Podcasts about turing

English mathematician and computer scientist

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Latest podcast episodes about turing

CodeNewbie
S22:E1 - The new wave of frontend developer tools are on their way (Chris Ferdinandi)

CodeNewbie

Play Episode Listen Later Nov 9, 2022 34:41


To welcome back our show for Season 22, we talk with a familiar face to the CodeNewbie Podcast, Chris Ferdinandi. Chris is the author of the Vanilla JS Pocket Guide series and the creator of the Vanilla JS Academy training program. On today's episode, Chris talks about what he's learned since coming on the show in 2020, how he sees the future of frontend development evolving over the next few years, and what tools might help in your next job search. Show Links Microsoft (sponsor) Turing (sponsor) 11ty API Angular Astro Browser Native JavaScript CSS Client-side vs. Server-side DOM Go HTML JavaScript jQuery Lodash Netlify Next Nuxt Petite Vue Preact React Ruby Static Site Generator Svelte Umbrella Underscore Vanilla JS Vue WordPress

Catholic Connection
2022-11-02 - The Miracle Hunter

Catholic Connection

Play Episode Listen Later Nov 2, 2022 60:00


Michael O'Neill begins a new season of "Explore with the Miracle Hunter on EWTN-TV. Louis Brown from Christ Medicus Foundation talks about "Turing a New Leaf in Autumn" : Start thinking about your health and wellness in union with the Divine Physician and His love.

Turing School Podcast

Marshall and Jesse chat with Erin Pintozzi about her work as an instructor at Turing, software development, her approach to coaching, problem solving, social justice, baby food, and hip stretching. Notes: Range The humble hash Hip stretching Feeding Littles “Don't let perfect take away the good” / “Don't search for perfect, search for done.” Turing Curriculum Blog Posts

New Books in Ancient History
John Stillwell, "The Story of Proof: Logic and the History of Mathematics" (Princeton UP, 2022)

New Books in Ancient History

Play Episode Listen Later Oct 31, 2022 57:58


The Story of Proof: Logic and the History of Mathematics (Princeton UP, 2022) investigates the evolution of the concept of proof--one of the most significant and defining features of mathematical thought--through critical episodes in its history. From the Pythagorean theorem to modern times, and across all major mathematical disciplines, John Stillwell demonstrates that proof is a mathematically vital concept, inspiring innovation and playing a critical role in generating knowledge. Stillwell begins with Euclid and his influence on the development of geometry and its methods of proof, followed by algebra, which began as a self-contained discipline but later came to rival geometry in its mathematical impact. In particular, the infinite processes of calculus were at first viewed as "infinitesimal algebra," and calculus became an arena for algebraic, computational proofs rather than axiomatic proofs in the style of Euclid. Stillwell proceeds to the areas of number theory, non-Euclidean geometry, topology, and logic, and peers into the deep chasm between natural number arithmetic and the real numbers. In its depths, Cantor, Gödel, Turing, and others found that the concept of proof is ultimately part of arithmetic. This startling fact imposes fundamental limits on what theorems can be proved and what problems can be solved. This book could well serve as a history of mathematics, because in developing the evolution of the concept of proof and how it has arisen in the various different mathematical fields. The author essentially traces the important milestones in the development of mathematics. It's an amazing job of collecting and categorizing many of the most important ideas in this area. Learn more about your ad choices. Visit megaphone.fm/adchoices

New Books in History
John Stillwell, "The Story of Proof: Logic and the History of Mathematics" (Princeton UP, 2022)

New Books in History

Play Episode Listen Later Oct 31, 2022 57:58


The Story of Proof: Logic and the History of Mathematics (Princeton UP, 2022) investigates the evolution of the concept of proof--one of the most significant and defining features of mathematical thought--through critical episodes in its history. From the Pythagorean theorem to modern times, and across all major mathematical disciplines, John Stillwell demonstrates that proof is a mathematically vital concept, inspiring innovation and playing a critical role in generating knowledge. Stillwell begins with Euclid and his influence on the development of geometry and its methods of proof, followed by algebra, which began as a self-contained discipline but later came to rival geometry in its mathematical impact. In particular, the infinite processes of calculus were at first viewed as "infinitesimal algebra," and calculus became an arena for algebraic, computational proofs rather than axiomatic proofs in the style of Euclid. Stillwell proceeds to the areas of number theory, non-Euclidean geometry, topology, and logic, and peers into the deep chasm between natural number arithmetic and the real numbers. In its depths, Cantor, Gödel, Turing, and others found that the concept of proof is ultimately part of arithmetic. This startling fact imposes fundamental limits on what theorems can be proved and what problems can be solved. This book could well serve as a history of mathematics, because in developing the evolution of the concept of proof and how it has arisen in the various different mathematical fields. The author essentially traces the important milestones in the development of mathematics. It's an amazing job of collecting and categorizing many of the most important ideas in this area. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

New Books Network
John Stillwell, "The Story of Proof: Logic and the History of Mathematics" (Princeton UP, 2022)

New Books Network

Play Episode Listen Later Oct 31, 2022 57:58


The Story of Proof: Logic and the History of Mathematics (Princeton UP, 2022) investigates the evolution of the concept of proof--one of the most significant and defining features of mathematical thought--through critical episodes in its history. From the Pythagorean theorem to modern times, and across all major mathematical disciplines, John Stillwell demonstrates that proof is a mathematically vital concept, inspiring innovation and playing a critical role in generating knowledge. Stillwell begins with Euclid and his influence on the development of geometry and its methods of proof, followed by algebra, which began as a self-contained discipline but later came to rival geometry in its mathematical impact. In particular, the infinite processes of calculus were at first viewed as "infinitesimal algebra," and calculus became an arena for algebraic, computational proofs rather than axiomatic proofs in the style of Euclid. Stillwell proceeds to the areas of number theory, non-Euclidean geometry, topology, and logic, and peers into the deep chasm between natural number arithmetic and the real numbers. In its depths, Cantor, Gödel, Turing, and others found that the concept of proof is ultimately part of arithmetic. This startling fact imposes fundamental limits on what theorems can be proved and what problems can be solved. This book could well serve as a history of mathematics, because in developing the evolution of the concept of proof and how it has arisen in the various different mathematical fields. The author essentially traces the important milestones in the development of mathematics. It's an amazing job of collecting and categorizing many of the most important ideas in this area. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

New Books in Intellectual History
John Stillwell, "The Story of Proof: Logic and the History of Mathematics" (Princeton UP, 2022)

New Books in Intellectual History

Play Episode Listen Later Oct 31, 2022 57:58


The Story of Proof: Logic and the History of Mathematics (Princeton UP, 2022) investigates the evolution of the concept of proof--one of the most significant and defining features of mathematical thought--through critical episodes in its history. From the Pythagorean theorem to modern times, and across all major mathematical disciplines, John Stillwell demonstrates that proof is a mathematically vital concept, inspiring innovation and playing a critical role in generating knowledge. Stillwell begins with Euclid and his influence on the development of geometry and its methods of proof, followed by algebra, which began as a self-contained discipline but later came to rival geometry in its mathematical impact. In particular, the infinite processes of calculus were at first viewed as "infinitesimal algebra," and calculus became an arena for algebraic, computational proofs rather than axiomatic proofs in the style of Euclid. Stillwell proceeds to the areas of number theory, non-Euclidean geometry, topology, and logic, and peers into the deep chasm between natural number arithmetic and the real numbers. In its depths, Cantor, Gödel, Turing, and others found that the concept of proof is ultimately part of arithmetic. This startling fact imposes fundamental limits on what theorems can be proved and what problems can be solved. This book could well serve as a history of mathematics, because in developing the evolution of the concept of proof and how it has arisen in the various different mathematical fields. The author essentially traces the important milestones in the development of mathematics. It's an amazing job of collecting and categorizing many of the most important ideas in this area. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/intellectual-history

New Books in Mathematics
John Stillwell, "The Story of Proof: Logic and the History of Mathematics" (Princeton UP, 2022)

New Books in Mathematics

Play Episode Listen Later Oct 31, 2022 57:58


The Story of Proof: Logic and the History of Mathematics (Princeton UP, 2022) investigates the evolution of the concept of proof--one of the most significant and defining features of mathematical thought--through critical episodes in its history. From the Pythagorean theorem to modern times, and across all major mathematical disciplines, John Stillwell demonstrates that proof is a mathematically vital concept, inspiring innovation and playing a critical role in generating knowledge. Stillwell begins with Euclid and his influence on the development of geometry and its methods of proof, followed by algebra, which began as a self-contained discipline but later came to rival geometry in its mathematical impact. In particular, the infinite processes of calculus were at first viewed as "infinitesimal algebra," and calculus became an arena for algebraic, computational proofs rather than axiomatic proofs in the style of Euclid. Stillwell proceeds to the areas of number theory, non-Euclidean geometry, topology, and logic, and peers into the deep chasm between natural number arithmetic and the real numbers. In its depths, Cantor, Gödel, Turing, and others found that the concept of proof is ultimately part of arithmetic. This startling fact imposes fundamental limits on what theorems can be proved and what problems can be solved. This book could well serve as a history of mathematics, because in developing the evolution of the concept of proof and how it has arisen in the various different mathematical fields. The author essentially traces the important milestones in the development of mathematics. It's an amazing job of collecting and categorizing many of the most important ideas in this area. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/mathematics

Matters of Life and Death
AI sentience 2: I-Thou relationships, talking to stuffed animals, thanking Alexa, and Turing red flag laws

Matters of Life and Death

Play Episode Listen Later Oct 26, 2022 32:26


Building on last week's discussion of AI chatbots, we consider the theology and sociology of why interacting with other human beings is so central to our personhood. But would it matter if we did enter into a counselling or caring relationship with a robot or AI software, if we felt it helped our loneliness or anxiety? How can we be raising young people, who cannot remember a world before smart speakers and digital assistants, to engage well and honestly with the AI all around them? And might there be a role for regulation to hem in the ambitions of the overmighty tech giants in this space? You can read John's briefing paper on AI and simulated relationships here - https://johnwyatt.com/2020/01/10/article-artificial-intelligence-and-simulated-relationships/ Subscribe to the Matters of Life and Death podcast: https://pod.link/1509923173 If you want to go deeper into some of the topics we discuss, visit John's website: http://www.johnwyatt.com For more resources to help you explore faith and the big questions, visit: http://www.premierunbelievable.com

The Nonlinear Library
AF - Beyond Kolmogorov and Shannon by Alexander Gietelink Oldenziel

The Nonlinear Library

Play Episode Listen Later Oct 25, 2022 9:26


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: Beyond Kolmogorov and Shannon, published by Alexander Gietelink Oldenziel on October 25, 2022 on The AI Alignment Forum. This post is the first in a sequence that will describe James Crutchfield's Computational Mechanics framework. We feel this is one of the most theoretically sound and promising approaches towards understanding Transformers in particular and interpretability more generally. As a heads up: Crutchfield's framework will take many posts to fully go through, but even if you don't make it all the way through there are still many deep insights we hope you will pick up along the way. EDIT: since there was some confusion about this in the comments: These initial posts are supposed to be an introductionary and won't get into the actually novel aspects of Crutchfield's framework yet. It's also not a dunk on existing information- theoretic measures - rather an ode! To better understand the capability and limitations of large language models it is crucial to understand the inherent structure and uncertainty ('entropy') of language data. It is natural to quantify this structure with complexity measures. We can then compare the performance of transformers to the theoretically optimal limits achieved by minimal circuits. This will be key to interpreting transformers. The two most well-known complexity measures are the Shannon entropy and the Kolmogorov complexity. We will describe why these measures are not sufficient to understand the inherent structure of language. This will serve as a motivation for more sophisticated complexity measures that better probe the intrinsic structure of language data. We will describe these new complexity measures in subsequent posts. Later in this sequence we will discuss some directions for transformer interpretability work. Compression is the path to understanding Imagine you are an agent coming across some natural system. You stick an appendage into the system, effectively measuring its states. You measure for a million timepoints and get mysterious data that looks like this: ...00110100100100110110110100110100100100110110110100100110110100... You want to gain an understanding of how this system generates this data, so that you can predict its output, so you can take advantage of the system to your own ends, and because gaining understanding is an intrinsic joy. In reality the data was generated in the following way: output 0, then 1, then you flip a fair coin, and then repeat. Is there some kind of framework or algorithm where we can reliably come to this understanding? As others have noted, understanding is related to abstraction, prediction, and compression. We operationalize understanding by saying an agent has an understanding of a dataset if it possesses a compressed generative model: i.e. a program that is able to generate samples that (approximately) simulate the hidden structure, both deterministic and random, in the data. Note that pure prediction is not understanding. As a simple example take the case of predicting the outcomes of 100 fair coin tosses. Predicting tails every flip will give you maximum expected predictive accuracy (50%), but it is not the correct generative model for the data. Over the course of this sequence, we will come to formally understand why this is the case. Standard measures of information theory do not work To start let's consider the Kolmogorov Complexity and Shannon Entropy as measures of compression, and see why they don't quite work for what we want. Kolmogorov Complexity Recall that the Kolmogorov(-Chaitin-Solomonoff) complexity K(x) of a bit string x is defined as the length of the shortest programme outputting x [given a blank output on a chosen universal Turing machine] One often discussed downside of the K complexity is that it is incomputable. But there is another more conceptual do...

How to B2B a CEO (with Ashu Garg)
How to Build a Hundred-Billion-Dollar Company (Jonathan Siddharth, Co-Founder & CEO of Turing)

How to B2B a CEO (with Ashu Garg)

Play Episode Listen Later Oct 25, 2022 42:00


Jonathan Siddharth is cofounder and CEO of Turing, the platform that helps companies source, vet, match, and manage the world's best software developers remotely. Jonathan started Turing in Foundation Capital's offices four years ago and he's since grown it into a $4B company. On this episode of B2BaCEO, Ashu gets him to talk about how he did it: from best practices for hiring execs and communicating with investors, to the unique fundraising machine that Turing's built, to navigating through choppy economic waters. Jonathan is one of the most methodical, forward-thinking entrepreneurs active today. For four wild years, he's been living the startup life, and fighting the founder's fight, and embodying this show's ideal of growing from engineer to CEO.

TechStuff
The Ghost in the Machine

TechStuff

Play Episode Listen Later Oct 24, 2022 46:59 Very Popular


What does "the ghost in the machine" mean? From philosophy to artificial intelligence, we explore this idiom to understand what it means, how it's used and if the dream of strong AI is realistic.See omnystudio.com/listener for privacy information.

Turing School Podcast
Remote Community

Turing School Podcast

Play Episode Listen Later Oct 19, 2022 68:21


Marshall and Jess chat with Turing alum and Handshake Engineer Priya Power about her experience doing Turing remotely, social justice, life at Handshake, education, and other topics. 

And the Winner Should Have Been...
Birdman or (The Unexpected Virtue of Ignorance)

And the Winner Should Have Been...

Play Episode Listen Later Oct 8, 2022 76:00


This week we're back in our bag and talking about whether or not the Best Picture winner from the 2015 Oscars deserved the win.  Even though these are not quite 10 years old, we still spoil them with abandon! Enjoy! 00:10 Bob's worst intro ever02:01 Why 2014?03:50 How many of the 8 nominees did we see?06:21 Birdman!09:33 Navel-gazing perspective12:30 We're actors! We're the opposite of people.15:05 Fame at the highest levels19:47 Chicago!20:09 Birdman verdicts22:13 Hail, Caesar! is a better movie about the world of acting22:42 Pandering to the academy23:57 What did the end of Birdman mean?28:14 Boyhood!31:36 It's more interesting what it would be like to make such a movie than to watch this movie32:29 Richard Linklater37:14 Teenagers are not interesting40:18 Wait, we're only two films in at the 40 minute mark?40:39 The Grand Budapest Hotel43:30 What kind of man is M. Gustave?47:40 Bob has only liked 2 of the 4 Wes Anderson films he's seen48:08 The French Dispatch digression49:27 A point of congruence with Birdman51:45 The Imitation Game/Whiplash53:07 JK Simmons as Fletcher54:05 Turing's personal life not that interesting55:53 The wartime stakes in The Imitation Game not well established57:04 Mark Strong as Stewart Menzies59:33 Mark's pick for the best movie of 2014: Nightcrawler1:05:44 Interstellar1:09:09 Our picks for Best Picture1:10:34 The legacy of the Best Picture nominees1:12:05 Mark's technical difficulties1:12:44 Preview of coming attractionsNote: Oscar® and Academy Awards® are the trademarks and service marks of the Academy of Motion Picture Arts and Sciences. This podcast is neither endorsed by nor affiliated with the Academy of Motion Picture Arts and Sciences.Music:Intro and Outro music excerpted without alteration other than length and volume from AcidJazz by Kevin McLeod under a Creative Commons (CC BY 3.0) license: https://creativecommons.org/licenses/by/3.0/legalcode

The Nonlinear Library
AF - Warning Shots Probably Wouldn't Change The Picture Much by Nate Soares

The Nonlinear Library

Play Episode Listen Later Oct 6, 2022 4:17


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: Warning Shots Probably Wouldn't Change The Picture Much, published by Nate Soares on October 6, 2022 on The AI Alignment Forum. One piece of advice I gave to EAs of various stripes in early 2021 was: do everything you can to make the government sane around biorisk, in the wake of the COVID pandemic, because this is a practice-run for AI. I said things like: if you can't get the world to coordinate on banning gain-of-function research, in the wake of a trillions-of-dollars tens-of-millions-of-lives pandemic "warning shot", then you're not going to get coordination in the much harder case of AI research. Biolabs are often publicly funded (rather than industry-funded). The economic forces arrayed behind this recklessly foolish and impotent research consists of “half-a-dozen researchers thinking it's cool and might be helpful”. (While the work that would actually be helpful—such as removing needless bureaucracy around vaccines and investing in vaccine infrastructure—languishes.) Compared to the problem of AI—where the economic forces arrayed in favor of “ignore safety and rush ahead” are enormous and the argument for expecting catastrophe much murkier and more abstract—the problem of getting a sane civilizational response to pandemics (in the wake of a literal pandemic!) is ridiculously easier. And—despite valiant effort!—we've been able to do approximately nothing. We're not anywhere near global bans on gain-of-function research (or equivalent but better feats of coordination that the people who actually know what they're talking about when it comes to biorisk would tell you are better targets than gain-of-function research). The government continues to fund research that is actively making things worse, while failing to put any serious funding towards the stuff that might actually help. I think this sort of evidence has updated a variety of people towards my position. I think that a variety of others have not updated. As I understand the counter-arguments (from a few different conversations), there are two main reasons that people see this evidence and continue to hold out hope for sane government response: 1. Perhaps the sorts of government interventions needed to make AI go well are not all that large, and not that precise. I confess I don't really understand this view. Perhaps the idea is that AI is likely to go well by default, and all the government needs to do is, like, not use anti-trust law to break up some corporation that's doing a really good job at AI alignment just before they succeed? Or perhaps the idea is that AI is likely to go well so long as it's not produced first by an authoritarian regime, and working against authoritarian regimes is something governments are in fact good at? I'm not sure. I doubt I can pass the ideological Turing test of someone who believes this. 2. Perhaps the ability to cause governance to be sane on some issue is tied very directly to the seniority of the government officials advising sanity. EAs only started trying to affect pandemic policy a few years ago, and aren't very old or recognized among the cacophony of advisors. But if another pandemic hit in 20 years, the sane EA-ish advisors would be much more senior, and a lot more would get done. Similarly, if AI hits in 20 years, sane EA-ish advisors will be much more senior by then. The observation that the government has not responded sanely to pandemic near-misses, is potentially screened-off by the inexperience of EAs advising governance. I have some sympathy for the second view, although I'm skeptical that sane advisors have significant real impact. I'd love a way to test it as decisively as we've tested the "government (in its current form) responds appropriately to warning shots" hypotheses. On my own models, the "don't worry, people will wake up as the cliff-edge come...

The Nonlinear Library
EA - Warning Shots Probably Wouldn't Change The Picture Much by So8res

The Nonlinear Library

Play Episode Listen Later Oct 6, 2022 4:17


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: Warning Shots Probably Wouldn't Change The Picture Much, published by So8res on October 6, 2022 on The Effective Altruism Forum. One piece of advice I gave to EAs of various stripes in early 2021 was: do everything you can to make the government sane around biorisk, in the wake of the COVID pandemic, because this is a practice-run for AI. I said things like: if you can't get the world to coordinate on banning gain-of-function research, in the wake of a trillions-of-dollars tens-of-millions-of-lives pandemic "warning shot", then you're not going to get coordination in the much harder case of AI research. Biolabs are often publicly funded (rather than industry-funded). The economic forces arrayed behind this recklessly foolish and impotent research consists of “half-a-dozen researchers thinking it's cool and might be helpful”. (While the work that would actually be helpful—such as removing needless bureaucracy around vaccines and investing in vaccine infrastructure—languishes.) Compared to the problem of AI—where the economic forces arrayed in favor of “ignore safety and rush ahead” are enormous and the argument for expecting catastrophe much murkier and more abstract—the problem of getting a sane civilizational response to pandemics (in the wake of a literal pandemic!) is ridiculously easier. And—despite valiant effort!—we've been able to do approximately nothing. We're not anywhere near global bans on gain-of-function research (or equivalent but better feats of coordination that the people who actually know what they're talking about when it comes to biorisk would tell you are better targets than gain-of-function research). The government continues to fund research that is actively making things worse, while failing to put any serious funding towards the stuff that might actually help. I think this sort of evidence has updated a variety of people towards my position. I think that a variety of others have not updated. As I understand the counter-arguments (from a few different conversations), there are two main reasons that people see this evidence and continue to hold out hope for sane government response: 1. Perhaps the sorts of government interventions needed to make AI go well are not all that large, and not that precise. I confess I don't really understand this view. Perhaps the idea is that AI is likely to go well by default, and all the government needs to do is, like, not use anti-trust law to break up some corporation that's doing a really good job at AI alignment just before they succeed? Or perhaps the idea is that AI is likely to go well so long as it's not produced first by an authoritarian regime, and working against authoritarian regimes is something governments are in fact good at? I'm not sure. I doubt I can pass the ideological Turing test of someone who believes this. 2. Perhaps the ability to cause governance to be sane on some issue is tied very directly to the seniority of the government officials advising sanity. EAs only started trying to affect pandemic policy a few years ago, and aren't very old or recognized among the cacophony of advisors. But if another pandemic hit in 20 years, the sane EA-ish advisors would be much more senior, and a lot more would get done. Similarly, if AI hits in 20 years, sane EA-ish advisors will be much more senior by then. The observation that the government has not responded sanely to pandemic near-misses, is potentially screened-off by the inexperience of EAs advising governance. I have some sympathy for the second view, although I'm skeptical that sane advisors have significant real impact. I'd love a way to test it as decisively as we've tested the "government (in its current form) responds appropriately to warning shots" hypotheses. On my own models, the "don't worry, people will wake up as the cliff-edge com...

Turing School Podcast
Authentic Community

Turing School Podcast

Play Episode Listen Later Oct 5, 2022 62:30


Turing friends and alumni Jeannine, Marshall, and Jesse speak with Asian Girls Ignite founder Joanne Liu about her Turing story, education, social justice, motherhood, Asian Girls Ignite, and other topics. 

The 7investing Podcast
Investing with 2 Honest Poker Players

The 7investing Podcast

Play Episode Listen Later Oct 4, 2022 56:31


What happens when two poker-playing veteran investors discuss all things loosely related to the stock market? Luke Hallard and Krzysztof Piekarski wanted to record a podcast about the most interesting ideas and happenings in the world of investing. In this introductory episode, we talk about running with our favorite podcasts, being an optimist, Zen, stoicism and the difference between the three; Artificial Intelligence and its limitations; the Turing test; an AI Elon Musk chat robot that will answer your questions and the difference between a bad beat and a sad outcome. Welcome to 7investing. We are here to empower you to invest in your future! We publish our 7 best ideas in the stock market to our subscribers for just $49 per month or $399 per year. Start your journey toward's financial independence: https://www.7investing.com/subscribe Stop by our website to level-up your investing education: https://www.7investing.com Join the 7investing Community Forum: https://discord.gg/6YvazDf9sw Subscribe to Zacks "Profit from the Pros" newsletter for more insights: https://www.zacks.com/7investingpodcast Follow us: ► https://www.facebook.com/7investing ► https://twitter.com/7investing ► https://instagram.com/7investing --- Send in a voice message: https://anchor.fm/7investing/message

The J Curve
Daniel Ibri / Mindset Ventures on building bridges between global tech markets, bootstrapping Fund I and why now is the best time to invest in startups

The J Curve

Play Episode Listen Later Oct 3, 2022 31:55


October 3: For this episode of the J Curve I am thrilled to bring you my conversation with Daniel Ibri, a founder and General Partner at Mindset Ventures, an early stage Sao Paulo based VC firm that invests in B2B startups in Israel and the United States, that just did the first closing of their brand new $100M Fund IV and whose portfolio includes tech unicorns Brex and  Turing.In today's episode we will learn:  1. How to build a high performing portfolio on competitive markets like Israel and the US?2. What's Daniel's biggest investment mistake and what are the key learnings from it?3. What is the logic of raising 3 subsequent funds in 6 years?4. Why recession in the best time to invest in startups?5. What are the challenges of valuating early stage tech companies?

Marketing Square : Méthodes Growth Marketing
188. 12 idées pour optimiser son business grâce à l'Intelligence Artificielle !

Marketing Square : Méthodes Growth Marketing

Play Episode Listen Later Sep 30, 2022 23:09


L'I.A. n'est plus un truc de geek ! On utilise déjà l'intelligence artificielle sans même le savoir... Savez-vous que vous pourriez déléguer une partie de votre business, à moindres frais ? Sébastien Fourault, ex-Googler, Consultant UX et Entrepreneur (zewelcome.com), dévoile les secrets pour mettre l'Intelligence Artificielle au service de nos business. Tous types de business !   Dans cet épisode décoiffant, découvrez... Quelles sont les différents types d'I.A. ? Qu'est-ce que le test de Turing ? Qu'est-ce qu'une I.A. "sentient" ? Qu'est-ce que "GPT-3" ? 5 idées pour utiliser la génération de textes 7 idées pour utiliser la génération d'images 20mn de Masterclass sur l'I.A. et des idées à emporter pour votre business. Attention, vous allez ENFIN tout comprendre !

The Nonlinear Library
EA - $5k challenge to quantify the impact of 80,000 hours' top career paths by NunoSempere

The Nonlinear Library

Play Episode Listen Later Sep 23, 2022 10:25


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: $5k challenge to quantify the impact of 80,000 hours' top career paths, published by NunoSempere on September 23, 2022 on The Effective Altruism Forum. Motivation 80,000 hours has identified a number of promising career paths. They have a fair amount of analysis behind their recommendations, and in particular, they have a list of top ten priority paths. However, 80,000 hours doesn't quite have quantitative estimates of these paths' value. Although their usefulness would not be guaranteed, quantitative estimates could make it clearer: how valuable their top career paths are relative to each other how valuable their top career paths are relative to options further down their list at which level of personal fit one should switch between different career paths where the expected impact is coming from, and which variables we are most uncertain about eventually, whether certain opportunities are valuable in themselves or for the value of information or career capital that they provide etc. The Prize Following up on the $1,000 Squiggle Experimentation Challenge and the Forecasting Innovation Prize we are offering a prize of $5k for quantitative estimates of the value of 80,000 hours' top 10 career paths. Rules Step 1: Make a public post online between now and December 1, 2022. Posts on the EA Forum (link posts are fine) are encouraged.Step 2: Complete this submission form. Further details Participants can use units or strategies of their choice—these might be QALYs, percentage points of reduction in existential risk, basis points of the future, basis points of existential risk reduced, career-dependent units, etc. Contestants could also use some other method, like relative values, estimating proxies, or some original option. We are specifically looking for quantitative estimates that attempt to estimate some magnitude reasonably close to the real world, similar to the units above. So for example, assigning valuations from 0 to 5 stars would not fulfil the requirements of the contest, but estimates in terms of the units above would qualify. Participants are free to estimate the value of one, several, or all ten career paths. Participants are free to use whatever tool or language they want to produce these estimates. Some possible tooling might be: Excel, Squiggle, Guesstimate, probabilistic languages or libraries (e.g., Turing.jl, PyMC3, Stan), Causal, working directly in a popular programming language, etc. Participants can provide point estimates of impact, but they are encouraged to provide their estimates as distributions instead. Participants are free to estimate the impact of a marginal person, of a marginal person with a good fit, the average value, etc. Participants are welcome to provide both average and marginal value—for example, they could provide a function which provides an estimate of marginal value at different levels of labor and capital. We provide some examples of possible rough submissions in an appendix. We are also happy to comment on estimation strategies: feel free to leave a comment on this post or to send a message to Nuño Sempere using the EA forum message functionality. Judging The judges will be Nuño Sempere, Eli Lifland, Alex Lawsen and Sam Nolan. These judges will judge on their personal capacities, and their stances do not represent their organizations. Judges will estimate the quality and value of the entries, and we will distribute the prize amount of $5k in proportion to an equally weighted aggregate of those subjective estimates. To reduce our operational burden, we are looking to send out around three to five prizes. If there are more than five submissions, we plan to implement a lottery system. For example, a participant who would have won $100 would instead get a 10% chance of receiving $1k. Acknowledgements This contest is a project of...

Beauty and the Biz
100 Percent Non-Surgical Practice by Surgeon — with Alexander Rivkin, MD (Ep.172)

Beauty and the Biz

Play Episode Listen Later Sep 23, 2022 71:35


Hello, and welcome to Beauty and the Biz where we talk about the business and marketing side of plastic surgery and how Dr. Rivkin has a 100 percent non-surgical practice. I'm your host, Catherine Maley, author of Your Aesthetic Practice – What your patients are saying, as well as consultant to plastic surgeons, to get them more patients and more profits. Now, today's episode is called "100 Percent Non-Surgical Practice by Surgeon — with Alexander Rivkin, MD." Why would someone go through years of training to be a facial plastic surgeon, only to drop it and focus on non-surgical techniques? Dr. Alexander Rivkin is a trained facial plastic surgeon who founded Rivkin Aesthetics in Los Angeles.  Since 2003, he has specialized in state-of-the-art NON-surgical aesthetic procedures that compete with the outcomes of plastic surgery, delivered in an intimate and luxurious setting. On this week's Beauty and the Biz Podcast, Dr. Rivkin explains his journey from surgery to non-surgical procedures as well as… Building a name as “The Best” by focusing Running a practice with a CEO and COO Staff issues being the biggest challenge Avoiding coat hangers (lasers you don't use) in your office and more He also tells an incredible story about watching the destruction of Ukraine (his mother country) and wanting to help and how he got an entire plane of medical supplies safely to them. Visit Dr. Rivkin's Website  

Turing School Podcast
How Does Turing Build Community?

Turing School Podcast

Play Episode Listen Later Sep 21, 2022 70:37


Three hosts and Turing Alumni Jeannie, Jesse & Marshall talk with EllenMary Hickmann, Managing Director at Turing School of Software about her path to Turing, her work building community, social justice, education, and other topics.

FMKlit Pod
75. Robits!

FMKlit Pod

Play Episode Listen Later Sep 21, 2022 102:02


As technology advances, it brings up so many very important questions. Is there a Turing test for the heart? Do androids dream of electric sheets? We endeavor to find out because today we're doing robits!! For this Asimovian episode, we read "The A.I. Who Loved Me" by Alyssa Cole and "Love Machine: An Erotic Robot Romance (The Body Electric Book 1)" by Electra Shepherd. Our definitely human and not replicant hosts discuss very helpful dishwashers, hydraulic cocks, and when things get too heavy for erotica. Support us on Patreon! patreon.com/fmklitpod

Monster Radio RX93.1's Official Podcast Channel
HOW YOU HANDLE CRITICISM [Full Episode] w/ TURING!

Monster Radio RX93.1's Official Podcast Channel

Play Episode Listen Later Sep 20, 2022 139:37


September 20, 2022 The Morning Rush Hosted by: Chico, Hazel, & Markki Special guest: The big, bold, and beautiful TURING from Drag Race PH

The Stephen Wolfram Podcast
History of Science and Technology Q&A (September 22, 2021)

The Stephen Wolfram Podcast

Play Episode Listen Later Sep 9, 2022 89:18


Stephen Wolfram answers questions from his viewers about the history science and technology as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-qa Questions include: Stephen, why are keyboards the default computer interface? - Why didn't someone invent the printing press earlier? It seems to have been quite financially valuable. Was it an engineering problem or a lack of entrepreneurship? - In your opinion if Physics didn't work out for Professor Feynman would he have been able to make it as a stand up Comedian - In the past or currently, how much 'science' and technology is published or publicly available... is the most 'advanced'/ useful science and tech in the published literature/ patents? Are there branches of secret science? Yes or No, are you personally or do you know a group sitting on tech that is not public facing? - Why is Turing's machine model today so dominant compared to the equivalent lambda calculus? - Would love to read an essay just about the printing of the NKS book. This is a wild saga. - ​Is it possible to identify the moment in history when scientific investigation as we know it today broke away from the study of philosophy? - When do the viewers get to hear more about the answer to the why there's something rather than nothing? - Why do you think certain cultures have had such a disproportional success in science discovery & business?

Turing School Podcast
Alumni Community

Turing School Podcast

Play Episode Listen Later Sep 7, 2022 57:39


An interview with Erin Bassity, founder of the Turing Alumni Committee, Turing grad, and current Turing board member.  Reading Recommendations: White Privilege: Unpacking the Invisible Knapsack by Peggy McIntosh  Difference Matters by Brenda Allen  Turing Q2 2022 Jobs and Hiring Report 

Catholic Answers Live
#10790 Open Forum - Jimmy Akin

Catholic Answers Live

Play Episode Listen Later Sep 3, 2022


Questions Covered: 01:38 – A family member with dementia has me thinking a lot about this: Say you’re Catholic and maybe haven’t been the most charitable or loving, but you do your basic duty of Mass and Confession. Dementia creeps up–either slowly or quickly–and you get to the point where you don’t know what day it is, much less is able to make an examination of conscience for confession. I just wonder about the state of the soul that can’t improve or offer things up. I know you’re not responsible for your actions if you’re not in your right mind, but do you think they can gain any merit just for the trial of what they’re going through? 05:37 – Any thoughts/ideas about when the Great Pyramid was built? Some say 4000 BC, but others claim 10,000 BC is more like it, even pre-flood, where its air shaft would then point to a different Star. 16:36 – What’s the best way to talk to people who just don’t see anything good about what the church teaches? 18:17 – I have been in a few debates about “limbo.” Does it exist? And do un-baptized children go there? So many different thoughts on this from many different theologians 21:44 – Is the Latin Mass evil, good, or wonderful, and why does Francis hate it so much? 24:00 – How would you answer Atheist regard the problem of “unresistant unbelief”? If a fellow says he wants to believe in God and be a Christian (as one famous Atheist YouTuber said recently), but just needs some confirming sign to push him over the hump, what explanation can we give for why God refrains from doing so? Or is there some other reply? 28:26 – How is it possible that when a semi-trailer is carrying a bunch of honeybee hives, a honeybee can fly outside of the hive and just hover right there while the truck is moving 75 miles an hour and the honeybee doesn't get left behind? This blows my mind lol I need Jimmy's help 33:18 – I know it is illicit for someone like a grandparent to baptize a child in secret, against both parents’ wishes, and against their priest's decision even though my understanding is valid. My question is, did the child ultimately benefit from this? If yes, why is it considered illicit? If not, how come? Was it just neutral? 36:23 – Do vampires have souls? Is it murder to slay a vampire (assuming the classical undead form)? 41:55 – What do you think of AI-created “art” (MidJourney, etc)? I used to think there could be a Turing test equivalent, i.e., that we'd always be able to tell the difference between computer-generated images and human-created art, but as I see what is being created by MidJourney and the like I'm having doubts 45:23 – Is there a distinction between Hell / Hades / Gehenna / Sheol (Shuh ull)/ Lake of Fire, or are they all the same thing? 50:08 – Why is sex outside of marriage sinful? This seems to be an overwhelming reality. …

Idea Machines
Institutional Experiments with Seemay Chou [Idea Machines #47]

Idea Machines

Play Episode Listen Later Sep 1, 2022 73:50


Seemay Chou talks about the process of building a new research organization, ticks, hiring and managing entrepreneurial scientists, non-model organisms, institutional experiments and a lot more! Seemay is the co-founder and CEO of Arcadia Science —  a research and development company focusing on underesearched areas in biology and specifically new organisms that haven't been traditionally studied in the lab.  She's also the co-founder of Trove Biolabs — a startup focused on harnessing molecules in tick saliva for skin therapies and was previously an assistant professor at UCSF.  She has thought deeply not just about scientific problems themselves, but the meta questions of how we can build better processes and institutions for discovery and invention. I hope you enjoy my conversation with Seemay Chou   Links Seemay on Twitter (@seemaychou) Arcadia's Research Trove Biolabs Seemay's essay about building Arcadia  Transcript [00:02:02] Ben: So since a lot of our conversation is going to be about it how do you describe Arcadia to a smart well-read person who has never actually heard of it before? [00:02:12] Seemay: Okay. I, I actually don't have a singular answer to this smart and educated in what realm. [00:02:19] Ben: oh, good question. Let's assume they have taken some undergraduate science classes, but perhaps are not deeply enmeshed in, in academia. So, so like, [00:02:31] Seemay: enmeshed in the meta science community.[00:02:35]  [00:02:35] Ben: No, no, no, no, but they've, they, they, they, they they're aware that it's a thing, but [00:02:40] Seemay: Yeah. Okay. So for that person, I would say we're a research and development company that is interested in thinking about how we explore under researched areas in biology, new organisms that haven't been traditionally studied in the lab. And we're thinking from first principal polls about all the different ways we can structure the organization around this to also yield outcomes around innovation and commercialization. [00:03:07] Ben: Nice. And how would you describe it to someone who is enmeshed in the, the meta science community? [00:03:13] Seemay: In the meta science community, I would, I would say Arcadias are meta science experiment on how we enable more science in the realm of discovery, exploration and innovation. And it's, you know, that that's where I would start. And then there's so much more that we could click into on that. Right. [00:03:31] Ben: And we will, we will absolutely do that. But before we get there I'm actually really [00:03:35] interested in, in Arcadia's backstory. Cuz cuz when we met, I feel like you were already , well down the, the path of spinning it up. So what's, there's, there's always a good story there. What made you wanna go do this crazy thing? [00:03:47] Seemay: So, so the backstory of Arcadia is actually trove. Soro was my first startup that I spun out together with my co-founder of Kira post. started from a point of frustration around a set of scientific questions that I found challenging to answer in my own lab in academia. So we were very interested in my lab in thinking about all the different molecules and tick saliva that manipulate the skin barrier when a tick is feeding, but basically the, the ideal form of a team around this was, you know, like a very collaborative, highly skilled team that was, you know, strike team for like biochemical, fractionation, math spec, developing itch assays to get this done. It was [00:04:35] not a PhD style project of like one person sort of open-endedly exploring a question. So I was struggling to figure out how to get funding for this, but that wasn't even the right question because even with the right money, like it's still very challenging to set up the right team for this in academia. And so it was during this frustration that I started exploring with Kira about like, what is even the right way to solve this problem, because it's not gonna be through writing more grants. There's a much bigger problem here. Right? And so we started actually talking to people outside of academia. Like here's what we're trying to achieve. And actually the outcome we're really excited about is whether it could yield information that could be acted on for an actually commercializable product, right. There's like skin diseases galore that this could potentially be helpful for. So I think that transition was really important because it went from sort of like a passive idea to, oh, wait, how do we act as agents to figure out how to set this up correctly? [00:05:35] We started talking to angel investors, VCs people in industry. And that's how we learned that, you know, like itch is a huge area. That's an unmet need. And we had tools at our disposal to potentially explore that. So that's how tr started. And that I think was. The beginning of the end or the, the start of the beginning. However you wanna think about it. Because what it did, was it the process of starting trove? It was so fun and it was not at all in conflict with the way I was thinking about my science, the science that was happening on the team was extremely rigorous. And I experienced like a different structure. And that was like the light bulb in my head that not all science should be structured the same way. It really depends on what you're trying to achieve. And then I went down this rabbit hole of trying to study the history of what you might call meta science. Like what are the different structures and iterations of this that have happened over, over the history of even the United States. And it's, hasn't always been the same. Right? And then I think [00:06:35] like, as a scientist, like once you grapple with that, that the way things are now is not how they always have been. Suddenly you have an experiment in front of you. And so that is how Arcadia became born, because I realize. Couched within this trove experiment is so many things that I've been frustrated about that I, I, I don't feel like I've been maximized as the type of scientist that I am. And I really want to think in my career now about not how I fit into the current infrastructure, but like what other infrastructures are available to us. Right? [00:07:08] Ben: Nice. [00:07:09] Seemay: Yeah. So that, that was the beginning. [00:07:11] Ben: and, and so you, you then, I, I, I'm just gonna extrapolate one more, more step. And so you sort of like looked at the, the real, the type of work that you really wanted to do and determined that, that the, the structure of Arcadia that you've built is, is like perhaps the right way to go about enabling that. [00:07:30] Seemay: Okay. So a couple things I, I don't even know yet if Arcadia is the right way to do it. So I [00:07:35] feel like it's important for me to start this conversation there that I actually don't know. But also, yeah, it's a hypothesis and I would also say that, like, that is a beautiful summary, but it's still, it was still a little clunkier than the way you described it and the way I described it. So there's this gap there then of like, okay, what is the optimal place for me to do my science? How do we experiment with this? And I was still acting in a pretty passive way. You know, I was around people in the bay area thinking about like new orgs. And I had heard about this from like ju and Patrick Collison and others, like people very interested in funding and experimenting with new structures. So I thought, oh, if I could find someone else to create an organization. That I could maybe like help advise them on and be a part of, and, and so I started writing up this proposal that I was trying to actually pitch to other people like, oh, would you be interested in leading something like this? [00:08:35] Like, and the more that went on and I, I had like lots and lots and lots of conversations with other scientists in academia, trying to find who would lead this, that it took probably about six months for me to realize like, oh, in the process of doing this, I'm actually leading this. I think and like trying to find someone to hand the keys over to when actually, like, I seem to be the most invested so far. And so I wrote up this whole proposal trying to find someone to lead it and. It came down to that like, oh, I've already done this legwork. Like maybe I should consider myself leading it. And I've, I've definitely asked myself a bunch of times, like, was that like some weird internalized sexism on my part? Cause I was like looking for like someone, some other dude or something to like actually be in charge here. So that's actually how it started. And, and I think a couple people started suggesting to this to me, like if you feel so strongly about this, why aren't you doing this? And I know [00:09:35] it's always an important question for a founder to ask themselves. [00:09:38] Ben: Yeah, yeah, no, that's, that's really clutch. I appreciate you sort of going into the, the, the, the, the, the, like, not straight paths of it. Because, because I guess when we, we put these things into stories, we always like to, to make it like nice and, and linear and like, okay, then this happened and this happened, and here we are. But in reality, it was it's, it's always that ambiguity. Can, can I actually ask two, two questions based on, on that story? One is you, you mentioned that. In academia, even if you had the money, you wouldn't be able to put together that strike team that you thought was necessary. Like why can, can you, can you unpack that a little bit? [00:10:22] Seemay: Yeah. I mean, I think there's a lot of reasons why one of the important reasons, which is absolutely not a criticism of academia, in fact, it's maybe like my support of the [00:10:35] mission in academia is around training and education. That like part of our job as PIs and the research projects we set up is to provide an opportunity for a scientist to learn how to ask questions. How to answer those, how to go through the whole scientific process. And that requires a level of sort of like openness and willingness to allow the person to take the reigns on that. That I think is very difficult if you're trying to hit like very concrete, aggressive milestones with a team of people, right. Another challenge of that is, you know, the way we set up incentive structures around, you know, publishing, like we also don't set up the way we, you know, publish articles in journals to be like very collaborative or as collaborative as you would want in this scenario. Right. At the end of the day, there's a first author, there's the last author. And that is just a reality. We all struggle with despite everyone's best intentions. And so that inherently now sets up yeah. [00:11:35] Another situation where you're trying to figure out how you, we, this collaborative effort with this reality and. Even in the best case scenario, it doesn't always feel great. Right? Like it just like makes it harder to do the thing. And then finally, like it just, you know, for the way we fund projects in, in academia, you know, this wasn't a very hypothesis driven project. Like it's very hard to lay out specific aims for it. Beyond just the things we're gonna be trying to like, what, what, what is our process that we can lay [00:12:08] Ben: Yeah, it's a  [00:12:09] Seemay: I can't tell you yeah. What the outcomes are gonna be. So I did write grants on that and that was repeatedly the feedback. And then finally, there's, you know, this other thing, which is that, like, we didn't want to accidentally land on an opportunity for invi innovation. We explicitly wanted to find molecules that could be, you know, engineered for products. Like that was [00:12:35] our hypothesis. If there is any that like. By borrowing the innovation from ticks who have evolved to feed for days to sometimes over a week that we are skipping steps to figure out the right natural product for manipulating processes in the skin that have been so challenging to, you know, solve. So we didn't want it to be an accident. We wanted to be explicitly translational quote unquote. So that again, poses another challenge within an academic lab where you, you have a different responsibility, right? [00:13:05] Ben: Yeah. And, and you it's there there's like that tension there between setting out to do that and then setting out to do something that is publishable, right? [00:13:14] Seemay: Mm-hmm mm-hmm . Yeah. Yeah. And I think one of the, the hard things that I'm always trying to think about is like, what are things that have out of the things that I just listed? What are things that are appropriately different about academia and what are the things that maybe are worth a second? [00:13:31] Ben: mm. [00:13:32] Seemay: they might actually be holding us back even [00:13:35] within academia. So the first thing I would say is non-negotiable that there's a training responsibility. So that is has to be true, but that's not necessarily mutually exclusive with also having the opportunity for this other kind of team. For example, we don't really have great ways in academia to properly, you know, support staff scientists at a, at a high level. Like there's a very limited opportunity for that. And I, you know, I'm not arguing with people about like the millions of reasons why that might be. That's just a fact, you know, so that's not my problem to solve. I just, I just see that as like a challenge also like of course publishing, right? Like I think [00:14:13] Ben: yeah, [00:14:14] Seemay: in a best case scenario publishing should be science should be in the driver's seat and publishing should be supporting those activities. I think we do see, you know, and I know there's a spectrum of opinions on this, but there are definitely more and more cases now where publishing seems to be in the [00:14:35] driver's seat, [00:14:36] Ben: yeah, [00:14:36] Seemay: dictating how the science goes on many levels. And, you know, I can only speak for myself that I, I felt that to be increasingly true as I advanced my career. [00:14:47] Ben: yeah. And just, just to, to make it, make it really explicit that it's like the, the publishing is driving because that's how you like, make your tenure case. That's how you make any sort of credibility. Everybody's gonna be judging you based on what you're publishing as opposed to any other. [00:15:08] Seemay: right. And more, I think the reason it felt increasingly heavy as I advanced my career was not even for those reasons, to be honest, it was because of my trainees,  [00:15:19] Ben: Hmm.  [00:15:20] Seemay: if I wanna be out. Doing my crazy thing. I have a huge responsibility now to my students, and that is something I'm not willing to like take a risk on. And so now my hands are tied in this like other way, and their [00:15:35] careers are important to me. And if they wanna go into academia, I have to safeguard that. [00:15:40] Ben: Yeah. I mean, it suggests. Sort of a, a distinction between sort of, regardless of academia or not academia between like training labs and maybe focused labs. And, and you could say like, yes, you, you want trainees in focus. Like you want trainees to be exposed to focused research. But like at least sort of like thinking about those differences seems really important. [00:16:11] Seemay: Yes. Yeah. And in fact, like, you know, because I don't like to, I don't like to spend too much time, like. Criticizing people in academia, like we even grapple with this internally at Arcadia,  [00:16:25] Ben: Yeah.  [00:16:25] Seemay: like there is a fundamentally different phase of a project that we're talking about sort of like new, creating new ideas, [00:16:35] exploring de-risking and then some transition that happens where it is a sort of strike team effort of like, how do you expand on this? How do you make sure it's executed well? And there's probably many more buckets than the, just the two I said, but it it's worthy of like a little more thought around the way we set up like approvals and budgets and management, because they're too fundamentally different things, you know? [00:17:01] Ben: Yeah, that's actually something I, I wanted to ask about more explicitly. And this is a great segue is, is sort of like where, where do ideas come from at Arcadia? Like how, you know, it's like, there's, there's some spectrum where everybody's from, like everybody's working on, you know, their own thing to like you dictating everything. Everything in between. So like, yeah. Can you, can you go more into like, sort of how that, that flow works almost? [00:17:29] Seemay: So I might even reframe the question a little bit to [00:17:35] not where do ideas come from, but how do ideas evolve? Because it's  [00:17:39] Ben: please. Yeah. That's a much better reframing. [00:17:41] Seemay: because it's rarely the case, regardless of who the idea is coming from at Arcadia, that it ends where it starts. and I think that that like fluidity is I the magic sauce. Right. And so by and large, the ideas tend to come from the scientists themselves. Occasionally of course, like I will have a thought or Che will have a thought, but I see our roles as much more being there to like shepherd ideas in the most strategic and productive direction. And so we like, you know, I spent a lot of time thinking about like, well, what kind of resources would this take? And, you know, Che definitely thinks about that piece as well as, you know, like what it, what would actually be the impact of this if it worked in terms of like both our innovation, as well as the knowledge base outside of Arcadia Practically speaking, something we've started doing, that's been really helpful because we've gone. We've already gone through different iterations of this too. Like we [00:18:35] started out of like, oh, let's put out a Google survey. People can fill out where they pitch a project to us. And that like fell really flat because there's no conversation to be had there. And now they're basically writing a proposal. Yeah. More streamlined, but it's not that qualitatively different of a process. So then we started doing these things called sandboxes, which I'm actually really enjoying right now. These are every Friday we have like an hour long session. The entire company goes and someone's up at the dry erase board. We call it, throwing them in the sandbox and they present some idea or set of ideas or even something they're really struggling. For everybody to like, basically converse with them about it. And this has actually been a much more productive way for us to source ideas. And also for me to think collaboratively with them about like the right level of like resources, the right sort of inflection points for like, when we decide go or no, go on things. And so that's how we're currently doing it. I mean, we're [00:19:35] like just shy of about 30 people. I, this process will probably break again. once we hit like 50 people or something, cuz it's actually just like logistically a lot of people to cram into a room and there is a level of sort of like, yeah, and then there's a level of formality that starts to happen when there's like that many people in the room. So we'll see how it goes, but that's how it's currently working today. [00:20:00] Ben: that's that's really cool. And, and, and so then, then like, let's, let's keep following the, the evolutionary path, right. So an idea gets sandboxed and you collectively come to some conclusion that it's like, okay, like this idea is, is like, well worth pursuing then what happens. [00:20:16] Seemay: So then and actually we're like very much still under construction right now around this. We're trying to figure out like, how do, how do we think about budget and stuff for this type of step? But then presumably, okay, the person starts working on it. I can tell you where we're trying to go. I, I'm not sure where there yet, where we're trying to go is turning our [00:20:35] publications into a way to like actually integrate into this process. Like, ideally I would love it as CEO, if I can be updated on what people in the order are doing through our pub site. [00:20:49] Ben: Oh [00:20:50] Seemay: And that, like, I'm not saying they publish every single thing they do every day. Of course, that's crazy, crazy talk, but like that it's somewhat in line with what's happening in real time. That that is an appropriate place for me to catch up on what they're doing and think about like high level decisions and get feedback and see the feedback from the community as well, because that matters, right? Like if, if our goal is to either generate products in the form of actual products in the world that we commercialize versus knowledge products that are useful to others and can stimulate either more thought or be used by others directly. Like I need to actually see that data in the form of like the outside world interacting with their releases. Right. [00:21:35] So that's what we're trying to move towards, but there's a lot of challenges associated with that. Like if a, if a scientist is like needing to publish very frequently, How do we make sure we have the right resources in place to help them with that? There may be some aspects of that, that like anyone can help with like formatting or website issues or, you know, even like schematic illustrations to try and just like reduce the amount of friction around this process as much as possible. [00:22:00] Ben: And I guess almost just like my, my concern with the like publishing everything openly very early. And this is, this is almost where, where I disagree with with some people is that there's what, what I believe Sahi Baca called like the, the like Wardy baby problem, where ideas, when you're first sort of like poking at them are just like really ugly and you like, can't even, you can't even, like, you can barely justify it to [00:22:35] anybody on your team who like, trust you let alone people who like don't have any insight into the process. And so. Do do you, do you worry at all about like, almost just being like completely demoralized, right? Like it's just, it's so much easier to point out why something won't work early on than why it will. [00:22:56] Seemay: Yeah, totally. Yeah. [00:22:59] Ben: how do you [00:22:59] Seemay: Well, I mean, yeah, no, I think that's a hard, hard challenge. I mean, and, and people, and I would say at a metal level, I get, I get a lot of that too. Like people pointing out all the ways Arcadia [00:23:09] Ben: Yeah, I'm [00:23:10] Seemay: or potentially going to fail. So a couple things, I mean, I think one is that just, of course I'm not asking our scientists to. They have a random thought in the shower, like put that out into the world. right. Like there's of course some balance, like, you know, go through some amount of like thinking and like, you know, feedback with, with their most local peers on it. More, more in terms more than anything, like [00:23:35] just to like make sure by the time it goes out into the world that you're capturing precious bandwidth strategically. Right. [00:23:41] Ben: Yeah, [00:23:41] Seemay: On the other hand though, like, you know, while we don't want like that totally raw thing, we are so far on the, under the spectrum right now in terms of like forgiveness of some wards. And, and it also ignores the fact that like, it's the process, right? Like ugly baby. Great. That's that's like, like the uglier the better, like put it out there because like you want that feedback. You're not trying to be. trying to get to some ground truth here. And rigor happens through lots of like feedback throughout the entire process, especially at the beginning. And it's not even like that, that rigor doesn't happen in our current system. It's just that it doesn't make it out into the public space. People do share their thoughts with others. They do it at the dry erase board. They share proposals with each other. There's a lot of this happening. It's just not visible. So I mean, the other thing just like culturally, what I've been trying to like emphasize at [00:24:35] Arcadia is like process, not outcomes that like, you know, talking about it directly, as well as we have like an exercise in the beginning of thinking about like, what is the correct level of like failure rate quote unquote, and like what's productive failure. And just like, if we are actually doing like high risk, interesting science that's worth doing fundamentally, there's gotta be some inherent level of failure built in that we expect. Otherwise, we are answering questions. We already know the answer to, and then what's the fucking point. Right? [00:25:05] Ben: Yeah, [00:25:06] Seemay: So it almost doesn't matter what the answer to that question is. Like people said like 20%, some people said 80%, there's a very wide range in people's heads. Cuz there's this, isn't not a precise question. Right. So there's not gonna be precise answers, but the point is like the acceptance of that fact. Right? [00:25:24] Ben: Yeah. And also, I, I think I'm not sure if you would agree with this, but like, I, I feel like even like failure is a very fuzzy concept. In this, in this context, [00:25:35] right? [00:25:35] Seemay: totally. I actually really hate that word. We, we are trying to rebrand it internally to pivots. [00:25:42] Ben: Yeah. Yeah. I like that. I also, I also hate in this context, the idea of like risk, right? Like risk makes sense when it's like, you're getting like cash on cash returns, but [00:25:54] Seemay: right. [00:25:54] Ben: when [00:25:55] Seemay: Yeah. Yeah. I mean, you can redefine that word in this case to say like, it's extremely risky for you to go down this safe path because you will be very likely, you know, uncovering boring things. That's a risk, right? [00:26:13] Ben: Yeah. And then just in terms of process, I wanna go one, one step further into the, sort of like the, the like strike teams around an idea. Is it like something like where, where people just volunteer do do they get, like how, how, how do you actually like form those teams? [00:26:30] Seemay: Yeah. So far there has not been like sort of top down forcing of people into things. I [00:26:35] mean, we are a small org at this point, but like, I think like personally, my philosophy is that like, people do their best work when they're, they feel agency and like sort of their own deep, inner inspiration to do it. And so I try to make things more ground up because of that. Not, not just because of like some fuzzy feeling, but actually I think you'll get the best work from people, if you'd set it up that way. Having said that, you know, there are starting to be situations where we see an opportunity for a strike team project where we can, like, we need to hire someone to come. [00:27:11] Ben: Mm-hmm [00:27:12] Seemay: because no one existing has that skill set. So that that's a level of like flexibility that like not everybody has in other organizations, right. That you have an idea now you can hire more people onto it. So I mean, that's like obviously a huge privilege. We have to be able to do that where now we can just like transparently be like, here's the thing who wants to do it? You know? [00:27:32] Ben: yeah, yeah. [00:27:35] That's, that's very cool.  [00:27:36] Seemay: One more thing else. Can I just say one more thing about that [00:27:39] Ben: of course you can see as many things as you [00:27:40] Seemay: yeah. Actually the fact that that's possible, I feel like really liberates people at Arcadia to think more creatively because something very different happens when I ask people in the room. What other directions do you think you could go in versus what other directions do you think this project should go, could go in that we could hire someone from the outside to come do. Because now they like, oh, it doesn't have to be me. Or maybe they're maybe it's because they don't have the skillset or maybe they're attached to something else that they're working on. So making sure that in their mind, it's not framed as like an either or, but in if, and, and that they can stay in their lane with what they most wanna do. If we decide to move forward on that, you know? Cause I, I think that's often something that like in academia, we don't get to think about things that way. [00:28:30] Ben: Yeah, absolutely. And then the, the people that you would hire onto a [00:28:35] project, would they, like, so say, say, say the, the project then ends it, it reaches some endpoint. Do they like then sort of go back into the, the pool of people who are, are sandboxing? How do, how does that [00:28:49] Seemay: So we, So we haven't had that challenge on a large scale yet. I would say from a human perspective, I would really like to avoid a situation where like standard biotech companies, you know, if an area gets closed out, there's a bunch of layoffs. Like it would be nice to figure out how we can like, sort of reshuffle everybody. One of the ways this has happened, but it's not a problem yet is like we have these positions called arcade scientists, which is kind of meant for this to allow people to kind of like move around. So there's actually a couple of scientists that Arcadia that are quote unquote arcade it's meant to be like a playful term for someone who's a, a generalist in some area like biochemistry, [00:29:35] generalist computational generalist, something like that, where their job is literally to just work on like the first few months of any project. [00:29:44] Ben: oh, [00:29:45] Seemay: And help kind of like, de-risk like, they're really tolerant of that process. They like it. They like trying to get something brand new off the ground. And then once it becomes like more mature with like clear milestones, then we can hire someone else and then they move on to like the next thing, I think this is a skill in itself that doesn't really get highlighted in other places. And I think it's a skillset that actually resonates with me very much personally, because if I were applying to Arcadia, that is the position that I would want. [00:30:14] Ben: I, I think I'm in the same boat. Yeah, that, and that's, that's critical is like, there aren't a lot of organizations where you sort of like get to like come in for a stage of a project. In research, like there, it it's generally like you're, you're on this project.  [00:30:29] Seemay: And how often do you hear people complain about that in science of like, oh, so and so they're, they're [00:30:35] really great at starting things, but not finishing things. It's like, well, like how do we capitalize on that then? [00:30:39] Ben: yeah. Make it a feature and not a bug. Yeah, no, it's like, it it's sort of like having, I I'm imagining like sort of just different positions on a, a sports team, for example. And, and I feel like I, I was thinking the other day that that analogies between like research organizations and sports teams are, are sort of underrated right. Like you don't expect like the goal to be going and like, like scoring. Right. And you don't, you don't say like, oh, you're underperforming goalie. You didn't score any goals.  [00:31:08] Seemay: Right. That's so funny. I like literally just had a call with Sam Aman before this, where, where we were talking about this a little bit, we were talking about in a slightly different context about a role that I feel like is important in our organization of someone to help connect the dots across the different projects. What we were sort of like conceptualizing in my call with him as like the cross pollinators, like the bees in the organization that like, know what get in the [00:31:35] mix, know what everyone's doing and help everybody connect the dots. And like, I feel like this is some sort of a supportive role. That's better understood on sports teams. Like there's always someone that's like the glue, right? Maybe they're not the MVP, but they're the, the other guy that's like, or, you know, girl, whatever, UN gendered, but very important. Everybody understands that. And like, it's like celebrated, you know, [00:31:58] Ben: Yeah. Yeah. And it's like, and, and the trick is, is really seeing it more like a team. Right. So that's like the, the overarching thing. [00:32:07] Seemay: And then I'll just like, I don't know, just to highlight again though, how like these realities that you and I are talking about that I think is actually very well accepted across scientists. We all understand these different roles. Those don't come out in the very hierarchical authorship, byline of publications, which is the main currency of the system. And so, yeah, that's been fascinating to like, sort of like relearn because when we started this publishing experiment, [00:32:35] I was primarily thinking about the main benefit being our ability to do different formats and in a very open way. But now I see that this there's this whole other thing that's probably had the most immediate impact on Arcadia science, which is the removal of the authorship byline. [00:32:52] Ben: Mm. So, so you don't, you don't say who wrote the thing at all. [00:32:57] Seemay: We do it's at the bottom of the article, first of all. And then it's listed in a more descriptive way of who did what, it's not this like line that's like hierarchical, whether implicitly or explicitly and for my conversations with the scientists at Arcadia, like that has been really like a, a wonderful release for them in terms of like, thinking about how do they contribute to projects and interact with each other, because it's like, it doesn't matter anymore that that currency is like off the table. [00:33:27] Ben: Yeah. That that's very cool. And can, can I, can I change tracks a little bit and ask you about model organisms? [00:33:34] Seemay: sure  [00:33:34] Ben: [00:33:35] so like, and this is, this is coming really from my, my naivete, but like, like what, what are model organisms? And like, why is having more of them important? [00:33:47] Seemay: So there's, this is super, super important for me to clarify there's model organisms and there's non-model organisms, but there's actually two different ways of thinking about non-model organisms. Okay. So let me start with model organisms. A model organism is some organism that provides an extremely useful proxy for studying typically like either human biology or some conserved element of biology. So, you know, the fact that like we have. Very similar genetic makeup to mice or flies. Like there's some shortcuts you can take in these systems that allow you to like quickly ask experimental questions that would not be easy to do in a human being. Right. Like we obviously can't do those kinds of experiments there.[00:34:35]  And so, and so, so the same is true for like ASIS, which can be a model for plants or for like biology more generally. And so these are really, really useful tools, especially if you think about historically how challenging it's been to set up new organisms, like, think about in the fifties before we could like sequence genomes as quickly or something, you know, like you really have to band together to like build some tools in a few systems that give you useful shortcuts in general, as proxies for biology now.  [00:35:11] Ben: can I, can I, can I just double click right there? What does it mean to like set it up? Like, like what, what does it mean? Like to like, yeah. [00:35:18] Seemay: Yeah. I mean, there's basic anything from like Turing, right? Like you have to learn how to like cultivate the organism, grow it, proliferate it. Yeah. You gotta learn how to do like basic processing of it. Like whether it's like dissections or [00:35:35] isolating cell types or something, usually some form of genetics is very useful. So you can perturb the system in some controlled way and then ask precise questions. So those are kinda like the range of things that are typically challenging to set up and different organisms. Like, I, you can think of them as like video game characters, they have like different strengths, right? Like different bars. Some are [00:35:56] Ben: Yeah. [00:35:59] Seemay: fantastic for some other reason. You know, whether it's cultivation or maybe something related to their biology. And so that's that's model organisms and. I am very much pro model organisms. Like our interest in non-model organisms is in no way in conflict with my desire to see model organisms flourish, right. That fulfills an important purpose. And we need more, I would say, non-model organisms. Now. This is where it gets a little murky with the semantics. There's two ways you could think about it. At least one is that these are organisms that haven't quite risen to the level of this, the [00:36:35] canonical model organisms in terms of like tooling and sort of community effort around it. And so they're on their way to becoming model, but they're just kinda like hipster, you know, model or model organisms. Maybe you could think about it like that. There's a totally different way to think about it, which is actually how Arcadia's thinking about it, is to not use them as proxy for shared biology at all. But focus on the biology that is unique about that organism that signals some unique biological innovation that happened for that organism or plate of organisms or something. So for example, ticks releasing a bunch of like crap in their saliva, into your skin. That's not a proxy for us, like feeding on other, you know, vertebrates that is an innovation that happened because ticks have this like enormous job they've had to evolve to learn, to do well, which is to manipulate everything about your [00:37:35] circulation, your skin barrier, to make sure it's one blood meal at each of its life stages happen successfully and can happen for days to over a week. It's extremely prolonged. It can't be detected. So that is a very cool facet about tech biology that we could now leverage to learn something different. That could be useful for human biology, but that's, it's not a proxy, right? [00:37:58] Ben: Yeah. And so, so I was gonna ask you why ticks are cool, but I think that that's sort of self explanatory. [00:38:05] Seemay: Oh, they're wild. Like they, like, they have this like one job to do, which is to drink your blood and not get found out. [00:38:15] Ben: and, and I guess like, is there, so, so like with ticks, I I'm trying to, to frame this, like, is there something useful in like comparing like ticks and mosquitoes? Do they like work by the same mechanisms? Are they like completely different [00:38:30] Seemay: yeah. There's no, there's definitely something interesting here to explore because blood [00:38:35] feeding as a behavior in some ways is a very risky behavior. Right. Any sort of parasitism like that. And actually blood [00:38:42] Ben: That's trying to drink my blood. [00:38:44] Seemay: Yes. That's the appropriate response. Blood feeding actually emerged multiple times over the course of evolution in different lineages and mosquitoes, leeches ticks are in very different clouds of organisms and they have like different strategies for solving the same problem that they've evolved independently. So there's some convergence there, but there's a lot of divergence there as well. So for example, mosquitoes, and if you think about mosquitoes, leaches, and tick, this is a great spectrum because what's critically different about them is the duration of the blood  [00:39:18] Ben: Mm,  [00:39:19] Seemay: feed for a few seconds. If they're lucky, maybe in the range of minutes, leaches are like minutes to hours. Ticks are dazed to over a week. Okay. So like temporally, like they have to deal with very different. For, for mosquitoes, they tend to focus on [00:39:35] like immediately numbing of the local area to getting it out. Right. Undetected, Lees. They they're there for a little bit longer, so they have very cool molecules around blood flow like that there's a dilation, like speeding up the amount of blood that they can intake during that period. And then ticks have to deal with not just the like immediate response, but also longer term response, inflammation, wound healing, all these other sensations that happen. If, imagine if you stuck a needle in yourself for a week, like a lot more is going on, right? [00:40:08] Ben: Yeah. Okay. That, that makes a lot of sense. And so, so they really are sort of unique in that temporal sense, which is actually important. [00:40:17] Seemay: Yeah. And whether it's positive or not, it does seem to track that duration of that blood meal at least correlates with sort of the molecular complexity in terms of Sliva composition from each of these different sets of organisms. I just list. So there's way more proteins in other molecules that [00:40:35] have been detected int saliva as opposed to mosquito saliva. [00:40:39] Ben: And, and so what you're sort of like one of your, your high level things is, is like figuring out which of those are important, what mixture of them are important and like how to replicate that for youthful purposes? [00:40:51] Seemay: Yeah. Right, exactly. Yeah. [00:40:54] Ben: and, and, and are there other, like, I mean, I, I guess we can imagine like farther into Arcadia's future and, and think about like, what do you have, like, almost like a, like a wishlist or roadmap of like, what other really weird organisms you want to start poking at? [00:41:13] Seemay: So actually, so that, that is originally how we were thinking about this problem for non-model organisms like which organisms, which opportunities and that itself has evolved in the last year. Well, we realized in part, because of our, just like total paralysis around this decision, because [00:41:35] what we didn't wanna do is say, okay, now Arcadia's basically decided to double down on these other five organisms. We've increased the Canon by five now. Great. Okay. But actually that's not what we're trying to do. Right. We're trying to highlight the like totally different way. You could think about capitalizing on interesting biology and our impact will be felt more strongly if it happens, not just in Arcadia, but beyond Arcadia for this to be a more common way. And, and I think like Symbio is really pushing for this as a field in general. So we've gone from sort of like which organisms to thinking about. Maybe one of our most important contributions is to ask the question, how do you decide which organism, like, what is even the right set of experiments to help you understand that? What is the right set of data? That you might wanna collect, that would help you decide, let's say for example, cuz this is an actual example. We're very interested in produce diatoms, algae, other things, which, [00:42:35] which species should you settle on? I don't know. Like there's so many, right? Like, so then we started collecting like as many we could get our hands on through publicly available databases or culture collections. And now we are asking the meta question of like, okay, we have these, what experiments should we be doing in a high throughput way across all of these to help us decide. And that itself, that process, that engine is something that I think could be really useful for us to share with the worlds that is like hard for an individual academic lab to think about. That is not aligned with realities of like grants and journal publications and stuff. And so, yeah. Is it like RNA seek data sets? What kind of like pheno assays might you want, you want to collect? And we now call this broadly organismal onboarding process. Like what do you need in the profile of the different organisms and like, is it, phenomics now there's structural [00:43:35] prediction pipelines that we could be running across these different genomes depending on your question, it also may be a different set of things, but wouldn't it be nice to sort of just slightly turn the ES serendipity around, like, you know, what was around you versus like, can we go in and actually systematically ask this question and get a little closer to something that is useful? You know, [00:43:59] Ben: Yeah. [00:43:59] Seemay: and I think the amazing thing about this is. You know, I, and I don't wanna ignore the fact that there's been like tons of work on this front from like the field of like integrative biology and evolutionary biologists. Like there's so much cool stuff that they have found. What I wanna do is like couple their thinking in their efforts with like the latest and greatest technologies to amplify it and just like broaden the reach of the way they ask those questions. And the thing that's awesome about biology is even if you didn't do any of this and you grabbed like a random butterfly, you would still find extremely cool stuff. So that's the [00:44:34] Ben: [00:44:35] Right. Yeah. [00:44:36] Seemay: like, where can we go from here now that we have all these different technologies at our disposal? [00:44:41] Ben: Yeah. No, that's, that's extremely cool. And I wanted to ask a few questions about Arcadia's business model. And so sort of like it's, it's a public fact, unlike a lot of research organizations, Arcadia is, is a for-profit organization now, of course, that's that's a, you and I know that that's a legal designation. And there's like, I, I almost think of there as being like some multidimensional space where it's like, on the one hand you have like, like the Chan Zuckerberg initiative, which is like, is nominally a for-profit right. In the sense of [00:45:12] Seemay: Yeah. [00:45:13] Ben: not a, it's not a non-profit organization. And then on the other hand, under the spectrum, you have maybe like something like a hedge fund where it's like, what is like the only purpose of this organization in the world is to turn money into more money. Right. And so like, I, I guess I'd love to know like how you, how you think about sort of like where in that domain you [00:45:34] Seemay: [00:45:35] Yeah. Yeah. So, okay. This [00:45:38] Ben: and like how you sort of came to that, that [00:45:41] Seemay: Yeah. This was not a straightforward decision because actually I originally conceived of the Arcadia as a, a non-profit entity. And I think there were a lot of assumptions and also some ignorance on my part going into that. So, okay. Lemme try and think about the succinct way to tell all this. So I [00:45:58] Ben: take, take, take your time. [00:46:00] Seemay: okay. I started talking to a lot of other people at organizations. Like new science type of organizations. And I'll sort of like refrain from naming names here out of respect for people. But like they ran into a lot of issues around being a nonprofit, you know, for one, it, it impacted sort of like just sort of like operational aspects, maintaining a nonprofit, which if, if you haven't done it before, and I learned like, by reading about all this and learning about all this, like it maintaining that status is in and [00:46:35] of itself and effort, it requires legal counsel. It requires boards, it requires oversight. It requires reporting. There's like a whole level of operations [00:46:45] Ben: Yeah. And you always sort of have the government looking over your shoulder, being [00:46:49] Seemay: Yep. And you have to go into it prepared for that. So it also introduces some friction around like how quickly you can iterate as an organization on different things. The other thing is that like Let's say we started as a nonprofit and we realized, oh, there's a bunch of like for-profit type activities. We wanna be doing the transition of converting a nonprofit to a for-profit is actually much harder than the other way around. [00:47:16] Ben: Mm. [00:47:17] Seemay: And so that sort of like reversibility was also important to me given that, like, I didn't know exactly what Arcadia would ultimately look like, and I still dunno [00:47:27] Ben: Yeah. So it's just more optionality. [00:47:29] Seemay: Yeah. And another point is that like I do have explicit for profit interests for [00:47:35] Arcadia. This is not like, oh, I like maybe no. Like we like really want to commercialize some of our products one day. And it's, it's not because we're trying to optimize revenue it's because it's very central to our financial experiment that we're trying to think about, like new structures. Basic scientists and basic science can be, can capture its own value in society a little bit more efficiently. And so if we believe the hypothesis that discovering new biology across a wide range of organisms could yield actionable lessons that could then be translated into real products. Then we have to make a play for figuring out how this, how to make all this work. And I like also see an opportunity to figure out how I can make it work, such that if we do have revenue, I make sure our basic scientists get to participate in that. You know, because that is like a huge frustration for me as a basic scientist that like we haven't solved this problem. [00:48:35] Like basic science. It's a bedrock for all downstream science. Yet we some have to have, yeah, we have to be like siloed away from it. Like we don't get to play a part in it. And also the scientists at our Katy, I would say are not like traditional academic scientists. Like I would, I, my estimate would be like, at least a third of them have an intentional explicit interest in being part of a company one day that they helped found or spin out. And so that's great. We have a lot of like very entrepreneurial scientists at Arcadia. And so I I'm, I'm not shying away from the fact that like, we are interested in a, for profit mission. Having said all of that, I think it's important to remember that like mission and values don't stem from tax structure, right? Like you, there are nonprofit organizations that have like rotten values. And there are also for-profit organizations that have rotten values, like that is not the [00:49:35] dividing line for this. And so I think it puts the onus on us at Arcadia though, to continuously be rigorous with ourselves accountable to ourselves, to like define our values and mission. But I don't think that they are like necessarily reliant on the tax structure, especially in a for-profit organization where there's only two people at the cap table and their original motivating reason to do doing this was to conduct a meta science experiment. So we have like a unique alignment with our funders on this that I think also makes us different from other for-profit orgs. We're not a C Corp, we're an LC. And actually we're going through the process right now of exploring like B Corp status, which means that you have a, a fundamental, like mix of mission and for profit. [00:50:21] Ben: Yeah. That was actually something that I was going to ask about just in, in terms of, I think, what sort of like implicitly. One of the reasons that people wonder about [00:50:35] the, the mixture of like research and for profit status is that like the, the, the time scales of research is just, are just long, right? Like, like re, re research research takes a long time and is expensive. And if, if you're like, sort of answering to investors who are like really like, primarily looking for a return on their investment I feel like that, I, I mean, at least just in, in my experience and like my, my thinking about this like that, that, that's, that's my worry about it is, is that like so, so what, like having like, really like a small number of really aligned investors seems like pretty critical to being able to like, stick to your values. [00:51:18] Seemay: Yeah, no, it's true. I mean, there were actually other people interested in funding, our Arcadian every once in a while I get reached out to still, but like me Jud and Sam and Che, like we went through the ringer together. Like we went on this journey together to get here, to [00:51:35] decide on this. And I think there is, I think built in an understanding that like, there's a chance this will fail financially and otherwise. Um, but, but I think the important case to consider is like that we discussed is like, what would happen if we are a scientific success, but a financial failure. What are each of you interested in doing. and that that's such an important answer. A question, right? So for both of them, the answer was we would consider the option of endowing this into a nonprofit, but only if the science is interesting. Okay. If that is, and I'm not saying that we're gonna target that end goal, like I'm gonna fight with all my might to figure out another way, but that is a super informative answer, right? Because [00:52:27] Ben: yeah, [00:52:27] Seemay: delineating what the priorities are. The priority is the science, the revenue is [00:52:35] subservient to that. And if it doesn't work fine, we will still iterate on that like top priority. [00:52:42] Ben: Yeah, it would also be, I mean, like that would be cool. It would also be cool if, if you, I mean, it's just like, everybody thinks about like growing forever, but I think it would be incredibly cool if you all just managed to make enough revenue that you can just like, keep the cycle going right.  [00:52:58] Seemay: Yeah. It also opens us up to a whole new pocket of investments that is difficult in like more standard sort of like LP funded situations. So, you know, given that our goal is sustainability now, like things that are like two to five X ROI are totally on the table. [00:53:22] Ben: Yeah. Yeah, yeah. [00:53:24] Seemay: actually that opens up a huge competitive edge for us in an area of like tools or products that like are not really that interesting to [00:53:35] LPs that are looking to achieve something else. [00:53:38] Ben: yeah, with like a normal startup. And I think that I, I, that that's, I think really important. Like I, I think that is a big deal because there's, there's so many things that I see And, and it's like the two to five X on the amount of money that you could capture. Right. But like the, the, the amount of value that you create could be much, much larger than that. Right. Like, and this is the whole problem. Like, I, I, I mean, it's just like the, the thing that I always run into is you look at just like the ability of people to capture the value of research. And it just is very hard to, to like capture the whole thing. And often when people try to do that, it ends up sort of like constraining it. And so you're, you're just like, okay, with getting a reasonable return then it just lets you do so many other cool things. [00:54:27] Seemay: yeah. I'm yeah. I think that's the vibe. [00:54:32] Ben: that is an excellent vibe. And, and speaking [00:54:35] with the vibe and, and you mentioned this I'm, I. Interested in both, like how you like find, and then convince people to, to join Arcadia. Right. Because it's, it's like, you are, you are to some extent asking people to like play a completely different game. Right? Like you're asking people who have been in this, this like you know, like citations and, and paper game to say like, okay, you're gonna like, stop playing that and play this other thing. And so like, yeah. [00:55:04] Seemay: yeah. It's funny. Like I get asked this all the time, like, how do you protect the careers or whatever of people that come to Arcadia? And the solution is actually pretty simple, even though people don't think of it, which is you Don. You don't try and convince people to come. Like we are not trying to grow into an infinitely large organization. I don't even know if we'll ever reach that number 150. Like I was just talking to Sam about like, we may break before that point. Like, that's just sort of like my cap. We may find that [00:55:35] 50 people is like the perfect number 75 is. And you know, we're actually just trying to figure out like, what is, what are the right ingredients for the thing we're trying to do? And so therefore we don't need everybody to join. We need the right people to join and we can't absorb the risk of people who ultimately see a career path that is not well supported by Arcadia. If we absorb that, it will pull us back to the means. because we don't want anyone at Arcadia to be miserable. We want scientists to succeed. So actually the easiest way to do that is to not try and convince people to do something they're not comfortable with and find the people for whom it feels like a natural fit. So actually think, I think I saw on Twitter, someone ask this question in your thread about what's like the, oh, an important question you asked during your interviews. And like one of the most important questions I ask someone is where else have you applied for jobs? [00:56:35] And if they literally haven't applied anywhere outside of academia, like that's an opportunity for me to push [00:56:43] Ben: Mm. [00:56:44] Seemay: I'm very worried about that. Like, I, I don't want them to be quote unquote, making a sacrifice that doesn't resonate with where they're trying to go in their career. Cuz I can't help them AF like once they come. Arcadia has to evolve like its own organism. And like, sometimes that means things that are not great for people who wanna be in academia, including like the publishing and journal bit. And so yeah, what I tell them is like, look, you have two jobs at Arcadia and both have to be equally exciting to you. And you have to fully understand that there both your responsibility, your job is to be a scientist and a meta scientist. And that those two things have to be. You understand what that second thing is that your job is to evolve with me, provide me with feedback on like, what is working and not working [00:57:35] for you and actively participate in all the meta science experiments that we're doing around publishing translation, technology, all these things, right? Like it can't be passive. It has to be active. If that sounds exciting to you, this is a great place for you. If you're trying to figure out how you're going to do that, have your cake and eat it too, and still have a CV that's competitive for academia in case like in a year, you know, like you go back, I, this is not the place for you. And I, I can't as a human being, like, that's, I, I can't absorb that because like, I like, I can't help, but have some empathy for you once you're here as an individual, like, I don't want you to suffer. Right. And so we need to have those hard conversations early before they join. And there's been a few times where like, yeah, I think like I sufficiently scared someone away. So I think it was better for them. Right? Like it's better for [00:58:25] Ben: Yeah, totally. [00:58:25] Seemay: if that happens. Yeah, it's harder once they're here. [00:58:29] Ben: and, and so, so the like, The, they tend to be people who are sort of like already [00:58:35] thinking, like already have like one foot out the door of, of academia in the sense of like, they're, they're already sort of like exploring that possibility. So they've so you don't have to like get them to that point. [00:58:48] Seemay: Right. Yes. Because like, like that's a whole journey they need to go on in, on their own, because there's so many reasons why someone might be excited to leave academia and go to another organization like this. I mean, there's push and pull. Right. So I think that's a challenge, like separating out, like, like what is just like push, because they're like upset with how things are going there versus like, do they actually understand what joining us will entail? And are they, do they have the like optimism and the agency to like, help me do this experiment. It does require optimism. Right. [00:59:25] Ben: absolutely. [00:59:25] Seemay: So like sometimes like, you know, I push people, like what, where else have you applied for jobs? And they, if they can't seem to answer that very well I say, okay, let me change [00:59:35] this question. You come to Arcadia and I die. Arcadia dissolves. It's, it's an easier way of like, it's like, I can own it. Okay. Like I died and like me and Che and Jed die. Okay. Like now what are you gonna do with your career? And like, I is a silly question, but it's kind of a serious question. Like, you know, just like, what is, how does this fit into your context of how you think about your career and is it actually going to move you towards where you're trying to go? Because, I mean, I think like that's yeah. Another problem we're trying to solve is like scientists need to feel more agency and they won't feel agency by just jumping to another thing that they think is going to solve problems for them. [01:00:15] Ben: Yeah, that's a really good point. And so, so this is almost a selfish question, but like where do you find these people? Right? Like you seem to, you seem to be very good at it. [01:00:26] Seemay: Yeah. I actually don't I don't, I, I don't know the answer to that question fully because we [01:00:35] only just recently said, oh my God, we need to start collecting some data through like voluntary surveys from applicants of like, how do they know about us? You know? It seems to be a lot of like word of mouth, social media, maybe they read something that I wrote or that Che wrote or something. And while that's been fine so far, we also like wanna think about how we like broaden that reach further. It's definitely not through their, for the most part, not through their institutes or PIs that I know. [01:01:03] Ben: Yeah, I, but, but it is, it is like, it sounds like it does tend to be inbounds, right? Like it tends to be people like reaching out to you as opposed to the other way around. [01:01:16] Seemay: Yeah. You know, and that's not for lack of effort. I mean, there have been definitely times where. We have like proactively gone out and tried to scout people, but it does run into that problem that I just described before of like, [01:01:29] Ben: Yeah. [01:01:30] Seemay: if you find them yourself, are you trying to pull them in and have they gone through their own [01:01:35] journey yet? And so in some of those cases, while it seemed like, like we entertain like conversations for a while with a couple of candidates, we tried to scout, but ultimately that's where it ended was like, oh, they like, they need to go on their own. And like, sort of like fully explore for a bit, you know, this would be a bit risky. But it hasn't, you know, it hasn't been all, you know a failure like that, but it, it happens a lot. [01:01:58] Ben: Yeah, no, I mean that, that, frankly, that, that squares with my, my experience sort of like roughly, roughly trying to find people who, who fit a similar mold. So that that's, I mean, and that, that suggests a strategy, right. Is like, be like, be good at setting up some kind of lighthouse, which you, you seem to have done. [01:02:17] Seemay: The only challenge with this, I would say, and, and we are still grappling with this is that sort of approach does make it hard to reach candidates that are sort of like historically underrepresented, because they may not see themselves as like strong candidates for such and such. And [01:02:35] so now we're, now we have this other challenge to solve of like, how do we make sure people have gone through their own process on their own, but also make sure that the opportunity is getting communicated to the right people and that they like all, everybody understands that they're a candidate, you know, [01:02:53] Ben: Yeah. And I guess so , as long as we're recording this podcast, like what, what is that like, like if you were talking to someone who was like, what does that process even look like? Like what would I start doing? Like what would you, what would you tell someone? [01:03:08] Seemay: Oh, to like explore a role at Arcadia. [01:03:11] Ben: yeah. Or just like to like, go through that, like, like to, to start going through that [01:03:16] Seemay: Yeah. Yeah, I mean, I guess like, there's probably a couple of different things. Like, I mean, one is just some deep introspection on like, what are your priorities in your life, right? Like what are you trying to achieve in your career? Beyond just like the sort of ladder thing, like what's the, what are the most important, like north stars for you? And I think [01:03:35] like for a place like Arcadia or any of the other sort of like meta science experiments, That has to be part of it somehow. Right. Like being really interested and passionate about being part of finding a solution and being one of the risk takers for them. I think the other thing is like very pragmatic, just like literally go out there and like explore other jobs, please. Like, I feel like, you know, like, like what is your market value? You know? Like what  [01:04:05] Ben: don't don't  [01:04:05] Seemay: Yeah. Like, and like go get that information for yourself. And then you will also feel a sense of like security, because like, even if I die and Arcadia dissolves, you will realize through that process that you have a lot of other opportunities and your skillset is highly valuable. And so there is like solid ground underneath you, regardless of what happens here, that they need to absorb that. Right. And then also just. Like, trust me, your negotiations with me will go way better. If you come in [01:04:35] armed with information, like one of my goals with like compensation for example, is to be really accurate about making sure we're hitting the right market val

Increments
#43 - Artificial General Intelligence and the AI Safety debate

Increments

Play Episode Listen Later Aug 28, 2022 67:50


Some people think (https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities) that advanced AI is going to kill everyone. Some people don't (https://www.nytimes.com/2019/10/31/opinion/superintelligent-artificial-intelligence.html). Who to believe? Fortunately, Ben and Vaden are here to sort out the question once and for all. No need to think for yourselves after listening to this one, we've got you covered. We discuss: - How well does math fit reality? Is that surprising? - Should artificial general intelligence (AGI) be considered "a person"? - How could AI possibly "go rogue?" - Can we know if current AI systems are being creative? - Is misplaced AI fear hampering progress? References: - The Unreasonable effectiveness of mathematics (https://www.maths.ed.ac.uk/~v1ranick/papers/wigner.pdf) - Prohibition on autonomous weapons letter (https://techlaw.uottawa.ca/bankillerai) - Google employee conversation with chat bot (https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917) - Gary marcus on the Turing test (https://garymarcus.substack.com/p/nonsense-on-stilts) - Melanie Mitchell essay (https://arxiv.org/pdf/2104.12871.pdf). - Did MIRI give up? Their (half-sarcastic?) death with dignity strategy (https://www.lesswrong.com/posts/j9Q8bRmwCgXRYAgcJ/miri-announces-new-death-with-dignity-strategy) - Kerry Vaughan on slowing down (https://twitter.com/KerryLVaughan/status/1545423249013620736) AGI development. Contact us - Follow us on Twitter at @IncrementsPod, @BennyChugg, @VadenMasrani - Check us out on youtube at https://www.youtube.com/channel/UC_4wZzQyoW4s4ZuE4FY9DQQ - Come join our discord server! DM us on twitter or send us an email to get a supersecret link Which prompt would you send to GPT-3 in order to end the world? Tell us before you're turned into a paperclip over at incrementspodcast@gmail.com

The Nonlinear Library
LW - The Solomonoff prior is malign. It's not a big deal. by Charlie Steiner

The Nonlinear Library

Play Episode Listen Later Aug 25, 2022 10:49


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: The Solomonoff prior is malign. It's not a big deal., published by Charlie Steiner on August 25, 2022 on LessWrong. Epistemic status: Endorsed at ~85% probability. In particular, there might be clever but hard-to-think-of encodings of observer-centered laws of physics that tilt the balance in favor of physics. Also, this isn't that different from Mark Xu's post. Previously, previously, previously I started writing this post with the intuition that the Solomonoff prior isn't particularly malign, because of a sort of pigeon hole problem - for any choice of universal Turing machine there are too many complicated worlds to manipulate, and too few simple ones to do the manipulating. Other people have different intuitions. So there was only one thing to do. Math. All we have to do is compare [wild estimates of] the complexities of two different sorts of Turing machines: those that reproduce our observations by reasoning straightforwardly about the physical world, and those that reproduce our observations by simulating a totally different physical world that's full of consequentialists who want to manipulate us. Long story short, I was surprised. The Solomonoff prior is malign. But it's not a big deal. Team Physics: If you live for 80 years and get 10^7 bits/s of sensory signals, you accumulate about 10^16 bits of memory to explain via Solomonoff induction. In comparison, there are about 10^51 electrons on Earth - just writing their state into a simulation is going to take somewhere in the neighborhood of 10^51 bits. So the Earth, or any physical system within 35 orders of magnitude of complexity of the Earth, can't be a Team Physics hypothesis for compressing your observations. What's simpler than the Earth? Turns out, simulating the whole universe. The universe can be mathematically elegant and highly symmetrical in ways that Earth isn't. For simplicity, let's suppose that "I" am a computer with a simple architecture, plus some complicated memories. The trick that allows compression is you don't need to specify the memories - you just need to give enough bits to pick out "me" among all computers with the same architecture in the universe. This depends only on how many similar computers there are in Hilbert space with different memories, not directly on the complexity of my memories themselves. So the complexity of a simple exemplar of Team Physics more or less looks like: Complexity of our universe + Complexity of my architecture, and rules for reading observations out from that architecture + Length of the unique code for picking me out among things with the right architecture in the universe. That last one is probably pretty big relative to the first two. The universe might only be a few hundred bits, and you can specify a simple computer with under a few thousand (or a human with ~10^8 bits of functional DNA). The number of humans in the quantum multiverse, past and future, is hard to estimate, but 10^8 bits is only a few person-years of listening to Geiger counter clicks, or being influenced by the single-photon sensitivity of the human eye. Team Manipulation: A short program on Team Manipulation just simulates a big universe with infinite accessible computing power and a natural coordinate system that's obvious to its inhabitants. Life arises, and then some of that life realizes that they could influence other parts of the mathematical multiverse by controlling the simple locations in their universe. I'll just shorthand the shortest program on Team Manipulation, and its inhabitants, as "Team Manipulation" full stop, since they dominate the results. So they use the limitless computing power of their universe to simulate a bunch of universes, and when they find a universe where people are using the Solomonoff prior to make decisions, they note what string of bits the p...

Turing School Podcast
What is Community?

Turing School Podcast

Play Episode Listen Later Aug 24, 2022 55:10


Three friends and Turing Alum talk about the Turing School, their experience as students and now software developers, the Turing community, and the future of the Hello Turing World Podcast Reading Recommendations: Marshall: Range by David Epstein Jesse: Mutual Aid by Dean Spade Mark: Riverside, a software for recording podcasts  

Trans Resister Radio
Quantum Computing Mysticism, AoT#361

Trans Resister Radio

Play Episode Listen Later Aug 22, 2022 58:53


Quantum computing may offer a way to power emerging tech such as Ai or AGI.  Topics include: livestreams, quantum computing, quantum theory, physics, entanglement, mysticism, Ai, qubits, time and space, Robin, Fibonacci laser, Golden Ratio, Ai generated artwork, text prompt to image, NFT market, Beeple, Jimmy James, metaverse, Paris Hilton, digital immortality, Ozzy Osbourne, The Congress movie, Robin Wright, Carrie Fischer deep fake in movie, Star Wars, Turing test

Money Talks: El otro lado de la moneda

Los mercados, los juegos del hambre reales por venir, Newman y el test de Turing a la inversa, echarle dinero a los problemas.

Startupification
38: Live at Turing Fest

Startupification

Play Episode Listen Later Aug 21, 2022


Matt and Steven recorded live at Turing Fest 2022. The big topic of conversation was the announcement of the Scottish Government's Tech Scaler contract to Codebase (disclosure: Steven works for Codebase). Hear questions from Matt and the audience about this and get the inside skinny from Steven on what it is, and what it might mean for the Scottish tech eco-system.

Our Film Fathers
Episode 117: Drop the Deus

Our Film Fathers

Play Episode Listen Later Aug 18, 2022 45:26


At what point does a machine, with human-like characteristics, cease to be viewed as a machine? At that point, are we obligated to treat it as a human? Is it still an "it"? We try to answer these questions by watching Alex Garland's Ex Machina (2014). Excellent performances by Oscar Isaac and Alicia Vikander help drive the story in this amazing re-imagining of Shakespeare's The Tempest.Subscribe, rate and review:Apple Podcasts: Our Film FathersSpotify: Our Film FathersGoogle Podcasts: Our Film FathersStitcher: Our Film FathersAmazon Music: Our Film Fathers-----------------------Follow us:Instagram: @ourfilmfathersTwitter: @ourfilmfathersEmail: ourfilmfathers@gmail.com

The Stack Overflow Podcast
The last technical interview you'll ever take

The Stack Overflow Podcast

Play Episode Listen Later Aug 17, 2022 24:52


Since the day a hiring manager first wheeled a whiteboard into a conference room, software engineers have dreaded the technical interview, which can be an all-day process (or multi-day homework assignment). If you're interviewing for multiple roles, you can expect to write out a bubble sort in pseudocode for each one. These technical interviews do no favors for hiring companies, either, because the investment needed from both parties limits the number of candidates a company can consider. In this age of data-driven decisions, perhaps there's a way that AI and ML can help candidates and companies find each other.  On this episode of the podcast, sponsored by Turing AI, we chat with Chief Revenue Officer Prakash Gupta about building a better hiring process with AI. Turing helps companies scale their engineering programs quickly with remote developers from around the world. We talk about how to vet a profession without standard markers, the benefits of soft skills, and how AI-assisted hiring helps everyone involved. While companies have been outsourcing development for years, COVID made the software industry almost entirely remote. Suddenly, every company has the ability to hire the best developers regardless of location. And good developers can find work at companies of all sizes without packing up and settling in Silicon Valley. But when any company could conceivably interview any candidate, how do you vet candidates at scale? There is no standardized board certification for software engineers, after all. Every interviewer has to vet the candidates themselves, and that's where human biases come in. On one side, you have Fortune 500 companies developing complex systems and undergoing digital transformation projects, plus startups looking to scale their engineering organizations as their product finds market fit. On the other, you have a new generation of engineers trained on bootcamps and online resources who may not have opportunities where they live. That's where Turing comes in, matching 1.7 million engineers from over 140 countries with jobs at hundreds of companies. Turing strives to mitigate bias by collecting hundreds of signals about candidates over a four- to six-hour process. This process covers projects candidates have worked on, technology aptitude, and soft skills through 30-minute tests, candidates' online presence in places like GitHub and Stack Overflow, and qualitative assessments refined over two years of feedback loops. A process that once consisted of ten interviews can now drop to two or three at the most. Some Turing customers have eliminated interviews altogether, relying on Turing's AI-powered solutions to surface and evaluate the best candidates. To see how Turing can streamline your interview process, either as a candidate or a company, check out turing.com today.

Into the Impossible
Part 2: Sir Roger Penrose & Stuart Hameroff: What is Consciousness?

Into the Impossible

Play Episode Listen Later Aug 8, 2022 78:53 Very Popular


A conversation with Nobel Prize Winner and renowned mathematical physicist Sir Roger Penrose and anesthesiologist Dr. Stuart Hameroff about consciousness and quantum mechanics. Sir Roger Penrose and Dr. Stuart Hameroff have tackled one of the most vexing problems in science -- how does consciousness work? Their theories of consciousness were selected by the Templeton Foundation for study. We will discuss Is the brain a sophisticated computer or an intuitive thinking device? Following on from their conference in Tucson which pitted Integrated Information Theory (IIT) against Orchestrated Objective Reduction (Orch-OR), Sir Roger Penrose OM and Stuart Hameroff discuss the current state of theories that might explain human consciousness and objections to them from FQXI and others. Sir Roger Penrose describe examples of ‘non-computability' in human consciousness, thoughts and actions such as the way we evaluate particular chess positions which cast doubt on ‘Turing' computation as a complete explanation of brain function. As a source of non-computability, Roger discuss his ‘objective reduction' (‘OR') self-collapse of the quantum wavefunction which is a potential resolution for the ‘measurement problem' in quantum mechanics, and a mechanism for non-computable physics. Dr. Stuart Hameroff reviews neuronal and biophysical aspects of Orch OR, in which ‘orchestrated' quantum vibrations occur among entangled brain microtubules and evolve toward Orch OR threshold and consciousness. The nature, feasibility, decoherence times and evidence for quantum vibrations in microtubules, their role and correlation with consciousness, effects upon them of anesthetic gases and psychedelic drug molecules will be discussed, along with Orch OR criticisms and predictions of microtubule quantum vibrations as therapeutic targets for mental and cognitive disorders. Be my friend:

Bannon's War Room
WarRoom Battleground EP 111: MAGA Is Turing Out Strong For Wisconsin, Ohio, Minnesota

Bannon's War Room

Play Episode Listen Later Aug 8, 2022


We discuss what is happening on the ground in battleground states ahead of the 2022 elections. Our guests are: Adam Steen, Jay Schroeder, Mike Gableman, Doug Wardlow, JR Majewski, Tina PetersStay ahead of the censors - Join us warroom.org/joinAired On: 08/08/2022Watch:On the Web: http://www.warroom.orgOn Gettr: @WarRoomOn Podcast: http://warroom.ctcin.bioOn TV: PlutoTV Channel 240, Dish Channel 219, Roku, Apple TV, FireTV or on https://AmericasVoice.news. #news #politics #realnews

Think Like A Nobel Prize Winner
Part 1: Sir Roger Penrose & Stuart Hameroff: What is Consciousness?

Think Like A Nobel Prize Winner

Play Episode Listen Later Aug 8, 2022 31:14


A conversation with Nobel Prize Winner and renowned mathematical physicist Sir Roger Penrose and anesthesiologist Dr. Stuart Hameroff about consciousness and quantum mechanics. Sir Roger Penrose and Dr. Stuart Hameroff have tackled one of the most vexing problems in science -- how does consciousness work? Their theories of consciousness were selected by the Templeton Foundation for study. We will discuss Is the brain a sophisticated computer or an intuitive thinking device? Following on from their conference in Tucson which pitted Integrated Information Theory (IIT) against Orchestrated Objective Reduction (Orch-OR), Sir Roger Penrose OM and Stuart Hameroff discuss the current state of theories that might explain human consciousness and objections to them from FQXI and others. Sir Roger Penrose describe examples of ‘non-computability' in human consciousness, thoughts and actions such as the way we evaluate particular chess positions which cast doubt on ‘Turing' computation as a complete explanation of brain function. As a source of non-computability, Roger discuss his ‘objective reduction' (‘OR') self-collapse of the quantum wavefunction which is a potential resolution for the ‘measurement problem' in quantum mechanics, and a mechanism for non-computable physics. Dr. Stuart Hameroff reviews neuronal and biophysical aspects of Orch OR, in which ‘orchestrated' quantum vibrations occur among entangled brain microtubules and evolve toward Orch OR threshold and consciousness. The nature, feasibility, decoherence times and evidence for quantum vibrations in microtubules, their role and correlation with consciousness, effects upon them of anesthetic gases and psychedelic drug molecules will be discussed, along with Orch OR criticisms and predictions of microtubule quantum vibrations as therapeutic targets for mental and cognitive disorders. Be my friend:

Think Like A Nobel Prize Winner
Part 2: Sir Roger Penrose & Stuart Hameroff: What is Consciousness?

Think Like A Nobel Prize Winner

Play Episode Listen Later Aug 8, 2022 76:38


A conversation with Nobel Prize Winner and renowned mathematical physicist Sir Roger Penrose and anesthesiologist Dr. Stuart Hameroff about consciousness and quantum mechanics. Sir Roger Penrose and Dr. Stuart Hameroff have tackled one of the most vexing problems in science -- how does consciousness work? Their theories of consciousness were selected by the Templeton Foundation for study. We will discuss Is the brain a sophisticated computer or an intuitive thinking device? Following on from their conference in Tucson which pitted Integrated Information Theory (IIT) against Orchestrated Objective Reduction (Orch-OR), Sir Roger Penrose OM and Stuart Hameroff discuss the current state of theories that might explain human consciousness and objections to them from FQXI and others. Sir Roger Penrose describe examples of ‘non-computability' in human consciousness, thoughts and actions such as the way we evaluate particular chess positions which cast doubt on ‘Turing' computation as a complete explanation of brain function. As a source of non-computability, Roger discuss his ‘objective reduction' (‘OR') self-collapse of the quantum wavefunction which is a potential resolution for the ‘measurement problem' in quantum mechanics, and a mechanism for non-computable physics. Dr. Stuart Hameroff reviews neuronal and biophysical aspects of Orch OR, in which ‘orchestrated' quantum vibrations occur among entangled brain microtubules and evolve toward Orch OR threshold and consciousness. The nature, feasibility, decoherence times and evidence for quantum vibrations in microtubules, their role and correlation with consciousness, effects upon them of anesthetic gases and psychedelic drug molecules will be discussed, along with Orch OR criticisms and predictions of microtubule quantum vibrations as therapeutic targets for mental and cognitive disorders. Be my friend:

Into the Impossible
Part 1: Sir Roger Penrose & Stuart Hameroff: What is Consciousness?

Into the Impossible

Play Episode Listen Later Aug 7, 2022 30:29 Very Popular


A conversation with Nobel Prize Winner and renowned mathematical physicist Sir Roger Penrose and anesthesiologist Dr. Stuart Hameroff about consciousness and quantum mechanics. 00:00 Intro 01:00 Happy Birthday to Sir Roger! 05:00 Updates to The Emperor's New Mind 07:00 What about Schrödinger's Cat? Part 2: https://youtu.be/OoDi856wLPM Sir Roger Penrose and Dr. Stuart Hameroff have tackled one of the most vexing problems in science -- how does consciousness work? Their theories of consciousness were selected by the Templeton Foundation for study. We will discuss Is the brain a sophisticated computer or an intuitive thinking device? Following on from their conference in Tucson which pitted Integrated Information Theory (IIT) against Orchestrated Objective Reduction (Orch-OR), Sir Roger Penrose OM and Stuart Hameroff discuss the current state of theories that might explain human consciousness and objections to them from FQXI and others. Sir Roger Penrose describe examples of ‘non-computability' in human consciousness, thoughts and actions such as the way we evaluate particular chess positions which cast doubt on ‘Turing' computation as a complete explanation of brain function. As a source of non-computability, Roger discuss his ‘objective reduction' (‘OR') self-collapse of the quantum wavefunction which is a potential resolution for the ‘measurement problem' in quantum mechanics, and a mechanism for non-computable physics. Dr. Stuart Hameroff reviews neuronal and biophysical aspects of Orch OR, in which ‘orchestrated' quantum vibrations occur among entangled brain microtubules and evolve toward Orch OR threshold and consciousness. The nature, feasibility, decoherence times and evidence for quantum vibrations in microtubules, their role and correlation with consciousness, effects upon them of anesthetic gases and psychedelic drug molecules will be discussed, along with Orch OR criticisms and predictions of microtubule quantum vibrations as therapeutic targets for mental and cognitive disorders. Be my friend:

The Chad & Cheese Podcast
Turing.com CEO talks Remote First

The Chad & Cheese Podcast

Play Episode Listen Later Aug 3, 2022 32:17


Don't believe the headlines. Hiring competent developers is still really, really challenging. One start-up that's helping solve the quandary for companies is Turing, who - by the way - has already raised $153 million to cure what ails employers. Jonathan Siddharth, CEO and founder at Turing, joins the boys to discuss what makes Turing different, how Upwork gets it wrong, and why pay transparency and equality are still such a hurdle.

Boozed and Confused
Episode 90: LaMDA

Boozed and Confused

Play Episode Listen Later Jul 25, 2022 58:08


Technology seems to get smarter and smarter with each passing day. Have we finally created truly feeling and sentient artificial intelligence? One ex-Google employee seems to think so. Join Carolanne and Matt as they explore the case of Google's LaMDA and Blake Lamoine. Our linktree: linktr.ee/boozedandconfused This week's booze of choice: Warpigs Brewing Foggy Geezer Hazy IPA Sources: https://twitter.com/cajundiscordian https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917 https://www.youtube.com/watch?v=kgCUn4fQTsc https://blog.google/technology/ai/lamda/ https://en.wikipedia.org/wiki/LaMDA https://en.wikipedia.org/wiki/Turing_test https://www.engadget.com/blake-lemoide-fired-google-lamda-sentient-001746197.html?src=rss https://www.washingtonpost.com/technology/2022/06/11/google-ai-lamda-blake-lemoine/

The Davina Podcast
Daily 1% x 228: Be Awesome

The Davina Podcast

Play Episode Listen Later Jul 20, 2022 8:21


Broken Silicon
161. Nvidia Lovelace Pricing, RDNA 3 Supply, PlayStation 5 Pro, Nintendo Switch 2

Broken Silicon

Play Episode Listen Later Jul 12, 2022 170:06 Very Popular


Meyer Tech Rants joins to discuss the current landscape of Gaming Hardware. SPON: Get 10% off Tasty Vite Ramen with Code “brokensilicon” at: https://bit.ly/3oyv4tR SPON: brokensilicon = -25% off Windows, dieshrink = -3% off Everything: https://biitt.ly/shbSk 0:00 Remembering Console SSD Hype and Nvidia RTX IO 5:32 Who is Meyer? 7:41 GPU Oversupply - How worried is Nvidia? Why are people surprised? 15:42 Nvidia RTX 4000 Series Pricing – Can Nvidia charge more for next gen? 23:13 Did people buy Ampere because it was impressive? Or because Turing sucked? 39:17 Will the RTX 4080 use AD103? What will it cost? 43:33 Will Nvidia Lovelace sell for MSRP? Will Nvidia save the 4090 Ti for later? 47:32 What will the RTX 4070 cost? Does it makes sense for the 4080 to use 400w? 54:08 RDNA 3 Supply & Pricing - Can AMD take Market Share? 1:08:00 Can RDNA 3 win mindshare? Does Nvidia Blackwell stand a chance? 1:14:22 Intel Raptor Lake vs AMD Zen 4, Z790 vs X670E 1:24:03 Does Z790 need to cost less than X670? Is Intel in trouble? 1:32:36 Is Intel running out of time to raise margins? 1:43:02 PlayStation 5 Pro & Vita 2 – What makes sense? 1:57:45 Nintendo Switch 2 - Insider whispers, specs, features, pricing... 2:22:34 XBOX Series S Pro 2:37:00 VR, x86 vs ARM, RISC-V, Apple Silicon https://twitter.com/MeyerRants meyertechrants.blogspot.com Previous MTR episode: https://youtu.be/Ups8FrRFNR0 Nvidia Oversupply Leak: https://youtu.be/PHZbgaAoFHI https://www.digitimes.com.tw/tech/dt/n/shwnws.asp?cnlid=1&id=0000638859_23P7Q8BU6YQGPN8X7TLNT https://arstechnica.com/gaming/2022/02/how-sonys-new-patent-filing-could-speed-up-playstation-ray-tracing/ https://www.techpowerup.com/review/diamond-hd-4870/3.html https://www.techpowerup.com/review/sapphire-hd-4890/ https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ https://www.nintendo.com/switch/oled-model/ https://www.techpowerup.com/gpu-specs/amd-rembrandt.g1004

The Remnant with Jonah Goldberg
The Disappearing Act

The Remnant with Jonah Goldberg

Play Episode Listen Later Jun 10, 2022 68:13 Very Popular


It's been a while since a recovering Marxist joined the Remnant, but Ruy Teixeira, political demographer and senior fellow at American Progress, is here to fill that void. In predictably wonky fashion, he and Jonah examine the future of the Democratic Party and how the progressive movement has changed in recent years. They also explore racism in American life, wokeism in universities, and Jonah's renowned distaste for primaries. Whatever happened to the FDR coalition? Is Maoism back in style? And should we still be optimistic about America? Show Notes:- Ruy's newsletter, The Liberal Patriot- Ruy and John B. Judis' The Emerging Democratic Majority- Ruy's The Optimistic Leftist- Ruy: “Eyes Wide Shut”- Ruy: “How to Fix the Democratic Brand”- The Remnant with Francis Fukuyama- David Shor and the Democrats- Jonah: “That Shor Sounds Good”- Tyler Cowen on the Turing test- The Remnant with Elaine Kamarck