron, that wasn't enough. The increasing evidence of the serious harms to those of us that were vaccinated. That's not enough. And now the cardiac deaths and the deaths of young people is apparently not enough either. It's high time these experimental vaccines were suspended and a full investigation into the harms they've caused initiated. History will be a harsh judge if we don't start using evidence-based medicine. We need to return to basic science, basic ethics immediately, which means listening to all voices and investigating all concerns. In conclusion, Mr Deputy Speaker, the experimental Covid-19 vaccines are not safe and they're not effective. Despite there only being limited interest in the chamber from colleagues, and I'm very grateful for those who have attended, we can see from the public gallery there is considerable public interest. I would implore all members of the House, present and those not. Support calls for a three-hour debate on this important issue. And Mr Deputy Speaker, this might be the first debate on excess deaths in our Parliament. Indeed, it might be the first debate on excess deaths in the world, but very sadly I promise you won't be the last. (Parliament Square Speech Andrew Bridgen MP) But without further ado let's welcome to the stage Mr Andrew Bridgen. Thank you ladies and gentlemen, thank you for coming down here to support the debate today, and thank you for supporting me and the cause. More? I just spoke for 25 minutes. Blood. It's been quite a week. Start of the week, get attacked from behind by a blunt instrument. But what an ending to this week. We have made history today. Nine months, more than 20 refused attempts to get a debate on excess deaths, the first debate on excess deaths in the UK, Parliament, the first proper debate on excess deaths in the world and I promise you, I absolutely promise you, it won't be the last. We will get a three hour debate in the next few weeks now on excess deaths. We've got two democracies under challenge all over the world. We're hanging over and using what we've got to make sure we get our message out there. On Tuesday next week I'm, I'm bringing in a bill, a ten minute rule motion, a bill called the Sovereignty and Referendums Bill. I'm going to put it to the House. That would stop, if we could bring that in, that would stop the WHO power grab of the people of the UK. I've been invited to speak as well next week on Zoom to some African political leaders, to try and persuade them to resist the WHO power grab, because it doesn't matter where we break this, we can break it in the UK, we can break it anywhere else in the world. This is a worldwide problem, an absolute assault on humanity, and we've all got to stick together. I've been an MP for nearly 14 years. I've given a lot of speeches in that chamber. That I was a bit nervous today because I knew there was never going to be a more important, speech I've ever given. I've never been in a more important speech than the one I was giving today. Can't you hear at the back? Turn up the PA. So, here we go. There was never going to be a more important speech than the one I was giving today, and, even after 14 years as an MP I was a little bit nervous standing up. But what really got me was, OK, there wasn't as many MPs in the chamber as I'd liked, but, the public gallery was full and the support from there was absolutely incredible. And they always say the politicians, that place over there, is in the Westminster bubble. We are going to burst the bubble in Westminster. Absolutely. Ultimately, my message to send you away with is that your determination, your cheerfulness, your resilience will deliver us victory. Thank you very much for coming today. (Hearts of Oak) Andrew, we've just been in on the debate on vaccine harms. Tell us about the process, because it's been a long, hard battle, which you talk about in the chamber. (Andrew Bridgen MP) Yeah, I've been putting in since January every week for a backbench business debate. That was refused. I've put in for a Westminster Hall debate on a weekly basis and I've put in for an adjournment debate. Eventually, after nine months and more than 20 rejections, we had the first debate on excess deaths in the UK Parliament. I think it's the first one in the world, but I promise you it won't be the last. I think the dozen or so MPs who attended today's debate, I'm hoping I'll be able to get a get them to sign up that we can have a three-hour debate well before Christmas and then it's going to grow from there because ultimately the data that I imparted in the chamber today, it's all backed up with the science. Every MP is going to be getting a copy of my Hansard speech and the full data pack of all the evidence that backs up everything I've said. There's no excuses now. So this goes to law because it's a no-brainer really to have these conversations because we've all seen excess deaths across Europe. Ask yourself in a democracy why don't they want to have a conversation about anything? I mean, I'm aware that in the Australian Senate four or five senators asked for a debate on excess deaths they ended up having a debate on whether you should have a debate on excess deaths and the consensus of the Australian Senate was they didn't want to have a debate on excess deaths. Well, I mean that's a red flag straight away, isn't it? (Hearts of Oak) Last question, I assume you believe that there are some MPs that can be won over, that public figures have kept quiet a further reputation, which you don't care about and you've walked away from the party. Tell us about those who you think you can possibly win over and then support you publicly on this. (Andrew Bridgen MP) Well certainly some of the ones that were there today, I know of some who weren't there today who will support calling for a much bigger debate on excess deaths. And ultimately it's the pressure of the electorate, the people, and you could see that although the House wasn't very full of members, the public gallery was full and that shows you that public opinion is they want this issue debated, they want to know what's gone on, and it's their right to have it happen. And that will become an irresistible force for politicians. That's how democracy works. (Hearts of Oak) Well, we've just had the debate in Parliament, a debate that I actually, to be honest, didn't think would happen. I thought that it would be stopped and held off. Only one member of 650 MPs in that place was willing to stand up and have this conversation, on vaccine arms as on excess deaths. He spoke for 24 minutes, presented everything in a measured calm manner, no emotion. One of the many things Andrew is great at, that he just lays it out gently, softly, step by step, that he doesn't raise the hyperball that maybe some others will rise to. And he laid it out in 24 minutes. And of course, the government's response is, Well, excess deaths are other factors, lifestyle factors, like smoking, like cholesterol, even fatty foods. So the government are blaming all the excess deaths over a period of a sudden spike in, smoking and a spike in eating fish and chips. That's what the government. Wow. Like ostriches with their heads in the sand. So Andrew presented his figures. The great thing is that we expect now there to be a much longer debate in Parliament. That was a short motion, a short debate, a 30 minute session. Andrew is hopeful that this can now go to a three hour fuller debate and that will be really interesting to see whether that gets tabled and whether it actually does go ahead and I would like to see other MPs backing Andrew and I think the more he speaks the more courage they will get. Andrew is someone with courage, with conviction, with a backbone, with a determination to speak truth and often, that is a rarity across there, it really is, really people want to, keep their heads down, they want to climb up the greasy pole and attain those higher levels of political achievement. So we obviously will watch this, follow Andrew. He is a hero. There's no one else in that Parliament across the way that's a hero like Andrew. And what else? I mean, it's the hill that he's chosen to die on. It's the hill that he has chosen to fight on. It's the hill that he has lost his career in the Conservative Party. And why? Because people are dying and no one is talking about it. What more important issue is there apart from life and death? And if something has been introduced and it's killing people, you need to look at it, you need to address, you need to understand it, to analyse it and then see what you do with that. So we have won here amongst 650. We will follow this and watch this closely as we see this move towards a fuller debate in Parliament and certainly my hope and prayer is that many other MPs stand up and speak, and that this happens across the world. We've seen a debate happening, I know, in the German Parliament with the AfD. I know we've seen debates happening in the Australian Parliament and the One Nation Party with Pauline and Malcolm are doing a fantastic job there. And here is one individual. Obviously, the Reclaim Party is behind Andrew Bridgen. He's a member of that of Lawrence Fox's party. And Andrew will continue to speak. And as he speaks, I believe that we will see ripple effects across the world because the world watches what happens here. This is called the mother of parliament and I believe that as Andrew continues to speak and continues to speak within this chamber that we will see other parliaments around the world address this issue. But this doesn't affect future debt, I mean, the damage is done, the deaths are happening. But at least you have to hold people to account. And for me, this is about justice. It's about honesty. It's about clarity. It is about truth, which is something that's been in short supply over the last couple of years during the COVID tyranny. So keep an eye on this space for Andrew to continue to push this. And when that longer three hour debate does happen, we will be here reporting on us and reporting on those who have come out to support Andrew today. Matt Le Tissier was here, Le God was in the chamber watching Andrew, Mike Yeadon was here speaking, Fiona Hine has done a great job in pulling people together. There is massive support and I think the parliamentarians in the government want individuals like Andrew Bridgton to feel they are alone, but they are not alone. They are backed by masses of the population and today was a small subset, of that, but Andrew knows he is not alone. Make sure and post this video, let others see what has happened here in the UK Parliament and have hope, because I think often that's also in short supply and I think what has happened today is a day of hope, is a day of reckoning and is a day of moving forward to actually presenting the truth and holding people to account.

Dr. Laura C. Green, Ph.D. and Dr. Edmund A.C. Crouch of Green Toxicology LLC breaks down the process of extrapolating results from blood alcohol testing in this podcast, excerpted from MCLE's 4/25/23 live webcast: Toxicology for Lawyers: Drug & Alcohol Testing. The full program is available as an on demand webcast or an MP3 here.  Get 24/7 instant access to hundreds of related eLectures like this one—and more—with a subscription to the MCLE OnlinePass. Learn more at www.mcle.org/onlinepass and start your free trial today!

There are facts, provable information, data, research results and opinions.  What is the right mix when presenting?  Should we just marshal the detail, lay it out for the audience and let them draw their own conclusions or do we need to direct them?  How expert do we have to be to start handing out advice to others?  Are we seeding the emergence of opposition to what we are talking about, because members of the audience don't want some speaker lecturing to them?  Are we setting ourselves up for a very hot Q&A session, where some of the assembled masses are about to tear shreds off us? These types of questions are difficult for those of us in industries where we have points of view and are recommending certain actions on the part of the audience.  The training industry is a hot crucible for advice and recommendations for others. We are suggesting things which we believe will help them do better in their companies.  Or it could be that through your own firm's experiences, you have observed some things to be careful of and you are going to enlighten the audience, so that they don't repeat the mistakes you made.  There is certainly a demand for case studies, warnings, examples and the sharing of experiences, in order to guide audiences about where the dangers are and the traps are set.  Just stating our opinion though won't cut it.  We have to set that up with some evidence, something relatable for the audience, so that they feel what we are saying is credible.  The best options are personal experiences.  These always have the most credence and authenticity.  The second best is the experience of others and the last is published, public information.  In Japan, any time you are tempted to use data to prove a point, you need to have the Japanese version too.  If it is only information collected in the US or in Europe, then Japanese audience members will just discount it, because as far as they are concerned, Japan is always different and the data won't travel well.  Often though, we start out with some data and we even raid previous presentations for slides brimming with graphs and diagrams, to use for the next presentation.  That data is too valuable to just leave for one presentation, so we want to recycle it.  Or we might have some recent survey data, which will be fresh for the audience and we want to impress them.  One of the dangers is we get stuck at the data provision level and we don't relate this to the realities of the audience members.  Data by itself is good, but “what does it mean for me”, is always in the minds of the audience. This is where we get into the advice business and we have to tread warily.  We have to remain the expert, without becoming the schoolteacher, bossing the audience members around and telling them how to fly straight. Extrapolating what the data shows is a good idea, but there is an element of prophesy built in and basically that is just our opinion.  Instead of getting sucked into the “listen to me now” business, we can approach it in another way. Rhetorical questions are brilliant for this.  We can lay out the facts or the argument and instead of moving into the advice component, we can ask the audience what they think, without requiring them to vocalise an answer.  We frame the construct and let the question hang there unanswered, so that the audience has to draw their one conclusions.  When we want to add in our point of view, we can do so in a very small target way.  Rather than spruking the answer, we can cloak it in camouflage. We can say, “there is a view that…” or “ a common conclusion has been….”, or “a perspective I quite like is….” or “most experts seem to agree that….”.  In this way we proffer an answer, without having to attach ourselves to it.  This reduces the friction with highly opinionated audience members, who may want to argue the point with us.  We come across as reasonable, balanced, open, flexible as well as humble.  We can say, “I will leave it to everyone to make up their own minds on this one”. That is fine but often we are asked to speak because we supposedly know something about the topic and this may come across as a cop out and audience members may feel cheated.  They don't want a lecture from us, but they are interested in what we think, and they want to hear about that. Rhetorical questions and a small target strategy will go a long way toward setting the right frame for the talk.  Audiences will vary of course, but if you don't know what you are facing then caution is a good policy.  You have assembled valuable information, given some guidance and have respected the audience to be capable of reaching their own interpretation of what it all means, while offering your humble insights.  That is a killer combo.

In this episode, Brian Auten and Chad Gross interview Richard P. Moore. They talk about the New Apostolic Reformation and its theological problems.0:23 - Intro to Richard Moore3:50 - Why is Richard in Germany, and why write this book?8:02 - What has been the influence of the NAR in Germany?19:42 - What differentiates NAR from traditional pentecostal/charismatic?25:42 - Why do those within NAR want to distance themselves from the label “New Apostolic Reformation”?33:10 - Where does NAR theology go off the rails?43:53 - Richard's visit to Bethel Church and what he found55:39 - Extrapolating scripture out of context, building doctrines out of thin air1:01:29 - The self-centeredness of the NAR 1:04:15 - Can we “take the good and leave the bad” in the NAR?1:11:53 - Further resources…Richard P. Moore is the author of Divergent Theology: An Inquiry into the Theological Characteristics of the Word of Faith, Third Wave Movement and the New Apostolic Reformation. Resources:Richard Moore's website:www.richardpmoore.netChurchepreneurs Podcastwww.richardpmoore.net/churchepreneurs-podcastRichard's podcast on YouTube https://www.youtube.com/@richardm23On Twitter:@richardpmoore23Richard's recent article on the New Apostolic Reformation in the Evangelical Review of Theology can be read here: https://a315.co/3OcwQM6Divergent Theology: An Inquiry into the Theological Characteristics of the Word of Faith, Third Wave Movement and the New Apostolic Reformation. https://a315.co/Divergent-TheologyGerman version:www.richardpmoore.net/entwurzelt-kaufen-2================================We appreciate your feedback.If you're on TWITTER, you can follow Chad @TBapologetics.You can follow Brian @TheBrianAutenAnd of course, you can follow @Apologetics315If you have a question or comment for the podcast, record it and send it our way using www.speakpipe.com/Apologetics315 or you can email us at podcast@apologetics315.com

This week Andrew talks with former P.F. Chang's CEO Damola Adamolekun. Damola took over as CEO during the heart of the COVID-19 pandemic — and led the company & its 20,000 team members through a variety of monumental strategic initiatives. In this conversation, Damola shares a behind-the-scenes look at the leadership mindsets & philosophies he put in action to power one of the world's most iconic brands to new heights. You'll hear actionable lessons on how to manage during times of disruption, communicate a clear vision, achieve quick wins to build momentum, & so much more. This episode will get you hungry to take on your next big challenge.  Programming Note: This conversation was recorded while Damola was still CEO at P.F. Chang's prior to the recent announcement that he stepped down as CEO. Show Highlights: (2:30) - Decision to invest in P.F. Chang's(3:25) - Current vs. potential(4:09) - Presenting investor pitch to John Paulson(4:31) - Extrapolating to what's possible(6:38) - The power of being present(8:32) - Using challenges/crisis to galvanize(10:12) - Balancing parallel initiatives (10:59) - Changing perceptions(11:34) - Shifting mindset of an organization(13:45) - Building momentum through quick wins(14:23) - Restaurant operator going above & beyond(18:34) - Attention to detail (20:47) - Getting others excited about role(23:51) - Damola's morning routine(24:38) - Starting your day with a conquest(26:27) - Creating positive habits(28:35) - Moving from Nigeria to US(30:22) - Damola's inspiration** Follow Andrew On Social Media **Twitter: @andrewhmosesInstagram: @AndrewMoses123Sign up for e-mails to keep up with Andrew's podcast at everybodypullsthetarp.com/newsletter

1) Facio, Facere, Feci, Factum 2) Opus, Operis 3) Ops, Opis 4) Ergon 5) Labor --- Support this podcast: https://podcasters.spotify.com/pod/show/liam-connerly/support

We take a look at two extraordinary astronomical news items in this episode. A deep dive into an astronomical survey has discovered an Earth-sized rogue planet and a Neptune-sized rogue planet. Extrapolating from those discoveries, there may be trillions of Earth-sized rogue planets roaming the galaxy. That's more than the number of stars in the Milky Way! We also explore the question of early galaxies and a model that suggests the universe is older than the standard model. As a bonus, we have some wacky Top quark trivia, space news, and more.

In this episode Saul and Jolene talk about a question saw on Facebook that asked, "Does being a paid GM change your game?  There were plenty of comments and Saul picked out a few to discuss how it correlated with being a paid GM. One comment was asking if the GM would be considered to be a player in an RPG game.  For Saul it seemed like a silly question but if you really dig into the issue it is not so easily answered.  Another commenter didn't like the use of the term play because they thought it didn't fully describe what a GM does.         Finally Saul and Jolene discuss how being paid might affect the GM in their approach to GMing.  Whether it included more prep, more props, maps, voices, and added stress to perform.     For Saul he used the idea of being a Con GM Vs Running Home games to show how he tackles a Con game in comparison to a home game.  Extrapolating that to being paid Saul believed it would surely impact his approach and responsibility over putting on a good RPG game.   Thank You all for listening.   Web Art by Jim Foster   Episode Art by Michael Shean-Jones   Music by Brentin Davis, song Getting Paid Over Here     Available from Tribeofnoise.com   Used using the Creative Commons License 4.0    https://creativecommons.org/licenses/by-sa/4.0/legalcode

We are posting some of our weekly Behind The Scenes Q&A live podcast is for Club Gym Nerd members because Jessica lost her voice from squealing too much about Gabby. Here's how to ask questions live. We discussed: Everything about the Gabby Douglas official return announcement! No, literally everything -- start values skills, her competition strategy, how she could petition to Classic, how she can do the World Cup circuit and only go to camp once, how to avoid national team camp all together if she doesn't want to ever go to camp etc. etc Spencer explains Gabby's Ling vs a Bi/Ono bars turn National Team Camp scores and start values Simone Biles's all-around domination and which vaults she's competing. Who got hit hardest on scoring E score drama Extrapolating an all black Olympic team from camp results (full scores on our website) To continue the conversation, just login to  your Club Gym Nerd account.  Not a member? Join here. Not sure if you are ready to join? Watch College & Cocktails week one for free here.

The Rational Root is a Bitcoin on-chain & cycle analyst. In this interview, 9 months on from our last interview, we look into the various Bitcoin price models Root has developed. We again review his Bitcoin Spiral Models, Bitcoin Hodl price models and Bitcoin halving & cycle charts. We discuss the growing evidence of Bitcoin scarcity, and bullish cases as we approach the next halving. - - - - In his first interview on What Bitcoin Did last August, the Rational Root made prescient predictions about Bitcoin bear market lows that occurred a few months later. The caveat is that he also stated “You always have to be a bit sceptical about models that predict the future, they can be wrong.” Nevertheless, the models Rational Root has developed have some conspicuous patterns that are worth consideration. The Bitcoin Spiral chart Rational Root uses is incredibly powerful. It clearly shows a correlation between Bitcoin's price and the halving events. Extrapolating this pattern forward suggests we could be on the cusp of a new bullish price triggered by next year's halving. This phase is predicted to see a material price appreciation between $100,000 and $1 million in the next cycle. We also debate whether Bitcoin is now correlated with other more significant asset markets, in particular the S&P 500. In essence, is there a correlation between Bitcoin's price and risk appetite in the investment market? If such a link has been established, and a rescission occurs, this could lead to a deviation from the Bitcoin halving cycle patterns. But perhaps the most bullish analysis Rational Root has undertaken involves the assessment of demand and supply indicators, such as his "Hodl Model", which predicts the growth rate of illiquid supply in Bitcoin. It's simple economics that price is affected by the relationship between demand and supply, and, as Rational Root states “Bitcoin is becoming more scarce, and this data is not being paid attention to by many people."

“If we keep on following the same pattern Bitcoin should reach between $100,000 and a million in the next cycle, and possibly higher after that. So that is quite bullish.”— Rational RootThe Rational Root is a Bitcoin on-chain & cycle analyst. In this interview, 9 months on from our last interview, we look into the various Bitcoin price models Root has developed. We again review his Bitcoin Spiral Models, Bitcoin Hodl price models and Bitcoin halving & cycle charts. We discuss the growing evidence of Bitcoin scarcity, and bullish cases as we approach the next halving.- - - - In his first interview on What Bitcoin Did last August, the Rational Root made prescient predictions about Bitcoin bear market lows that occurred a few months later. The caveat is that he also stated “You always have to be a bit sceptical about models that predict the future, they can be wrong.” Nevertheless, the models Rational Root has developed have some conspicuous patterns that are worth consideration.The Bitcoin Spiral chart Rational Root uses is incredibly powerful. It clearly shows a correlation between Bitcoin's price and the halving events. Extrapolating this pattern forward suggests we could be on the cusp of a new bullish price triggered by next year's halving. This phase is predicted to see a material price appreciation between $100,000 and $1 million in the next cycle. We also debate whether Bitcoin is now correlated with other more significant asset markets, in particular the S&P 500. In essence, is there a correlation between Bitcoin's price and risk appetite in the investment market? If such a link has been established, and a rescission occurs, this could lead to a deviation from the Bitcoin halving cycle patterns.But perhaps the most bullish analysis Rational Root has undertaken involves the assessment of demand and supply indicators, such as his "Hodl Model", which predicts the growth rate of illiquid supply in Bitcoin. It's simple economics that price is affected by the relationship between demand and supply, and, as Rational Root states “Bitcoin is becoming more scarce, and this data is not being paid attention to by many people." - - - - This episode's sponsors:Iris Energy - Bitcoin Mining. Done Sustainably Ledn - Financial services for Bitcoin hodlersBitcasino - The Future of Gaming is hereLedger - State of the art Bitcoin hardware walletWasabi Wallet - Privacy by defaultUnchained - Secure your bitcoin with confidence-----WBD672 - Show Notes-----If you enjoy The What Bitcoin Did Podcast you can help support the show by doing the following:Become a Patron and get access to shows early or help contributeMake a tip:Bitcoin: 3FiC6w7eb3dkcaNHMAnj39ANTAkv8Ufi2SQR Codes: BitcoinIf you do send a tip then please email me so that I can say thank youSubscribe on iTunes | Spotify | Stitcher | SoundCloud | YouTube | Deezer | TuneIn | RSS FeedLeave a review on iTunesShare the show and episodes with your friends and familySubscribe to the newsletter on my websiteFollow me on Twitter Personal | Twitter Podcast | Instagram | Medium | YouTubeIf you are interested in sponsoring the show, you can read more about that here or please feel free to drop me an email to discuss options.

It's Wednesday, June 14th, A.D. 2023. This is The Worldview in 5 Minutes heard at www.TheWorldview.com. I'm Adam McManus. (Adam@TheWorldview.com) By Jonathan Clark Religious buildings are targeted in 100 countries Pew Research released a report last week on property crimes against religious groups in 2020. Out of nearly 200 countries, properties in over 100 countries were targeted in incidents tied to religion.  Europe had the highest share of countries with such property attacks. Churches filed restitution cases to recover confiscated property as well as vandalism of their property. The Middle East, North Africa, and the Asian-Pacific region also saw high levels of attacks on religious property. Sub-Saharan Africa and the Americas experienced the least attacks.  Overall, social groups perpetrated the attacks in 81 countries, while governments were the perpetrators in 56 countries.  Britain bans puberty blockers for kids Last Friday, the National Health Service of England announced restrictions on puberty blockers for children. The latest guidance still allows the drugs but limits them to clinical trials. The NHS previously acknowledged that children who pretend to be the opposite sex may be going through a “transient phase.”  The latest guidance noted, “A significant proportion of children and young people who are concerned about or distressed by issues of gender incongruence experience coexisting mental health, neuro-developmental, or personal, family or social complexities in their lives.” Sexual perversion is transient phase for many in U.K. A new study from Duke University found sexually perverted lifestyles are indeed a transient phase for many in the U.K. The study analyzed over 20,000 respondents in two waves. The first wave was between 2011 and 2013; the second one was between 2017 and 2019. The survey found 8.6% of people who identified as gay or lesbian in the first wave, switched to heterosexual by the second wave. Among those identifying as bisexual, 44% changed to heterosexual. And a massive 69.6% of those identifying as “other” made the switch. The “other” category includes people pretending to be the opposite sex. Only 3.3% of heterosexuals in the first wave identified differently by the second wave. Jesus said in Mark 10:6, “But from the beginning of creation, ‘God made them male and female.'" Missouri protects kids with two new laws Meanwhile, in the U.S., Missouri Republican Governor Mike Parson signed two bills last week to protect children. One bill prohibits male students from participating in female sports at schools. The other bill outlaws surgical or chemical procedures meant to “transition” children to the opposite sex. Governor Parson said, “These decisions have permanent consequences for life and should not be made by impressionable children who may be in crisis or influenced by the political persuasions of others.” Rising percentage of Americans are conservative A new survey from Gallup found more Americans say they are conservative on social issues. Thirty-eight percent of U.S. adults say they are conservative on social issues this year, up from 30% in 2021. Meanwhile, 29% of Americans say they are liberal on social issues this year, down from 34% in 2021. Gallup noted this increase in conservative identification on social issues was present in nearly all political and demographic subgroups.  Southern Baptists eager to ban female pastors The Southern Baptist Convention is holding its annual meeting this week in New Orleans. Delegates at the meeting will vote on a measure to prevent women from serving as pastors in the denomination.  The SBC's statement of faith states, “the office of pastor is limited to men as qualified by Scripture.” However, some churches in the denomination are practicing differently.  The annual meeting will also consider whether or not to remove two churches for having women in pastoral roles. One of the churches is Saddleback Church, a megachurch in California founded by Rick Warren. Approximately 1,800 female pastors in Southern Baptist Convention And finally, a new study analyzes how many female pastors are in the Southern Baptist Convention. The research was led by Kevin McClure, a PhD student at The Southern Baptist Theological Seminary in Louisville, Kentucky. The study analyzed the SBC's online church database of 47,614 churches. Of those, 22,000 churches have websites listed. The study randomly selected 3,847 churches out the 22,000. Of that sample, there were 149 female pastors across 99 churches. Extrapolating that out to the rest of the denomination would mean there are 1,844 female pastors in 1,225 SBC churches. McClure noted, “This is a staggering number, and it dwarfs the previously known information about female pastors by a factor of ten.” The Apostle Paul wrote in 1 Timothy 2:12, “I do not permit a woman to teach or to exercise authority over a man.” Close And that's The Worldview in 5 Minutes on this Wednesday, June 14th in the year of our Lord 2023. Subscribe by iTunes or email to our unique Christian newscast at www.TheWorldview.com. Or get the Generations app through Google Play or The App Store. I'm Adam McManus (Adam@TheWorldview.com). Seize the day for Jesus Christ.

Lamya H. speaks about writing an unapologetically queer and Muslim text in her debut work, Hijab Butch Blues: A Memoir, which chronicles her formative years in a Middle Eastern country and her continuing education in the United States. She recalls writing “Hajar” as a standalone essay, and how she formed and shaped a narrative arc that shaped the memoir extrapolating foundational texts like the Quran to share stories about her upbringing, relationships, academia, critical nostalgia, geographies, and intertextualities. 

Are you getting the right amount of chlorophyll in your body for the right detoxification, mitochondrial health, and blood-purifying benefits? Paleovalley's Organic Supergreens powder is my favorite for ensuring you're getting the right digestive enzymes to keep you healthy, as it contains none of the potentially gut-irritating ingredients like cereal grasses.  Simply take a scoop of Paleovalley's Organic Supergreens with water and start reaping the benefits of a healthier body and a clearer state of mind. Visit paleovalley.com/jockers.   LMNT is a tasty electrolyte drink mix with everything you need and nothing you don't. It's also got no sugar and is perfectly suited for those following a keto, low-carb, or paleo diet. LMNT contains a science-backed electrolyte ratio with none of the junk! That means no gluten and no fillers.  You'll notice a pickup in your energy and mental clarity when you drink LMNT. You can now get a free sample pack in a variety of tasty flavors with any order from LMNT's site. Simply go to drinkLMNT.com/drjockers and you're good to go with your electrolyte boost.    Are your joints painful? Do you suffer from daily discomfort? As it stands today, chronic pain affects more Americans than DIABETES, HEART DISEASE, and CANCER combined.   In fact, chronic pain is THE NUMBER ONE reason Americans access the Healthcare system today. However, many people believe their only option for pain relief is found in drugstore options such as Ibuprofen, Advil, and Tylenol (to name a few). Tragically, most people are unaware that these pain meds do more HARM than good.    Common side effects include Headaches, Liver and Kidney issues, High Blood Pressure, and in rare cases – even death. Not to mention, they are just masking your pain... Giving you temporary relief...   While this may sound all doom and gloom, I do have good news... There is an all-natural solution that addresses the root cause of your discomfort... A REAL solution that allows you to get back to being YOU!   And that solution is called Curcumin Gold made by my friends at Purality Health. Curcumin Gold contains Turmeric Curcumin Extract, Vegan Omega-3s, & Ginger Oil. These carefully selected ingredients support healthy joint function and address the root cause of inflammation within your body. Trust me when I say you won't find anything else on the market quite like this. In fact, my friends over at Purality Health have a patented formula that utilizes something called ‘MICELLE LIPOSOMAL' technology which delivers the nutrients into your bloodstream – proven to be 800% more efficient!   Even better, it's backed by a 180-day money-back guarantee. And today, we have a 30% OFF coupon for you! Visit puralityhealth.com and use the coupon "DRJ" to access 30% OFF today!   What is the connection between the gut microbiome and Alzheimer's Disease? Most of us know someone affected by the onset of old age and perhaps Alzheimer's Disease. So what can we do to prevent this gradual decline in memory, thinking, behavior, and social skills from happening to us or those we love?   It turns out that we can start by looking at our gut for the answers. Today's expert is Dr. David Perlmutter, a board-certified neurologist, and six-time bestselling author. Understand the relationship between the intestinal barrier and the blood-brain barrier, as well as the connection between inflammation, 'leaky gut', and brain health.    As always, there's a lot of good information in this episode featuring two experts in all things health. From the gut biome to the ‘viome', and the difference between good and bad bacteria, fats, and oils, Dr. Perlmutter brings a proactive urgency to how we need to take care of our brains by looking after our guts.    Please share this episode with anyone that you care about. It may just save, or at the very least radically improve somebody's life. Thanks for being a part of our community! Let's dive into the show!     "The relationship between the gut (in any way, not just the microbiome) in any of its functionality to the brain really is still, I think, pretty much neglected as it relates to the challenges that we as neurologists face, like Alzheimer's and MS." ~ Dr. Perlmutter Subscribe to the podcast on: Apple Podcast Stitcher Spotify PodBean TuneIn Radio In This Episode:   05:35 What did Dr. Perlmutter originally learn about the gut in relation to the brain? 07:05 When did Dr. Perlmutter start looking into what was happening in the gut? 08:35 What is modern medicine doing to combat Alzheimer's and dementia? 11:35 What is amyloid (plaque)?  12:10 Why keeping our metabolism intact is so crucial 14:20 What's the first thing we should do when blood sugar goes up? 16:00 Why it's such a stretch for (some) neurologists to make the link to our gut 18:50 What's going on with the gut (to keep our brains happy)? 20:55 What is BDNF? 22:40 The crucial role of gut bacteria in preserving our gut lining  25:40 How many people are suffering from major depressive orders? 30:30 The difference between good and bad gut bacteria 32:00 Why the over-prescription of antibiotics is bad for our gut bacteria 35:20 Why you shouldn't compromise your stomach acid 37:40 Learning to 'go upstream' with your acid reflux 39:10 The difference between good and bad fats 42:30 What is the richest source of DHA in nature? 45:20 The benefits of quercetin 47:40 Why eating a diversity of vegetables is so important 51:00 Fossilized poop and what we can learn from our ancestors 52:50 Extrapolating the Hygiene Hypothesis as it relates to our guts 56:10 What is the 'viome'? 57:40 Are all bacteria and viruses bad for our gut?   58:30 The importance of creating gut resilience 1:01:00 How do we innoculate our gut with good bacteria? 1:02:40 What happens when we deprive newborn babies of healthy bacteria? 1:03:20 What are Dr.Perlmutter's daily habits to support his BDNF levels? 1:08:40 'Be your own advocate' and other last words of inspiration Resources: Paleovalley Organic Supergreens  LMNT (electrolyte drink mix) - Go to drinkLMNT.com/drjockers for a free sample with any purchase Curcumin Gold -  Visit puralityhealth.com and use coupon "DRJ" to access 30% OFF today! Connect with Dr. David Perlmutter: Website - https://www.drperlmutter.com Book: Drop Acid -  https://amzn.to/3BtuGmp Book: Grain Brain - https://amzn.to/3QnEVNe Instagram - https://www.instagram.com/davidperlmutter/   Connect with Dr. Jockers: Instagram – https://www.instagram.com/drjockers/ Facebook – https://www.facebook.com/DrDavidJockers YouTube – https://www.youtube.com/user/djockers Website – https://drjockers.com/ If you are interested in being a guest on the show, we would love to hear from you! Please contact us here! - https://drjockers.com/join-us-dr-jockers-functional-nutrition-podcast/

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: PaLM-2 & GPT-4 in "Extrapolating GPT-N performance", published by Lukas Finnveden on May 30, 2023 on LessWrong. Two and a half years ago, I wrote Extrapolating GPT-N performance, trying to predict how fast scaled-up models would improve on a few benchmarks. One year ago, I added PaLM to the graphs. Another spring has come and gone, and there are new models to add to the graphs: PaLM-2 and GPT-4. (Though I only know GPT-4's performance on a small handful of benchmarks.) Converting to Chinchilla scaling laws In previous iterations of the graph, the x-position represented the loss on GPT-3's validation set, and the x-axis was annotated with estimates of size+data that you'd need to achieve that loss according to the Kaplan scaling laws. (When adding PaLM to the graph, I estimated its loss using those same Kaplan scaling laws.) In these new iterations, the x-position instead represents an estimate of (reducible) loss according to the Chinchilla scaling laws. Even without adding any new data-points, this predicts faster progress, since the Chinchilla scaling laws describes how to get better performance for less compute. The appendix describes how I estimate Chinchilla reducible loss for GPT-3 and PaLM-1. Briefly: For the GPT-3 data points, I convert from loss reported in the GPT-3 paper, to the minimum of parameters and tokens you'd need to achieve that loss according to Kaplan scaling laws, and then plug those numbers of parameters and tokens into the Chinchilla loss function. For PaLM-1, I straightforwardly put its parameter- and token-count into the Chinchilla loss function. To start off, let's look at a graph with only GPT-3 and PaLM-1, with a Chinchilla x-axis. Here's a quick explainer of how to read the graphs (the original post contains more details). Each dot represents a particular model's performance on a particular category of benchmarks (taken from papers about GPT-3 and PaLM). Color represents benchmark; y-position represents benchmark performance (normalized between random and my guess of maximum possible performance). The x-axis labels are all using the Chinchilla scaling laws to predict reducible loss-per-token, number of parameters, number of tokens, and total FLOP (if language models at that loss were trained Chinchilla-optimally). Compare to the last graph in this comment, which is the same with a Kaplan x-axis. Some things worth noting: PaLM is now ~0.5 OOM of compute less far along the x-axis. This corresponds to the fact that you could get PaLM for cheaper if you used optimal parameter- and data-scaling. The smaller GPT-3 models are farther to the right on the x-axis. I think this is mainly because the x-axis in my previous post had a different interpretation. The overall effect is that the data points get compressed together, and the slope becomes steeper. Previously, the black "Average" sigmoid reached 90% at ~1e28 FLOP. Now it looks like it reaches 90% at ~5e26 FLOP. Let's move on to PaLM-2. If you want to guess whether PaLM-2 and GPT-4 will underperform or outperform extrapolations, now might be a good time to think about that. PaLM-2 If this CNBC leak is to be trusted, PaLM-2 uses 340B parameters and is trained on 3.6T tokens. That's more parameters and less tokens than is recommended by the Chinchilla training laws. Possible explanations include: The model isn't dense. Perhaps it implements some type of mixture-of-experts situation that means that its effective parameter-count is smaller. It's trained Chinchilla-optimally for multiple epochs on a 3.6T token dataset. The leak is wrong. If we assume that the leak isn't too wrong, I think that fairly safe bounds for PaLM-2's Chinchilla-equivalent compute is: It's as good as a dense Chinchilla-optimal model trained on just 3.6T tokens, i.e. one with 3.6T/20=180B parameters. This would make it 6180e...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: PaLM-2 & GPT-4 in "Extrapolating GPT-N performance", published by Lukas Finnveden on May 30, 2023 on The AI Alignment Forum. Two and a half years ago, I wrote Extrapolating GPT-N performance, trying to predict how fast scaled-up models would improve on a few benchmarks. One year ago, I added PaLM to the graphs. Another spring has come and gone, and there are new models to add to the graphs: PaLM-2 and GPT-4. (Though I only know GPT-4's performance on a small handful of benchmarks.) Converting to Chinchilla scaling laws In previous iterations of the graph, the x-position represented the loss on GPT-3's validation set, and the x-axis was annotated with estimates of size+data that you'd need to achieve that loss according to the Kaplan scaling laws. (When adding PaLM to the graph, I estimated its loss using those same Kaplan scaling laws.) In these new iterations, the x-position instead represents an estimate of (reducible) loss according to the Chinchilla scaling laws. Even without adding any new data-points, this predicts faster progress, since the Chinchilla scaling laws describes how to get better performance for less compute. The appendix describes how I estimate Chinchilla reducible loss for GPT-3 and PaLM-1. Briefly: For the GPT-3 data points, I convert from loss reported in the GPT-3 paper, to the minimum of parameters and tokens you'd need to achieve that loss according to Kaplan scaling laws, and then plug those numbers of parameters and tokens into the Chinchilla loss function. For PaLM-1, I straightforwardly put its parameter- and token-count into the Chinchilla loss function. To start off, let's look at a graph with only GPT-3 and PaLM-1, with a Chinchilla x-axis. Here's a quick explainer of how to read the graphs (the original post contains more details). Each dot represents a particular model's performance on a particular category of benchmarks (taken from papers about GPT-3 and PaLM). Color represents benchmark; y-position represents benchmark performance (normalized between random and my guess of maximum possible performance). The x-axis labels are all using the Chinchilla scaling laws to predict reducible loss-per-token, number of parameters, number of tokens, and total FLOP (if language models at that loss were trained Chinchilla-optimally). Compare to the last graph in this comment, which is the same with a Kaplan x-axis. Some things worth noting: PaLM is now ~0.5 OOM of compute less far along the x-axis. This corresponds to the fact that you could get PaLM for cheaper if you used optimal parameter- and data-scaling. The smaller GPT-3 models are farther to the right on the x-axis. I think this is mainly because the x-axis in my previous post had a different interpretation. The overall effect is that the data points get compressed together, and the slope becomes steeper. Previously, the black "Average" sigmoid reached 90% at ~1e28 FLOP. Now it looks like it reaches 90% at ~5e26 FLOP. Let's move on to PaLM-2. If you want to guess whether PaLM-2 and GPT-4 will underperform or outperform extrapolations, now might be a good time to think about that. PaLM-2 If this CNBC leak is to be trusted, PaLM-2 uses 340B parameters and is trained on 3.6T tokens. That's more parameters and less tokens than is recommended by the Chinchilla training laws. Possible explanations include: The model isn't dense. Perhaps it implements some type of mixture-of-experts situation that means that its effective parameter-count is smaller. It's trained Chinchilla-optimally for multiple epochs on a 3.6T token dataset. The leak is wrong. If we assume that the leak isn't too wrong, I think that fairly safe bounds for PaLM-2's Chinchilla-equivalent compute is: It's as good as a dense Chinchilla-optimal model trained on just 3.6T tokens, i.e. one with 3.6T/20=180B parameters. This would ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Announcing Innovate Animal Ag (like GFI but for Animal Welfare Tech), published by RobertY on April 14, 2023 on The Effective Altruism Forum. I'm very excited to announce the launch of Innovate Animal Ag, a new nonprofit whose mission is to support agricultural technologies that directly improve animal welfare. Our first focus is on helping introduce in-ovo egg sexing technologies into the US. We're currently hiring and fundraising! The problem Companies improve their treatment of animals when the cost of the status quo is higher than the cost of change. Historically, pressure from activists, consumers, and regulators has been effective at increasing the cost of the inhumane practices that are part of the status quo. But significantly less work has been done on the other side of the equation: decreasing the cost of change. Similarly to how The Good Food Institute, New Harvest, and The Material Innovation Initiative support the field of alternative proteins, Innovate Animal Ag supports the field of animal welfare technology. Through education, networking building, and other ecosystem-level interventions, we aim to make it as easy as possible for companies to adopt new technologies that improve animal welfare. The technologies that we focus on will be guided by the EA principles of importance, tractability and neglectedness. Based on these criteria, some classes of technology we could be interested in include humane seafood slaughter machines, humane poultry euthanasia techniques, humane pest control technologies, and in-ovo egg sexing. Initial focus: in-ovo egg sexing Most of our work in the short term will be spent on in-ovo egg sexing technologies, specifically on helping introduce them to the US market. We chose this focus because it addresses an important problem that's particularly tractable right now: The culling of male chicks in the egg industry. We hope to use this as a test-case for our overall approach. For each of the more than 6 billion egg-laying hens in the world, there was a male chick that was fertilized, incubated, hatched, manually identified by a human, sorted, and then immediately killed. This practice is inhumane, wasteful for the industry, and unpopular with the consumers that know about it. Fortunately, there are a number of solutions being developed by companies and labs around the world. Some are even in the early stages of commercialization in Europe, where governments have started to ban the practice of chick culling. This rollout is going better than many realize: Extrapolating from publicly available partnership announcements, we estimate that current in-ovo sexing companies already supply between 10 and 20% of the entire EU market, with a cost impact of 1–3 euro cents per table egg (similar to, if not lower than cage-free). This leads us to believe that current technology is already ready for the high-end specialty egg market in the US. A small market foothold at the high end is then the first step towards wider adoption. Once companies demonstrate that there is demand for these products, they can more easily invest in lowering costs and scaling. Eventually the goal is to fulfill The United Egg Producers promise to completely eliminate chick culling across the industry. In our conversations with the companies developing this technology, a common refrain is that they're interested in the US market, but have little direct engagement with the US egg industry. For the most part, companies are focused on Europe because that's where governments are banning chick culling. Jumpstarting the market in the US will be challenging. Producers may not be aware that this technology is ready, and if they are, they may not be confident that consumers will be willing to pay a price premium. We aim to help solve these problems through interventions such as:...

This is a podcast episode about the James Webb Telescope The internet recently celebrated images from far-off areas of the universe from the latest eye in the sky - James Webb Space Telescope. JWST isn't the first time astronomers pointed a space telescope at various heavenly bodies. Years ago, Hubble Space Telescope was launched and brought back the first images of deep space, something that humans have never been able to see before. But the history of space exploration started many centuries before JWST lifted off from the earth's surface to bring back the images of the cosmos that broke the internet. From the time human beings started walking on the face of the earth, we have always looked up with wonder at the sky. Heavenly bodies fascinated us in many ways and became an integral part of cultures across the world. But before we dwell into JWST's existence and the story behind it, let us rewind some time in the past. Actually, a long time in the past, to the big daddy of all bangs - the Big Bang! The Big Bang theory describes how the universe expanded from an initial state of high density and temperature. It is the prevailing cosmological model explaining the evolution of the observable universe from the earliest known periods through its subsequent large-scale form. The model offers a comprehensive explanation for a broad range of observed phenomena, including the abundance of light elements, the cosmic microwave background (CMB) radiation, and large-scale structure. Big Bang theory is supported by Hubble-Lemarite law which states that further, an object is in the universe, the faster it is moving away from earth. Extrapolating this cosmic expansion backwards in time using the known laws of physics, the theory describes an increasingly concentrated cosmos preceded by a singularity in which space and time lose meaning. Detailed measurements of the expansion rate of the universe place the Big Bang singularity at around 13.8 billion years ago, which is thus considered the age of the universe. After its initial expansion, an event that is by itself often called "the Big Bang", the universe cooled sufficiently to allow the formation of subatomic particles, and later atoms. Giant clouds of these primordial elements—mostly hydrogen, with some helium and lithium—later coalesced through gravity, forming early stars and galaxies, the descendants of which are visible today. Besides these primordial building materials, astronomers observe the gravitational effects of an unknown dark matter surrounding galaxies. Georges Lemaître first noted in 1927 that an expanding universe could be traced back in time to an originating single point, which he called the "primeval atom". Edwin Hubble confirmed through analysis of galactic redshifts in 1929 that galaxies are indeed drifting apart; this is important observational evidence for an expanding universe.  For several decades, the scientific community was divided between supporters of the Big Bang and the rival steady-state model which both offered explanations for the observed expansion, but the steady-state model stipulated an eternal universe in contrast to the Big Bang's finite age. In 1964, the CMB was discovered, which convinced many cosmologists that the steady-state theory was falsified, since, unlike the steady-state theory, the hot Big Bang predicted uniform background radiation throughout the universe caused by the high temperatures and densities in the distant past. One of the most important factors supporting the Big Bang Theory is the expansion of the universe. Contrary to popular belief, Big Bang is not an explosion of matter that moves outward to fill the empty space, it is the expansion of the space itself with time, resulting in each point moving away. It is an expansion of space and not an explosion in space. The need for James Webb came in because it turned out that Hubble wasn't enough for more deep space investigation. To study about the universe as it existed during its formation, it was essential to be able to see deeper into space. JWST uses infrared cameras instead of visible light that enables it to look deeper into space. Infrared light travels through the gas clouds and dust in space and enables JWST to see further and detect fainter objects than a regular visible light spectrum camera. Since James Webb uses infrared cameras, it is important to maintain the temperature of the telescope as low as possible to avoid the images being tainted. For this reason, Webb orbits much further away from earth to avoid contamination of the images being captured. Any amount of heat would result in the contamination of images since anything that emits heat radiates infrared radiation. As a result of this distance from earth, JWST is not serviceable like the Hubble telescope. Hubble had issues with its mirrors when it was launched and then had to be serviced to add a corrective “lens” to ensure that the images were not blurred. Also, unlike the Hubble telescope, James Webb couldn't be assembled in the orbit but had to b launched in one piece which would unwrap itself. This was a complicated process with many points of failure. JWST is parked in an orbit around a point about a million kilometres from the earth where the effects of the gravitational force of the Sun and Earth act with equal force. James Webb was NASA's second administrator. He lead the Apollo Moon missions Projected cost was $1bn which was surpassed in 2007 and crossed $10bn in 2021.  Launched Dec 25th, 2021 During its inception and early years, it was called Next Generation Space Telescope (NGST) Cold telescope in space is required for capturing the infrared radiations coming from far away objects. An expected lifetime of 10 years, but may last more than that Since Webb orbits around 1.5 million kilometres from Earth, assembly in space and servicing is impossible The primary mirror in Webb is about 6.5 metres in diameter with about 6 times larger area than Hubble Mirrors are Beryllium mirrors and are lighter than Hubble's mirrors although bigger in size Five laters of sun shield with vacuum in between acting as insulator Located in the second Lagrange point where it is shielded from the Sun and the moon at all the times Webb operates at 50K which is 50 degrees above absolute zero (-223 degree C) You can follow us and leave us feedback on Facebook, Instagram, and Twitter @eplogmedia, For partnerships/queries send you can send us an email at bonjour@eplog.media   DISCLAIMER: The views expressed on all the shows produced and distributed by Ep.Log Media are personal to the host and the guest of the shows respectively and with no intention to harm the sentiments of any individual/organization.The said content is not obscene or blasphemous or defamatory of any event and/or person deceased or alive or in contempt of court or breach of contract or breach of privilege, or in violation of any provisions of the statute, nor hurt the sentiments of any religious groups/ person/government/non-government authorities and/or breach or be against any declared public policy of any nation or state.See omnystudio.com/listener for privacy information.

This is a podcast episode about the James Webb Telescope The internet recently celebrated images from far-off areas of the universe from the latest eye in the sky - James Webb Space Telescope. JWST isn't the first time astronomers pointed a space telescope at various heavenly bodies. Years ago, Hubble Space Telescope was launched and brought back the first images of deep space, something that humans have never been able to see before. But the history of space exploration started many centuries before JWST lifted off from the earth's surface to bring back the images of the cosmos that broke the internet. From the time human beings started walking on the face of the earth, we have always looked up with wonder at the sky. Heavenly bodies fascinated us in many ways and became an integral part of cultures across the world. But before we dwell into JWST's existence and the story behind it, let us rewind some time in the past. Actually, a long time in the past, to the big daddy of all bangs - the Big Bang! The Big Bang theory describes how the universe expanded from an initial state of high density and temperature. It is the prevailing cosmological model explaining the evolution of the observable universe from the earliest known periods through its subsequent large-scale form. The model offers a comprehensive explanation for a broad range of observed phenomena, including the abundance of light elements, the cosmic microwave background (CMB) radiation, and large-scale structure. Big Bang theory is supported by Hubble-Lemarite law which states that further, an object is in the universe, the faster it is moving away from earth. Extrapolating this cosmic expansion backwards in time using the known laws of physics, the theory describes an increasingly concentrated cosmos preceded by a singularity in which space and time lose meaning. Detailed measurements of the expansion rate of the universe place the Big Bang singularity at around 13.8 billion years ago, which is thus considered the age of the universe. After its initial expansion, an event that is by itself often called "the Big Bang", the universe cooled sufficiently to allow the formation of subatomic particles, and later atoms. Giant clouds of these primordial elements—mostly hydrogen, with some helium and lithium—later coalesced through gravity, forming early stars and galaxies, the descendants of which are visible today. Besides these primordial building materials, astronomers observe the gravitational effects of an unknown dark matter surrounding galaxies. Georges Lemaître first noted in 1927 that an expanding universe could be traced back in time to an originating single point, which he called the "primeval atom". Edwin Hubble confirmed through analysis of galactic redshifts in 1929 that galaxies are indeed drifting apart; this is important observational evidence for an expanding universe.  For several decades, the scientific community was divided between supporters of the Big Bang and the rival steady-state model which both offered explanations for the observed expansion, but the steady-state model stipulated an eternal universe in contrast to the Big Bang's finite age. In 1964, the CMB was discovered, which convinced many cosmologists that the steady-state theory was falsified, since, unlike the steady-state theory, the hot Big Bang predicted uniform background radiation throughout the universe caused by the high temperatures and densities in the distant past. One of the most important factors supporting the Big Bang Theory is the expansion of the universe. Contrary to popular belief, Big Bang is not an explosion of matter that moves outward to fill the empty space, it is the expansion of the space itself with time, resulting in each point moving away. It is an expansion of space and not an explosion in space. The need for James Webb came in because it turned out that Hubble wasn't enough for more deep space investigation. To study about the universe as it existed during its formation, it was essential to be able to see deeper into space. JWST uses infrared cameras instead of visible light that enables it to look deeper into space. Infrared light travels through the gas clouds and dust in space and enables JWST to see further and detect fainter objects than a regular visible light spectrum camera. Since James Webb uses infrared cameras, it is important to maintain the temperature of the telescope as low as possible to avoid the images being tainted. For this reason, Webb orbits much further away from earth to avoid contamination of the images being captured. Any amount of heat would result in the contamination of images since anything that emits heat radiates infrared radiation. As a result of this distance from earth, JWST is not serviceable like the Hubble telescope. Hubble had issues with its mirrors when it was launched and then had to be serviced to add a corrective “lens” to ensure that the images were not blurred. Also, unlike the Hubble telescope, James Webb couldn't be assembled in the orbit but had to b launched in one piece which would unwrap itself. This was a complicated process with many points of failure. JWST is parked in an orbit around a point about a million kilometres from the earth where the effects of the gravitational force of the Sun and Earth act with equal force. James Webb was NASA's second administrator. He lead the Apollo Moon missions Projected cost was $1bn which was surpassed in 2007 and crossed $10bn in 2021.  Launched Dec 25th, 2021 During its inception and early years, it was called Next Generation Space Telescope (NGST) Cold telescope in space is required for capturing the infrared radiations coming from far away objects. An expected lifetime of 10 years, but may last more than that Since Webb orbits around 1.5 million kilometres from Earth, assembly in space and servicing is impossible The primary mirror in Webb is about 6.5 metres in diameter with about 6 times larger area than Hubble Mirrors are Beryllium mirrors and are lighter than Hubble's mirrors although bigger in size Five laters of sun shield with vacuum in between acting as insulator Located in the second Lagrange point where it is shielded from the Sun and the moon at all the times Webb operates at 50K which is 50 degrees above absolute zero (-223 degree C) You can follow us and leave us feedback on Facebook, Instagram, and Twitter @eplogmedia, For partnerships/queries send you can send us an email at bonjour@eplog.media   DISCLAIMER: The views expressed on all the shows produced and distributed by Ep.Log Media are personal to the host and the guest of the shows respectively and with no intention to harm the sentiments of any individual/organization.The said content is not obscene or blasphemous or defamatory of any event and/or person deceased or alive or in contempt of court or breach of contract or breach of privilege, or in violation of any provisions of the statute, nor hurt the sentiments of any religious groups/ person/government/non-government authorities and/or breach or be against any declared public policy of any nation or state.See omnystudio.com/listener for privacy information.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is "A Scandinavian Perspective on Industrial Operator Independence." Our guest is Johan Stahre (https://www.linkedin.com/in/jstahre/), Professor and Chair of Production Systems at Chalmers University in Sweden. In this conversation, we talk about how the field of human-centered automation has evolved, the contemporary notion of operator 4.0, Scandinavian worker independence, shop floor innovation at Volvo, factories of the future, modern production systems, robots, and cobots in manufacturing. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich (https://www.augmentedpodcast.co/84). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Human-centered automation is the only kind of automation that we should be thinking about, and this is becoming more and more clear. Operators are fiercely independent, and so should they be. This is the only way they can spot problems on the shop floor, by combining human skills with automation in new ways augmenting workers. It seems the workforce does not so much need engagement as they need enablement. Fix that, and a lot can happen. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is A Scandinavian Perspective on Industrial Operator Independence. Our guest is Johan Stahre, Professor and Chair of Production Systems at Chalmers University in Sweden. In this conversation, we talk about how the field of human-centered automation has evolved, the contemporary notion of operator 4.0, Scandinavian worker independence, shop floor innovation at Volvo, factories of the future, modern production systems, robots, and cobots in manufacturing. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Johan, Welcome. How are you? JOHAN: I'm fine, thank you, Trond. It's really nice to see you. TROND: Yeah, likewise. JOHAN: Fellow Nordic person. TROND: Fellow Nordic person. And I apologize for this very American greeting, you know, how are you? As you know, I'm from the Nordic region. I actually mean it, [laughs] you know, it was a question. So I do wonder. [laughs] JOHAN: I'm actually fine. It's just ending the vacation, so I'm a little bit sad about that because everyone...but it's a very nice time now because the rest of the world seems to be on vacation, so you can get a lot of work done. TROND: I concur; that is a wonderful time. Johan, I wanted to just briefly talk about your exciting background. You are an engineer, a mechanical engineer from Sweden. And you had your initial degree from Linköping University. Then you went on to do your Ph.D. a while back in manufacturing automation, and this was at Chalmers, the University in Sweden. And that's where you have done your career in manufacturing research. You are, I think, the first Scandinavian researcher certainly stationed currently in Sweden that we've had on the podcast. So I'm kind of curious, what is manufacturing like in Scandinavia? And what is it that fascinated you about this topic so that you have moved so deeply into it? JOHAN: Manufacturing in Sweden is the core; it's the backbone of our country in a sense. We have statistically too many large manufacturing companies in Sweden as compared to, I mean, we're only 10 million people, but we have like 10, 12 pretty large companies in the manufacturing area in automotive but also in electronics like Ericsson, you have Volvo, we have SKF. We have a lot of big companies. Sweden has an industrial structure that we have several small companies and a couple of large companies, not so many in the middle section there. This happened, actually, in the 1800s somewhere. There was a big growth of big companies, and there was a lot of effort from the government to support this, and that has been continued. So the Swedish government has supported the growth of industry in Sweden, and therefore we have a very strong industry and also quite good digital growth and maturity. TROND: So the Scandinavian background to me when I was there, I remember that one of the things that at least Scandinavian researchers think is distinct about Scandinavia is worker independence. And it's something that I kind of wanted to just tease out a little bit in the beginning of this podcast. Am I wrong in this, or is there something distinct about the relationship between, I guess, workers and managers in Scandinavia, particularly? One speaks about the Scandinavian model. Can you outline a little bit what that means in manufacturing if it still exists? It's an open question. JOHAN: From my perspective, Sweden usually ranks very high in innovation, also when it comes to international rankings. And I think some of that has to do with the openness and the freedom of thinking in a sense and not so hierarchical, more consensus-oriented, ability to test and check and experiment at work without getting repercussions from top management. And it is much easier. In fact, if you are at one department in a manufacturing company or in university as such and you want to collaborate with another colleague across the aisle, if you have a two hierarchical system, you need to go three levels up in order to be able to do that. But here, I think it's easier to just walk across the aisle to have this collaboration and establish a cooperative environment. I think that that's part of the reason. Also, we're not so many; I mean, I think historically, we needed to do a lot of things ourselves in Sweden. We were a country up north with not so many people, and we have harsh environments, and I think it's the same as Norway. I mean, you need to be self-sustainable in that sense, and that creates, I think, environmental collaboration. TROND: We'll go more deeply into your research on manufacturing and to what extent a question I asked here matters to that. But do you have a sense just at the outset here that this type of worker and operators sort of independence, relative independence, perhaps compared to other regions, is it changing at all? Or is this kind of a feature that is a staple of Scandinavian culture and will be hard to change both for good and for bad? JOHAN: I think that as everything...digitalization has sort of erased a lot of the cultural differences across the world in that sense. Because when I was a student, there was not this expressed digital environment, of course. The information environment was less complex. But I think now all the young people, as well as my mother, does her banking...she's 90, but she does her banking on her iPad; I mean, it's very well-spread. And I think that we are all moving towards a similar culture, and the technology is spreading so quick. So you cannot really have cultural differences in that sense. But I think that's still the way that we're using this. And I think that the collaborative sense I think that that is still there. The reason why Sweden is comparatively innovative still is that we still maintain our culture and use the technology to augment that capability. TROND: So, Johan, we'll talk about a bunch of your experiences because you obviously are based in Sweden. And because of Sweden's industrial situation, you have some examples, you know, Volvo, a world-famous company obviously, and also famous for its management practices, and its factory practices, we'll get into that. But you've also worked, and you're advising entities such as the World Economic Forum, and you are active on the European stage with the European Institute of Technology. Your activity clearly goes way, way beyond these borders. But why don't we maybe start with some of these Scandinavian experiences and research projects that you've done maybe with Volvo? What is it with Volvo that captured people's attention early on? And what sort of experience and research have you done with Volvo? JOHAN: I think that Volvo is very innovative, and Volvo today is two types of companies; one is the car company that has now gone fully electric. It was introduced at the stock market, most recently owned by a Chinese company, and before that, it was owned by Ford, and before that, it was also public. But you also have the other part, which is the Volvo Group, which is looking at trucks, and boats, and things like that. And they both share a high level of innovation, ambition, innovation, and power, I think, using the experiences already from the '60s, where you had a lot of freedom as an employee. And also very good collaboration with the union in investments and in all the changes in the company I think that has been very beneficial. And it's made them...what is now Volvo Cars was very, very early, for example, with digital twins. They were experimenting with digital twins already in the 1990s. And we work together with Volvo but also with SKF, which is a roller-bearing company here to look at how we can support frontline workers and augment their capabilities because they're very skilled and they're very experienced. But sometimes you need to have sensor input, and you need to have structures, and rules, and procedures, and instructions. So we worked quite early with them already, maybe in 2009, 2010, to see how can we transform their work situation, provide them with work instructions through wearable devices. It was very popular at that time. MIT was experimenting with cyborgs. And the people that were...I think it was Thad Starner; he was trying to put on a lot of computer equipment. Then he went through the security at the airport and had some problems there. But that's not the case for the operators. But it was a little bit too early, I think. We tried to experiment with some of the maintenance people at Volvo cars. And they were very interested in the technology, but the use for it was a little bit obscure. And this was at the time when you had the mobile connectivity was 9,600 kilobits through a mobile phone or in the modem, so Wi-Fi more or less did not exist. And the equipment: the batteries weighed two kilos, and the computer weighed one kilo. And then you had a headset that looked like you came from deployment in a war zone. So it was a little bit...it looked a little bit too spacy for them to be actually applicable. And then some 10 years later, we actually did a similar experiment with SKF, the roller bearing company where we deployed the first iPod touch, I think they were called. That was right before the iPhone. I think it was an experiment by Steve Jobs to see how can we create what then became the iPhone screen. And we put that on the arms of the operators and tried to see how can we give them an overview of the process situation. So they were constantly aware, and they were quite happy about this. And then, we wanted to finish the experiment. The operators actually said, "Well, we don't want to give the equipment back." And then we said, "Well, we need to have it back. Of course, you can use the software." So they brought their own phones, and they downloaded the software. And they're still using it, actually, not on their own phones anymore. But they use this kind of software that we developed at that time together with them. So that was quite interesting. TROND: That's fascinating. Extrapolating from some of these early experiences up until now, I wanted to just ask you this from a research perspective, but also, I guess, from a management perspective. So you work on production systems. What is really the goal here, or what has the objective been early on? You talked about these early MIT experiments. And I know control systems is a very old area of research. And from what I understand, in the early days, the use cases weren't just factories; they were also on spacecraft and things. But to your point, especially earlier, we were working with very, very different technology interfaces. But now, obviously, we are starting to roll out 5G, which gives a whole other type of richness. But does it really matter how rich the technology interface is? Or does it matter more what the objective is with these various types of augmentations that have been attempted really throughout the decades? Can you just give us a little sense of what researchers and yourself what you were trying to augment and how that depends or doesn't depend on the quality of technology? JOHAN: First, we need to realize that the manufacturing industry has always been a very, very early adopter. The first computers were used for war simulations and for making propellers for submarines to see how you can program the milling machines. This was in the 1950s. And the industrial robots in the '60s in the '70s were also very early applications of digitalization. Before anything else had computers, the manufacturing industry was using it, and that's still the case. That might surprise some people. When they walk out into a shop floor, they see no computers around because all the computers are built into the machines already. What is still missing is the link, perhaps to the people. So they are still using the screens. And they are the ones...people are the key components of handling complex and unforeseeable situations. So you need to provide them, I think...to be really productive, you need to provide the frontline staff with the equipment for them to avoid and to foresee and to handle unforeseen situations because that's what differs between the man and machine or a human and the machine. People are much more apt to solve a complex situation that was not programmed before. That's the augmentation part here; how can we augment the human capabilities? And people talk about augmented reality; I mean, I don't think it's the reality that needs to be augmented; it's the human to be handling the reality that needs to be augmented. TROND: Johan, this is so fascinating because, first of all, it's quite easy to dismiss manufacturing a little bit these days because, to the untrained eye, all the excitement is in the consumer space because that's where the new devices get released, and that's, obviously, where all the attention is these days unless you obviously are in manufacturing. But can you bring us back to those early days of computing when a lot of the use cases for computing were first explored with manufacturing? So you talked about MIT, and back at MIT and at Stanford, all the way back to the '60s, they were exploring this new and fascinating field of even artificial intelligence, but before that, just regular control systems, electronic interfaces. What fork in the road would you say happened there? Because clearly, the fascination has been with digitalizing everything and software kind of one for 30 years, but in manufacturing, it's more complicated. You say people, so it's people, and then it's kind of these production systems that you research. That's not the same as the use case of an individual with their phone, and they're sort of talking to people. There are many, many more variables in play here. What is the real difference? JOHAN: Last year actually the European Commission put forth industry 5.0, which should be the follower after industry 4.0. And they based that on three main challenges. One is sustainability, one is resilience, and the various kinds of resilience towards the shock of the war but also by climate, et cetera. And the third one is actually human-centeredness to see how can we really fully deploy human capabilities in a society and also in industry, of course. I think what you're referring to is the two guys at Stanford in the '60s; one was John McCarthy. He was the inventor of the artificial intelligence concept. His aim then was to replace human work. That was the ambition with the artificial intelligence because human work is not as productive as computing work, but it still has some drawbacks. But in the same place not so far away, in another department at Stanford, was a guy called Douglas Engelbart. And he was actually the father of...he called it intelligence augmentation. So it was AI and IA at that time. But his ambition was to augment human work to see how can you have this. And he was the one that invented hypertext and the mouse. And he put up the first hypermedia set in Silicon Valley. So this was a guy that inspired companies like Apple, and Xerox PARC, those kinds of institutions that had a huge bearing. There was a book by a research colleague at Oxford. He was comparing that over time, from the early industrial days and then forward, technology that replaces people always has more complications when introduced and scaled than technology that augments people. If you look at the acceptance and the adoption of the iPhone, that took months, or weeks, or whatever, seconds for some people, for me, for example. If you look at what happened in the industrial revolutions in the 1800s and the 1700s, you had a lot of upheaval, and already in the 1960s...I'm starting to sound like a university professor. But in '96, in the U.S., there was a Senate hearing about is automation taking the jobs from people or not? And the conclusion was that it is not, it is actually creating companies that then employ more people because of the productivity gains and the innovation gains. And you allow people to use the automation as augmentation, not only cognitive augmentation. We think a lot about augmentation as something that you do with your eyes and your brain. But robots are also augmenting people. It lifts heavy objects like cars or big containers, whatever. That's the kind of augmentation that maybe you don't consider when you look at it from an artificial or an augmented reality perspective. TROND: Well, so many things to pick up here. But the variety of meanings of augmentation are kind of astounding, aren't they? And you've written about this operator 4.0 several times. There's obviously cognitive augmentation, and then there's physical augmentation. Are there other types of augmentation that you can speak of? JOHAN: I really can't think of any. TROND: But those are the main ones. So it's either kind of your mentality or sort of your knowledge. So the work instruction parts go to the skills-based, I guess, augmentation, which perhaps is an additional one. Or I'm just thinking if manufacturing wants to make progress in these things, it would perhaps make sense to really verify what workers at any moment actually themselves express that they need. And I guess that's what I was fishing for a little bit here in this history of all of this, whether the technology developers at all moments really have a clear idea of what it is that the workers are saying themselves they're missing or that they obviously are missing. Because automation and augmentation, I mean, do you find them diametrically opposed, or are they merely complementary when it works well? JOHAN: I mean, automation traditionally has been the way to scale, and, I mean, in the beginning, you want to see what the machine is doing, right? And then you really don't want to see it. You just want it to work. So it's really helping you to scale up your work. And in that sense, automation, like collaborative robots, for example, which people are talking about robots, are something that is replacing jobs, but if you look at it, it is a very small portion of statistics. In Singapore, which is the highest user of robots installed, there were 950 maybe robots per 10,000 employees. And the average in the Americas is 100 robots per 10,000 employees, and that's not really a lot. And so there is plenty of space for robots to be the tools for people. So if you don't treat them as something that will replace you but something that will actually augment you, I think it would be much easier. What could happen, though, and I think that is maybe part of your question, is that, well, these tools are becoming so complex that you cannot use them unless you increase your skill. How do you do that? Because no company would like to end up in a situation where the tools that you have bought and invested a lot of money in are too complex for your employees to use. That's a lost investment. It's like you're building a big factory out in a very remote place, and you don't have enough electric power to run it. You don't want to end up in that situation. Like you expressed, I think that maybe what's missing and what's trending right now is that the upskilling of the workforce is becoming extremely important. TROND: And how do you do that, Johan? Because there's obviously...there's now an increased attention on upskilling. But that doesn't mean that everyone has the solution for it. And employers are always asking for other people to pay for it, for example, governments, or the initiative of the worker, perhaps. It seems like Europe has taken this challenge head-on. Germany, at least, is recognized as a leader in workforce training. The U.S. is a latecomer to the game from that perspective. But it typically shows up in a big way. So something is going to happen here in the U.S. when it comes to workforce training. What is the approach? I mean, there seems to be two approaches to me; one is to simplify the technology, so you need less training. And the other would be obviously an enormous reskilling effort that either is organized, perhaps ideally in the workplace itself, so it's not removed from the tasks. Or some enormous schooling effort that is highly efficient and perhaps online. What do you think are the winning approaches to re-skilling that entire manufacturing workforce continuously? Because it's not like you have to rescale them once, you have to rescale them every time. JOHAN: Well, I can only guess. I think that you need to do all of these, all of the above. One complicating factor is the demographics of, especially Japan; of course, we know that from a long time that, they have an aging population. But Europe is now becoming the new Japan in that sense. We have a very big problem in terms of aging populations, especially countries like Italy and perhaps Germany but also in northern countries. And we don't have perhaps...there's a lot of discussion on immigration right now. But actually, the workforce would need a lot of immigration to be able to respond to the needs of our industry in the forthcoming situation. I think that China is maybe 4 or 5 years behind Europe, and the U.S. is maybe 10-12 years behind Europe as well. So that will happen...the only non-affected regions right now are India and Africa. And that means that the European, and Chinese, and U.S. industries will have to compete with a rather young population in Africa and India. And so that will become over time, but it is a long time, so that means that it's not always on the political agenda. Things that take a long time are usually not the things that you speak about when you have election times that we have in Sweden right now. It's mostly what's on the table. So I think that how to do that is really complex. We had some collaboration within the World Economic Forum. It is a fantastic organization because it spans the whole globe. So that means that the information comes from different parts of the world, and you can see different aspects of this. And a country that has done a lot about this is Singapore, very good experiments, very nice projects, initiatives regarding upskilling. And Europe is now launching an innovation program where they want to go deeper into deep tech to try to...the commissioner for research and education in June launched a big initiative around innovation and how that can be supported by deep technology. So we'll see what comes out of that. It'll be very, very interesting to see. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com, and pick up the book in a bookstore near you. TROND: Speaking about the World Economic Forum for a minute, Johan, you have been part of this group project called the Augmented Workforce Initiative. You told me when we spoke earlier that, in your opinion, this initiative couldn't have existed even just five years ago. Can you explain what you mean by that? Because augmentation, the way that you've been speaking about it now, is a perspective that was nascent, even in the early days of computing and manufacturing control systems. Yet, it seems to have disappeared a little bit, at least from the top end of the political and research agenda. Yet here we are and you said this initiative couldn't have existed five years ago. Can you explain what you meant by that? JOHAN: That is a very, very nice initiative by the World Economic Forum, and it's run by the forum and Cambridge University, who has a very, very good group on this and some very nice people. And I'm honored to be part of that group together with my colleague from Mexico, David Romero. You may know him as well. And I think that what they're looking at is the increased understanding. And that was actually one of the sessions at this World Economic Forum, you know, the Davos days that were run this year. And it was actually part of those days as a theme about how to engage, and how to support, and to augment the workforce, which has never happened before on that level. So it's really, really high on the agenda. The Forum has been running previous projects also on the future of work and how the demographic situation is affecting or how the skill situation is affecting the companies. They have come up with suggestions that more or less half the workforce needs to be upskilled within the next couple of years. And that's a huge undertaking. TROND: The novelty here is that the world's elite managers, I guess, who are represented at the World Economic Forum are increasingly aware of the complexity of workforce issues generally, and then specifically of upskilling, and maybe even upskilling in this very specific meaning of augmenting a worker which, I guess to my mind, is a little bit different from just generally speaking about robotic automation and hammering these efficiency points. But obviously, it's a much more challenging debate because it's one thing to find a budget for an automation effort and introduce a lot of computers or introduce a lot of whatever technology, usually hardware, but what we're talking about here is a lot more challenging because you need to tailor it to these workers. And there are many workers, obviously, so it's a complicated phenomenon. How is that going? What would you say are some of the findings of the Augmented Workforce Initiative? JOHAN: I think that companies like Tulip, companies like Black & Decker, and others have a lot of good use cases actually already, which may or may not before have been labeled augmentation. It might have been labeled as operator support, or decision-making support, or things like that, or upskilling. But I think that the findings are that there is a lot out there, but it's not emphasized as something that is really important for the company's survival in that sense. TROND: It wasn't so glorified before. A lot of the decision support systems were viewed as lower-level systems that were just kind of more like HR systems or just tinkering with necessary stuff that people had to know kind of a thing. And so you're saying it's been elevated now, yeah, as having a much more essential impact on the quality of work. JOHAN: It has a leveraging impact for the whole company, I would say, but that's also part of this industry 4.0 approach. And you have the hierarchical integration of companies where the CEO should be aware of what's going on on the shop floor and vice versa, as well as the horizontal integration where you have the companies up and down the supply chain and value chain knowing what's going on early. And that is really something that maybe stopped at mid-management level before, but now it needs to be distributed out to the places where the complexity is higher, and that's the frontline workers. Maybe...now I'm guessing, but I think that also the understanding that the investments done by this company in complex manufacturing equipment could be at risk if you don't have the right skills to use them is now penetrating, I think, a lot of the companies. In Europe, in 2019 or something like that, there were almost 30 million people employed in the manufacturing industry. And if you look at the number of...if you say that half of these need to be upskilled somehow over a period of three years...and I actually made a mock calculation that the re-skilling need for in-person months in Europe if we were to fulfill this is 50 million person-months, 50 million person-months, just the time for the people to participate in these trainings. So that's a huge undertaking. And I think that that scares companies as well as governments because just imagine taking 50 million person-months out of productivity or the production equation. But the alternative might be worse. If you lose your capability to use your equipment, that might even be worse. TROND: Wow, these are daunting things. I guess that brings me to the last section here and some thoughts from you on the future outlook. When it comes to technology and these tools for human augmentation, what are the timelines for, well, either making the improvements or, as you said, not losing competitiveness because of this skills crisis? What are we looking at here? Is there some imminent challenge and opportunity? Or is this going to play out over 25 years? JOHAN: I think that in 25 years, the demographic situations will have changed again, so I assume that they will look different. But right now, we have a problem with an aging population. And we have a lot of people going into retirement. A lot of knowledge will disappear unless we can store it somehow. A lot of people will not go into industry. I mean, when I talk to colleagues, they say, "Well, we need to make the manufacturing industry more sexy. It should be cleaner, or it should be nicer because young people don't go to industry." But if I go to the healthcare section, they will say the same thing, "Oh, we need to make it much better because people are not applying for these educations." TROND: [laughs] Where are people applying, the tech companies? JOHAN: No, that's the problem. They don't exist. They were never born. TROND: [laughs] Right. JOHAN: So the demographic bomb is that they are actually not there. So you cannot rely on employing young people because they are not existing in Europe and soon not in the U.S. to the extent that they were before. So therefore, you need to focus on the older people. So you need to re-upskill not only the middle-aged people but the people in their 50s and even in their 60s. That adds to the complexity. In the next 5 to 10 years, there will be a lot of discussions on how to fill the missing places in industry to remain competitive. I also think that you can see the augmentation here as a fantastic tool together with the upskilling because upskilling the new skills together with the augmented tools like collaborative robots, like cognitive support, like whatever you can put in an iPhone, or whatever phone, or tool, or watch, or whatever, you can add the capability to make decisions. And that's the augmentation you will see. And you will see a lot of digital twins try to foresee problems. You will see a lot of transversal technologies going from different high-tech industry into manufacturing industry to support especially the frontline people and to enable their innovation capabilities. TROND: Johan, you said earlier that the complexity is higher at the level of frontline workers. Did you mean that, basically, the complexity of frontline work of itself at an individual level is also underestimated? Or were you simply saying that because there are so many frontline workers and the various situations of various types of frontline workers is so different that it's obviously an underappreciated management challenge? Or were you truly saying that frontline work in and of itself is either complicated or becoming more complex? JOHAN: If a task was not automated, it is inherently complex. So you couldn't automate it, right? TROND: Right. JOHAN: Because if you can teach a robot or whatever to do tasks, then it's not difficult, and you can foresee the results. There was a lady called Lisanne Bainbridge. She put out The Paradox of Automation that the more you automate, the more dependent you become on the few people that are still there to handle the situations that are so complex that you could not foresee them. So everything that is programmed is programmed by a programmer, and the programmer tries to foresee every foreseeable situation, and to that extent, the robots and the automation works. But if these situations go out of hand, if they're too complex, and something happens, then there is no robot that can fix that. Unfortunately, AI is not there yet. TROND: Well, you said, "Unfortunately, AI is not there yet," but I would also conjecture that, fortunately, AI is not there yet because you're pointing to something missing, I think. And a lot of the AI debate is starting to come back now. And it was there in the '60s because people realized that for lots of different reasons, to have a human oversight over robotic processes is actually a good thing. And you talked to me earlier about the experiments with imagining a trip to Mars and having to execute robotic actions on Mars in a control system environment where you actually had to foresee the action and plan; it was always a supervised type of situation. So the supervisory control concept has been there from the beginning of computing. If you were to think of a future where AI actually does get more advanced, and a lot of people feel like that's imminent, maybe you and I don't, but in any case, let's imagine that it does become more advanced and becomes sort of a challenge, how do we maintain human control over those kinds of decisions? I mean, there are researchers that have imagined, you know, famously in Superintelligence, Bostrom imagines this paperclip factory that goes amok and starts to optimize for producing paperclips, and everyone is suddenly producing, you know, and the machine then just reallocates resources to this enormously ridiculous task of producing only paper clips. It's a very memorable example. But a lot of people feel that AI could soon or at some point reach that level. How do we, as a failsafe, avoid that that becomes an issue? Or do you see it as such a far-fetched topic in manufacturing that it would be decades, if not centuries, away? JOHAN: I think that AI has been seasonal if you allow the expression. There's talk about these AI winters every now and then, and they tend to come every 10 or 15 years, and that matches two Ph.D. lifetimes, Ph.D. development. I mean, people tend to forget the problems, and then they tend to use these Gartner curves. If you look at the Gartner curve, you have the expectation part. I'm not being arrogant towards the AI research. I think that AI is fantastic, but it should be seen, from my perspective, as what it is, as an advanced form of automation that can be used as an augmentation tool. I think it was Kasparov that started to collaborate with a chess computer maker or developer, and they won every tournament because the combination of the human and the chess computer was astounding. And now I think there are even competitions with chess computers plus chess experts comes with them. There was, I think, in the 1800s, there was a traveling exhibitionist where they had the Mechanical Turk, I think it was called. It was a chess player that was competing then against the people in the audience. And actually, inside this box, there was a small human that was making all the chess moves. And they were beating all the chess champions. So there was a man inside this. I think that there is still a man inside a lot of the automation. TROND: A man and a woman. I wanted to just lastly end on a more positive note because you told me earlier that you are more optimistic now than ten years ago on behalf of your industry that you've researched for so many years. Why is that? JOHAN: I think that the technology, I mean, I'm a techno-optimist. And I think that we have also the full scale, the full attention from the ICT industry on various industrial processes right now. It was a lot of service-oriented. And I think that that is playing out now in the platform wars, the different services, but these different services are actually making a lot of good in the manufacturing and the tougher industries. And so, there is a bigger focus now on creating CO2-less steel. And there's an exploration of different industries that are going across; you look at the electrification of vehicles which is cutting across several sectors in the industry, automotive industry, electronics industry. And I think that the problems in industry are becoming so complex. So the ICT attention is on industry now more than perhaps on consumers, as it were, and I think that that's promising. I see companies like Ericsson promoting 5G. I see companies doing the Amazon Web Services and such companies looking at services that are useful for industry. And that's also augmenting the people's capability in that sense, so that's why I'm so positive. I see all the sensors coming. I see all the computing power coming into the hands of the frontline operators. And I see also the use for the upskilling and the skilling technologies that are emerging. How do you do that? What they do in Matrix when the leading lady downloads the instructions for the helicopter or motorcycle or whatever it is. But how do you do that in real life? How do you prepare for something that's coming in the next few minutes? That is something that people are now looking at using technologies, augmenting technologies, digital twins, and things like that in a completely different way than they were five years ago. TROND: Wow. So these are exciting moments for learning in manufacturing with perhaps wide-ranging consequences if we succeed. Johan, I thank you so much for these reflections. You've spent a career investigating production systems, and manufacturing, and workers. And these are very rich debates. And it seems like they're not over, Johan. So, hopefully, we'll have you back when something happens. And we'll have you comment on some developments. Thank you very much. JOHAN: Thank you, Trond. Thank you for a very interesting discussion. You always learn a lot by being asked a lot of questions, so thank you so much for this learning experience. Thank you. TROND: You're very gracious. Thank you, Johan. You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was a Scandinavian Perspective on Industrial Operator Independence. Our guest was Johan Stahre, Professor and Chair of Production Systems at Chalmers University of Sweden. In this conversation, we talked about how the field of human-centered automation has evolved. My takeaway is that human-centered automation is the only kind of automation that we should be thinking about, and this is becoming more and more clear. Operators are fiercely independent, and so should they be. This is the only way they can spot problems on the shop floor, by combining human skills with automation in new ways augmenting workers. It seems the workforce does not so much need engagement as they need enablement. Fix that, and a lot can happen. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich. Hopefully, you'll find something awesome in these or in other episodes and if so, do let us know by messaging us. We would love to share your thoughts with other listeners. The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, devices, and systems in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially about where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Johan Stahre.

The Joy Revolution and TEDxOcala proudly present... The Golden Mic Podcast w/ Marc Cordon Ep. 0108 The 4th Trimester: The Power of a Doctor & Her Black Bag/w Dr Sonal Patel https://play.goldenmicpodcast.com/0108_patel Meet Sonal, a mother of four boys. During her pregnancy, Sonal was the Hollywood prototype of a pregnant woman - a perfectly round belly, glowing skin, and luscious flowing hair. But life after birth was much different for Sonal. Postpartum tore her apart physically, emotionally, and mentally - leaky, engorged breasts accompanied by waves of intense pelvic pain, diapers on both baby and her, a sense of isolation, and tears never portrayed in any Hollywood blockbuster. Sonal is a physician, specifically a pediatrician further trained in neonatology. Her story concerning postpartum, or the 4th trimester, is not an isolated tale. Many women undergo it far worse. Serena Williams, the greatest of women's tennis, almost died after her c-section. She developed a pulmonary embolism, a lung clot, requiring two surgical operations, and spent her first six weeks of motherhood in bed, half her 4th trimester. In doing more research on the 4th trimester, the statistics are staggering. America's maternal mortality rate of 23.8 maternal deaths per 100,000 live births ranks last in first world nations though spending the most in healthcare dollars.The lastest data reported in September 2022 showed that 22% mothers die in pregnancy, while 78% die after birth, with 25% in the first week of delivery. 4 in 5 deaths are preventable.Interstate disparities find that a mother in Georgia has ten times higher chance of dying versus a California mother.Among black women, maternal mortality is three times higher than that of her white counterparts. Extrapolating the data further, a black woman's higher education level and socioeconomic status does not protect her from these alarming disparities.Women are income-earners through outside avenues of employment as well as through traditional household duties. A new mother's unexpected death not only leaves behind a newborn and maybe other children, but jolts a household's economic foundation.  In 2022, the Council of Foreign Relations estimated that if women were part of the US labor force at the same rate as men, America's economy would grow by $4.3 trillion in five years.   As Dr. Patel says, "There must be a better way." ...and a better way for the 4th trimester will take everyone. Sonal is doing her part. After her own experience, Dr. Sonal Patel transitioned from her Neonatal ICU job and began a new career as a traveling 4th-trimester doctor. She wrote the book, "The Doctor & Her Black Bag: How Old-Fashioned Care Tackles Maternal Mortality and Benefits America's Economy" In the spirit of bringing awareness to the 4th trimester, Dr. Patel will speak at TEDxOcala. Dr. Patel will be talking about how the failure not to action during the 4th trimester impacts everyone. If ever a time for you to subscribe to The Golden Mic Podcast, it's now as I have the privilege of having a conversation with many of the speakers you'll see at TEDxOcala 2022: The Power of... See Sonal live on Saturday, November 12, 2022, from 10am - 4pm EST at the College of Central Florida, Fine Arts Auditorium. Go to www.tedxocala.com to register now! This show will sell out. #thepowerof #TEDx #TEDxOcala #thejoyrev #goldenmicpodcast #podquest #expertisereimagined #4thtrimester Enough copy!

Things discussed: Offense: Let's talk about the tight ends! Putting three of them out there overwhelmed Maryland's size. They're spilling so when they run out of guys it's Corum versus a cornerback. Weird looks: pulling tackles messes with your keys. Duo with a counter step looks like Split is coming. McCarthy: A few bad throws, his footwork might be at fault for overthrows, Maryland used new coverages. OL did fine—don't know what PFF is doing to grade that. Extrapolating to Iowa: different response needed. What do you want to see Michigan practice with McCarthy: more weird zone or reading weird zone coverages? Fascinating to see JJ versus Iowa who's trying to confuse you and make you throw INTs. Let's run some trick plays and close it down. During break: Mel Pearson's side of the story. Believe Shields was mad, not that he can turn a third of the program against him. It's not fair to make this a he said/she said; the WilmerHale Report and Mel getting a friendly to tell his side are different things. The one part of his story that checks out is it's at least plausible that "making people lie about COVID" is about the contact tracing forms instruction, not something nefarious by the coach. Defense: Mazi Smith and Kris Jenkins fixed the linebacker problem by two-gapping and not letting the ball get to that level. High-level metagame between the Maryland OL reacting to Michigan's blitzes. Cornerbacks! Gemon Green ran everybody's routes. Sainristil is Daxhillian in coverage, not in the box. Turner huge plays. Rough game from the LBs and Mullings in particular. Need Nikhai Hill-Green's aggressiveness. Maryland was well-prepped for this game. Lots of tempo, weird plays, scripted drives. Got their best shot by far. Pressure with four? Upshaw no. Harrell no, maybe sometimes, but when not emphatically no. DTs don't get a lot of chances—when they got to rush they got upfield but M's system wants a DT to hang back to prevent an escape run down the middle.

Ep. 36 | Philosopher, author, and social entrepreneur Tomas Björkman's claims are convincing: our culture needs to go through a new developmental paradigm shift. Either it will grow more complex—or crumble, as empires have crumbled in the past. The collective worldview needs to change, and to that end Tomas' focus is on the relationship between growing our personal psychological maturity and societal change, a relationship Nordic countries recognized to their great advantage towards the end of the nineteenth century. Extrapolating from his vast experience with business leadership, where inner psychological maturity turns out to be a foremost aspect of success, Tomas extends this knowledge, applying it to all of society, and emphasizes the importance of supporting a lifelong inner process of development for every individual. The only hope for our shared future seems to lie in wiser decision making by individuals who have expanded both mind and heart to encompass the greater complexities of our time. Recorded on December 2019, with Dr. Roger Walsh, John Dupuy, and Douglas Prater. “Conscious effort on large-scale consciousness development actually worked.” (For Apple Podcast users, https://deeptransformation.io/tomas-bjorkman-cultivating-psychological-maturity-race-between-maturity-and-catastrophe/ (click here to view the complete show notes on the episode page.)) Note: This podcast was recorded live and includes, at times, some extraneous noises in the background. Please excuse them -- we felt the conversation was very valuable and well worth sharing with our audience. We hope you enjoy it as much as we do. Topics & Time StampsIntroducing Tomas Björkman: philosopher, author, entrepreneur (02:28) Tomas' unique contribution: cultivating psychological maturity individually and collectively, in order to co-create a new culture and survive as a species (03:39) Inner psychological maturation is one of the most important aspects of a good businessman, a good leader; this knowledge needs to be applied to all of us and society as a whole (05:12) The importance of getting the corporate culture right, societal culture right, and support inner psychological development (06:37) Our worldview in the West hasn't changed since the Enlightenment, the Age of Reason: it's time for another deep shift to encompass greater complexity (09:49) Looking at culture (and consciousness) as a complex, self-organizing, evolving system: we have reached the bifurcation point (14:47) The Nordic secret: Scandinavian countries realized the connection between the maturity of our inner world and our society at the end of the 1800s, becoming the happiest, richest, most stable countries in the world (24:24) The establishment of retreat centers for young people to find their inner compass and creating a critical mass of self-authoring people (28:20) Maturing from the socialized mind to the self-authoring mind, from conventional to postconventional (30:15) Consciously navigating psychological transformation and the self-transforming mind (34:24) The corporate realization that to succeed in business, you need your employees to mature to the level of self-authoring mind: Deliberately Developmental Organizations (37:54) The German idealistic philosophers' reaction against Enlightenment philosophy and the evolution of mind and heart: Bildung (39:30) What constitutes depth of relationship? Inner development, deepening of relationship, and a multicultural society created by conscious effort (48:24) Growing together: how to hold and scaffold the building of a multicultural society (54:34) Changing our collective worldview will have to be a collective movement (59:01) The effects of the popularization of contemplative practice on the development of psychological maturity (01:04:31) Resources & ReferencesTomas Björkman, https://amzn.to/3IUCcbV (The World We Create)* Tomas Björkman & https://en.wikipedia.org/wiki/Lene_Rachel_Andersen (Lene...

The Almighty gave us His Torah so we would know how to serve Him by following His laws or Halacha. One of those sets of instructions were for our sages to put fences around the mitzvos and extrapolate Torah law to apply to the myriad of situations that we would encounter throughout time. Through time […]

The Almighty gave us His Torah so we would know how to serve Him by following His laws or Halacha. One of those sets of instructions were for our sages to put fences around the mitzvos and extrapolate Torah law to apply to the myriad of situations that we would encounter throughout time. Through time […]

About ChrisChris is the Co-founder and Chief Product Officer at incident.io, where they're building incident management products that people actually want to use. A software engineer by trade, Chris is no stranger to gnarly incidents, having participated (and caused!) them at everything from early stage startups through to enormous IT organizations.Links Referenced: incident.io: https://incident.io Practical Guide to Incident Management: https://incident.io/guide/ TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: DoorDash had a problem. As their cloud-native environment scaled and developers delivered new features, their monitoring system kept breaking down. In an organization where data is used to make better decisions about technology and about the business, losing observability means the entire company loses their competitive edge. With Chronosphere, DoorDash is no longer losing visibility into their applications suite. The key? Chronosphere is an open-source compatible, scalable, and reliable observability solution that gives the observability lead at DoorDash business, confidence, and peace of mind. Read the full success story at snark.cloud/chronosphere. That's snark.cloud slash C-H-R-O-N-O-S-P-H-E-R-E.Corey: Let's face it, on-call firefighting at 2am is stressful! So there's good news and there's bad news. The bad news is that you probably can't prevent incidents from happening, but the good news is that incident.io makes incidents less stressful and a lot more valuable. incident.io is a Slack-native incident management platform that allows you to automate incident processes, focus on fixing the issues and learn from incident insights to improve site reliability and fix your vulnerabilities. Try incident.io, recover faster and sleep more.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. Today's promoted guest is Chris Evans, who's the CPO and co-founder of incident.io. Chris, first, thank you very much for joining me. And I'm going to start with an easy question—well, easy question, hard answer, I think—what is an incident.io exactly?Chris: Incident.io is a software platform that helps entire organizations to respond to recover from and learn from incidents.Corey: When you say incident, that means an awful lot of things. And depending on where you are in the ecosystem in the world, that means different things to different people. For example, oh, incident. Like, “Are you talking about the noodle incident because we had an agreement that we would never speak about that thing again,” style, versus folks who are steeped in DevOps or SRE culture, which is, of course, a fancy way to say those who are sad all the time, usually about computers. What is an incident in the context of what you folks do?Chris: That, I think, is the killer question. I think if you look at organizations in the past, I think incidents were those things that happened once a quarter, maybe once a year, and they were the thing that brought the entirety of your site down because your big central database that was in a data center sort of disappeared. The way that modern companies run means that the definition has to be very, very different. So, most places now rely on distributed systems and there is no, sort of, binary sense of up or down these days. And essentially, in the general case, like, most companies are continually in a sort of state of things being broken all of the time.And so, for us, when we look at what an incident is, it is essentially anything that takes you away from your planned work with a sense of urgency. And that's the sort of the pithy definition that we use there. Generally, that can mean anything—it means different things to different folks, and, like, when we talk to folks, we encourage them to think carefully about what that threshold is, but generally, for us at incident.io, that means basically a single error that is worthwhile investigating that you would stop doing your backlog work for is an incident. And also an entire app being down, that is an incident.So, there's quite a wide range there. But essentially, by sort of having more incidents and lowering that threshold, you suddenly have a heap of benefits, which I can go very deep into and talk for hours about.Corey: It's a deceptively complex question. When I talk to folks about backups, one of the biggest problems in the world of backup and building a DR plan, it's not building the DR plan—though that's no picnic either—it's okay. In the time of cloud, all your planning figures out, okay. Suddenly the site is down, how do we fix it? There are different levels of down and that means different things to different people where, especially the way we build apps today, it's not is the service or site up or down, but with distributed systems, it's how down is it?And oh, we're seeing elevated error rates in us-tire-fire-1 region of AWS. At what point do we begin executing on our disaster plan? Because the worst answer, in some respects is, every time you think you see a problem, you start failing over to other regions and other providers and the rest, and three minutes in, you've irrevocably made the cutover and it's going to take 15 minutes to come back up. And oh, yeah, then your primary site comes back up because whoever unplugged something, plugged it back in and now you've made the wrong choice. Figuring out all the things around the incident, it's not what it once was.When you were running your own blog on a single web server and it's broken, it's pretty easy to say, “Is it up or is it down?” As you scale out, it seems like that gets more and more diffuse. But it feels to me that it's also less of a question of how the technology has scaled, but also how the culture and the people have scaled. When you're the only engineer somewhere, you pretty much have no choice but to have the entire state of your stack shoved into your head. When that becomes 15 or 20 different teams of people, in some cases, it feels like it's almost less than a technology problem than it is a problem of how you communicate and how you get people involved. And the issues in front of the people who are empowered and insightful in a certain area that needs fixing.Chris: A hundred percent. This is, like, a really, really key point, which is that organizations themselves are very complex. And so, you've got this combination of systems getting more and more complicated, more and more sort of things going wrong and perpetually breaking but you've got very, very complicated information structures and communication throughout the whole organization to keep things up and running. The very best orgs are the ones where they can engage the entire, sort of, every corner of the organization when things do go wrong. And lived and breathed this firsthand when various different previous companies, but most recently at Monzo—which is a bank here in the UK—when an incident happened there, like, one of our two physical data center locations went down, the bank wasn't offline. Everything was resilient to that, but that required an immediate response.And that meant that engineers were deployed to go and fix things. But it also meant the customer support folks might be required to get involved because we might be slightly slower processing payments. And it means that risk and compliance folks might need to get involved because they need to be reporting things to regulators. And the list goes on. There's, like, this need for a bunch of different people who almost certainly have never worked together or rarely worked together to come together, land in this sort of like empty space of this incident room or virtual incident room, and figure out how they're going to coordinate their response and get things back on track in the sort of most streamlined way and as quick as possible.Corey: Yeah, when your bank is suddenly offline, that seems like a really inopportune time to be introduced to the database team. It's, “Oh, we have one of those. Wonderful. I feel like you folks are going to come in handy later today.” You want to have those pathways of communication open well in advance of these issues.Chris: A hundred percent. And I think the thing that makes incidents unique is that fact. And I think the solution to that is this sort of consistent, level playing field that you can put everybody on. So, if everybody understands that the way that incidents are dealt with is consistent, we declare it like this, and under these conditions, these things happen. And, you know, if I flag this kind of level of impact, we have to pull in someone else to come and help make a decision.At the core of it, there's this weird kind of duality to incidents where they are both kind of semi-formulaic and that you can basically encode a lot of the processes that happen, but equally, they are incredibly chaotic and require a lot of human impact to be resilient and figure these things out because stuff that you have never seen happen before is happening and failing in ways that you never predicted. And so, this is where incident.io plays into this is that we try to take the first half of that off of your hands, which is, we will help you run your process so that all of the brain capacity you have, it goes on to the bit that humans are uniquely placed to be able to do, which is responding to these very, very chaotic, sort of, surprise events that have happened.Corey: I feel as well—because I played around in this space a bit before I used to run ops teams—and, more or less I really should have had a t-shirt then that said, “I am the root cause,” because yeah, I basically did a lot of self-inflicted outages in various environments because it turns out, I'm not always the best with computers. Imagine that. There are a number of different companies that play in the space that look at some part of the incident lifecycle. And from the outside, first, they all look alike because it's, “Oh, so you're incident.io. I assume you're PagerDuty. You're the thing that calls me at two in the morning to make sure I wake up.”Conversely, for folks who haven't worked deeply in that space, as well, of setting things on fire, what you do sounds like it's highly susceptible to the Hacker News problem. Where, “Wait, so what you do is effectively just getting people to coordinate and talk during an incident? Well, that doesn't sound hard. I could do that in a weekend.” And no, no, you can't.If this were easy, you would not have been in business as long as you have, have the team the size that you do, the customers that you do. But it's one of those things that until you've been in a very specific set of a problem, it doesn't sound like it's a real problem that needs solving.Chris: Yeah, I think that's true. And I think that the Hacker News point is a particularly pertinent one and that someone else, sort of, in an adjacent area launched on Hacker News recently, and the amount of feedback they got around, you know, “You're a Slack bot. How is this a company?” Was kind of staggering. And I think generally where that comes from is—well, first of all that bias that engineers have, which is just everything you look at as an engineer is like, “Yeah, I can build that in a weekend.” I think there's often infinite complexity under the hood that just gets kind of brushed over. But yeah, I think at the core of it, you probably could build a Slack bot in a weekend that creates a channel for you in Slack and allows you to post somewhere that some—Corey: Oh, good. More channels in Slack. Just when everyone wants.Chris: Well, there you go. I mean, that's a particular pertinent one because, like, our tool does do that. And one of the things—so I built at Monzo, a version of incident.io that we used at the company there, and that was something that I built evenings and weekends. And among the many, many things I never got around to building, archiving and cleaning up channels was one of the ones that was always on that list.And so, Monzo did have this problem of littered channels everywhere, I think that sort of like, part of the problem here is, like, it is easy to look at a product like ours and sort of assume it is this sort of friendly Slack bot that helps you orchestrate some very basic commands. And I think when you actually dig into the problems that organizations above a certain size have, they're not solved by Slack bots. They're solved by platforms that help you to encode your processes that otherwise have to live on a Google Doc somewhere which is five pages long and when it's 2 a.m. and everything's on fire, I guarantee you not a single person reads that Google Doc, so your process is as good as not in place at all. That's the beauty of a tool like ours. We have a powerful engine that helps you basically to encode that and take some load off of you.Corey: To be clear, I'm also not coming at this from a position of judging other people. I just look right now at the Slack workspace that we have The Duckbill Group, and we have something like a ten-to-one channel-to-human ratio. And the proliferation of channels is a very real thing. And the problem that I've seen across the board with other things that try to address incident management has always been fanciful at best about what really happens when something breaks. Like, you talk about, oh, here's what happens. Step one: you will pull up the Google Doc, or you will pull up the wiki or the rest, or in some aspirational places, ah, something seems weird, I will go open a ticket in Jira.Meanwhile, here in reality, anyone who's ever worked in these environments knows that step one, “Oh shit, oh shit, oh shit, oh shit, oh shit. What are we going to do?” And all the practices and procedures that often exist, especially in orgs that aren't very practiced at these sorts of things, tend to fly out the window and people are going to do what they're going to do. So, any tool or any platform that winds up addressing that has to accept the reality of meeting people where they are not trying to educate people into different patterns of behavior as such. One of the things I like about your approach is, yeah, it's going to be a lot of conversation in Slack that is a given we can pretend otherwise, but here in reality, that is how work gets communicated, particularly in extremis. And I really appreciate the fact that you are not trying to, like, fight what feels almost like a law of nature at this point.Chris: Yeah, I think there's a few things in that. The first point around the document approach or the clearly defined steps of how an incident works. In my experience, those things have always gone wrong because—Corey: The data center is down, so we're going to the wiki to follow our incident management procedure, which is in the data center just lost power.Chris: Yeah.Corey: There's a dependency problem there, too. [laugh].Chris: Yeah, a hundred percent. [laugh]. A hundred percent. And I think part of the problem that I see there is that very, very often, you've got this situation where the people designing the process are not the people following the process. And so, there's this classic, I've heard it through John Allspaw, but it's a bunch of other folks who talk about the difference between people, you know, at the sharp end or the blunt end of the work.And I think the problem that people are facing the past is you have these people who sit in the, sort of, metaphorical upstairs of the office and think that they make a company safe by defining a process on paper. And they ship the piece of paper and go, “That is a good job for me done. I'm going to leave and know that I've made the bank—the other whatever your organization does—much, much safer.” And I think this is where things fall down because—Corey: I want to ambush some of those people in their performance reviews with, “Cool. Just for fun, all the documentation here, we're going to pull up the analytics to see how often that stuff gets viewed. Oh, nobody ever sees it. Hmm.”Chris: It's frustrating. It's frustrating because that never ever happens, clearly. But the point you made around, like, meeting people where you are, I think that is a huge one, which is incidents are founded on great communication. Like, as I said earlier, this is, like, a form of team with someone you've never ever worked with before and the last thing you want to do is be, like, “Hey, Corey, I've never met you before, but let's jump out onto this other platform somewhere that I've never been or haven't been for weeks and we'll try and figure stuff out over there.” It's like, no, you're going to be communicating—Corey: We use Slack internally, but we have a WhatsApp chat that we wind up using for incident stuff, so go ahead and log into WhatsApp, which you haven't done in 18 months, and join the chat. Yeah, in the dawn of time, in the mists of antiquity, you vaguely remember hearing something about that your first week and then never again. This stuff has to be practiced and it's important to get it right. How do you approach the inherent and often unfortunate reality that incident response and management inherently becomes very different depending upon the specifics of your company or your culture or something like that? In other words, how cookie-cutter is what you have built versus adaptable to different environments it finds itself operating in?Chris: Man, the amount of time we spent as a founding team in the early days deliberating over how opinionated we should be versus how flexible we should be was staggering. The way we like to describe it as we are quite opinionated about how we think incidents should be run, however we let you imprint your own process into that, so putting some color onto that. We expect incidents to have a lead. That is something you cannot get away from. However, you can call the lead whatever makes sense for you at your organization. So, some folks call them an incident commander or a manager or whatever else.Corey: There's overwhelming militarization of these things. Like, oh, yes, we're going to wind up taking a bunch of terms from the military here. It's like, you realize that your entire giant screaming fire is that the lights on the screen are in the wrong pattern. You're trying to make them in the right pattern. No one dies here in most cases, so it feels a little grandiose for some of those terms being tossed around in some cases, but I get it. You've got to make something that is unpleasant and tedious in many respects, a little bit more gripping. I don't envy people. Messaging is hard.Chris: Yeah, it is. And I think if you're overly virtuoustic and inflexible, you're sort of fighting an uphill battle here, right? So, folks are going to want to call things what they want to call things. And you've got people who want to import [ITIL 00:15:04] definitions for severity ease into the platform because that's what they're familiar with. That's fine.What we are opinionated about is that you have some severity levels because absent academic criticism of severity levels, they are a useful mechanism to very coarsely and very quickly assess how bad something is and to take some actions off of it. So yeah, we basically have various points in the product where you can customize and put your own sort of flavor on it, but generally, we have a relatively opinionated end-to-end expectation of how you will run that process.Corey: The thing that I find that annoys me—in some cases—the most is how heavyweight the process is, and it's clearly built by people in an ivory tower somewhere where there's effectively a two-day long postmortem analysis of the incident, and so on and so forth. And okay, great. Your entire site has been blown off the internet, yeah, that probably makes sense. But as soon as you start broadening that to things like okay, an increase in 500 errors on this service for 30 minutes, “Great. Well, we're going to have a two-day postmortem on that.” It's, “Yeah, sure would be nice if we could go two full days without having another incident of that caliber.” So, in other words, whose foot—are we going to hire a new team whose full-time job it is, is to just go ahead and triage and learn from all these incidents? Seems to me like that's sort of throwing wood behind the wrong arrows.Chris: Yeah, I think it's very reductive to suggest that learning only happens in a postmortem process. So, I wrote a blog, actually, not so long ago that is about running postmortems and when it makes sense to do it. And as part of that, I had a sort of a statement that was [laugh] that we haven't run a single postmortem when I wrote this blog at incident.io. Which is probably shocking to many people because we're an incident company, and we talk about this stuff, but we were also a company of five people and when something went wrong, the learning was happening and these things were sort of—we were carving out the time, whether it was called a postmortem, or not to learn and figure out these things. Extrapolating that to bigger companies, there is little value in following processes for the sake of following processes. And so, you could have—Corey: Someone in compliance just wound up spitting their coffee over their desktop as soon as you said that. But I hear you.Chris: Yeah. And it's those same folks who are the ones who care about the document being written, not the process and the learning happening. And I think that's deeply frustrating to me as—Corey: All the plans, of course, assume that people will prioritize the company over their own family for certain kinds of disasters. I love that, too. It's divorced from reality; that's ridiculous, on some level. Speaking of ridiculous things, as you continue to grow and scale, I imagine you integrate with things beyond just Slack. You grab other data sources and over in the fullness of time.For example, I imagine one of your most popular requests from some of your larger customers is to integrate with their HR system in order to figure out who's the last engineer who left, therefore everything immediately their fault because lord knows the best practice is to pillory whoever was the last left because then they're not there to defend themselves anymore and no one's going to get dinged for that irresponsible jackass's decisions, even if they never touched the system at all. I'm being slightly hyperbolic, but only slightly.Chris: Yeah. I think [laugh] that's an interesting point. I am definitely going to raise that feature request for a prefilled root cause category, which is, you know, the value is just that last person who left the organization. That it's a wonderful scapegoat situation there. I like it.To the point around what we do integrate with, I think the thing is actually with incidents that's quite interesting is there is a lot of tooling that exists in this space that does little pockets of useful, valuable things in the shape of incidents. So, you have PagerDuty is this system that does a great job of making people's phone making noise, but that happens, and then you're dropped into this sort of empty void of nothingness and you've got to go and figure out what to do. And then you've got things like Jira where clearly you want to be able to track actions that are coming out of things going wrong in some cases, and that's a great tool for that. And various other things in the middle there. And yeah, our value proposition, if you want to call it that, is to bring those things together in a way that is massively ergonomic during an incident.So, when you're in the middle of an incident, it is really handy to be able to go, “Oh, I have shipped this horrible fix to this thing. It works, but I must remember to undo that.” And we put that at your fingertips in an incident channel from Slack, that you can just log that action, lose that cognitive load that would otherwise be there, move on with fixing the thing. And you have this sort of—I think it's, like, that multiplied by 1000 in incidents that is just what makes it feel delightful. And I cringe a little bit saying that because it's an incident at the end of the day, but genuinely, it feels magical when some things happen that are just like, “Oh, my gosh, you've automatically hooked into my GitHub thing and someone else merged that PR and you've posted that back into the channel for me so I know that that happens. That would otherwise have been a thing where I jump out of the incident to go and figure out what was happening.”Corey: This episode is sponsored in part by our friend EnterpriseDB. EnterpriseDB has been powering enterprise applications with PostgreSQL for 15 years. And now EnterpriseDB has you covered wherever you deploy PostgreSQL on-premises, private cloud, and they just announced a fully-managed service on AWS and Azure called BigAnimal, all one word. Don't leave managing your database to your cloud vendor because they're too busy launching another half-dozen managed databases to focus on any one of them that they didn't build themselves. Instead, work with the experts over at EnterpriseDB. They can save you time and money, they can even help you migrate legacy applications—including Oracle—to the cloud. To learn more, try BigAnimal for free. Go to biganimal.com/snark, and tell them Corey sent you.Corey: The problem with the cloud, too, is the first thing that, when there starts to be an incident happening is the number one decision—almost the number one decision point is this my shitty code, something we have just pushed in our stuff, or is it the underlying provider itself? Which is why the AWS status page being slow to update is so maddening. Because those are two completely different paths to go down and you are having to pursue both of them equally at the same time until one can be ruled out. And that is why time to identify at least what side of the universe it's on is so important. That has always been a bit of a tricky challenge.I want to talk a bit about circular dependencies. You target a certain persona of customer, but I'm going to go out on a limb and assume that one explicit company that you are not going to want to do business with in your current iteration is Slack itself because a tool to manage—okay, so our service is down, so we're going to go to Slack to fix it doesn't work when the service is Slack itself. So, that becomes a significant challenge. As you look at this across the board, are you seeing customers having problems where you have circular dependency issues with this? Easy example: Slack is built on top of AWS.When there's an underlying degradation of, huh, suddenly us-east-1 is not doing what it's supposed to be doing, now, Slack is degraded as well, as well as the customer site, it seems like at that point, you're sort of in a bit of tricky positioning as a customer. Counterpoint, when neither Slack nor your site are working, figuring out what caused that issue doesn't seem like it's the biggest stretch of the imagination at that point.Chris: I've spent a lot of my career working in infrastructure, platform-type teams, and I think you can end up tying yourself in knots if you try and over-optimize for, like, avoiding these dependencies. I think it's one of those, sort of, turtles all the way down situations. So yes, Slack are unlikely to become a customer because they are clearly going to want to use our product when they are down.Corey: They reach out, “We'd like to be your customer.” Your response is, “Please don't be.” None of us are going to be happy with this outcome.Chris: Yeah, I mean, the interesting thing that is that we're friends with some folks at Slack, and they believe it or not, they do use Slack to navigate their incidents. They have an internal tool that they have written. And I think this sort of speaks to the point we made earlier, which is that incidents and things failing or not these sort of big binary events. And so—Corey: All of Slack is down is not the only kind of incident that a company like Slack can experience.Chris: I'd go as far as that it's most commonly not that. It's most commonly that you're navigating incidents where it is a degradation, or some edge case, or something else that's happened. And so, like, the pragmatic solution here is not to avoid the circular dependencies, in my view; it's to accept that they exist and make sure you have sensible escape hatches so that when something does go wrong—so a good example, we use incident.io at incident.io to manage incidents that we're having with incident.io. And 99% of the time, that is absolutely fine because we are having some error in some corner of the product or a particular customer is doing something that is a bit curious.And I could count literally on one hand the number of times that we have not been able to use our products to fix our product. And in those cases, we have a fallback which is jump into—Corey: I assume you put a little thought into what happened. “Well, what if our product is down?” “Oh well, I guess we'll never be able to fix it or communicate about it.” It seems like that's the sort of thing that, given what you do, you might have put more than ten seconds of thought into.Chris: We've put a fair amount of thought into it. But at the end of the day, [laugh] it's like if stuff is down, like, what do you need to do? You need to communicate with people. So, jump on a Google Chat, jump on a Slack huddle, whatever else it is we have various different, like, fallbacks in different order. And at the core of it, I think this is the thing is, like, you cannot be prepared for every single thing going wrong, and so what you can be prepared for is to be unprepared and just accept that humans are incredibly good at being resilient, and therefore, all manner of things are going to happen that you've never seen before and I guarantee you will figure them out and fix them, basically.But yeah, I say this; if my SOC 2 auditor is listening, we also do have a very well-defined, like, backup plan in our SOC 2 [laugh] in our policies and processes that is the thing that we will follow that. But yeah.Corey: The fact that you're saying the magic words of SOC 2, yes, exactly. Being in a responsible adult and living up to some baseline compliance obligations is really the sign of a company that's put a little thought into these things. So, as I pull up incident.io—the website, not the company to be clear—and look through what you've written and how you talk about what you're doing, you've avoided what I would almost certainly have not because your tagline front and center on your landing page is, “Manage incidents at scale without leaving Slack.” If someone were to reach out and say, well, we're down all the time, but we're using Microsoft Teams, so I don't know that we can use you, like, the immediate instinctive response that I would have for that to the point where I would put it in the copy is, “Okay, this piece of advice is free. I would posit that you're down all the time because you're the kind of company to use Microsoft Teams.” But that doesn't tend to win a whole lot of friends in various places. In a slightly less sarcastic bent, do you see people reaching out with, “Well, we want to use you because we love what you're doing, but we don't use Slack.”Chris: Yeah. We do. A lot of folks actually. And we will support Teams one day, I think. There is nothing especially unique about the product that means that we are tied to Slack.It is a great way to distribute our product and it sort of aligns with the companies that think in the way that we do in the general case but, like, at the core of what we're building, it's a platform that augments a communication platform to make it much easier to deal with a high-stress, high-pressure situation. And so, in the future, we will support ways for you to connect Microsoft Teams or if Zoom sought out getting rich app experiences, talk on a Zoom and be able to do various things like logging actions and communicating with other systems and things like that. But yeah, for the time being very, very deliberate focus mechanism for us. We're a small company with, like, 30 people now, and so yeah, focusing on that sort of very slim vertical is working well for us.Corey: And it certainly seems to be working to your benefit. Every person I've talked to who is encountered you folks has nothing but good things to say. We have a bunch of folks in common listed on the wall of logos, the social proof eye chart thing of here's people who are using us. And these are serious companies. I mean, your last job before starting incident.io was at Monzo, as you mentioned.You know what you're doing in a regulated, serious sense. I would be, quite honestly, extraordinarily skeptical if your background were significantly different from this because, “Well, yeah, we worked at Twitter for Pets in our three-person SRE team, we can tell you exactly how to go ahead and handle your incidents.” Yeah, there's a certain level of operational maturity that I kind of just based upon the name of the company there; don't think that Twitter for Pets is going to nail. Monzo is a bank. Guess you know what you're talking about, given that you have not, basically, been shut down by an army of regulators. It really does breed an awful lot of confidence.But what's interesting to me is the number of people that we talk to in common are not themselves banks. Some are and they do very serious things, but others are not these highly regulated, command-and-control, top-down companies. You are nimble enough that you can get embedded at those startup-y of startup companies once they hit a certain point of scale and wind up helping them arrive at a better outcome. It's interesting in that you don't normally see a whole lot of tools that wind up being able to speak to both sides of that very broad spectrum—and most things in between—very effectively. But you've somehow managed to thread that needle. Good work.Chris: Thank you. Yeah. What else can I say other than thank you? I think, like, it's a deliberate product positioning that we've gone down to try and be able to support those different use cases. So, I think, at the core of it, we have always tried to maintain the incident.io should be installable and usable in your very first incident without you having to have a very steep learning curve, but there is depth behind it that allows you to support a much more sophisticated incident setup.So, like, I mean, you mentioned Monzo. Like, I just feel incredibly fortunate to have worked at that company. I joined back in 2017 when they were, I don't know, like, 150,000 customers and it was just getting its banking license. And I was there for four years and was able to then see it scale up to 6 million customers and all of the challenges and pain that goes along with that both from building infrastructure on the technical side of things, but from an organizational side of things. And was, like, front-row seat to being able to work with some incredibly smart people and sort of see all these various different pain points.And honestly, it feels a little bit like being in sort of a cheat mode where we get to this import a lot of that knowledge and pain that we felt at Monzo into the product. And that happens to resonate with a bunch of folks. So yeah, I feel like things are sort of coming out quite well at the moment for folks.Corey: The one thing I will say before we wind up calling this an episode is just how grateful I am that I don't have to think about things like this anymore. There's a reason that the problem that I chose to work on of expensive AWS bills being very much a business-hours only style of problem. We're a services company. We don't have production infrastructure that is externally facing. “Oh, no, one of our data analysis tools isn't working internally.”That's an interesting curiosity, but it's not an emergency in the same way that, “Oh, we're an ad network and people are looking at ads right now because we're broken,” is. So, I am grateful that I don't have to think about these things anymore. And also a little wistful because there's so much that you do it would have made dealing with expensive and dangerous outages back in my production years a lot nicer.Chris: Yep. I think that's what a lot of folks are telling us essentially. There's this curious thing with, like, this product didn't exist however many years ago and I think it's sort of been quite emergent in a lot of companies that, you know, as sort of things have moved on, that something needs to exist in this little pocket of space, dealing with incidents in modern companies. So, I'm very pleased that what we're able to build here is sort of working and filling that for folks.Corey: Yeah. I really want to thank you for taking so much time to go through the ethos of what you do, why you do it, and how you do it. If people want to learn more, where's the best place for them to go? Ideally, not during an incident.Chris: Not during an incident, obviously. Handily, the website is the company name. So, incident.io is a great place to go and find out more. We've literally—literally just today, actually—launched our Practical Guide to Incident Management, which is, like, a really full piece of content which, hopefully, will be useful to a bunch of different folks.Corey: Excellent. We will, of course, put a link to that in the [show notes 00:29:52]. I really want to thank you for being so generous with your time. Really appreciate it.Chris: Thanks so much. It's been an absolute pleasure.Corey: Chris Evans, Chief Product Officer and co-founder of incident.io. I'm Cloud Economist Corey Quinn and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this episode, please leave a five-star review on your podcast platform of choice along with an angry comment telling me why your latest incident is all the intern's fault.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.Announcer: This has been a HumblePod production. Stay humble.

The goal of Proverbs is "To know wisdom and instruction, To perceive the words of understanding" - Proverbs 1:2 "Whoever loves instruction loves knowledge, But he who hates correction is stupid." - Proverbs 12:1 “Extrapolating from the wisdom of Proverbs one would conclude that the divine plan calls for a society in which people work hard, observe each other's rights, respect each other, and treat the less fortunate kindly. It is a society in which people are friendly, enjoy the pleasures of moderation, and love their families and homes. It is a society in which people are sincere, modest, self-controlled, temperate, reliable, chaste, willing to listen and learn. Those who live in this society are forgiving, considerate, discreet, kind to animals, sweet-tempered, liberal, yet prudent.” (The Wisdom Literature and Psalms, James E. Smith)

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Where I agree and disagree with Eliezer, published by Paul Christiano on June 19, 2022 on The AI Alignment Forum. (Partially in response to AGI Ruin: A list of Lethalities. Written in the same rambling style. Not exhaustive.) Agreements Powerful AI systems have a good chance of deliberately and irreversibly disempowering humanity. This is a much easier failure mode than killing everyone with destructive physical technologies. Catastrophically risky AI systems could plausibly exist soon, and there likely won't be a strong consensus about this fact until such systems pose a meaningful existential risk per year. There is not necessarily any “fire alarm.” Even if there were consensus about a risk from powerful AI systems, there is a good chance that the world would respond in a totally unproductive way. It's wishful thinking to look at possible stories of doom and say “we wouldn't let that happen;” humanity is fully capable of messing up even very basic challenges, especially if they are novel. I think that many of the projects intended to help with AI alignment don't make progress on key difficulties and won't significantly reduce the risk of catastrophic outcomes. This is related to people gravitating to whatever research is most tractable and not being too picky about what problems it helps with, and related to a low level of concern with the long-term future in particular. Overall, there are relatively few researchers who are effectively focused on the technical problems most relevant to existential risk from alignment failures. There are strong social and political pressures to spend much more of our time talking about how AI shapes existing conflicts and shifts power. This pressure is already playing out and it doesn't seem too likely to get better. I think Eliezer's term “the last derail” is hyperbolic but on point. Even when thinking about accident risk, people's minds seem to go to what they think of as “more realistic and less sci fi” risks that are much less likely to be existential (and sometimes I think less plausible). It's very possible this dynamic won't change until after actually existing AI systems pose an existential risk. There is a good chance that an AI catastrophe looks like an abrupt “coup” where AI systems permanently disempower humans with little opportunity for resistance. People seem to consistently round this risk down to more boring stories that fit better with their narratives about the world. It is quite possible that an AI coup will be sped up by humans letting AI systems control killer robots, but the difference in timeline between "killer robots everywhere, AI controls everything" and "AI only involved in R&D" seems like it's less than a year. The broader intellectual world seems to wildly overestimate how long it will take AI systems to go from “large impact on the world” to “unrecognizably transformed world.” This is more likely to be years than decades, and there's a real chance that it's months. This makes alignment harder and doesn't seem like something we are collectively prepared for. Humanity usually solves technical problems by iterating and fixing failures; we often resolve tough methodological disagreements very slowly by seeing what actually works and having our failures thrown in our face. But it will probably be possible to build valuable AI products without solving alignment, and so reality won't “force us” to solve alignment until it's too late. This seems like a case where we will have to be unusually reliant on careful reasoning rather than empirical feedback loops for some of the highest-level questions. AI systems will ultimately be wildly superhuman, and there probably won't be strong technological hurdles right around human level. Extrapolating the rate of existing AI progress suggests you don't get too much time ...

Extrapolating from what they've learned from these playoffs and last season, Pete, Darius & Mike discuss and debate what the team needs from Anthony Davis, his offseason, his shooting and what types of players the Lakers need to support Davis best. Learn more about your ad choices. Visit podcastchoices.com/adchoices

  In This Episode, We Talk About: The doctrine of horizontal atonement, reconciliation, and salvation. Beliefs and traditions of the early church and early church fathers. The Biblical basis behind cosmic powers and demonic forces. Cross-cultural and multi-ethnic unity in the church and how it honors and glorifies God. Resources & Links: Follow Mission ONE on Instagram | @partnerwithmissionone   Learn more on mission1.org  Show Notes: Do you know how to honor God in a unifying, fulfilling way? Our guest today is Kristin Caynor, a researcher working on the Ephesians 2 Gospel Project! Kristin has her MTS from Duke Divinity and is currently working on her PhD at Trinity College as she dedicates her life to serving the global church the best way she can. She will be sharing her knowledge of Biblical text as we examine Ephesians 2 and its doctrine to horizontal atonement and salvation. We will discuss the beliefs and traditions of the early church in comparison to the modern church as we take a look at the text from the early church fathers' eyes.   Then, it's time to explore the Biblical basis behind cosmic and demonic powers at play in our world. We also talk about unity within the church and embracing a multi-cultural doctrine to bring glory to God. Why has Ephesians 2 been overlooked for so long? How does culture and context play a big role in how we interpret scripture today? What did the first 300 years of the church look like, and what has changed since then? By the end of this episode, you'll be able to see an oft-overlooked text in a new light and new appreciation, so you can bring glory to God in a whole new way! 00:00 Say hello to researcher Kristin Caynor working on the Ephesians 2 Gospel Project! 02:20 What is the Ephesians 2 Gospel Project? 04:55 Examining the horizontal reconciliation in Ephesians 2:14-16. 06:40 What is significant about this collective reconciliation? 09:50 How did the early church fathers view this text?  10:50 How did Kristin get involved in the Gospel Project? 13:20 Why do you think this text was overlooked for so long? How is the Project helping people to see it in a new perspective? 15:10 What did the first 300 years of the early church look like? 19:25 Why do we think differently about salvation compared to the early church fathers? 22:40 How has our culture affected the way we view and read the text of Ephesians 2? 24:15 Extrapolating the reconciliation of the Jews and gentiles. 28:10 What did slavery look like in the early church? 30:30 How did Gregory of Nyssa view slavery? 35:40 What scriptures tell us about the cosmic powers? 38:50 What is the Biblical basis behind demonic forces? 41:40 The mystery of the gospel. 43:30 How does God show his glory in Ephesians 2? 46:20 What do the early church fathers have to say about this? 51:10 How does disunity in the church dishonor God? 54:20 What do you hope people take away from this episode?

(Apologies for the audio in this episode, there was an issue with my mic, promise to have it fixed next episode!) In this episode… I introduce you to Dr. Thomas Sowell… One of the greatest thinkers of our time who played a major part in my unveiling and I can't recommend him enough to my audience. We will consolidate a specific section within his book Black Rednecks & White Liberals… this'll be fun. Extrapolating specifically on Cracker Culture and Butter. This book is nothing short of brilliance when it comes to the much needed historical context on how the complexities of culture develop overtime and are inherited from overseas. Many historians and scholars, such as Dr. Sowell, identify counterproductive cultural values and behavior patterns in the SOUTH of the US and see similar patterns connecting to their ancestry in Great Britain. Let us dive a bit deeper into the specific values and behaviors relating to VIOLENCE, AVERSION TO WORK, ENTREPRENEURSHIP and EDUCATION… and see how these compare with the northerners of the US….   Butter is involved I swear.   It is astonishing, on how different these regions are in various sectors relating to culture, success, economic development and productivity. These statistical disparities and observations that existed between SOUTHERN WHITES and NORTHERN WHITES in the past… are often taken as EVIDENCE OR PROOF of RACIAL DISCRIMINATION when similar disparities are found between black and white populations today. I don't buy the blanket term of systemic racism… neither racial discrimination or inferiority can explain these differences we discuss in this episode between northern and southern whites from earlier centuries. Even the differences between northern blacks vs. southern whites… I hope this opens some doors and raises eyebrows for further discussion.   Enjoy. Or not.   Yours Truly.   “What the REDNECKS or CRACKERS brought with them… was a whole constellation of values and behavior patterns that might of made sense in the world they lived in for centuries… but were counterproductive in the world to which they were going… and counterproductive to the blacks… who would live in the midst for centuries, taking with them… similar values…”   REFERENCES:   Thomas Sowell - Black Rednecks & White Liberals Grady McWhiney - Cracker Culture: Celtic Ways in the South David Hackett Fischer - Albion's Seed Alexis de Tocqueville - Democracy in America --- Send in a voice message: https://anchor.fm/unveiledpatriot/message Support this podcast: https://anchor.fm/unveiledpatriot/support

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: PaLM in "Extrapolating GPT-N performance", published by Lukas Finnveden on April 6, 2022 on The AI Alignment Forum. A bit more than a year ago, I wrote Extrapolating GPT-N performance, trying to predict how fast scaled-up models would improve on a few benchmarks. Google Research just released a paper reporting benchmark performance of PaLM: a 540B parameter model trained on 780B tokens. This post contains an updated version of one of the old graphs, where I've added PaLM's performance. You can read the original post for the full details, but as a quick explainer of how to read the graph: Each dot represents a particular model's performance on a particular benchmark (taken from the GPT-3 paper). Color represents benchmark; y-position represents benchmark performance (normalized between random and my guess of maximum possible performance); and the x-position represents loss on GPT-3's validation set. The x-axis is also annotated with the required size+data that you'd need to achieve that loss (if you trained to convergence) according to the original scaling laws paper. (After the point at which OpenAI's scaling-laws predicts that you'd only have to train on each data point once, it is also annotated with the amount of FLOP you'd need to train on each data point once.) The crosses represent Google's new language model, PaLM. Since they do not report loss, I infer what position it should have from the size and amount of data it was trained on. (The relationship between parameters and data is very similar to what OpenAI's scaling laws recommended.) The sigmoid lines are only fit to the GPT-3 dots, not the PaLM crosses. Some reflections: SuperGLUE is above trend (and happens to appear on the Cloze & completion trendline — this is totally accidental). ANLI sees impressive gains, though nothing too surprising given ~sigmoidal scaling. Common sense reasoning + Reading tasks are right on trend. Cloze & completion, Winograd, and Q&A are below trend. The average is amusingly right-on-trend, though I wouldn't put a lot of weight on that, given that the weighting of the different benchmarks is totally arbitrary. (The current set-up gives equal weight to everything — despite e.g. SuperGLUE being a much more robust benchmark than Winograd.) And a few caveats: The GPT-3 paper was published 2 years ago. I would've expected some algorithmic progress by now — and the PaLM authors claim to have made some improvements. Accounting for that, this looks more like it's below-trend. The graph relies a lot on the original scaling laws paper. This is pretty shaky, given that the Chinchilla paper now says that the old scaling laws are sub-optimal. The graph also relies on a number of other hunches, like what counts as maximum performance for each benchmark. And using sigmoids in particular was never that well-motivated. Since GPT-3 was developed, people have created much harder benchmarks, like MMLU and Big-bench. I expect these to be more informative than the ones in the graph above, since there's a limit on how much information you can get from benchmarks that are already almost solved. On the graph, it looks like the difference between GPT-3 (the rightmost dots) and PaLM is a lot bigger than the difference between GPT-3 and the previous dot. However, the log-distance in compute is actually bigger between the latter than between the former. The reason for this discrepancy is that GPT-3 slightly underperformed the scaling laws, and therefore appears relatively more towards the left than you would have expected from the compute invested in it. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: My attitude towards death, published by Richard Ngo on February 25, 2022 on LessWrong. The philosophy and psychology of death seem weirdly under-discussed - particularly by the wider silicon valley community, given how strongly anti-death many people in it are. This post is an attempt to think through some relevant considerations, primarily focused on my own intuitions and emotions. See also this old blog post - I mostly still agree with the points I made in it, but when thinking about it now I frame things pretty differently. Fearing death, loving life Let's first distinguish two broad types of reasons for wanting to avoid death: fearing death, and loving life. Perhaps these seem like two sides of the same coin - but, psychologically speaking, they feel very distinct to me. The latter was particularly dominant when I was in primary school, when a part of me emerged that was very afraid of death (in a way that wasn't closely linked to fear of missing out on any particular aspects of life). That part is still with me - but when it comes to the surface, its fear feels viscerally unpleasant, so I learned to suppress it pretty strongly. Arguments for why death is bad usually focus on positive reasons - living longer allows people to experience more happiness, and more of the other good things in life. These have resonated with me more over time, as I started to think about death on a more intellectual level. However, one difficulty with these arguments is that many parts of me pursue goals in a fairly myopic way which doesn't easily extrapolate to centuries, millennia, or longer. For example, it's hard to imagine what career success or social success look like on the scale of millennia - and even when I try, those visions are pretty different from the versions of those concepts that I currently find motivating on a gut level. Extrapolating hedonistic goals is easier in some ways (it's easy to imagine being happy for a very long time) but harder in other ways (the parts of me which care most about happiness are also the most myopic). Dissolving fear In practice, then, most of my motivation for avoiding death in the long term stems from fear of death. Although that fear comes out only rarely, I have a strong heuristic that fear-based motivation should be transmuted to other forms of motivation wherever possible. So what would happen if I talked more to the part that's scared of death, to try and figure out where it's coming from? By default, I expect it'd be uncooperative - it wants to continue being scared of death, to make sure that I act appropriately (e.g. that I stay ambitious). Can I assure it that I'll still try hard to avoid death if it becomes less scared? One source of assurance is if I'm very excited about a very long life - which I am, because the future could be amazing. Another comes from the altruistic part of me, whose primary focus is increasing the probability that the future will in fact be amazing. Since I believe that we face significant existential risk this century, working to make humanity's future go well overlaps heavily with working to make my own future go well. I think this broad argument (along with being in communities which reward longtermist altruism) has helped make the part of me that's scared of death more quiescent. Indeed, probably my main concern with my current attitude towards death is actually that I'm not scared enough about existential risk - I think that, if my emotions better matched my credences, that'd help motivate me (especially to pursue particularly unusual or ambitious interventions). This doesn't seem like a crucial priority, though, since my excitement- and interest-based motivations have been working fairly well so far (modulo some other productivity gaps which seem pretty orthogonal). Generalising to others So far I'...

You should listen to this episode. Barbara Bain, Martin Landau, Space 1999, Dragon's Domain.