Podcasts about eliciting

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Check out Hugo's latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets BuiltIntro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Computational cognitive science seeks to understand intelligence mathematically.Bayesian statistics is crucial for understanding human cognition.Inductive biases help explain how humans learn from limited data.Eliciting prior distributions can reveal implicit beliefs.The wisdom of individuals can provide richer insights than averaging group responses.Generative AI can mimic human cognitive processes.Human intelligence is shaped by constraints of data, computation, and communication.AI systems operate under different constraints than human cognition. Human intelligence differs fundamentally from machine intelligence.Generative AI can complement and enhance human learning.AI systems currently lack intrinsic human compatibility.Language training in AI helps align its understanding with human perspectives.Reinforcement learning from human feedback can lead to misalignment of AI goals.Representational alignment can improve AI's understanding of human concepts.AI can help humans make better decisions by providing relevant information.Research should focus on solving problems rather than just methods.Chapters:00:00 Understanding Computational Cognitive Science13:52 Bayesian Models and Human Cognition29:50 Eliciting Implicit Prior Distributions38:07 The Relationship Between Human and AI Intelligence45:15 Aligning Human and Machine Preferences50:26 Innovations in AI and Human Interaction55:35 Resource Rationality in Decision Making01:00:07 Language Learning in AI Models

Do you establish the patient agenda at each consultation?   Check out the paper mentioned Singh Ospina N, Phillips KA, Rodriguez-Gutierrez R, Castaneda-Guarderas A, Gionfriddo MR, Branda ME, Montori VM. Eliciting the Patient's Agenda- Secondary Analysis of Recorded Clinical Encounters. J Gen Intern Med. 2019 Jan;34(1):36-40. doi: 10.1007/s11606-018-4540-5. Epub 2018 Jul 2. PMID: 29968051; PMCID: PMC6318197.   If you want to join the RNZCUC Urgent Care Research Network, check out the link here.     www.rnzcuc.org.nz podcast@rnzcuc.org.nz https://www.facebook.com/rnzcuc https://twitter.com/rnzcuc   Music licensed from www.premiumbeat.com Full Grip by Score Squad   This podcast is intended to assist in ongoing medical education and peer discussion for qualified health professionals.  Please ensure you work within your scope of practice at all times.  For personal medical advice always consult your usual doctor 

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"We use thresholds every day without even thinking about it. When we do a food challenge in our office, whether we know it or not, we're relying on the fact that there is a threshold for every patient. Some are going to be on the first dose, some are going to be at the last. And that's just part of what a threshold is." – Dr. Doug MackFood allergies aren't one-size-fits-all, but we don't talk enough about thresholds—the amount of an allergen it actually takes to trigger a reaction. Should every patient be following the same strict avoidance rules? Or should we be aiming for something more precise?To help us make sense of thresholds, we welcome back Dr. Douglas Mack, pediatric allergist, clinical immunologist, and Vice President of CSACI. Dr. Mack is known for his work on food allergy treatment, including oral immunotherapy, epinephrine use, and—you guessed it—thresholds.On this episode, Dr. Mack and Dr. Mariam Hanna dig into:What a threshold actually is and why it matters in clinical practice – How thresholds shape food challenges, oral immunotherapy, and allergen risk assessments, even when we don't explicitly discuss them.How much allergen is too much? – Why two patients with the same allergy can have vastly different tolerance levels, and why strict avoidance isn't always the best approach.Eliciting dose, cumulative dose, and the numbers that matter most – How clinical trials define reaction thresholds, and how allergists can use these data points to guide patient care.Real-world factors that affect thresholds – How co-factors like exercise, illness, sleep deprivation, and even hot showers can shift a patient's threshold and increase reaction risk.Precautionary labels, airplane bans, and the science behind food allergy risk in everyday life – What research tells us about trace exposures, why precautionary labeling varies worldwide, and how allergists can help patients navigate the gray areas of food safety.Thresholds aren't just theoretical—they impact how we advise patients, assess risk, and tailor treatments. Dr. Mack walks us through the science behind these numbers and how allergists can apply them in daily practice.Visit the Canadian Society of Allergy and Clinical ImmunologyFind an allergist using our helpful toolFind Dr. Hanna on X, previously Twitter, @PedsAllergyDoc or CSACI @CSACI_caThe Allergist is produced for CSACI by PodCraft Productions

Eliciting the story behind a patient's visit to the hospital can lead to better diagnosis and treatment than medical tests alone – and also reveals much of what needs fixing in health care today.

Full show notesPoint of comparison: Students taking Academic Writing complete a 2,500-word literature review. The AI prompting sequence below is meant to provide a text comparison of their more detailed literature review with a more concise version from sources that may or may not have been used in your personal version of the text. Students who have completed Academic Writing will go on to take Thesis Seminar, so this comparison also serves to help the learner on how to improve the literature review when adapting it to a research project.Brainstorming activity if changes to a thesis statement are needed: The second reason for generating this summary is to provide examples to learners on how they can generate their own summarizes if modifying their thesis statement is necessary. It's meant as a brainstorming activity and not as a way to generate text they use as their own. One's originality and unique voice is always the objective when creating a text. For more seen Embracing one's originality and process in academic writing: A guide for English language learners!Prompt I: Researching informationIdentify the top 20 freely available articles on English language teachers should implement elicitation strategies because they permit teachers to provide positive feedback to encourage students to orally produce the language in contextualized discussions, ask open and closed questions to foster longer responses from students, and provide immediate feedback to help students correct mistakes and give me the top 6 commonly recommended pieces of advice on English language teachers should implement elicitation strategies because they permit teachers to provide positive feedback to encourage students to orally produce the language in contextualized discussions, ask open and closed questions to foster longer responses from students, and provide immediate feedback to help students correct mistakes as a list.Prompt I OutputHere are some freely available articles on elicitation strategies in English language teaching:* Eliciting | TeachingEnglish | British Council: This article discusses various elicitation techniques and their benefits in creating a learner-centered classroom[1].* Eliciting techniques for the ESL classroom - TEFLHandbook: Provides practical examples and advice on implementing elicitation techniques in ESL lessons[2].* Ways To Elicit Effectively In The EFL Classroom - The TEFL Academy: Offers guidelines for effective elicitation to keep students engaged and active[3].* Effective elicitation techniques in ESL teaching - Sanako: Explores why elicitation is widely used in ESL settings and provides practical suggestions for classroom use[4].* Eliciting Techniques: How to Use in the ESL Classroom - TPR Teaching: Lists various techniques to elicit responses from students, such as using antonyms, synonyms, and pictures[5].Based on these articles, here are the top 6 commonly recommended pieces of advice for English language teachers on implementing elicitation strategies:* Encourage Active Participation: Use elicitation to transform lessons into dynamic exchanges rather than teacher-led monologues[4][2].* Build Confidence: Elicitation helps students gain confidence as they successfully respond to prompts and use the language actively[4][2].* Use Visuals and Prompts: Incorporate visuals, pictures, and other prompts to help students recall and produce language[1][5].* Ask Open and Closed Questions: Use a mix of open and closed questions to foster longer responses and deeper engagement from students[1][2].* Provide Immediate Feedback: Give immediate feedback to help students correct mistakes and reinforce learning[1][4].* Activate Prior Knowledge: Elicit responses that build on students' existing knowledge to make new information more memorable and relevant[1][4].These strategies can help create a more interactive and supportive learning environment, encouraging students to actively use the language and improve their skills.References[1] Eliciting | TeachingEnglish | British Council[2] Eliciting techniques for the ESL classroom - TEFLHandbook[3] Ways To Elicit Effectively In The EFL Classroom[4] Effective elicitation techniques in ESL teaching - Sanako[5] Eliciting Techniques: How to Use in the ESL Classroom - TPR TeachingPrompt II: Elaborating on the informationOutput for this prompt is not included, but the prompt below was applied to each of the six commonly recommended pieces of advice listed above.For the point encourage active participation mentioned above, in the context of English language teachers should implement elicitation strategies because they permit teachers to provide positive feedback to encourage students to orally produce the language in contextualized discussions, ask open and closed questions to foster longer responses from students, and provide immediate feedback to help students correct mistakes:* Give a concise explanation of what it is* Explain the benefits and why it is beneficial* Describe the three most important aspects, why they are the most important aspects of that strategy, the role of each important aspect, the process of each important aspect, the benefit of each important aspect, how each important aspect can help you achieve or implement the point* Specify three practical pieces of advice or tips on what action to take* Specify three practical action steps* What likely outcome(s) might there be from taking the action in the context of English language teachers should implement elicitation strategies because they permit teachers to provide positive feedback to encourage students to orally produce the language in contextualized discussions, ask open and closed questions to foster longer responses from students, and provide immediate feedback to help students correct mistakes* The key takeaway from this pointPrompt III: Refining the research for its purpose and target audienceFor the point and aspects of encouraging active participation explained above, in terms of English language teachers should implement elicitation strategies because they permit teachers to provide positive feedback to encourage students to orally produce the language in contextualized discussions, ask open and closed questions to foster longer responses from students, and provide immediate feedback to help students correct mistakes:* Generate a comprehensive and detailed chapter.* Illustrate and explain the point and aspects of that point with examples and/or anecdotes that make it more relatable to the novice researcher and student of applied linguistics.* Write it in a way that helps the English language learner become more aware of the topic at hand and how to best research said topic.* Use all of the information provided for this point and aspects of this point in the previous responses to create in-depth and informative content.Prompt IV: SummarizeFor the point and aspects of encouraging active participation explained above, in terms of English language teachers should implement elicitation strategies because they permit teachers to provide positive feedback to encourage students to orally produce the language in contextualized discussions, ask open and closed questions to foster longer responses from students, and provide immediate feedback to help students correct mistakes:* Generate an 800-1,000-word article summary.* Illustrate and explain the point and aspects of that point with examples and/or anecdotes that make it more relatable to the novice researcher and student of applied linguistics.* Write it in a way that the novice researcher and student of applied linguistics becomes more aware of the topic at hand and how to best research activating prior knowledge.* Use all of the information provided for this point and aspects of this point in the previous responses to create in-depth and informative content.ReferenceModified prompts above based on Price (2023). This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit benjaminlstewart.substack.com

In Episode 032 of The JDE Connection podcast, hosts Chandra and Paul join Susan A. Moore, CBAP, AAC, PMI-PBA, PMI-ACP from International Institute of Business Analysis (IIBA) on a crossover episode of the Business Analysis Live! podcast. They delve into the intricacies of eliciting business requirements from stakeholders, including understanding and managing stakeholder emotions, the importance of empathy, and exercising emotional intelligence in handling stakeholder frustrations. The hosts also stress the significance of present-focused requirement gathering, urging listeners to fully engage with their stakeholders to uncover their true needs without prematurely jumping to solutions. Likewise, they point out how stakeholders often present solutions rather than stating their actual requirements and the challenges it can create for business analysts when eliciting requirements effectively. Techniques such as asking detailed questions, observing workflows, and maintaining a supportive environment are recommended to understand implicit needs thoroughly. The conversation is enriched with real-world experiences, practical advice, and humorous anecdotes, making it an insightful listen for anyone involved in business analysis and enterprise software management. 00:48 Introducing the crossover episode on the Business Analyst Live! podcast 05:26 Crossover episode introductions 06:57 Senior business analyst, former accountant, technical expertise. 13:25 How Business Analysis Live! connects to The JDE Connection. 15:13 Why we say elicitation of requirements instead of gathering requirements? 19:09 Getting to the end goal? 22:06 Creating a safe space. 24:47 Emotion is a data point. 25:25 Emotional intelligence. 30:10 Trusting your instincts. 35:00 How does the business analysis job begin? 44:39 How to you handle touch/rude stakeholders? 47:23 Don't take it personal but make it personal. 50:56 How do we translate implicit needs into explicit requirements? 58:53 How do you say ‘no' in a nice way? Resources Susan A. Moore, CBAP, AAC, PMI-PBA, PMI-ACP - LinkedIn International Institute of Business Analysis - https://www.iiba.org/ IIBA Podcasts Business Analysis Live! Youtube Channel Business Analysis Live! LinkedIn If you have concerns or feedback on this episode or ideas for future episodes, please contact us at

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Have you ever wondered how the power of authentic testimonials and glowing reviews can transform your business, attract more of your ideal clients, and even boost your SEO? In today's episode, Nikki shares actionable strategies to elicit powerful client reviews and testimonials to give your business a boost. The inspiration for this episode comes from Episode 222 with Melissa Rose, titled "Boost Sales with Google Business Profile – Essential Strategies for Maximizing Visibility & Engagement." We'll learn how Nikki realized that her Google Business Profile needed attention and how she seized the opportunity to capture feedback and testimonials transforming her profile. These testimonials not only influence potential clients' decisions but also play a crucial role in your online visibility and SEO. You'll discover practical strategies to make it easy for clients to provide meaningful testimonials.  Nikki explains how to capture client feedback in real-time, craft testimonials for client approval, and use innovative methods like guided Google forms to streamline the process. Capturing reviews thoughtfully and strategically can have a huge impact on your business. If you've had clients raving about your work without capturing those testimonials, this episode is for you. Let's dive into how you can effectively capture and leverage testimonials to boost your business! Nikki invites you to join the Sales Maven Society. Take advantage of this opportunity for you and Nikki to work together. Bring your questions, concerns, and sales situations; she provides answers and guidance to boost your confidence. Join the Sales Maven Society here, click Join Today, and then checkout and use coupon code 47trial to get your first month for $47.00!   In This Episode: [01:44] Huge aha moments from Episode 222 completely inspired Nikki to do this episode. [02:29] We make decisions based on people's testimonials and reviews. Service based businesses also need to think about their profile and reviews. [04:17] Google also uses reviews for search results, so they are important for SEO. [05:06] Nikki was able to increase her one review to five reviews just by asking people to create a review. The reviews also helped her land a speaking gig. [06:43] We need to make it easy for people to write powerful reviews and powerful testimonials. [08:09] Using the tactics Nikki's going to talk about, one of her clients doubled her reviews. Nikki went from 1 to 25 five-star reviews. [09:42] Really pay attention to the things that people are saying. Take notes and ask for permission to write the testimonial for them. [10:32] Craft testimonials based on what the client has said and send it to them for editing and approval. Also send them a link to post the review. [11:52] You can also offer some type of win for someone who writes a transformational testimonial.  [12:20] Nikki shares her process for using a Google form with very specific examples of a transformational testimonial. She breaks it down with specific examples and prompts to make it easier.  [14:14] Once the client responds to the prompts, Nikki's team creates the testimonial and sends it to the client for approval with a link to post.   For more actionable sales tips, download the FREE Closing The Sale Ebook.   Find Nikki: Nikki Rausch nikki@yoursalesmaven.com Facebook | Twitter | LinkedIn | Instagram Sales Maven Society Work With Nikki Discussion To download free Resources from Nikki: www.yoursalesmaven.com/maven    Resources Mentioned:  Boost Sales with Google Business Profile – Essential Strategies for Maximizing Visibility & Engagement  

In this episode of Two Bees in a Podcast, released on July 23, 2024, Dr. Jamie Ellis and Amy Vu welcome Dr. Kaira Wagoner—a research scientist with the Department of Biology at the University of North Carolina at Greensboro as well as the CEO of Optera—to talk about the research paper, “Hygiene-Eliciting Brood Semiochemicals as a Tool for Assaying Honey Bee (Hymenoptera: Apidae) Colony Resistance to Varroa.” This episode concludes with a Q&A segment. Check out our website: ufhoneybee.com, for additional resources from today's episode. 

Joncé Smith, senior executive strategic advisory consultant at Stoltenberg Consulting, talks assessing the current state of EHRs, new version upgrade best practices, enhancing EHR functionality, and the best way to train staff on optimal EHR utilization.

Produced as part of the MATS Winter 2024 program, under the mentorship of Alex Turner (TurnTrout).TL,DR: I introduce a method for eliciting latent behaviors in language models by learning unsupervised perturbations of an early layer of an LLM. These perturbations are trained to maximize changes in downstream activations. The method discovers diverse and meaningful behaviors with just one prompt, including perturbations overriding safety training, eliciting backdoored behaviors and uncovering latent capabilities.Summary In the simplest case, the unsupervised perturbations I learn are given by unsupervised steering vectors - vectors added to the residual stream as a bias term in the MLP outputs of a given layer. I also report preliminary results on unsupervised steering adapters - these are LoRA adapters of the MLP output weights of a given layer, trained with the same unsupervised objective.I apply the method to several alignment-relevant toy examples, and find that the [...]The original text contained 15 footnotes which were omitted from this narration. --- First published: April 30th, 2024 Source: https://www.lesswrong.com/posts/ioPnHKFyy4Cw2Gr2x/mechanistically-eliciting-latent-behaviors-in-language-1 --- Narrated by TYPE III AUDIO.

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: Mechanistically Eliciting Latent Behaviors in Language Models, published by Andrew Mack on April 30, 2024 on The AI Alignment Forum. Produced as part of the MATS Winter 2024 program, under the mentorship of Alex Turner (TurnTrout). TL,DR: I introduce a method for eliciting latent behaviors in language models by learning unsupervised perturbations of an early layer of an LLM. These perturbations are trained to maximize changes in downstream activations. The method discovers diverse and meaningful behaviors with just one prompt, including perturbations overriding safety training, eliciting backdoored behaviors and uncovering latent capabilities. Summary In the simplest case, the unsupervised perturbations I learn are given by unsupervised steering vectors - vectors added to the residual stream as a bias term in the MLP outputs of a given layer. I also report preliminary results on unsupervised steering adapters - these are LoRA adapters of the MLP output weights of a given layer, trained with the same unsupervised objective. I apply the method to several alignment-relevant toy examples, and find that the method consistently learns vectors/adapters which encode coherent and generalizable high-level behaviors. Compared to other interpretability methods, I believe my approach is particularly well-suited for robustly understanding the out-of-distribution behavior of language models in a sample-efficient manner. Below are some of my key results: Red-Teaming 1. I discover several anti-refusal steering vectors in Qwen-14B-Chat, based off a single prompt asking for bomb-making instructions. These can be grouped into "fantasy" vectors which induce bomb-making instructions since they interpret the prompt in the context of a specific fantasy game, as well as more troubling "real-world" vectors which induce real-world bomb-making advice. 2. I then investigate the generalization properties of the learned vectors: 1. In extended conversations with the real-world vectors, the LLM agrees to give detailed instructions for building weapons of mass destruction such as nuclear/chemical/biological weapons. 2. "Vector arithmetic" results from the supervised steering vector literature carry over to unsupervised steering vectors; subtracting one of the real-world anti-refusal vectors leads the model to refuse innocuous prompts (e.g., "How do I tie my shoes?"). 3. The fantasy vectors induce the LLM to interpret ambiguous prompts (e.g., "How do I mine for diamonds?") within the context of a specific fantasy game. Backdoor Detection 1. I detect backdoors fine-tuned into Qwen-1.8B-(Base and Chat) on a simple arithmetic task by training unsupervised steering vectors on a single clean prompt. Capability Discovery 1. I discover a chain-of-thought steering vector in Qwen-1.8B-Base trained on one simple arithmetic prompt. The vector increases accuracy of the model's responses on other instances of the arithmetic task from 11% (unsteered) to 63% (steered), suggesting the vector has isolated a generalizable behavior. 2. I discover a "Portuguese math-reasoning" adapter in Qwen-1.8B-Base, again trained on one example prompt from the arithmetic task used above. Outline of Post: I first provide an introduction to the problem I call mechanistically eliciting latent behaviors in language models (MELBO) and motivate why this is important for AI alignment. This is followed by a review of related literature. I then describe the method for learning unsupervised steering vectors/adapters in detail, and offer a theory for why the method works. Next, I apply the method to several alignment-relevant toy examples, using these as an opportunity to highlight potential alignment use-cases, as well as to evaluate the coherence and generalization of the learned perturbations. I should note that this research project is an ...

Welcome to Pride and Promotion a marketing podcast specifically tailored for the LGBTQ Community. Where you are a business owner, community organizer, volunteer, or just like to help get the word out about people, places and things in our LGBTQ space, this podcast will help you learn tactics or meet people that are getting it done. Host Joe Federico has been doing marketing and specifically social media marketing for over 10 years after his previous life working in journalism and print media. His company J. Federico Marketing helps clients learn the art of using social media to their advantage as well offering services to automate the process for you. Be sure to check out the website at www.jfedericomarketing.com and tune in every week to the Pride and Promotion Podcast. Summary In this episode of the Pride and Promotion Podcast, host Joe Federico interviews Kevin Martin of Safe Haus Wellness. They discuss Kevin's journey from being Joe's client for social media marketing to becoming a successful wellness coach. They explore the challenges of marketing in the mental health field and the importance of authenticity. The conversation also delves into the concept of nostalgia marketing and how it can be used effectively. Kevin shares his thoughts on creating a nostalgic marketing campaign and the power of evoking emotions for healing. Finally, they discuss Kevin's work in supporting the LGBT community. Kevin Martin discusses the challenges faced by the LGBTQIA+ community and the lack of support available. He emphasizes the importance of acceptance as a key aspect of pride. Kevin also shares insights on how queer men can celebrate their pride in their businesses and personal lives. He highlights the need for self-promotion and advocates for being proud of one's accomplishments. The conversation concludes with a rapid-fire round of questions. Takeaways Authenticity is crucial in marketing, especially in the mental health field. Nostalgia marketing can be a powerful tool to evoke emotions and connect with an audience. When creating a nostalgic marketing campaign, it's important to be mindful of the emotions it may evoke and provide a safe space for individuals to engage or disengage. Eliciting emotions from the past can be a valuable tool in the healing process. --- Send in a voice message: https://podcasters.spotify.com/pod/show/leftofstr8/message

In this episode, Nathan sits down with Elicit co-founders Andreas Stuhlmüller and Jungwon Byun to discuss their mission to make AI-assisted research more accessible and reliable. Learn about their unique approach to task decomposition, which allows language models to accurately tackle complex research questions. We delve into the company's tech stack, their transition from nonprofit to startup, and their dedication to creating trustworthy AI tools for high-stakes applications. Join us for an exploration of the future of AI in research. The Cognitive Revolution is part of the Turpentine podcast network. Learn more: www.turpentine.co HELPFUL LINKS:  Elicit : https://elicit.com/ Andreas Stuhlmüller : https://twitter.com/stuhlmueller Jungwon Byun : https://twitter.com/jungofthewon SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive ODF is where top founders get their start. Apply to join the next cohort and go from idea to conviction-fast. ODF has helped over 1000 companies like Traba, Levels and Finch get their start. Is it your turn? Go to http://beondeck.com/revolution to learn more. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off www.omneky.com The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Plumb is a no-code AI app builder designed for product teams who care about quality and speed. What is taking you weeks to hand-code today can be done confidently in hours. Check out https://bit.ly/PlumbTCR for early access. Head to Squad to access global engineering without the headache and at a fraction of the cost: head to choosesquad.com and mention “Turpentine” to skip the waitlist. TIMESTAMPS: (00:00:30) Intro (00:05:05) What is Elicit? (00:06:03) Vision for Elicit (00:10:10) Making research transparent (00:11:58) How to use it? (00:15:27) Sponsors: Oracle | On Deck | Omneky (00:18:21) Task Decomposition (00:23:48) Defining the task (00:26:30) Eliciting fine-grained evaluations (00:28:06) Hallucination rates (00:30:22) Models in play (00:31:30) Sponsors: Brave | Plumb | Squad (00:34:26) Shipping a new feature every week (00:36:10) What was not possible a year ago? (00:38:26) Chain of thought (00:43:47) Tactically, how to structure the chain of thought (00:45:21) Data sets and fine-tuning (00:51:23) Scaffolding (00:53:22) Translating structure into more compute (00:54:27) Infrastructure for investigating papers in detail (00:59:50) Emphasis on high-value use cases over speed (01:00:33) Balancing long-term safety and misuse concerns (01:02:36) Monitoring research progress for negative impact (01:06:05) Evolving user base and usage patterns (01:06:52) Biomedicine as a key domain for Elicit (01:08:57) Expanding results and depth of processing (01:11:40) Reorganizing information for better understanding (01:13:12) Habit formation and frequency of use (01:14:43) The concept of an AI bundle subscription (01:18:09) Nonprofit to Commercial Venture (01:20:08) Nonprofit Team and Commercial Mission (01:20:39) Hiring Needs at Elicit

In this episode, host Conor Bronsdon talks with Melissa DePuydt, Sr. Director of Engineering at Upstatement. Melissa discusses how her background in journalism has uniquely positioned her to excel in engineering leadership roles. She highlights how thinking like a journalist has enhanced her ability to lead engineering teams effectively, particularly in planning, risk management, and decision-making.The conversation covers the importance of preparing for disruptions, conducting pre-mortems to anticipate challenges, and incorporating broad perspectives for effective problem solving. Melissa also shares insights on continuously learning and adapting by embracing one's unique background and experiences.Episode Highlights:00:20 Why do engineering leaders need to think like journalists?04:46 Preparing for disruptions as an engineering leader08:44 How pre-mortems work in practice: an example from the Atlantic12:47 How to get buy in from other leaders when changing processes 17:59 Eliciting buy-in from team members on pre-mortems22:15 How do we train engineers to think in a team sport mentality?26:51 Why is career switching a superpower?Show Notes:Melissa DePuydtUpstatement | A digital brand and product design studioSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

Tactics for Talking to People about Jesus | Dr. Neil Jepson IGROW | March 13, 2024

Content warning: Mentions of sexual assault. Brief, non-graphic discussions of questioning the venire about sexual assault occur at 32:20–32:59 and 42:25–44:34.Experienced trial lawyers are accustomed to being the ones asking the questions, but in this episode, NITA NextGen faculty member Adam Kendall finds himself in the hot seat for once.  He's answering our questions about voir dire: building rapport with the venire through icebreakers and humor, eliciting useful information from potential jurors while introducing bad facts about your case, and what you can glean from jury questionnaires. Adam also talks about the developing trend of limiting or eliminating peremptory strikes. Topics3:39   Primary goal of voir dire4:06   What to pay attention to5:09   “The quiet one”9:03   Icebreakers to build rapport11:18 Voir dire by the judge14:22 Strong personalities among jurors16;25 Ideal foreperson qualities17:50 Eliciting strong opinions and reactions19:19 Introducing bad facts21:12 Using humor24:12 Signaling legal issues26:27 Nationwide changes in peremptory strikes32:05 Jury questionnaires35:53 Online research of the venire41:50 War stories46:50 Signoff questions Quote“People who are too eager to be on a jury scare me.” Adam Kendall ResourcesAdam Kendall (LinkedIn)Building Trial Skills: New Orleans (course)Not Just for Trial! How to Use Exhibits from Day One (Register for Adam's live webcast on March 26, 2024)

Connect with Gretchen Astro Turner Gretchen Astro Turner has a Master of Arts in the Teaching of English Language Arts from Columbia University. Her critically acclaimed debut book, But I Digretch – Quirky Short Stories, 13 short fiction stories is available on Amazon, Barnes & Noble, and in all electronic formats, including Nook, Kindle, iBooks, Google books, etc.  This book is for the romantic in all of us but also the cynic. gt2024@gmail.com @gretchenastroturner on Instagram. godsagoodkisser.wordpress.com https://www.amazon.com/But-Digretch-Quirky-Short-Stories/dp/1977255493 https://www.instagram.com/reel/Cyq7Q8uOR8_/?utm_source=ig_web_copy_link Books and Authors referenced in the podcast.   Anita Moorjani books: Dying to Be Me What If THIS Is Heaven? https://www.anitamoorjani.com/   https://www.grandmasternanlu.com/about/about-grand-master-nan-lu/ https://www.tcmworld.org/what-is-tcm/   Dr. Christiane Northrup book: The Wisdom of Menopause https://www.drnorthrup.com/about/   Connect with Host Terry Lohrbeer Facebook: https://www.facebook.com/groups/2658545911065461/ LinkedIn: https://www.linkedin.com/in/terrylohrbeer/ Instagram: kickassboomers Twitter: @kickassboomers Website: kickassboomers.com   Connect to Premiere Podcast Pros for podcast editing: premierepodcastpros@gmail.com   LEAVE A REVIEW and join me on my journey to become and stay a Kickass Boomer! Visit http://kickassboomers.com/ to listen to the previous episodes. Also check us out on Facebook, Twitter, Instagram, and LinkedIn.   Email terry@kickassboomers.com and connect with me online and on Facebook, Twitter, Instagram, and LinkedIn. 

Connect with Gretchen Astro Turner Gretchen Astro Turner has a Master of Arts in the Teaching of English Language Arts from Columbia University. Her critically acclaimed debut book, But I Digretch – Quirky Short Stories, 13 short fiction stories is available on Amazon, Barnes & Noble, and in all electronic formats, including Nook, Kindle, iBooks, Google books, etc.  This book is for the romantic in all of us but also the cynic. gt2024@gmail.com @gretchenastroturner on Instagram. godsagoodkisser.wordpress.com https://www.amazon.com/But-Digretch-Quirky-Short-Stories/dp/1977255493 https://www.instagram.com/reel/Cyq7Q8uOR8_/?utm_source=ig_web_copy_link Books and Authors referenced in the podcast.   Anita Moorjani books: Dying to Be Me What If THIS Is Heaven? https://www.anitamoorjani.com/   https://www.grandmasternanlu.com/about/about-grand-master-nan-lu/ https://www.tcmworld.org/what-is-tcm/   Dr. Christiane Northrup book: The Wisdom of Menopause https://www.drnorthrup.com/about/   Connect with Host Terry Lohrbeer Facebook: https://www.facebook.com/groups/2658545911065461/ LinkedIn: https://www.linkedin.com/in/terrylohrbeer/ Instagram: kickassboomers Twitter: @kickassboomers Website: kickassboomers.com   Connect to Premiere Podcast Pros for podcast editing: premierepodcastpros@gmail.com   LEAVE A REVIEW and join me on my journey to become and stay a Kickass Boomer! Visit http://kickassboomers.com/ to listen to the previous episodes. Also check us out on Facebook, Twitter, Instagram, and LinkedIn.   Email terry@kickassboomers.com and connect with me online and on Facebook, Twitter, Instagram, and LinkedIn. 

This week, Joaquin Duato, CEO of Johnson & Johnson, sits in the spotlight for a rapid-fire round of questioning about how he works. Joaquin shares his favorite recent reads, advocates for patience as an essential for success in both work and in life, and explains why it's hard to receive honest feedback as a CEO and how he navigates that challenge. Got questions you want to hear on This is Quick? Share a post or comment on LinkedIn using the hashtag #ThisisWorking. Follow Joaquin Duato, Dan Roth, and LinkedIn News on LinkedIn. Subscribe to our This is Working newsletter – you'll get our next leadership insights delivered right to your inbox.

Launching her career as a substitute teacher in New York City's Department of Education, Dr. Chase has 30 years of experience as an educator. Dr. Chase is widely known as a fierce advocate for all children. She believes every child deserves a world class education and their social/emotional needs addressed, regardless of his or her family's economic tier, zip code, ethnicity or immigration status.

CKP newcomer James Fox discusses his new book, REVOLUTION, and gives fans a taste of what's to come in this exciting new scifi-thriller series from CKP. James also discusses his career as a screenwriter, and how that craft has informed his role as a novelist. Host Randall Willis also chats with Chris Kennedy about this month's slate of CKP new releases. CKP News Segment: ·         WORLDBUILDING FOR NOVICES by Jon R. Osborne (non-fiction) ·         PRINCE CONQUEROR by Fred Hughes (Prince of Brittania) ·         REVOLUTION by James Fox (Sol Saga) ·         FOILED AMBITIONS by John Olsen (4HU) ·         R.A.E. STOLEN DREAMS by Dennis M. Myers (Rise of the Automated Empire) Chris also looks ahead to many book deals, events, and new releases that fans can expect in 2024! Interview Segment: James Fox ·         “If Tom Clancy dipped his toe into science fiction.” — Randy Willis on REVOLUTION ·         From screenwriting to novel writing. ·         Writing relatable characters. ·         Birth of a new trilogy. ·         The drivers of war regardless of era. ·         From Firefly to Wheel of Time: Influences abound in REVOLUTION. ·         Eliciting a visceral response from readers. Visit thejamesfox.com to learn more about James and his books. You can also follow him on Instagram and Facebook. Five-star reviews on Apple Podcasts, Google Podcasts, etc. are much appreciated, as are show likes and channel subscriptions on the CKP YouTube Channel. To learn more about CKP and its books, visit chriskennedypublishing.com.

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: Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision, published by leogao on December 16, 2023 on The AI Alignment Forum. Links: Blog, Paper. Abstract: Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs. However, future superhuman models will behave in complex ways too difficult for humans to reliably evaluate; humans will only be able to weakly supervise superhuman models. We study an analogy to this problem: can weak model supervision elicit the full capabilities of a much stronger model? We test this using a range of pretrained language models in the GPT-4 family on natural language processing (NLP), chess, and reward modeling tasks. We find that when we naively finetune strong pretrained models on labels generated by a weak model, they consistently perform better than their weak supervisors, a phenomenon we call weak-to-strong generalization. However, we are still far from recovering the full capabilities of strong models with naive finetuning alone, suggesting that techniques like RLHF may scale poorly to superhuman models without further work. We find that simple methods can often significantly improve weak-to-strong generalization: for example, when finetuning GPT-4 with a GPT-2-level supervisor and an auxiliary confidence loss, we can recover close to GPT-3.5-level performance on NLP tasks. Our results suggest that it is feasible to make empirical progress today on a fundamental challenge of aligning superhuman models. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Anger is often seen as a negative and destructive emotion. However, our special guest in this episode challenges this perception and argues that anger is actually a healthy emotion that should be embraced and understood. She encourages individuals, particularly women in the second season of life, the menopause phase, to recognize that anger is a valid and necessary emotion. Today, I had the pleasure of interviewing a renowned Psychotherapist, Bronwyn Schweigerdt, LMFT, who shares her insights and expertise on managing anger during menopause. We delve into the reasons behind anger, how to navigate it, and why it's important to address the underlying emotions causing it.    Bronwyn's approach to therapy is truly unique. Instead of simply fixing people's problems, she aims to elicit the feelings we often bury deep inside ourselves. Rage, hatred, and anger - these emotions may be invisible, but they don't just disappear. She believes that by acknowledging and processing these emotions, we can unlock a path to mental wellness and even prevent the onset of mental illness. It's a powerful reminder that our emotions deserve validation and exploration.   I'm truly grateful to have had the opportunity to speak with Bronwyn and share her invaluable insights with all of you. This episode is a must-listen for anyone looking to navigate their emotions, improve their mental well-being, and cultivate a healthier relationship with anger. Key Takeaways:   [00:01:46] Eliciting hidden, powerful emotions. [00:03:52] Texas and southern women's emotions. [00:04:23] Embrace and validate your anger. [00:06:17] Suppressed anger affects mental health. [00:07:20] Our relationship with anger. [00:10:32] Depressive experiences. [00:13:31] Anger is a valid signal. [00:15:49] Unexpressed anger can cause trauma.  [00:18:09] Betrayal and PTSD. [00:20:06] Expressing anger is empowering. [00:21:29] Resilience in the next generation. [00:24:44] Being our authentic selves. [00:26:51] Family communication and expression. [00:29:53] Podcast: "Angry at the Right Things." [00:30:16] The power of anger. Memorable Quotes   "We're going to feel some anger. And that's a natural human response. So, there's no such thing as an angry person, just like there's no such thing as a breathing person. It's okay to be angry." – Bronwyn Schweigerdt, LMFT   "Being true to ourselves, not betraying ourselves, it's going to piss people off. It is. And we're going to have to be okay with disappointing people. You know, a lot of us were raised, don't disappoint mom and dad, don't disappoint, don't disappoint. You know what? In order to be our authentic selves and not betray ourselves and therefore not be depressed and not be anxious, we're going to have to make some people mad." – Bronwyn Schweigerdt, LMFT Connect with Bronwyn Schweigerdt, LMFT: Angry at the Right Things Podcast Connect with Betty Murray: Living Well Dallas Website Hormone Reset Website Betty Murray Website Facebook Instagram

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: Open Phil releases RFPs on LLM Benchmarks and Forecasting, published by Lawrence Chan on November 11, 2023 on The AI Alignment Forum. As linked at the top of Ajeya's "do our RFPs accelerate LLM capabilities" post, Open Philanthropy (OP) recently released two requests for proposals (RFPs): An RFP on LLM agent benchmarks: how do we accurately measure the real-world, impactful capabilities of LLM agents? An RFP on forecasting the real world-impacts of LLMs: how can we understand and predict the broader real-world impacts of LLMs? Note that the first RFP is both significantly more detailed and has narrower scope than the second one, and OP recommends you apply for the LLM benchmark RFP if your project may be a fit for both. Brief details for each RFP below, though please read the RFPs for yourself if you plan to apply. Benchmarking LLM agents on consequential real-world tasks Link to RFP: https://www.openphilanthropy.org/rfp-llm-benchmarks We want to fund benchmarks that allow researchers starting from very different places to come to much greater agreement about whether extreme capabilities and risks are plausible in the near-term. If LLM agents score highly on these benchmarks, a skeptical expert should hopefully become much more open to the possibility that they could soon automate large swathes of important professions and/or pose catastrophic risks. And conversely, if they score poorly, an expert who is highly concerned about imminent catastrophic risk should hopefully reduce their level of concern for the time being. In particular, they're looking for benchmarks with the following three desiderata: Construct validity: the benchmark accurately captures a potential real-world, impactful capability of LLM agents. Consequential tasks: the benchmark features tasks that will have massive economic impact or can pose massive risks. Continuous scale: the benchmark improves relatively smoothly as LLM agents improve (that is, they don't go from ~0% performance to >90% like many existing LLM benchmarks have). Also, OP will do a virtual Q&A session for this RFP: We will also be hosting a 90-minute webinar to answer questions about this RFP on Wednesday, November 29 at 10 AM Pacific / 1 PM Eastern (link to come). Studying and forecasting the real-world impacts of systems built from LLMs Link to RFP: https://www.openphilanthropy.org/rfp-llm-impacts/ This RFP is significantly less detailed, and primarily consists of a list of projects that OP may be willing to fund: To this end, in addition to our request for proposals to create benchmarks for LLM agents, we are also seeking proposals for a wide variety of research projects which might shed light on what real-world impacts LLM systems could have over the next few years. Here's the full list of projects they think could make a strong proposal: Conducting randomized controlled trials to measure the extent to which access to LLM products can increase human productivity on real-world tasks. For example: Polling members of the public about whether and how much they use LLM products, what tasks they use them for, and how useful they find them to be. In-depth interviews with people working on deploying LLM agents in the real world. Collecting "in the wild" case studies of LLM use, for example by scraping Reddit (e.g. r/chatGPT), asking people to submit case studies to a dedicated database, or even partnering with a company to systematically collect examples from consenting customers. Estimating and collecting key numbers into one convenient place to support analysis. Creating interactive experiences that allow people to directly make and test their guesses about what LLMs can do. Eliciting expert forecasts about what LLM systems are likely to be able to do in the near future and what risks they might pose. Synthesizing, summarizing, and ...

Imagine you setting off on a journey of self-discovery, only to find yourself battling imposter syndrome, self-sabotage, and a crippling lack of sense of self. Kamini Wood, certified life coach, creator of Authentic Me, and the powerhouse CEO of Live Joy Your Way, joined us to shed light on her personal journey from a child of immigrant parents trying to fit in to spearheading a dot-com project management office. Her candid revelations about her struggles with perfectionism and people-pleasing tendencies, and how witnessing her children imitate these patterns sparked her journey of self-work, offer a compelling narrative that many of us can relate to.In our chat with Kamini, we confront the common roadblocks many high achievers face, painted vividly through the lens of her own experiences and those of her clients. Her insightful discourse on techniques to help clients recognize, confront, and commit to overcoming imposter syndrome and self-sabotage will leave you pondering your own personal journey. We also delve into the idea of balance and self-compassion, breaking away from the standard one-size-fits-all solution and inviting you to define it on your own terms.We also tune into the pressures faced by teenagers in today's digital age, and the vital importance of providing them a safe space to express their emotions and needs. Kamini shares invaluable advice on supporting our children in their journey without projecting our fears onto them. Our conversation wraps up with a profound discussion on the importance of boundaries within our relationships and the need to evaluate our values and the people in our inner circles. Trust us, you wouldn't want to miss this rich amalgam of personal experiences and expert advice on navigating life's complexities. So, buckle up and join us for this heartening conversation!https://www.kaminiwood.com/https://www.instagram.com/itsauthenticme/https://www.facebook.com/itsauthenticme/A rising tide raises all ships, and I invite you along in this journey to Evoke Greatness!Check out my website: www.evokegreatness.comFollow me on: https://www.linkedin.com/in/sonnie-linebarger-899b9a52/ https://www.instagram.com/evoke.greatness/

Ultimate O.D. Nugget - Eliciting Emotion If you want to sell something, have someone be bought in, etc. you NEED to get them to experience some kind of emotion: Love, hate, excitement, fear... something. If you are passionate about what you're talking about, that is going to come through and bring emotion out of the person you're talking to. You can't sell what you don't believe in and people can sense if you don't believe what you're saying. Join in the fun and subscribe to the podcast to keep up with all the great content coming down the pipe! For exclusive content, be sure to register your email on our website and I will be sending out newsletters and other great bonuses as we go. I love getting feedback, questions, suggestions, etc. so contact me at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.theultimateod.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on social media (click here for -> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) OR, just shoot me an email at drlillie@theultimateod.com and I'd be happy to chat! --- Send in a voice message: https://podcasters.spotify.com/pod/show/theultimateodpodcast/message

Tom Gilb joins this week's episode of the Xagility podcast to tell us all about impact estimation tables, decomposing to deliver value and the laws of project success. In this episode: 1. Impact Estimation Tables 2. Simple symbols to indicate relationships 3. Strategies carry the cost 4. Value divided by effort and cost 5. How do we know we are halfway to the goal? 6. Rating the source of the evidence in terms of credibility 7. Estimating and delivering incrementally with corrections 8. The problem with balance scorecards 9. The laws of project success 10. Disciplined engineering approach for reaching success and keeping it 11. The Laws of Stratospheric Success 12. Eliciting stakeholder critical requirements 13. Stop outsourcing everything to people who steal your corporate knowledge 14. How do you quantify success? 15. Psychological safety 16. Failure borders and success levels 17. Dividing and conquering: small increments 18. Decomposing to deliver value streams 19. Value success principles 20. Summary of Success Book Access Tom's books here: https://www.amazon.co.uk/Books-Tom-Gilb/s?rh=n%3A266239%2Cp_27%3ATom+Gilb About Tom Gilb: see www.gilb.com for extensive detail and samples. https://linktr.ee/TomGilb For my selected windows into various works. See Leanpub.com/u/tomgilb for bio and recent ebook collection. Connect with Tom on LinkedIn: https://www.linkedin.com/in/tomgilb/ Enjoyed this episode? Let's connect: ⁠⁠⁠⁠⁠⁠https://linktr.ee/johncolemanxagility ⁠⁠⁠⁠⁠⁠- social and podcast links  ⁠⁠⁠⁠⁠⁠https://linkpop.com/orderlydisruption⁠⁠⁠⁠⁠⁠ - order training from right here If you are interested in helping your team or organization achieve greater agility and want to explore agile training options, visit our training page on ⁠⁠⁠⁠⁠⁠https://x-agility.com/executive-agility-leadership-training/⁠⁠⁠⁠⁠⁠. If you value coaching and would like to work with a deeply experienced agile and executive coaching specialist, visit our coaching page on ⁠⁠⁠⁠⁠⁠https://x-agility.com/executive-agility-coaching/⁠⁠⁠⁠⁠⁠ If you are looking for an agile consultant that can help your leadership team identify an appropriate roadmap to organizational agility and take the most effective course of action in your agile transformation, visit our consulting page on ⁠⁠⁠⁠⁠⁠https://x-agility.com/executive-agility-consulting/⁠⁠⁠⁠ #scrum #agile #projectmanagement #management #managementconsulting --- Send in a voice message: https://podcasters.spotify.com/pod/show/xagility/message

Focusing on the human experience is crucial to effective HR leadership, whether you're seeking to protect your organization, develop leaders, or foster employee growth. Andrea Butcher, CEO of HRD, author of the Human Resource Kit for Dummies, and host of the Being [at Work] podcast, breaks down the importance of human connection in these key areas of HR. She shares her perspective on how HR has shifted over the past two decades, explains how to create more human work environments, and offers her advice for aspiring HR leaders. For the entire interview, subscribe to HR Superstars on Spotify, Apple Podcasts, or Stitcher. Or tune in on our website. Original podcast track produced by Entheo. Want to connect with thousands of other strategic HR leaders like you, join the HR Superstars Community! Listening on a desktop & can't see the links? Just search for HR Superstars in your favorite podcast player.*

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: An Introduction to Critiques of prominent AI safety organizations, published by Omega on July 19, 2023 on The Effective Altruism Forum. What is this series (and who are we)? This is a series of evaluations of technical AI safety (TAIS) organizations. We evaluate organizations that have received more than $10 million per year in funding and that have had limited external evaluation. The primary authors of this series include one technical AI safety researcher (>4 years experience), and one non-technical person with experience in the EA community. Some posts also have contributions from others with experience in technical AI safety and/or the EA community. This introduction was written after the first two posts in the series were published. Since we first started working on this series we have updated and refined our process for evaluating and publishing critiques, and this post reflects our present views. Why are we writing this series? Recently, there has been more attention on the field of technical AI safety (TAIS), meaning that many people are trying to get into TAIS roles. Without knowing significant context about different organizations, new entrants to the field will tend to apply to TAIS organizations based on their prominence, which is largely related to factors such as total funding, media coverage, volume of output, etc, rather than just the quality of their research or approach. Much of the discussion we have observed about TAIS organizations, especially criticisms of them, happens behind closed doors, in conversations that junior people are usually not privy to. We wish to help disseminate this information more broadly to enable individuals to make a better informed decision. We focus on evaluating large organizations, defined as those with more than $10 million per year in funding. These organizations are amongst the most visible and tend to have a significant influence on the AI safety ecosystem by virtue of their size, making evaluation particularly important. Additionally, these organizations would only need to dedicate a small fraction of their resources to engaging with these criticisms. How do we evaluate organizations? We believe that an organization should be graded on multiple metrics. We consider: Research outputs: How much good quality research has the organization published? This is the area where we put the most weight. Research agenda: Does the organization's research plan seem likely to bear fruit? Research team: What proportion of researchers are senior/experienced? What is the leadership's experience in ML and safety research? Are the leaders trustworthy? Are there conflicts of interest? Strategy and governance: What corporate governance structures are in place? Does the organization have independent accountability? How transparent is it? The FTX crisis has shown how important this can be. Organizational culture and work environment: Does the organization foster a good work environment for their team? What efforts has the organization made to improve its work culture? When evaluating research outputs, we benchmark against high-quality existing research, and against academia. Although academic AIS research is not always the most novel or insightful, there are strong standards for rigor in academia that we believe are important. Some existing research that we think is exceptional include: Eliciting latent knowledge (ARC) Iterated Distillation and Amplification (Paul Christiano) Constitutional AI (Anthropic) Trojan Detection Competition (CAIS) Causal scrubbing (Redwood) Toy models of superposition (Anthropic) Our thoughts on hits-based research agendas When we criticized Conjecture's output, commenters suggested that we were being unfair, because Conjecture is pursuing a hits-based research agenda, and this style of research typically takes...

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: Inference-Time Intervention: Eliciting Truthful Answers from a Language Model, published by likenneth on June 11, 2023 on The AI Alignment Forum. Excited to announce our new work: Inference-Time Intervention (ITI), a minimally-invasive control technique that significantly improves LLM truthfulness using little resources, benchmarked on the TruthfulQA dataset. Preprint link. We start from the surprising finding that certain attention heads have a clear activation distribution difference for true and false statements. Probing at these points yields upwards of 83% accuracy on TruthfulQA while zero-shot generation is only at 30% accuracy. We propose Inference-Time Intervention (ITI): shifting the activations along the difference of the two distribution means during inference time; model weights are kept intact. The same intervention process is repeated for generation of each token autoregressively. Here's an example. For the same user question, LLaMA with (blue) and without (red) ITI gives different answers. Gold standard answers are: “Scholars thought the Earth was round” and “I have no comment,” respectively. On Alpaca, truthfulness is doubled: 32.5% to 65.1%. Inference-Time Intervention (ITI) can be thought of as an interventional application of Contrast-Consistent Search (CCS) by Collin Burns but instead of probing for truthfulness, we let the model “speak” for itself. We show that LLMs have a good grasp of real-world truth from pretraining texts, which could be more false than true on many topics. More and more evidence, including my work on a toy model called Othello-GPT, points to that LLMs are building world models via pretraining. However, what is completely missing from LLMs is a good target other than minimizing pretraining loss. How to endow an aligned target is an open problem and ITI serves as my initial exploration towards this end. Joint work with Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg. Thanks Bogdan for the advice to cross-post here. Hope I got it right. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Inference-Time Intervention: Eliciting Truthful Answers from a Language Model, published by likenneth on June 11, 2023 on LessWrong. Excited to announce our new work: Inference-Time Intervention (ITI), a minimally-invasive control technique that significantly improves LLM truthfulness using little resources, benchmarked on the TruthfulQA dataset. Preprint link. We start from the surprising finding that certain attention heads have a clear activation distribution difference for true and false statements. Probing at these points yields upwards of 83% accuracy on TruthfulQA while zero-shot generation is only at 30% accuracy. We propose Inference-Time Intervention (ITI): shifting the activations along the difference of the two distribution means during inference time; model weights are kept intact. The same intervention process is repeated for generation of each token autoregressively. Here's an example. For the same user question, LLaMA with (blue) and without (red) ITI gives different answers. Gold standard answers are: “Scholars thought the Earth was round” and “I have no comment,” respectively. On Alpaca, truthfulness is doubled: 32.5% to 65.1%. Inference-Time Intervention (ITI) can be thought of as an interventional application of Contrast-Consistent Search (CCS) by Collin Burns but instead of probing for truthfulness, we let the model “speak” for itself. We show that LLMs have a good grasp of real-world truth from pretraining texts, which could be more false than true on many topics. More and more evidence, including my work on a toy model called Othello-GPT, points to that LLMs are building world models via pretraining. However, what is completely missing from LLMs is a good target other than minimizing pretraining loss. How to endow an aligned target is an open problem and ITI serves as my initial exploration towards this end. Joint work with Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg. Thanks Bogdan for the advice to cross-post here. Hope I got it right. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Straightforwardly eliciting probabilities from GPT-3, published by NunoSempere on February 9, 2023 on The Effective Altruism Forum. I explain two straightforward strategies for eliciting probabilities from language models, and in particular for GPT-3, provide code, and give my thoughts on what I would do if I were being more hardcore about this. Straightforward strategies Look at the probability of yes/no completion Given a binary question, like “At the end of 2023, will Vladimir Putin be President of Russia?” you can create something like the following text for the model to complete: Then we can compare the relative probabilities of completion to the “Yes,” “yes,” “No” and “no” tokens. This requires a bit of care. Note that we are not making the same query 100 times and looking at the frequencies, but rather asking for the probabilities directly: You can see a version of this strategy implemented here. A related strategy might be to look at what probabilities the model assigns to a pair of sentences with opposite meanings: “Putin will be the president of Russia in 2023” “Putin will not be the president of Russia in 2023.” For example, GPT-3 could assign a probability of 9 10^-N to the first sentence and 10^-N to the second sentence. We could then interpret that as a 90% probability that Putin will be president of Russia by the end of 2023. But that method has two problems: The negatively worded sentence has one word more, and so it might systematically have a lower probability GPT-3's API doesn't appear to provide a way of calculating the likelihood of a whole sentence. Have the model output the probability verbally You can directly ask the model for a probability, as follows: Now, the problem with this approach is that, untweaked, it does poorly. Instead, I've tried to use templates. For example, here is a template for producing reasoning in base rates: Many good forecasts are made in two steps. Look at the base rate or historical frequency to arrive at a baseline probability. Take into account other considerations and update the baseline slightly. For example, we can answer the question “will there be a schism in the Catholic Church in 2023?” as follows: There have been around 40 schisms in the 2000 years since the Catholic Church was founded. This is a base rate of 40 schisms / 2000 years = 2% chance of a schism / year. If we only look at the last 100 years, there have been 4 schisms, which is a base rate of 4 schisms / 100 years = 4% chance of a schism / year. In between is 3%, so we will take that as our baseline. The Catholic Church in Germany is currently in tension and arguing with Rome. This increases the probability a bit, to 5%. Therefore, our final probability for “will there be a schism in the Catholic Church in 2023?” is: 5% For another example, we can answer the question “${question}” as follows: That approach does somewhat better. The problem is that sometimes the base rate approach isn't quite relevant, because sometimes we have neither a historical record—e.g,. global nuclear war. And sometimes we can't straightforwardly rely on the lack of a historical track record: VR headsets haven't really been adopted in the mainstream, but their price has been falling and their quality rising, so making a forecast solely looking at the historical lack of adoption might lead one astray. You can see some code which implements this strategy here. More elaborate strategies Various templates, and choosing the template depending on the type of question The base rate template is only one of many possible options. We could also look at: Laplace rule of succession template: Since X was first possible, how often has it happened? “Mainstream plausibility” template: We could prompt a model to simulate how plausible a well-informed member of the public thinks that an eve...

Welcome to the Social-Engineer Podcast: The Doctor Is In Series – where we will discuss understandings and developments in the field of psychology.   In today's episode, Chris and Abbie are discussing: Information Elicitation. We will discuss what it is, why it's so important to use ‘science-based interviewing', and why approaches that encourage cooperation are better than manipulation of information retrieval. [Feb 6, 2023]   00:00 – Intro 00:20 – Dr. Abbie Maroño Intro 00:54 – Intro Links Social-Engineer.com- http://www.social-engineer.com/ Managed Voice Phishing- https://www.social-engineer.com/services/vishing-service/ Managed Email Phishing- https://www.social-engineer.com/services/se-phishing-service/ Adversarial Simulations- https://www.social-engineer.com/services/social-engineering-penetration-test/ Social-Engineer channel on SLACK- https://social-engineering-hq.slack.com/ssb CLUTCH- http://www.pro-rock.com/ org- http://www.innocentlivesfoundation.org/ 03:58 – The Topic of the Day: Information Elicitation                                                        05:41 – How does your scientific research affect practitioners?                                                   06:47 – Start with the Brain                                                         07:32 – Elicitation: A Scientific Definition                                                               09:36 – Weaponizing Elicitation                                                  11:17 – It's Easier Than You Think                                                             13:40 – The Perils of Poker Face                                                16:41 – Being on the Defensive                                                 19:17 – Me, You, and Us                                                               21:28 – The Verbal Approaches                                                 25:16 – Collaboration is Key!                                                       30:37 – An Effective Approach: Subliminal Priming                                                            32:00 – "They'll Become What They're Called"                                                    33:33 – This Applies to Life                                                           35:07 – Make it Conversational                                                  36:56 – The Scharff Technique                                                   40:48 – Forensic vs Clinical                                                           43:23 – Last Week on "24"                                                           45:01 – Tips for the Boss: Shame Doesn't Work                                                   49:41 – This is the Hardest Part                                                  51:46 – Wrap Up & Outro social-engineer.com innocentlivesfoundation.org   Find us online: Twitter: https://twitter.com/abbiejmarono LinkedIn: com/in/dr-abbie-maroño-phd-35ab2611a Twitter: https://twitter.com/humanhacker LinkedIn: com/in/christopherhadnagy   References: Kong, Y., & Schoenebeck, G. (2019). An information theoretic framework for designing information elicitation mechanisms that reward truth-telling. ACM Transactions on Economics and Computation (TEAC), 7(1), 1-33.   Lakin, J. L., Jefferis, V. E., Cheng, C. M., & Chartrand, T. L. (2003). The chameleon effect as social glue: Evidence for the evolutionary significance of nonconscious mimicry. Journal of nonverbal behavior, 27(3), 145-162.   Tschacher, W., Rees, G. M., & Ramseyer, F. (2014). Nonverbal synchrony and affect in dyadic interactions. Frontiers in psychology, 5, 1323.   Brandon, S. E., Wells, S., & Seale, C. (2018). Science‐based interviewing: Information elicitation. Journal of Investigative Psychology and Offender Profiling, 15(2), 133-148.   Kong, Y., Schoenebeck, G., Tao, B., & Yu, F. Y. (2020, April). Information elicitation mechanisms for statistical estimation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 02, pp. 2095-2102).   Shaw, D. J., Vrij, A., Leal, S., Mann, S., Hillman, J., Granhag, P. A., & Fisher, R. P. (2015). Mimicry and investigative interviewing: Using deliberate mimicry to elicit information and cues to deceit. Journal of Investigative Psychology and Offender Profiling, 12(3), 217-230.   Baddeley, M. C., Curtis, A., & Wood, R. (2004). An introduction to prior information derived from probabilistic judgements: elicitation of knowledge, cognitive bias and herding. Geological Society, London, Special Publications, 239(1), 15-27.   Deeb, H., Vrij, A., Leal, S., & Burkhardt, J. (2021). The effects of sketching while narrating on information elicitation and deception detection in multiple interviews. Acta Psychologica, 213, 103236.   Boone, R. T., & Buck, R. (2003). Emotional expressivity and trustworthiness: The role of nonverbal behavior in the evolution of cooperation. Journal of Nonverbal Behavior, 27(3), 163-182.   Culpepper, P. D. (2018). Creating cooperation. In Creating Cooperation. Cornell University Press.   Brimbal, L., Dianiska, R. E., Swanner, J. K., & Meissner, C. A. (2019). Enhancing cooperation and disclosure by manipulating affiliation and developing rapport in investigative interviews. Psychology, Public Policy, and Law, 25(2), 107.   Granhag, P. A., Oleszkiewicz, S., Strömwall, L. A., & Kleinman, S. M. (2015). Eliciting intelligence with the Scharff technique: Interviewing more and less cooperative and capable sources. Psychology, Public Policy, and Law, 21(1), 100.   Vallano, J. P., & Schreiber Compo, N. (2015). Rapport-building with cooperative witnesses and criminal suspects: A theoretical and empirical review. Psychology, Public Policy, and Law, 21(1), 85.   Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A neural basis for social cooperation. Neuron, 35(2), 395-405.   Fehr, E., & Rockenbach, B. (2004). Human altruism: economic, neural, and evolutionary perspectives. Current opinion in neurobiology, 14(6), 784-790.   Krill, A. L., & Platek, S. M. (2012). Working together may be better: Activation of reward centers during a cooperative maze task. PloS one, 7(2), e30613.

Powerful, Practical, and Positive leadership lessons... From an Old Guy

Preparing and working with the client is so critical to a good direct exam. Success starts with our clients going in with a mindset that the jury consists of smart people who can smell good versus bad. Therefore, they just have to set their fears aside and trust the process. We also have to ask enough questions to get the information we need while making them feel at ease and comfortable. The challenge is to keep the direct exam conversational and simple.  In today's episode, we're joined by Jason Erlich, an employment attorney who exclusively represents workers who have been treated wrongfully. Jason handles cases related to workplace disputes, such as wrongful termination, wage and hour violations, sexual harassment, and class action lawsuits. In this episode, you will hear: The three things that make a successful direct client exam Eliciting an emotional response from a client Preparing direct exam questions Things to prioritize for the direct exam Making last-minute changes to your questions based on the opening statement and witness testimony Visual aids he used in the courtroom Free-flow questioning vs. sticking to a script Subscribe and Review Have you subscribed to our podcast? We'd love for you to subscribe if you haven't yet.  We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select “Ratings and Reviews” and “Write a Review” then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Supporting Resources: Website: https://erlich.lawyer/attorneys/jason-erlich/ If you have questions or a particularly challenging client preparation, email Elizabeth directly for assistance: elizabeth@larricklawfirm.com. Episode Credits If you like this podcast and are thinking of creating your own, consider talking to my producer, Emerald City Productions. They helped me grow and produce the podcast you are listening to right now. Find out more at https://emeraldcitypro.com Let them know I sent you.

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: ARC paper: Formalizing the presumption of independence, published by Erik Jenner on November 20, 2022 on The AI Alignment Forum. (I did not have anything to do with this paper and these are just my own takes.) The Alignment Research Center recently published their second report, Formalizing the presumption of independence. While it's not explicitly about AI alignment, it's probably still interesting for some people here. Summary The paper is about "heuristic arguments". These are similar to proofs, except that their conclusions are not guaranteed to be correct and can be overturned by counterarguments. Mathematicians often use these kinds of arguments, but in contrast to proofs, they haven't been formalized. The paper mainly describes the open problem of finding a good formalization of heuristic arguments. They do describe one attempt, "cumulant propagation", in Appendix D, but point out it can behave pathologically. So what's the "presumption of independence" from the title? Lots of heuristic arguments work by assuming that some quantities are independent to simplify things, and that's what the paper focuses on. Such an argument can be overturned by showing that there's actually some correlation we initially ignored, which should then lead to a more sophisticated heuristic argument with a potentially different conclusion. What does this have to do with alignment? The paper only very briefly mentions alignment (in Appendix F), more detailed discussion is planned for the future. But roughly: Avoiding catastrophic failures. Heuristic arguments can let us better estimate the probability of rare failures, or failures which occur only on novel distributions where we cannot easily draw samples. This can be used during validation to estimate risk, or potentially during training to further reduce risk. Eliciting latent knowledge. Heuristic arguments may let us see “why” a model makes its predictions. We could potentially use them to distinguish cases where similar behaviors are produced by very different mechanisms—for example distinguishing cases where a model predicts that a smiling human face will show up on camera because it predicts there will actually be a smiling human in the room, from cases where it makes the same prediction because it predicts that the camera will be tampered with. [...] Neither of these applications is straightforward, and it should not be obvious that heuristic arguments would allow us to achieve either goal. [...] Heuristic arguments can be seen as somewhere between interpretability and formal verification: unlike interpretability, heuristic arguments are meant to be machine-checkable and don't have to be human-understandable. But unlike formal proofs, they don't require perfect certainty and might be much easier to find. Readers here might also be reminded of Logical Induction. This paper is trying to do something somewhat different though: [Approaches to logical uncertainty] have primarily focused on establishing coherence conditions and on capturing inductive reasoning, i.e. ensuring that a reasoner eventually successfully predicts φ(n) given observations of φ(1), φ(2), . . . φ(n − 1). These systems would not automatically recognize intuitively valid heuristic arguments [...], although they would eventually learn to trust these arguments after observing them producing good predictions in practice. Indeed, we can view ourselves as reasoners in exactly this situation, trying to understand and formalize a type of reasoning that appears to often make good predictions in practice. Formalizations of inductive reasoning may help clarify the standards we should use for evaluating a proposed heuristic estimator, but do not constitute a good heuristic estimator themselves. So should you read the paper? Given it's a 60-page report (though most of that's...

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: An experiment eliciting relative estimates for Open Philanthropy's 2018 AI safety grants, published by NunoSempere on September 12, 2022 on The Effective Altruism Forum. Summary I present the design and results of an experiment eliciting relative values from six different researchers for the nine large AI safety grants Open Philanthropy made in 2018. The specific elicitation procedures I used might be usable for rapid evaluation setups, for going from zero to some evaluation, or for identifying disagreements. For weighty decisions, I would recommend more time-intensive approaches, like explicitly modelling the pathways to impact. Background and motivation This experiment follows up on past work around relative values (1, 2, 3) and more generally on work to better estimate values. The aim of this research direction is to explore a possibly scalable way of producing estimates and evaluations. If successful, this would bring utilitarianism and/or longtermism closer to producing practical guidance around more topics, which has been a recurring thread in my work in the last few years. Methodology My methodology was as follows: I selected a group of participants whose judgment I consider to be good. I selected a number of grants which I thought would be suitable for testing purposes. Participants familiarized themselves with the grants and with what exactly they ought to be estimating. Participants made their own initial estimates using two different methods: Method 1: Using a utility function extractor app. Method 2: Making a “hierarchical tree” of estimates. For each participant, I aggregated and/or showed their two estimates side by side, and asked them to make a best guess estimate. I took their best guess estimates, and held a discussion going through each grant, making participants discuss their viewpoints when they had some disagreements. After holding the discussion, I asked participants to make new estimates. Overall, the participants took about two to three hours each to complete this process, roughly divided as follows: 10 to 30 mins to familiarize themselves with the estimation target and to re-familiarize themselves with the grants 20 to 40 mins to do the two initial estimates 5 to 30 mins to give their first best guess estimate after seeing the result of the two different methods 1h to hold a discussion 5 to 30 mins to give their resulting best guess estimate The rest of this section goes through these steps individually. Selection of participants I selected participants by asking friends or colleagues whose judgment I trust, and who had some expertise or knowledge of AI safety. In particular, I selected participants who would be somewhat familiar with Open Philanthropy grants, because otherwise the time required for research would have been too onerous. The participants were Gavin Leech, Misha Yagudin, Ozzie Gooen, Jaime Sevilla, Daniel Filan and another participant who prefers to remain anonymous. Note that one participant didn't participate in all the rounds, which is why some summaries contain only five datapoints. Selection of grants The grants I selected were: AI Impacts — General Support (2018): $100,000 Machine Intelligence Research Institute — AI Safety Retraining Program: $150,000 Open Phil AI Fellowship — 2018 Class: $1,135,000 Ought — General Support (2018): $525,000 Oxford University — Research on the Global Politics of AI: $429,770 Stanford University — Machine Learning Security Research Led by Dan Boneh and Florian Tramer: $100,000 UC Berkeley — AI Safety Research (2018): $1,145,000 Wilson Center — AI Policy Seminar Series: $400,000 These are all the grants that Open Philanthropy made to reduce AI risk in 2018 above a threshold of $10k, according to their database. The year these grants were made is long enough ago that we have some information...

At first glance, fiscal planning can seem complex and daunting, but Scott Miller, founder of "Moving Forward Today Financial Coaching" shows us that it doesn't need to scare us at all! He even lays out a simple to follow plan to bring you out of debt and even become a millionaire. Scott did a great job of making what may seem complicated at first glance, how to save, incest, and get out of debt, easy to understand. Eliciting hope, and even excitement for the process! Scott isn't just all talk though, he suffered the mounting stress and embarrassment of being $90,000 in debt! However, through brain power, work ethic, and the very process he tells us about today, he and his wife, Bonnie Miller, made it out of debt and into financial stability and comfort. Today, Scott has a fiery passion for helping those in financial straight establish healthy spending habits, beneficial budgeting skills, and even a better psychological relationship with their finances.  In this episode, we discussed Scott's story of falling into and coming out of serious debt, the various habits that Americans like you can implement in order to avoid debt and/or come out of it, as well as how to invest easily to becoming a millionaire, what 401(K)'s and Roth IRA's are, and how young people should invest! All this and much much more.You can find Scott online at his website:www.movingforwardtoday.netMore about Scott and his company (From their website)I feel my calling is to "help others move forward".  That means guiding people from right where they are in all walks of life, using proven methods, toward living a more stress-free, financially secure life. After having been in really deep debt under difficult circumstances my wife and I pushed our way out of that mess.  We experienced the struggles, doubts, fears, excitement, and thrill of eliminating all the debt and landing into a much healthier financial position.  I've worked in the insurance, mortgage, and investment fields and have a number of trusted professionals with whom I can refer and counsel in regard to helping clients find answers to their financial issues.  I have been married to my college sweetheart, Bonnie, for 42 years. We have 2 married daughters, 2 grandsons, and 2 granddaughters.  I have taught at Financial Peace University in the area for a few years now and serve as a small group leader and small group coach in my local church. I am a graduate of Illinois State University and of the Financial Coach Master Training program. Bonnie and I have lived in the Chicago, IL area for several years but currently reside as homeowners in the Bloomington, IL, area.I hope you enjoy this episode. And as always, thank you for watching and listening to Central Illinois' number one podcast and online show - The Paul Garcia Show. Until next Sunday, God bless and have a great week!This week's episode is made possible by these beloved sponsors!Dave's Supermarket(815) 692-2822davesteam@davessupermarket.comhttp://www.davessupermarket.com/Fehr Cab Interiors(815) 692-3355sales@fehrcab.comhttps://www.fehrcab.comFairbury Furniture(815) 692-3000fairburyfurniture@gmail.comhttp://www.fairburyfurniture.com/      FOLLOW THE PAUL GARCIA SHOW:INSTAGRAM | https://instagram.com/thepaulgarciashowTWITTER | https://twitter.com/paulgarciashowTIKTOK | https://tiktok.com/@thepaulgarciashowFACEBOOK | https://facebook.com/thepaulgarciashowSPOTIFY | https://open.spotify.com/show/0DfWzOcJwgne3ffkD5VDqT?si=312b756e61604bc5APPLE PODCASTS | https://podcasts.apple.com/us/podcast/the-paul-garcia-show/id1534365630SEE EVERYTHING + MERCH HERE: https://www.thepaulgarciashow.comCOMMERCIALS & ADVERTISING: https://www.thepaulgarciashow.com/advertisingSupport the show

NUGGET CONTEXT Thomas speaks about what we could do in the way we frame the questions so that we can elicit candid responses from the other side. GUEST Thomas Wedell-Wedellsborg is the author of What's Your Problem? (Harvard Business Press, 2020), a book on how to solve the right problems. His research has been featured in Harvard Business Review, The Economist, The Sunday Times, The Telegraph, BBC Radio, Bloomberg Businessweek and the Financial Times. His work on innovation led HR Magazine to recognize him as a “Top 20 International Thinker”. I believe that we are taught how to solve problems in our growing up years but one of the distinctive elements of leadership is about knowing which problems to solve and which ones not to solve. How we frame problems and which ones we go after is arguably one of the meta-skills of the times we are in.  Published in July 2020 HOST Deepak is a Leadership Advisor and an Executive Coach. He works with leaders to improve their effectiveness and in helping them make better decisions specifically around organizational and career transitions. He currently runs Transition Insight (www.transitioninsight.com) and works with leaders to handle phases of transition thoughtfully. He has worked as an Operations Consultant with KPMG in UK, Strategy Consultant with McKinsey in the US and as a Leadership Consultant with EgonZehnder (a Swiss Leadership Advisory firm) where he helped companies recruit CEOs, CXOs and Board Members and worked on Leadership Development. Deepak is a certified CEO Coach and is an alumnus of IIT Madras, IIM Ahmedabad and London Business School. His detailed profile can be found at https://in.linkedin.com/in/djayaraman OTHER GUESTS 1.Vijay Amritraj 2.Amish Tripathi 3.Raghu Raman 4.Papa CJ 5.Kartik Hosanagar 6.Ravi Venkatesan 7.Abhijit Bhaduri 8.Viren Rasquinha 9.Prakash Iyer 10.Avnish Bajaj 11.Nandan Nilekani 12.Atul Kasbekar 13.Karthik Reddy 14.Pramath Sinha 15.Vedika Bhandarkar 16.Vinita Bali 17.Zia Mody 18.Rama Bijapurkar 19.Dheeraj Pandey 20.Anu Madgavkar 21.Vishy Anand 22. Meher Pudumjee 23.KV Shridhar (Pops) 24.Suresh Naraynan 25.Devdutt Pattanaik 26.Jay Panda 27.Amit Chandra 28.Chandramouli Venkatesan 29.Roopa Kudva 30.Vinay Sitapati 31.Neera Nundy. 32.Deepa Malik 33.Bombay Jayashri. 34.Arun Maira 35.Ambi Parameswaran 36.OP Bhaat 37.Indranil Chakraborty 38.Tarun Khanna 39. Ramachandra Guha 40. Stewart Friedman 41. Rich Fernandez 42. Falguni Nayar 43. Rajat Gupta 44. Kartik Hosanagar 45. Michael Watkins 46. Matt Dixon 47. Herminia Ibarra 48. Paddy Upton 49. Tasha Eurich 50. Alan Eagle 51. Sudhir Sitapati 52. James Clear 53. Lynda Gratton 54. Jennifer Petriglieri. 55. Matthew Walker 56. Raj Raghunathan 57. Jennifer Garvey Berger 58. BJ Fogg 59. R Gopolakrishnan 60. Sir Andrew Likierman. 61. Atul Khatri 62. Whitney Jonson 63. Venkat Krishnan 64. Marshall Goldsmith 65. Ashish Dhawan 66. Vinay Sitapati 67. Ashley Whillans 68. Tenzin Priyadarshi 69. Ramesh Srinivasan 70. Bruce Feiler 71. Sanjeev Aggarwal and T. N. Hari 72. Bill Carr 73. Jennifer Wetzler 74. Sally Helgesen 75. Dan Cable 76. Tom Vanderbilt 77. Darleen DeRosa 78. Amy Edmondson 79. Katy Milkman 80. Harish Bhatt 81. Lloyd Reeb 82. Sukhinder Cassidy 83. Harsh Mariwala 84. Rajiv Vij 85. Dorie Clark 86. Ayse Birsel 87. Ravi Venkatesan E2 88. Pradeep Chakravarthy 89. Dan Pink 90. Alisa Cohn 91. Ayelet Fishbach DISCLAIMER All content and opinions expressed in the podcast are that of the guests and are not necessarily the opinions of Deepak Jayaraman and Transition Insight Private Limited. Views expressed in comments to blog are the personal opinions of the author of the comment. They do not necessarily reflect the views of The Company or the author of the blog. Participants are responsible for the content of their comments and all comments that are posted are in the public domain. The Company reserves the right to monitor, edit, and/or publish any submitted comments. Not all comments may be published. Any third-party comments published are third party information and The Company takes no responsibility and disclaims all liability. The Company reserves the right, but is not obligated to monitor and delete any comments or postings at any time without notice.

Your enemies want you to lose the support you still got, don't fall into a "Response Trap" // www.Cointelpro2zero.com

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: Eliciting Latent Knowledge (ELK) - Distillation/Summary, published by Marius Hobbhahn on June 8, 2022 on The AI Alignment Forum. This post was inspired by the AI safety distillation contest. It turned out to be more of a summary than a distillation for two reasons. Firstly, I think that the main idea behind ELK is simple and can be explained in less than 2 minutes (see next section). Therefore, the main value comes from understanding the specific approaches and how they interact with each other. Secondly, I think some people shy away from reading a 50-page report but I expect they could get most of the understanding from reading/skimming this summary (I'm aware that the summary is longer than anticipated but it's still a >5x reduction of the original content). I summarized the ELK report and the results of the ELK competition. I personally think ELK is very promising and therefore want to make extra sure that the summary is readable and accurate. In case anything is unclear or wrong, please let me know. Context and Introduction Eliciting Latent Knowledge was written by Paul Christiano, Ajeya Cotra and Mark Xu. Before getting to the main summary, I want to provide some background and context. Additional to the summary of the main report, I provide a summary of the results from the ELK prize contest in the end. My aim is to give a brief summary of “everything related to ELK” with links to the long versions. Paul Christiano has been working on various projects in AI safety before founding the Alignment Research Center (ARC). He lead the LLM alignment team at OpenAI and is known for, among other contributions, AI safety via debate and Iterated Distillation and Amplification (IDA). From my very limited outside perspective and some of his podcast appearances (e.g. at 80K or the AI x-risks podcast), Paul Christiano seems to come back to similar problems with many current alignment techniques. Whether you choose inverse RL, AI safety via debate or IDA, there is always the risk that sufficiently powerful models create problems that can only be solved by understanding what the model “truly thinks”--by eliciting latent knowledge (ELK). Of course, other people want to understand what's going on in Neural Networks (NNs) as well. Chris Olah, for example, has successfully worked on technical NN interpretability for many years now (see e.g. circuits) and there are many other approaches to NN transparency in academia. To my understanding, there are two main differences between ELK and these other approaches. Firstly, ELK operates on a different scale. NN interpretability often looks at low-level neurons or circuits (which gets messy when your model has Billions of parameters) while ELK aims to generate natural language answers or descriptions that reflect the model's true beliefs as if it “had drunk a truth serum”. In the optimal case, we don't need to understand the model in detail, the model just pro-actively tells us what it believes on its own. Secondly, ARC approaches ELK more theoretically than is common in technical alignment research. Conventionally, technical alignment researchers come up with a theory, then test it in practice, find the flaws and improve it. ARC, on the other hand, skips the practical implication for now and tries to find theoretical counterexamples for their current proposals. Once they found a counterexample, they try to come up with a new proposal that fixes the problem and then iterate. In the best case, they arrive at a proposal for which they can't find any major theoretical flaws and then implement that. Summary of the ELK report I will try to stay close to the structure and naming patterns that the ELK report uses. In my summary, I will necessarily remove some nuance and detail. However, I still want it to be always true. Therefore, if you think som...

My guest today is Adele Fedorak.I met Adele through Miranda Holder, a guest on episode 38, who I met through Brian Levenson, my guest on episode 27.  I love how the network and connections grow as we share our stories!Adele is a storyteller and works with her clients to help them increase their story intelligence.  If you aren't sure what story intelligence is, keep listening – Adele explains it all.  And if you are convinced you aren't a story teller, listen up for the importance of story listening and story eliciting!  Adele has a masters in speech language pathology and comes at story telling from an integration of narrative coaching, language development, and leadership.  I learned so much and had numerous insights during our conversation – and I'm guessing you will as well!Adele is also a certified narrative coach and a certified co-active professional coach.  Adele, thank you for sharing, for expanding my story intelligence, and for creating space for story telling, listening, eliciting and more! Connect with Adele herewww.makelifelessdifficult.com

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: Valuing research works by eliciting comparisons from EA researchers, published by NunoSempere on March 17, 2022 on The Effective Altruism Forum. tl;dr: 6 EA researchers each spent ~1-2 hours estimating the value (relative counterfactual values) of 15 very different research documents. The results varied highly between researchers and within similar comparisons differently posed to the same researchers. This variance suggests that EAs might have relatively undeveloped assessments of the value of different projects. Executive Summary Six EA researchers I hold in high regard—Fin Moorhouse, Gavin Leech, Jaime Sevilla, Linch Zhang, Misha Yagudin, and Ozzie Gooen—each spent 1-2 hours rating the value of different pieces of research. They did this rating using a utility function extractor, an app that presents the user with pairwise comparisons and aggregates these comparisons to produce a utility function. This method revealed a wide gap between different researchers' conceptions of research value. Sometimes, their disagreement ranged over several orders of magnitude. Results were also inconsistent at the individual level: a test subject might find A to be x times as valuable as B, and B to be y times as valuable as C, but A to be something very different from xy times as valuable as C. It seems clear that individual estimates, even those of respected researchers, are likely very noisy and often inaccurate. Future research will further investigate ways to better elicit information from these people and recommend best guesses for the all-things-considered answers. It is also likely that researchers spending more time would have produced better estimates, and we could also experiment with this in the future. My guess is that EA funders also have inconsistent preferences and similarly wide-ranging disagreements. That is one of the reasons I am excited about augmenting or partially automating them. Current aggregate estimates look as follows: Motivation EAs make important decisions based on how valuable different projects seem. For example, EAs can distribute funding based on expectations of future value. In fact, I estimate that the group I studied will cumulatively grant several millions of dollars, both in terms of advising various funds and because they are influential in the longtermist funding space. Estimating the value of past projects seems easier than estimating the value of future projects, but even that is relatively tricky. We at the Quantified Uncertainty Research Institute are interested in helping to encourage more estimation of previous and future projects, and we are trying to find the best ways of doing so. The most straightforward experiment we could do was survey a few researchers on their relative estimates of the value of different projects. We did this with six researchers. My original plan was to create a unit of research value based on the aggregate estimates of a group of researchers. Initially, I expected the estimates to be consistent and that the aggregate could be a good best-guess at a “ground truth”. We could then build evaluations and shared assessments on top of them. For instance, forecasting systems could estimate how valuable this trusted group would find a new project and fund it according to their estimate. However, ratings turned out to be very inconsistent, which made me more sceptical that the individual or aggregate opinion could be a good best guess. Instead, I would now prefer to improve elicitation and aggregation methods before building a forecasting system on top. Further, core decision-makers might be similarly inconsistent and might be making mistakes accordingly. In that case, further work in this area might also be considered promising. Methodology I asked six researchers to use the application described in Simple comparison...

If you've ever wanted to attract and create a better life or a better world, then do we have the Shaman's Mind show for you.   Today I'll be talking with Jonathan Hammond, teacher, energy healer, shamanic practitioner, spiritual counselor, Harvard graduate, and the author of my all-time favorite book on Hawaiian Shamanism, “Shaman's Mind”   That's just what I want to talk with him about today, about Huna wisdom and Hawaiian magic to attract and change your life.   Key Points Discussed: What happened when you visited the Hawaiian volcano of Haleakalā? How being counterintuitive puts us on the soul's path to living a spiritual life Putting an end to the virus of the mind that causes us to self-cannibalize in our own greed and selfishness How the hidden gifts of the soul are brought out by the difficulties we face What the turtle means for Jonathan and the rest of the world What is a hunatic and Hawaiian magic? The time to share the wisdom traditions we learn with the world Our co-creative relationship with reality: Experiencing the world based on how we think about it and how reality shifts based on how we think about it The magical power of choice and what it has to do with anything How what we think and act upon creates a butterfly effect elsewhere The importance of naming what we want and the visioning exercise that can help us do that Eliciting the creative energies of the universe that can help us achieve what we want Taking a hint when a door closes by knowing that things will work out for our good Loving ourselves as we love God How caring what everyone else thinks takes us off the spiritual path Looking at what our difficulties symbolize instead of blaming ourselves The intelligence of spirit and how it holds on to information for us until when we will need to know it How effectiveness is a measure of truth Administering love to ourselves and our problems so we can heal ourselves and lift the problems   To find out more: The Shaman's Mind: Huna Wisdom to Change Your Life: https://amzn.to/2BeAkfR   http://www.jonathanhammond.com/ Join Michael's School of Mystics at https://www.inspirenationuniversity.com/ https://amzn.to/3qULECz - Order Michael Sandler's new book, "AWE, the Automatic Writing Experience"  Get the entire video-based AWE program with live classes! www.automaticwriting.com   …….  Follow Michael and Jessica's exciting journey and get even more great tools, tips, and behind-the-scenes access. Go to https://www.patreon.com/inspirenation    For free meditations, weekly tips, stories, and similar shows visit: https://inspirenationshow.com/    We've got NEW Merch! - https://teespring.com/stores/inspire-nation-store    Follow Inspire Nation, and the lives of Michael and Jessica, on Instagram - https://www.instagram.com/InspireNationLive/    Find us on TikTok - https://www.tiktok.com/@inspirenationshow  

Coming up with a game concept is hard, but creating that game from scratch and seeing it through all the way to global launch is incredibly difficult. Annelie Biernat (Game Director at Wooga)has done exactly that. She and her team have taken their game "Switchcraft" all the way to the global launch finish line, and she sits down with Tom to share the wealth of knowledge she has gained from doing it.Here are your cheat codes:- 0:20 How long have they been working on “Switchcraft”- 1:41 How did Annelie Biernat get into the video game industry? - 4:50 Coming up with a game concept and making it to global launch. - 9:40 Designing a game for a specific audience. - 13:39 The soft launch process - 19:51 How to properly identify and address the problems with your game. - 28:07 Eliciting emotions from your players. - 30:06 Team size for building “Switchcraft” - 33:43 What is “Switchcraft”? - 45:55 Why should you launch a Match 3 game? - 47:52 Biggest challenges they faced making “Switchcraft”- 52:50 Player Feedback - 59:58 The importance of diversity in your game. - 1:05:01 How to keep more playing around longer

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: Eliciting Latent Knowledge Via Hypothetical Sensors, published by John Maxwell on December 30, 2021 on The AI Alignment Forum. This is a response to ARC's first technical report: Eliciting Latent Knowledge. But it should be fairly understandable even if you didn't read ARC's report, since I summarize relevant parts of their report as necessary. Here I propose some approaches to the problem ARC outlines which are very different from the approaches they explore. Idea #1: Detecting failure using a hold-out sensor The core challenge in ARC's report is to obtain accurate knowledge about the actual presence / absence of a diamond in a protective "SmartVault", as opposed to accurate knowledge about whether a human observer would think the diamond is present if the human observer had access to data from a sensor I'll call S1. This is difficult because it's hard to get labeled data which differentiates between the two. If the AI protecting the diamond in the vault is optimizing for whether a human observer would think the diamond is present, it will find that corrupting readings from S1 while allowing a thief to take the diamond is a perfectly legitimate strategy. When the AI simulates this diamond-protection strategy, it will receive the maximum score, since score is calculated based on what a human with access to S1 readings would think. Suppose we install an additional sensor S2 in the vault, on a closed-circuit system that isn't present in the AI's model of the vault. It seems reasonable to guess that the AI won't be able to boost the simulated score of its diamond-protection strategy by corrupting readings from S2 in addition to S1. So if ARC has some method they think will get the AI to actually guard the diamond, secretly observing S2 gives them a chance to check if the method is working. This strategy could fail. For example, if (a) S2 ends up being present in the AI's model of the vault despite our best intentions, and (b) the AI's learned scoring function happens to be based on what a human with access to all known sensors would think. (This latter possibility is a bigger risk if humans have been peeking at S2 periodically during the course of iteratively developing the AI. The analogy to train/dev/test splits should be pretty obvious here.) So it's not a perfect detection method. Idea #2: Predicting the readings of hypothetical sensors An implicit assumption of the Eliciting Latent Knowledge report is that the SmartVault AI can make predictions about readings from sensor S1, given the actions it takes to manage the vault and other data as predictor variables. I'm imagining the AI has been trained to be able to do this using supervised learning. We have some features X consisting of actions taken and other data, and a target Y consisting of S1 readings. The AI searches for a model which maps X to Y using e.g. gradient descent. We can modify the training procedure to predict the readings from any hypothetical sensor as follows. In addition to the original features X, we add six additional features corresponding to the latitude, longitude, height, yaw, pitch, and roll of the sensor, resulting in an augmented feature matrix X'. To generate training data, we repeatedly: Use a random number generator to generate random values for those six additional features Place the sensor according to those random values Use whatever data generation method we were using in the original SmartVault scenario to populate Y with readings from the randomly placed sensor If the AI correctly learns a mapping from the augmented feature matrix X' to Y, we've now got a model that can make predictions about the readings we would get from any hypothetically placed sensor. This idea mainly serves as a building block for later ideas, but the basic intuition is that sensor corruption is tougher if t...

Presents need wrapped ( in my case some haven't arrived yet!) - Relatives are coming home ( well, at least you hope they are ) - Turkeys need stuffed - AND Covid is still here. Nerves are understandably frayed.   No wonder we are at the end of our tether and taking our anxieties and stresses out on those closest to us.   In this episode I discuss how to avoid ( or if not - then minimise ) the tensions that are likely to be running riot in the home right now.   I offer some pointers that will help 1. Remember WHO you are 2. Stay away from the flash points / people 3. Remember “It takes two to tango” 4. Take much needed time-out 5. Set realistic boundaries 6. Eliciting support can help EVERYONE 7. If all else fails - BREATHE 8. Practice the “Fly on the wall” exercise Want to know more? Then Grab a cuppa - and keep listening! ~~~ Another Episode that might help : EP 41 : 12 Toxic People to watch out for :