Podcasts about bayesian

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Best podcasts about bayesian

Latest podcast episodes about bayesian

Muscle For Life with Mike Matthews
Menno Henselmans on Stretch-Mediated Hypertrophy

Muscle For Life with Mike Matthews

Play Episode Listen Later Nov 16, 2022 61:10


Does training at different muscle lengths affect how quickly the muscles grow? In this interview, Menno Henselmans and I discuss new research on stretch-mediated hypertrophy and the role muscle lengths play in combination with mechanical tension. This is something Mike Israetel and I briefly touched on in our recent interview on partial reps versus full-ROM training, but in this discussion, Menno and I talk about the latest science of resistance training at long muscle lengths, including a new meta-analysis that isn't published yet. Menno has been on my podcast many times on my podcast, but in case you're not familiar with him, he's a former business consultant turned international public speaker, educator, writer, published scientist, and physique coach who's passionate about helping serious athletes attain their ideal physiques. In this interview, Menno and I talk about . . . What stretch-mediated hypertrophy is, possible mechanisms behind it, and whether you should modify your training to incorporate more of it Active tension versus passive tension The actual reason why full-ROM training is effective Specific guidance on how to modify and tweak exercises for more loaded stretching (including Bayesian curls, flyes, leg extension tips, and “skull-overs”) Static stretching between sets (its effects and whether you should do it) And more . . . So if you want to learn what the science says about training at longer muscle lengths, and how to incorporate more stretch-mediated hypertrophy in your program, definitely check out this interview! --- Timestamps 0:00 - My award-winning fitness books for men and women: https://legionathletics.com/products/books/ 4:22 - What is stretch-mediated hypertrophy? 6:28 - What is passive tension and active tension? 11:00 - Can muscles get longer, not just bigger? 15:34 - What are your thoughts on modifying full range of motion training? 28:06 - Are there modifications to exercises that can make them more efficient? 42:21 - What are your thoughts on different height positions for flyes? 45:03 - Can you explain skull overs? 48:17 - Are there any other modifications you want to cover? 50:15 - Does the position of the wrists affect pec activation? 58:22 - Where can we find you? -- Mentioned on the Show: My award-winning fitness books for men and women: https://legionathletics.com/products/books/ Menno's Youtube channel: https://www.youtube.com/channel/UCmO2dykYM3nlb5BtsXxp9ZQ Menno's Patreon: https://www.patreon.com/mennohenselmans Menno's Instagram: https://www.instagram.com/menno.henselmans/

Analytics Exchange: Podcasts from SAS
Health Pulse Podcast S3E12: From radical to routine: The use of Bayesian statistics in clinical trials

Analytics Exchange: Podcasts from SAS

Play Episode Listen Later Nov 15, 2022 14:25


More than a decade ago, Bruno Boulanger made a big bet on applying Bayesian statistics in clinical trials. At the time, very few in the industry thought the method, which applies probabilities to statistical problems, had a place in clinical development. Boulanger saw an opportunity, founding a company that quickly grew and was acquired by CRO PharmaLex in 2018, where he now serves as global head of statistics and data science.In this episode, Boulanger explains how Bayesian statistics uses probability and prediction to solve challenges in the increasingly complex world of clinical research and clinical trial design. Bayesian statistics allows researchers to expand decision making for clinical trials beyond its participants, which is imperative for trials targeting rare diseases. Looking forward, Boulanger is optimistic about the expansion of therapeutic innovation combined with digitalization and data science to meet the unmet needs of patients. All presentations represent the opinions of the presenter and do not represent the position or the opinion of SAS.

Learning Bayesian Statistics
#71 Artificial Intelligence, Deepmind & Social Change, with Julien Cornebise

Learning Bayesian Statistics

Play Episode Listen Later Nov 14, 2022 65:08


Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!This episode will show you different sides of the tech world. The one where you research and apply algorithms, where you get super excited about image recognition and AI-generated art. And the one where you support social change actors — aka the “AI for Good” movement.My guest for this episode is, quite naturally, Julien Cornebise. Julien is an Honorary Associate Professor at UCL. He was an early researcher at DeepMind where he designed its early algorithms. He then worked as a Director of Research at ElementAI, where he built and led the London office and “AI for Good” unit.After his theoretical work on Bayesian methods, he had the privilege to work with the NHS to diagnose eye diseases; with Amnesty International to quantify abuse on Twitter and find destroyed villages in Darfur; with Forensic Architecture to identify teargas canisters used against civilians.Other than that, Julien is an avid reader, and loves dark humor and picking up his son from school at the 'hour of the daddies and the mommies”.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek and Paul Cox.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Julien's website: https://cornebise.com/julien/Julien on Twitter: https://twitter.com/JCornebiseJulien on LinkedIn:

Slate Star Codex Podcast
ACX Grants: Project Updates

Slate Star Codex Podcast

Play Episode Listen Later Nov 6, 2022 26:54


https://astralcodexten.substack.com/p/acx-grants-project-updates         Thanks to everyone who got ACX Grants (see original grants here) and sent me a one-year update. Below are short summaries of the updates everyone sent. If for some reason you want one of the full updates, which are longer and more technical, let me know and I‘ll see if I have permission to send them to you. I've also included each grantee's assessment on a scale of 1-10 for how well they're doing, where 5/10 is “about as well as expected”. A few grantees are asking for extra help - I've included those requests in italics at the end of the relevant updates, and I've collected all of them together below. Updates   1: Discover Molecular Targets Of Antibiotics (8/10)Pedro Silva planned to use in silico screening to identify the biochemical targets of seven promising natural antibiotics, which could potentially help develop better versions of them. He says he's finished most of the simulations and determined the 5-20 most stable complexes for each antibiotic. Once he finishes this, he can start additional simulations on the best complexes to obtain better estimates of their stability and construct hypotheses on which of these is most involved in the antibiotic's efficacy. 2: Ballot Proposition For Approval Voting In Seattle (?/10)They have asked me not to discuss their progress until after the November election. 3: Software To Validate New FDA Drug Trial Designs (10/10)Michael Sklar and Confirm Solutions have gotten further funding from FTX and now have 2-3 people working full-time on the project. They are building new statistical techniques and software to help regulators quickly assess designs for clinical trials. Here is a recent conference poster on the methods. They have written proof-of-concept code and are writing a white paper to show regulators and pharma companies.  They also claim to have developed software that has "sped up their simulations for some standard Bayesian trial designs by a factor of about 1 million." They are looking for more employees and collaborators; if you're interested, contact research@confirmsol.org 4: Alice Evans' Research On “The Great Gender Divergence” (?/10)Dr. Evans has done over four months of research in Morocco, Italy, India, and Turkey. You can find some of her most recent thoughts at her blog here. Her book is still on track to be published from Princeton Press, more details tbd. 5: Develop Safer Immunosuppressants (7/10)Trevor Klee planned to continue his work to develop a safer slow-release form of cyclosporine. He realized this would be too expensive to do in humans in the current funding environment, and has pivoted to getting his medication approved for a feline autoimmune disease as both a proof-of-concept and as a cheaper, faster way to start making revenue. He recently raised $100,000 in crowdfunding (in addition to getting $200,000 from angel investors to run a feline trial, which will finish in January. He still anticipates eventually moving back to humans. Trevor wants to talk to bloggers or writers who might be interested in covering his work. 6: Promote Economically Literate Climate Policy In US States (4/10)Yoram Bauman and Climate 24x7 have written a policy paper about their ideas. They were able to get a bill in front of the Nebraska Legislature, but it died in committee. They have a promising measure in Utah, and an off chance of getting something rolling in Pennsylvania. Overall they report frustration, as many of the legislators they worked with have been voted out or term-limited. If you are a legislator or activist interested in helping with this project - especially in Utah, Pennsylvania, or South Dakota - please contact Yoram at yoram@standupeconomist.com. 7: Repository / Search Engine For Forecasting Questions (8/10)Nuno Sempere at metaforecast.org was able to hire a developer to “make the backend significantly better and add a bunch of functionality” - you can see a longer list of updates here. The developer has since left for other forecasting-related work and the project is moving more slowly. 8: Help [Anonymous] Interview For A Professorship (8/10)[Anonymous] was a grad student who wanted to interview for professorships at top schools where he might work on AI safety in an academic environment. The grant was to help make it financially easier for him to go on a long round of interviews [Anonymous] successfully got a job offer from a top school, and will be going there and researching AI safety.

The Nonlinear Library
LW - K-types vs T-types — what priors do you have? by strawberry calm

The Nonlinear Library

Play Episode Listen Later Nov 4, 2022 12:44


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: K-types vs T-types — what priors do you have?, published by strawberry calm on November 3, 2022 on LessWrong. Summary: There are two types of people, K-types and T-types. K-types want theories with low kolmogorov-complexity and T-types want theories with low time-complexity. This classification correlates with other classifications and with certain personality traits. Epistemic status: I'm somewhat confident that this classification is real and that it will help you understand why people believe the things they do. If there are major flaws in my understanding then hopefully someone will point that out. K-types vs T-types What makes a good theory? There's broad consensus that good theories should fit our observations. Unfortunately there's less consensus about to compare between the different theories that fit our observations — if we have two theories which both predict our observations to the exact same extent then how do we decide which to endorse? We can't shrug our shoulders and say "let's treat them all equally" because then we won't be able to predict anything at all about future observations. This is a consequence of the No Free Lunch Theorem: there are exactly as many theories which fit the seen observations and predict the future will look like X as there are which fit the seen observations and predict the future will look like not-X. So we can't predict anything unless we can say "these theories fitting the observations are better than these other theories which fit the observations". There are two types of people, which I'm calling "K-types" and "T-types", who differ in which theories they pick among those that fit the observations. K-types and T-types have different priors. K-types prefer theories which are short over theories which are long. They want theories you can describe in very few words. But they don't care how many inferential steps it takes to derive our observations within the theory. In contrast, T-types prefer theories which are quick over theories which are slow. They care how many inferential steps it takes to derive our observations within the theory, and are willing to accept longer theories if it rapidly speeds up derivation. Algorithmic characterisation In computer science terminology, we can think of a theory as a computer program which outputs predictions. K-types penalise the kolmogorov complexity of the program (also called the description complexity), whereas T-types penalise the time-complexity (also called the computational complexity). The T-types might still be doing perfect bayesian reasoning even if their prior credences depend on time-complexity. Bayesian reasoning is agnostic about the prior, so there's nothing defective about assigning a low prior to programs with high time-complexity. However, T-types will deviate from Solomonoff inductors, who use a prior which exponentially decays in kolmogorov-complexity. Proof-theoretic characterisation. When translating between proof theory and computer science, (computer program, computational steps, output) is mapped to (axioms, deductive steps, theorems) respectively. Kolmogorov-complexity maps to "total length of the axioms" and time-complexity maps to "number of deductive steps". K-types don't care how many steps there are in the proof, they only care about the number of axioms used in the proof. T-types do care how many steps there are in the proof, whether those steps are axioms or inferences. Occam's Razor characterisation. Both K-types and T-types can claim to be inheritors of Occam's Razor, in that both types prefer simple theories. But they interpret "simplicity" in two different ways. K-types consider the simplicity of the assumptions alone, whereas T-types consider the simplicity of the assumptions plus the derivation. This is the key idea. Both can accuse the other of ...

Learning Bayesian Statistics
#70 Teaching Bayes for Biology & Biological Engineering, with Justin Bois

Learning Bayesian Statistics

Play Episode Listen Later Oct 22, 2022 65:31


Proudly sponsored by https://www.pymc-labs.io/ (PyMC Labs), the Bayesian Consultancy. https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf (Book a call), or get in touch! Back in 2016, when I started dedicating my evenings and weekends to learning how to code and do serious stats, I was a bit lost… Where do I start? Which language do I pick? Why are all those languages just named with one single letter?? Then I found some stats classes by Justin Bois — and it was a tremendous help to get out of that wood (and yes, this was a pun). I really loved Justin's teaching because he was making the assumptions explicit, and also explained them — which was so much more satisfying to my nerdy brain, which always wonders why we're making this assumption and not that one. So of course, I'm thrilled to be hosting Justin on the show today! Justin is a Teaching Professor in the Division of Biology and Biological Engineering at Caltech, California, where he also did his PhD. Before that, he was a postdoc in biochemistry at UCLA, as well as the Max Planck Institute in Dresden, Germany. Most importantly for the football fans, he's a goalkeeper — actually, the day before recording, he saved two penalty kicks… and even scored a goal! A big fan of Los Angeles football club, Justin is a also a magic enthusiast — he is indeed a member of the Magic Castle… Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken and Or Duek. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Justin's website: http://bois.caltech.edu/index.html (http://bois.caltech.edu/index.html)  Justin on GitHub: https://github.com/justinbois/ (https://github.com/justinbois/) Justin's course on Data analysis with frequentist inference: https://bebi103a.github.io/ (https://bebi103a.github.io/) Justin's course on Bayesian inference: https://bebi103b.github.io/ (https://bebi103b.github.io/) LBS #6, A principled Bayesian workflow, with Michael Betancourt:  https://learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt/ (https://learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt/) Physical Biology of the Cell: https://www.routledge.com/Physical-Biology-of-the-Cell/Phillips-Kondev-Theriot-Garcia-Phillips-Kondev-Theriot-Garcia/p/book/9780815344506 (https://www.routledge.com/Physical-Biology-of-the-Cell/Phillips-Kondev-Theriot-Garcia-Phillips-Kondev-Theriot-Garcia/p/book/9780815344506) Knowledge Illusion – Why We Never Think Alone:...

The Nonlinear Library: LessWrong Daily
LW - Introduction to abstract entropy by Alex Altair

The Nonlinear Library: LessWrong Daily

Play Episode Listen Later Oct 20, 2022 29:01


Link to original articleWelcome 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: Introduction to abstract entropy, published by Alex Altair on October 20, 2022 on LessWrong. This post, and much of the following sequence, was greatly aided by feedback from the following people (among others): Lawrence Chan, Joanna Morningstar, John Wentworth, Samira Nedungadi, Aysja Johnson, Cody Wild, Jeremy Gillen, Ryan Kidd, Justis Mills and Jonathan Mustin. Illustrations by Anne Ore. Introduction & motivation In the course of researching optimization, I decided that I had to really understand what entropy is. But there are a lot of other reasons why the concept is worth studying: Information theory: Entropy tells you about the amount of information in something. It tells us how to design optimal communication protocols. It helps us understand strategies for (and limits on) file compression. Statistical mechanics: Entropy tells us how macroscopic physical systems act in practice. It gives us the heat equation. We can use it to improve engine efficiency. It tells us how hot things glow, which led to the discovery of quantum mechanics. Epistemics (an important application to me and many others on LessWrong): The concept of entropy yields the maximum entropy principle, which is extremely helpful for doing general Bayesian reasoning. Entropy tells us how "unlikely" something is and how much we would have to fight against nature to get that outcome (i.e. optimize). It is relevant to the fate of the universe. And it's also a fun puzzle to figure out! I didn't intend to write a post about entropy when I started trying to understand it. But I found the existing resources (textbooks, Wikipedia, science explainers) so poor that it actually seems important to have a better one as a prerequisite for understanding optimization! One failure mode I was running into was that other resources tended only to be concerned about the application of the concept in their particular sub-domain. Here, I try to take on the task of synthesizing the abstract concept of entropy, to show what's so deep and fundamental about it. In future posts, I'll talk about things like: How abstract entropy can be made meaningful on continuous spaces Exactly where the "second law of thermodynamics" comes from, and exactly when it holds (which turns out to be much broader than thermodynamics) How several domain-specific types of entropy relate to this abstract version Many people reading this will have some previous facts about entropy stored in their minds, and this can sometimes be disorienting when it's not yet clear how those facts are consistent with what I'm describing. You're welcome to skip ahead to the relevant parts and see if they're re-orienting; otherwise, if you can get through the whole explanation, I hope that it will eventually be addressed! But also, please keep in mind that I'm not an expert in any of the relevant sub-fields. I've gotten feedback on this post from people who know more math & physics than I do, but at the end of the day, I'm just a rationalist trying to understand the world. Abstract definition Entropy is so fundamental because it applies far beyond our own specific universe, the one where something close to the standard model of physics and general relativity are true. It applies in any system with different states. If the system has dynamical laws, that is, rules for moving between the different states, then some version of the second law of thermodynamics is also relevant. But for now we're sticking with statics; the concept of entropy can be coherently defined for sets of states even in the absence of any "laws of physics" that cause the system to evolve between states. The example I keep in my head for this is a Rubik's Cube, which I'll elaborate on in a bit. The entropy of a state is the number of bits you need to use to uniquely distinguish it. Some useful things t...

The Nonlinear Library
LW - Introduction to abstract entropy by Alex Altair

The Nonlinear Library

Play Episode Listen Later Oct 20, 2022 29:01


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: Introduction to abstract entropy, published by Alex Altair on October 20, 2022 on LessWrong. This post, and much of the following sequence, was greatly aided by feedback from the following people (among others): Lawrence Chan, Joanna Morningstar, John Wentworth, Samira Nedungadi, Aysja Johnson, Cody Wild, Jeremy Gillen, Ryan Kidd, Justis Mills and Jonathan Mustin. Illustrations by Anne Ore. Introduction & motivation In the course of researching optimization, I decided that I had to really understand what entropy is. But there are a lot of other reasons why the concept is worth studying: Information theory: Entropy tells you about the amount of information in something. It tells us how to design optimal communication protocols. It helps us understand strategies for (and limits on) file compression. Statistical mechanics: Entropy tells us how macroscopic physical systems act in practice. It gives us the heat equation. We can use it to improve engine efficiency. It tells us how hot things glow, which led to the discovery of quantum mechanics. Epistemics (an important application to me and many others on LessWrong): The concept of entropy yields the maximum entropy principle, which is extremely helpful for doing general Bayesian reasoning. Entropy tells us how "unlikely" something is and how much we would have to fight against nature to get that outcome (i.e. optimize). It is relevant to the fate of the universe. And it's also a fun puzzle to figure out! I didn't intend to write a post about entropy when I started trying to understand it. But I found the existing resources (textbooks, Wikipedia, science explainers) so poor that it actually seems important to have a better one as a prerequisite for understanding optimization! One failure mode I was running into was that other resources tended only to be concerned about the application of the concept in their particular sub-domain. Here, I try to take on the task of synthesizing the abstract concept of entropy, to show what's so deep and fundamental about it. In future posts, I'll talk about things like: How abstract entropy can be made meaningful on continuous spaces Exactly where the "second law of thermodynamics" comes from, and exactly when it holds (which turns out to be much broader than thermodynamics) How several domain-specific types of entropy relate to this abstract version Many people reading this will have some previous facts about entropy stored in their minds, and this can sometimes be disorienting when it's not yet clear how those facts are consistent with what I'm describing. You're welcome to skip ahead to the relevant parts and see if they're re-orienting; otherwise, if you can get through the whole explanation, I hope that it will eventually be addressed! But also, please keep in mind that I'm not an expert in any of the relevant sub-fields. I've gotten feedback on this post from people who know more math & physics than I do, but at the end of the day, I'm just a rationalist trying to understand the world. Abstract definition Entropy is so fundamental because it applies far beyond our own specific universe, the one where something close to the standard model of physics and general relativity are true. It applies in any system with different states. If the system has dynamical laws, that is, rules for moving between the different states, then some version of the second law of thermodynamics is also relevant. But for now we're sticking with statics; the concept of entropy can be coherently defined for sets of states even in the absence of any "laws of physics" that cause the system to evolve between states. The example I keep in my head for this is a Rubik's Cube, which I'll elaborate on in a bit. The entropy of a state is the number of bits you need to use to uniquely distinguish it. Some useful things t...

The Nonlinear Library
EA - Announcing Squigglepy, a Python package for Squiggle by Peter Wildeford

The Nonlinear Library

Play Episode Listen Later Oct 19, 2022 1:37


Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Announcing Squigglepy, a Python package for Squiggle, published by Peter Wildeford on October 19, 2022 on The Effective Altruism Forum. Squiggle is a "simple programming language for intuitive probabilistic estimation". It serves as its own standalone programming language with its own syntax, but it is implemented in JavaScript. I like the features of Squiggle and intend to use it frequently, but I also frequently want to use similar functionalities in Python, especially alongside other Python statistical programming packages like Numpy, Pandas, and Matplotlib. The squigglepy package here implements many Squiggle-like functionalities in Python. The package also has useful utility functions for Bayesian networks (using rejection sampling), pooling forecasts (via weighted geometric mean of odds and others), laplace (including the time-invariant version), and kelly betting. The package and documentation are available on GitHub. The package can be downloaded from Pypi using pip install squigglepy. This package is unofficial and supported by myself and Rethink Priorities. It is not affiliated with or associated with the Quantified Uncertainty Research Institute, which maintains the Squiggle language (in JavaScript). This package is also new and not yet in a stable production version, so you may encounter bugs and other errors. Please report those so they can be fixed. It's also possible that future versions of the package may introduce breaking changes. This package is available under an MIT license. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Astro arXiv | all categories
Planetary system around LTT 1445A unveiled by ESPRESSO: Multiple planets in a triple M-dwarf system

Astro arXiv | all categories

Play Episode Listen Later Oct 18, 2022 1:04


Planetary system around LTT 1445A unveiled by ESPRESSO: Multiple planets in a triple M-dwarf system by B. Lavie et al. on Tuesday 18 October We present radial velocity follow-up obtained with ESPRESSO of the M-type star LTT 1445A (TOI-455), for which a transiting planet b with an orbital period of~5.4 days was detected by TESS. We report the discovery of a second transiting planet (LTT 1445A c) and a third non-transiting candidate planet (LTT 1445A d) with orbital periods of 3.12 and 24.30 days, respectively. The host star is the main component of a triple M-dwarf system at a distance of 6.9 pc. We used 84 ESPRESSO high-resolution spectra to determine accurate masses of 2.3$pm$0.3 $mathrm{M}_oplus$ and 1.0$pm$0.2 $mathrm{M}_oplus$ for planets b and c and a minimum mass of 2.7$pm$0.7 $mathrm{M}_oplus$ for planet d. Based on its radius of 1.43$pm0.09$ $mathrm{R}_oplus$ as derived from the TESS observations, LTT 1445A b has a lower density than the Earth and may therefore hold a sizeable atmosphere, which makes it a prime target for the James Webb Space Telescope. We used a Bayesian inference approach with the nested sampling algorithm and a set of models to test the robustness of the retrieved physical values of the system. There is a probability of 85$%$ that the transit of planet c is grazing, which results in a retrieved radius with large uncertainties at 1.60$^{+0.67}_{-0.34}$ $mathrm{R}_oplus$. LTT 1445A d orbits the inner boundary of the habitable zone of its host star and could be a prime target for the James Webb Space Telescope. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2210.09713v1

Astro arXiv | all categories
Strong lensing constraints on primordial black holes as a dark matter candidate

Astro arXiv | all categories

Play Episode Listen Later Oct 18, 2022 0:28


Strong lensing constraints on primordial black holes as a dark matter candidate by Veronica Dike et al. on Tuesday 18 October Dark matter could comprise, at least in part, primordial black holes (PBH). To test this hypothesis, we present an approach to constrain the PBH mass ($M_{rm{PBH}}$) and mass fraction ($f_{rm{PBH}}$) from the flux ratios of quadruply imaged quasars. Our approach uses an approximate Bayesian computation (ABC) forward modeling technique to directly sample the posterior distribution of $M_{rm{PBH}}$ and $f_{rm{PBH}}$, while marginalizing over the subhalo mass function amplitude, spatial distribution, and the size of the lensed source. We apply our method to 11 quadruply-imaged quasars and derive a new constraint on the intermediate-mass area of PBH parameter space $10^4 $M$_{odot}

Astro arXiv | astro-ph.EP
Planetary system around LTT 1445A unveiled by ESPRESSO: Multiple planets in a triple M-dwarf system

Astro arXiv | astro-ph.EP

Play Episode Listen Later Oct 18, 2022 1:04


Planetary system around LTT 1445A unveiled by ESPRESSO: Multiple planets in a triple M-dwarf system by B. Lavie et al. on Tuesday 18 October We present radial velocity follow-up obtained with ESPRESSO of the M-type star LTT 1445A (TOI-455), for which a transiting planet b with an orbital period of~5.4 days was detected by TESS. We report the discovery of a second transiting planet (LTT 1445A c) and a third non-transiting candidate planet (LTT 1445A d) with orbital periods of 3.12 and 24.30 days, respectively. The host star is the main component of a triple M-dwarf system at a distance of 6.9 pc. We used 84 ESPRESSO high-resolution spectra to determine accurate masses of 2.3$pm$0.3 $mathrm{M}_oplus$ and 1.0$pm$0.2 $mathrm{M}_oplus$ for planets b and c and a minimum mass of 2.7$pm$0.7 $mathrm{M}_oplus$ for planet d. Based on its radius of 1.43$pm0.09$ $mathrm{R}_oplus$ as derived from the TESS observations, LTT 1445A b has a lower density than the Earth and may therefore hold a sizeable atmosphere, which makes it a prime target for the James Webb Space Telescope. We used a Bayesian inference approach with the nested sampling algorithm and a set of models to test the robustness of the retrieved physical values of the system. There is a probability of 85$%$ that the transit of planet c is grazing, which results in a retrieved radius with large uncertainties at 1.60$^{+0.67}_{-0.34}$ $mathrm{R}_oplus$. LTT 1445A d orbits the inner boundary of the habitable zone of its host star and could be a prime target for the James Webb Space Telescope. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2210.09713v1

Astro arXiv | all categories
Bayesian Accretion Modeling: Axisymmetric Equatorial Emission in the Kerr Spacetime

Astro arXiv | all categories

Play Episode Listen Later Oct 16, 2022 1:08


Bayesian Accretion Modeling: Axisymmetric Equatorial Emission in the Kerr Spacetime by Daniel C. M Palumbo et al. on Sunday 16 October The Event Horizon Telescope (EHT) has produced images of two supermassive black holes, Messier~87* (M 87*) and Sagittarius~A* (Sgr A*). The EHT collaboration used these images to indirectly constrain black hole parameters by calibrating measurements of the sky-plane emission morphology to images of general relativistic magnetohydrodynamic (GRMHD) simulations. Here, we develop a model for directly constraining the black hole mass, spin, and inclination through signatures of lensing, redshift, and frame dragging, while simultaneously marginalizing over the unknown accretion and emission properties. By assuming optically thin, axisymmetric, equatorial emission near the black hole, our model gains orders of magnitude in speed over similar approaches that require radiative transfer. Using 2017 EHT M 87* baseline coverage, we use fits of the model to itself to show that the data are insufficient to demonstrate existence of the photon ring. We then survey time-averaged GRMHD simulations fitting EHT-like data, and find that our model is best-suited to fitting magnetically arrested disks, which are the favored class of simulations for both M 87* and Sgr A*. For these simulations, the best-fit model parameters are within ${sim}10%$ of the true mass and within ${sim}10^circ$ for inclination. With 2017 EHT coverage and 1% fractional uncertainty on amplitudes, spin is unconstrained. Accurate inference of spin axis position angle depends strongly on spin and electron temperature. Our results show the promise of directly constraining black hole spacetimes with interferometric data, but they also show that nearly identical images permit large differences in black hole properties, highlighting degeneracies between the plasma properties, spacetime, and most crucially, the unknown emission geometry when studying lensed accretion flow images at a single frequency. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2210.07108v1

The Nonlinear Library
LW - A common failure for foxes by Rob Bensinger

The Nonlinear Library

Play Episode Listen Later Oct 15, 2022 3:09


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: A common failure for foxes, published by Rob Bensinger on October 14, 2022 on LessWrong. A common failure mode for people who pride themselves in being foxes (as opposed to hedgehogs): Paying more attention to easily-evaluated claims that don't matter much, at the expense of hard-to-evaluate claims that matter a lot. E.g., maybe there's an RCT that isn't very relevant, but is pretty easily interpreted and is conclusive evidence for some claim. At the same time, maybe there's an informal argument that matters a lot more, but it takes some work to know how much to update on it, and it probably won't be iron-clad evidence regardless. I think people who think of themselves as being "foxes" often spend too much time thinking about the RCT and not enough time thinking about the informal argument, for a few reasons: 1. A desire for cognitive closure, confidence, and a feeling of "knowing things" — of having authoritative Facts on hand rather than mere Opinions. A proper Bayesian cares about VOI, and assigns probabilities rather than having separate mental buckets for Facts vs. Opinions. If activity A updates you from 50% to 95% confidence in hypothesis H1, and activity B updates you from 50% to 60% confidence in hypothesis H2, then your assessment of whether to do more A-like activities or more B-like activities going forward should normally depend a lot on how useful it is to know about H1 versus H2. But real-world humans (even if they think of themselves as aspiring Bayesians) are often uncomfortable with uncertainty. We prefer sharp thresholds, capital-k Knowledge, and a feeling of having solid ground to rest on. 2. Hyperbolic discounting of intellectual progress. With unambiguous data, you get a fast sense of progress. With fuzzy arguments, you might end up confident after thinking about it a while, or after reading another nine arguments; but it's a long process, with uncertain rewards. 3. Social modesty and a desire to look un-arrogant. It can feel socially low-risk and pleasantly virtuous to be able to say "Oh, I'm not claiming to have good judgment or to be great at reasoning or anything; I'm just deferring to the obvious clear-cut data, and outside of that, I'm totally uncertain." Collecting isolated facts increases the pool of authoritative claims you can make, while protecting you from having to stick your neck out and have an Opinion on something that will be harder to convince others of, or one that rests on an implicit claim about your judgment. But in fact it often is better to make small or uncertain updates about extremely important questions, than to collect lots of high-confidence trivia. It keeps your eye on the ball, where you can keep building up confidence over time; and it helps build reasoning skill. High-confidence trivia also often poses a risk: either consciously or unconsciously, you can end up updating about the More Important Questions you really care about, because you're spending all your time thinking about trivia. Even if you verbally acknowledge that updating from the superficially-related RCT to the question-that-actually-matters would be a non sequitur, there's still a temptation to substitute the one question for the other. Because it's still the Important Question that you actually care about. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

The Nonlinear Library
AF - Instrumental convergence: single-agent experiments by Edouard Harris

The Nonlinear Library

Play Episode Listen Later Oct 12, 2022 13:43


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: Instrumental convergence: single-agent experiments, published by Edouard Harris on October 12, 2022 on The AI Alignment Forum. Thanks to Alex Turner and Vladimir Mikulik for pointers and advice, and for reviewing drafts of this sequence. Thanks to Simon Suo for his invaluable suggestions, advice, and support with the codebase, concepts, and manuscript. And thanks to David Xu, whose comment inspired this work. Work was done while at Gladstone AI, which Edouard is a co-founder of.

SuperDataScience
Causal Modeling and Sequence Data | SDS 617

SuperDataScience

Play Episode Listen Later Oct 11, 2022 70:33


Dr. Sean Taylor, Co-Founder and Chief Scientist of Motif Analytics, joins Jon Krohn this week for yet another perspective on causal modeling. Tune in for a great conversation that covers large-scale causal experimentation, Information Systems, Bayesian parameter searches, and more. This episode is brought to you by Datalore (https://datalore.online/SDS), the collaborative data science platform, and by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@jonkrohn.com for sponsorship information. In this episode you will learn: • Sean on his new venture, Motif Analytics [4:23] • The relationship between causality and sequence analytics [15:26] • Sean's data science work at Lyft [22:21] • The key investments for large-scale causal experimentation [27:25] • Why and when is causal modeling helpful [32:34] • Causal modeling tools and recommendations [36:52] • Facebook's Prophet automation tool for forecasting [40:02] • What Sean looks for in data science hires [50:57] • Sean on his PhD in Information Systems [53:34] Additional materials: www.superdatascience.com/617

UCL Minds
Teaching Bayesian Statistics and Accessibility in Education

UCL Minds

Play Episode Listen Later Oct 7, 2022 25:37


In this interview from the Department of Statistical Science at UCL, we speak with Dr Mine Dogucu who is a Lecturer in the department of Statistical Science at UCL. Dr Dogucu shares with us her experiences of teaching both frequentist and Bayesian statistics to undergraduates. She also explains what accessibility means in education and in the context of statistics, including being part of changing knitr and R Markdown to improve accessibility with image alternative text. Bayes Rules! book: www.bayesrulesbook.com/ New in knitr: Improved Accessibility with Image Alt Text: www.rstudio.com/blog/knitr-fig-alt/ Teach Access: teachaccess.org/ BrailleR: cran.r-project.org/web/packages/BrailleR/ Writing Alt Text for Data Visualization, Amy Cesal: medium.com/nightingale/writing…zation-2a218ef43f81 gradetools R package: federicazoe.github.io/gradetools/ Papers: Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses: arxiv.org/abs/2110.06355 Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations: arxiv.org/abs/2208.02565 Date of episode recording: 2022-09-29 Duration: 00:25:37 Language of episode: English Presenter:Nathan Green Guests: Mine Dogucu Producer: Nathan Green

Joe DeFranco's Industrial Strength Show
#389 Max Schmarzo On Training for Power, Programming Isometrics [and other interesting/random stuff!]

Joe DeFranco's Industrial Strength Show

Play Episode Listen Later Oct 6, 2022 105:13 Very Popular


This week Joe welcomes Max Schmarzo to the Industrial Strength Show. Max is a sports scientist, coach/educator, founder of Strong by Science and co-founder of EdgeU - a platform that helps others develop their craft as trainers, coaches, and therapists. On today's episode you'll hear Max & Joe have a thought-provoking conversation that entails a number of different topics. Highlights include: When should athletes train their weaknesses vs doubling down on their strengths; The 3 reasons to lift weights; The NEGATIVE aspects of training; Best practices when programming for Power; Traditional sets & reps vs "% drop-off of best effort"; How & Why to use "yielding" isometrics vs "overcoming" isometrics; Research/Science vs Anecdotal experience; Bayesian decision making (and what it has to do with "internet arguing")...and so much more! *For a full list of Show Notes + Timestamps goto www.IndustrialStrengthShow.com Important Links / People Mentioned DeFranco Supplements [*Use coupon: MuscleUpOctober] Max Schmarzo (@strong_by_science) The Max Schmarzo Podcast  Jake Tuura (@jaketuura) ZBiotics [*Use coupon: JOED]

Joe DeFranco's Industrial Strength Show
#389 Max Schmarzo On Training for Power, Programming Isometrics [and other interesting/random stuff!]

Joe DeFranco's Industrial Strength Show

Play Episode Listen Later Oct 6, 2022 105:13


This week Joe welcomes Max Schmarzo to the Industrial Strength Show. Max is a sports scientist, coach/educator, founder of Strong by Science and co-founder of EdgeU - a platform that helps others develop their craft as trainers, coaches, and therapists. On today's episode you'll hear Max & Joe have a thought-provoking conversation that entails a number of different topics. Highlights include: When should athletes train their weaknesses vs doubling down on their strengths; The 3 reasons to lift weights; The NEGATIVE aspects of training; Best practices when programming for Power; Traditional sets & reps vs "% drop-off of best effort"; How & Why to use "yielding" isometrics vs "overcoming" isometrics; Research/Science vs Anecdotal experience; Bayesian decision making (and what it has to do with "internet arguing")...and so much more! *For a full list of Show Notes + Timestamps goto www.IndustrialStrengthShow.com Important Links / People Mentioned DeFranco Supplements [*Use coupon: MuscleUpOctober] Max Schmarzo (@strong_by_science) The Max Schmarzo Podcast  Jake Tuura (@jaketuura) ZBiotics [*Use coupon: JOED]

Learning Bayesian Statistics
#69 Why, When & How to use Bayes Factors, with Jorge Tendeiro

Learning Bayesian Statistics

Play Episode Listen Later Oct 5, 2022 53:41


Proudly sponsored by https://www.pymc-labs.io/ (PyMC Labs), the Bayesian Consultancy. https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf (Book a call), or get in touch! A great franchise comes with a great rivalry: Marvel has Iron Man and Captain America; physics has General Relativity and Quantum Physics; and Bayesian stats has Posterior Estimation and… Bayes Factors! A few months ago, I had the pleasure of hosting EJ Wagenmakers, to talk about these topics. This time, I'm talking with Jorge Tendeiro, who has a different perspective on Null Hypothesis Testing in the Bayesian framework, and its relationship with generative models and posterior estimation. But this is not your classic, click-baity podcast, and I'm not interested in pitching people against each other. Instead, you'll hear Jorge talk about the other perspective fairly, before even giving his take on the topic. Jorge will also tell us about the difficulty of arguing through papers, and all the nuances you lose compared to casual discussions. But who is Jorge Tendeiro? He is a professor at Hiroshima University in Japan, and he was recommended to me by Pablo Bernabeu, a listener of this very podcast. Before moving to Japan, Jorge studied math and applied stats at the University of Porto, and did his PhD in the Netherlands. He focuses on item response theory (specifically person fit analysis), and, of course, Bayesian statistics, mostly Bayes factors. He's also passionate about privacy issues in the 21st century, an avid Linux user since 2006, and is trying to get the hang of the Japanese language. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas and Robert Yolken. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Jorge's website: https://www.jorgetendeiro.com/ (https://www.jorgetendeiro.com/) Jorge on Twitter: https://twitter.com/jntendeiro (https://twitter.com/jntendeiro) Jorge on GitHub: https://github.com/jorgetendeiro (https://github.com/jorgetendeiro) A Review of Issues About Null Hypothesis Bayesian Testing:  https://pure.rug.nl/ws/portalfiles/portal/159021509/2019_26880_001.pdf (https://pure.rug.nl/ws/portalfiles/portal/159021509/2019_26880_001.pdf) Advantages Masquerading as ‘Issues' in Bayesian Hypothesis Testing – A Commentary on Tendeiro and Kiers: https://psyarxiv.com/nf7rp (https://psyarxiv.com/nf7rp) On the white, the black, and the many shades of gray in between – Our reply to van Ravenzwaaij and Wagenmakers: https://psyarxiv.com/tjxvz/ (https://psyarxiv.com/tjxvz/) LBS

The Social-Engineer Podcast
Ep. 181 - The Doctor Is In Series - Can You Fake It Till You Make It

The Social-Engineer Podcast

Play Episode Listen Later Oct 3, 2022 53:30 Very Popular


Welcome to the Social-Engineer Podcast: The Doctor Is In Series – where we will discuss understandings and developments in the field of psychology.     This is Episode 181 and hosted by Chris Hadnagy, CEO of Social-Engineer LLC, and The Innocent Lives Foundation, as well as Social-Engineer.Org and The Institute for Social Engineering.    Joining Chris is co-host Dr. Abbie Maroño. Abbie is Director of education at Social-Engineer, LLC, and a perception management coach. She has a PhD in Behaviour analysis and specializes in nonverbal communication, trust, and cooperation.    Today's conversation will be on the topic of Can You Fake It Till You Make It. [Oct 03, 2022]    00:00 – Intro  00:21 – Dr. Abbie Maroño Intro  01:16 – Intro Links  Social-Engineer.com Managed Voice Phishing  Managed Email Phishing Adversarial Simulations  Social-Engineer channel on SLACK  CLUTCH  innocentlivesfoundation.org  03:45 – The topic of the day: Can you fake it till you make it?  05:15 – The Power of the Mind  06:53 – The Placebo Milkshake  12:07 – The difference with disorders  14:09 – “I'm gonna be happy!”  15:55 – Facial Feedback Hypothesis  21:00 – The power of expression  22:18 – Botox for happiness?  30:27 – Power Posing  37:39 – V is for Victory!  39:07 – The basis of non-verbals  41:34 – Self Talk  44:34 – All or Nothing  47:37 – Public Speaking or Firing Squad?  49:34 – Book Recommendations  You, Only Better – Nicholas Bate - https://amzn.to/3LTGkul  Don't Sweat the Small Stuff – Richard Carlson - https://amzn.to/3C0eg3I  50:26 – Wrap Up   50:58 – Find us online  Twitter: https://twitter.com/abbiejmarono  LinkedIn: linkedin.com/in/dr-abbie-maroño-phd-35ab2611a  Twitter: https://twitter.com/humanhacker  LinkedIn: linkedin.com/in/christopherhadnagy  51:48 – Outro  www.social-engineer.com  www.innocentlivesfoundation.org    Select research:    Carney, D. R., Cuddy, A. J., & Yap, A. J. (2010). Power posing: Brief nonverbal displays affect neuroendocrine levels and risk tolerance. Psychological science, 21(10), 1363-1368.     Coles, N. A., Larsen, J. T., & Lench, H. C. (2019). A meta-analysis of the facial feedback literature: Effects of facial feedback on emotional experience are small and variable. Psychological bulletin, 145(6), 610.     Crum, A. J., Corbin, W. R., Brownell, K. D., & Salovey, P. (2011). Mind over milkshakes: mindsets, not just nutrients, determine ghrelin response. Health Psychology, 30(4), 424.     Fischer, J., Fischer, P., Englich, B., Aydin, N., & Frey, D. (2011). Empower my decisions: The effects of power gestures on confirmatory information processing. Journal of Experimental Social Psychology, 47(6), 1146-1154.     Garrison, K. E., Tang, D., & Schmeichel, B. J. (2016). Embodying power: A preregistered replication and extension of the power pose effect. Social Psychological and Personality Science, 7(7), 623-630.     Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E. J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Comprehensive Results in Social Psychology, 2(1), 123-138.     Hardy, J., Gammage, K., & Hall, C. (2001). A descriptive study of athlete self-talk. The sport psychologist, 15(3), 306-318.     Kross, E., Bruehlman-Senecal, E., Park, J., Burson, A., Dougherty, A., Shablack, H., ... & Ayduk, O. (2014). Self-talk as a regulatory mechanism: how you do it matters. Journal of personality and social psychology, 106(2), 304.     McIntosh, D. N. (1996). Facial feedback hypotheses: Evidence, implications, and directions. Motivation and emotion, 20(2), 121-147.     Neal, D. T., & Chartrand, T. L. (2011). Embodied emotion perception: amplifying and dampening facial feedback modulates emotion perception accuracy. Social Psychological and Personality Science, 2(6), 673-678.     Neary, N. M., Small, C. J., & Bloom, S. R. (2003). Gut and mind. Gut, 52(7), 918-921.     Shackell, E. M., & Standing, L. G. (2007). Mind Over Matter: Mental Training Increases Physical Strength. North American Journal of Psychology, 9(1).    Zamanian, A., Jolfaei, A. G., Mehran, G., & Azizian, Z. (2017). Efficacy of botox versus placebo for treatment of patients with major depression. Iranian journal of public health, 46(7), 982.     Khademi, M., Roohaninasab, M., Goodarzi, A., Seirafianpour, F., Dodangeh, M., & Khademi, A. (2021). The healing effects of facial BOTOX injection on symptoms of depression alongside its effects on beauty preservation. Journal of cosmetic dermatology, 20(5), 1411-1415.     Carter, Bradin T., "Is Botox A Safe And Effective Treatment To Reduce Symptoms Of Depression?" (2017). PCOM Physician Assistant Studies Student Scholarship. 404. https://digitalcommons.pcom.edu/pa_systematic_reviews/404   

Stats + Stories
Inclusive Data Collection | Stats + Stories Episode 247

Stats + Stories

Play Episode Listen Later Sep 29, 2022 27:31


Measurement accuracy is something all quantitative researchers strive for, as you want to make sure you're measuring what you want to be measuring. When it comes to gathering gender and sex data, though measurements are complicated, beyond simply teasing apart sex and gender, there's also the imperative to ensure the language and measurement tools researchers use are inclusive of all experiences. That's the focus of this episode of stats and stories with guests Dooti Roy and Suzanne Thornton. Dr. Dooti Roy is a people leader, global product owner and a methodology statistician at Boehringer Ingelheim (she didn't give me where she worked in her bio so she might not want this) who enjoys developing/deploying innovative clinical research and statistical visualization tools with expertise in creating and leading dynamic cross-functional collaborations to efficiently solve complex problems. She is currently focused on research and methodological applications of Bayesian statistics, artificial intelligence and machine learning on clinical efficacy analyses, patient adherence, and dose-finding. She is passionate about promoting diversity and inclusion, mentoring, cross-cultural collaborations, and competent leadership development. She unwinds with painting, reading, traveling and heavy metal. Suzanne Thornton professor of Statistics at Swarthmore College, a liberal arts undergraduate-only institution. As an educator, she strives to teach students to understand statistics as the language of science and prepare them to become stewards of the discipline. In 2020 she chaired an ASA presidential working group on LGBTQ+ representation and inclusion in the discipline and earlier this year, she was appointed to a three year term to serve on the National Advisory Committee for the US Census.

The Option Genius Podcast: Options Trading For Income and Growth

Allen: All right, everybody, welcome passive traders. I have one of my good friends with me today, Denny is going to be here. He's going to be talking about trading life in general, and everything that he's learned along the way. Denny, you know, we've, you've been in our programs for a little bit now we've seen your success. And I'm, we're friends on Facebook. So I see you with your posts from Hawaii, sitting on a beach house and all that and we're on the coaching calls, you're always you know, you're always making me jealous. You're always like, "well, I'm going to Hawaii next week, or I'm going on vacation. I'm going golfing". I'm like, Come on, man. So I'm glad that we finally got to talk, you know, thank you for thank you for taking the time to be out here and talk with us. And I can't wait to learn from you. Denny: Okay. Well, the way I you know, the way I originally got hooked up with you is I saw one of your marketing deals on the internet. And I thought, you know, well, you know, let's give this a look. And so I talked with Cory and and I said to her, hey, look, you know, I've got I said, I'd like an honest answer that if I come in and buy the program and everything, and I've got $10,000. Is it possible for me to make $2,000 a month on the $10,000? And she said, Well, we've got people doing it. She was very honest. You know, and then so so I got in on the oil deal. One. I think it's blank check trading is that was the oil is. And boy, I learned a whole lot. The first year, I was just sailing along making money hand over fist. And that was when oil was not very volatile. And it was just making, you know, moving sideways, which is perfect for if you want to trade oil futures, you know, it's perfect. Allen: Yeah. Yeah. All markets are our friend.  Denny: And, and then all of a sudden, oil shot up. And I think it was November two years ago might have been three. Now I know I've been doing it quite a while. All of a sudden, I went in. And I looked and the market had dropped. And I and I was in a position where I was going to end up getting a margin call. So I liquidated my position was $4,700 that day, and I'll be damned the next day, boom, it pops right back up. And that was the day after Thanksgiving. And then on the next call, you talked about the Friday after Thanksgiving is not a very high volume deal. And so one big guy in there can make the market he can make it drop, you can make it rise, and I fell prey to that because I didn't know but you know, you can learn from your mistakes. And I made made plenty of them. But now I make money every month. Allen: That 4700, did that wipe you out? Denny: Out? No, no, I had 10 Okay. Okay, so I started all back over. And it took me it took me damn near a year to get it to get it back. And in the meantime, you had your program on stocks. Okay, so I signed up for that. And I fooled around with the stocks for a while and I went back to oil because to me, it's a little more passive where I can put a trade on and I will look at it once a week you know, and I feel comfortable with it. But then what happened is we got get them the next chapter Benny Alan COVID here. And my advertising agency that I own I do direct mail advertising for automotive industry. And I don't know if you've been reading but the car dealers don't have any new cars. Allen: Yeah, they don't need advertising. Denny: So, I my business the first year of COVID was down 2,000,400 and some $1,000 Right now, the second year is about 2.8 million and now we're into the third year of the car shortage and so far this year I'm down $1,976,000 From where my normal years would be so I went from a mid six figure income guaranteed down I collected my Social Security check with my wife, okay. And so I go okay, let's start fooling around with your knowledge with oil and with stock options and get yourself a little income so I took $25,000 out of our savings account and put it into my tasty works account and I make on an average trading two ETFs and oil and I just started doing spreads on weekly options in oil and that I've been doing okay on it but you got to watch that a little quicker because you'll, you can get caught up in a margin call on everything pretty quick on that. But since I have no other job, okay, I can watch it. You know, I just make sure that that when I go to the golf course on my daily trip I've got my phone with me. And I can hop in on the tasty works phone app and protect myself if I need to. But what I learned most from you was paid.. Allen: So how are you doing there? So you're like, Okay, so you Alright, so I'm following the story. Right? So you were you were learning like, you've been in our program, I think two years. So three, three, okay, three. So you learn how to do the oil you were doing great. And then you had one bad day where it crashed and you basically went back to zero and you had to start over? Right so that at least you didn't lose it you had you know you get back your gains then you know COVID hit so you had to basically all hands on deck for the business trying to figure that out. Now you're at the point where like, okay, you know what, I got this stuff that I know how to do let me see if I can make some money on the side. So you've been trading oil you've been doing you said you doing 2 ETFs. So what are you doing on? Yeah, what type I do? I do SPX and (inaudible). So what strategy are you doing on those? Okay, well, Denny: Let's go back to my educational background. Okay. Okay. I have a master's degree in Environmental Engineering. My master's thesis was the statistical modeling of dam failures due to excess runoff. Okay, so I'm a numbers guy, a numbers game, I understand standard deviations, regression lines, Bayesian coordinates, you know, all of this fancy mathematics that all of these indicators that when they write them, you know, I know how they get there. So I started looking at the stuff and I started looking for patterns, because standard deviation and stuff like that is nothing other than patterns, okay, that create a probability statement of the same thing occurring, okay. So, I started looking and I found the correlation between the VIX that, you know, on the CMOE, right, the VIX, right? And what happens with it? And so, I take the VIX and say it was it traded at 2588 and open this morning at 2588. I can't I can't remember exactly what it is. I go in, and I divide the VIX by 16. Now, why do I divide by 16? Allen: I have no idea. Denny: There are 256 trading days in the market. Right? The square root of 256 is 16. Okay. So I take the 68 divided by 16. And that gives me a percentage that's 87% accurate as to the upward or downward movement of SPX or rut on a daily basis. From what it opens that not what it closed that yesterday. But when the opening bell dings like, this morning, yesterday, right? Close to 1806. Okay. But this morning, when the bell rang, it was 1843 just for a short period of time until the CPI stuff caught up in the rear end dropped out of it. Okay, right. But so what I do is I go in and take what it opens at, and take the percentage and what it opens at, say it's one point it was 1.61 today, so you take 1.61% of the opening bell, and you subtract that from what it opened that and you add it to what it opened that and you gives you a high and a low rate. Okay? Allen: Say that again, do make doing so. Okay. The VIX divided by 16. Okay, then what do you do that? Denny: Okay, you multiply that the 1.61% Okay? Times when it opened that, okay, and that comes out to roughly what, close to 30 bucks. I don't have my calculator here. Okay. So you would take, you would take it and if it opened at 1843, you take the 30 off of that, that would be 1813. And then you take the 1843 and add the 32, which would be 1873. So that means that you've got an 87 point something percent chance that the right is going to close somewhere between the 1813 and 1873. Okay, okay, so now, we wait until the Between 1030 and 11 o'clock central time, okay. And the reason that I wait until then, is if you look, the market goes in and opens it bounces up and down. And if it's on the way up between 1030 and 11 o'clock you have what what usually happens and happens most days is a mid morning reversal of some sort where people are in taking profits or, or getting rid of losses. So okay. And at that point, it gives you a direction of the momentum of the market for the rest of the day. And the rest of the day barring no news or anything, it pretty much goes sideways or slightly up or slightly down. And I go in and sell a put put spread or a call spread at the bottom or the top that was ranges away from the way the momentum of the markets going. And I do that on a daily basis. Allen: So if you think is going down you sell calls if you think it's going up you sell puts at the end of that range. So is that like you said 87% So what is that like as like one and a half standard deviation?  Denny: One and a half standard deviations?  Allen: Okay. All right. But but why do you do the VIX because what does the VIX have to do with the rut? The VIX is based on the VIX, SPX the VIX Denny: Gives you the volatility, the market as a whole. Allen: Right. But it has to do with the volatility of the SPX, the RUT has its own.. Denny: Okay, okay. But the RUT is based on 2000 stocks, okay. And vix takes into account the volatility of what's happening in the 2000 stocks, the Dow Jones and the standards and poors. The way they calculate the bets, Allen: Okay, because I thought the VIX was just only on the SPX the 500. The large ones. Denny: Yeah, yeah. Well, but it is, but they just weren't right. There's yeah, there's a there's a correlation between what's happening in SPX and what happens in RUT. Okay. Allen: Yeah, they're, yeah, okay. Right. They are correlated. So it just it just happened correlated workout, right?  Denny: And it's just and it's just like if you want to see what's going on with gonna happen for disaster time, with the SPX. Go in and look at what's going on with QQQ. If QQQ is dropping, you better watch yourself on the SPX, with about, I forget what percentage of the SPX is Fang stocks now? Right? Yeah. Okay. Allen: So how long? How long have you been doing this? Denny: I've been doing for about four months. Allen: Four months. Okay. And you back tested it? Denny: Yeah. Oh, yeah. I spent a couple, couple $100 and got some good back testing software and back tested it. And if you go through the thing and wins about 80 some percent of the time, okay. Allen: And how much are you trying to make on each trade? Denny: Okay, I'm trying to make 4% Three and a half to 4% on a trade, okay. Allen: And these are weekly trades or daily trades daily. So you want the SPX, Denny: The SPX, the SPX has a closing every day. Okay, Allen: So these are at the close. Yes. Okay. Denny: And the rut has Monday, Wednesday and Friday. So I only trade the rut on Monday, Wednesday and Friday. Allen: Cool. So now your results been so far? Denny: That I'm doubling my money every month. Allen: Wow. 100% every month? Denny:  When Putin cut the pipeline off, okay. And the market and the rear end fell out of the market that day. I was at my computer when it started happening. And I closed everything out. If if I hadn't closed it out, I probably would have lost about three or 4000 that day, but I don't you know, what I do, Allen is I take a future value calculator, okay. And if this month, I want to make $10,000. I plug in $10,000. And I put three and a half percent of $10,000 times 21 or 22 trading days. And I print it out. And it tells me how much I need to make each day in order for that to occur. And then I keep a spreadsheet that I'm plus or minus off of the predicted number that I was supposed to be asked. And I adjust my trading from there now like right now for this month. So far. I'm up 900 bucks as a closing day. So I'm actually today is the 13th. Yeah, and I'm actually to where the tweet where I should be on the 20th of them. month. Okay, so if I think the markets going to be a little volatile or, or there might be some bad news coming, I can lay off, okay, and skip a day and see what's happening. Okay. That's where what you taught me is the patience. Is that it? You don't have to do it every day. Allen: Right? Right. So okay, so you're saying that you're doubling to 25? Every every month or no, Denny: Not doubling how much I want to make God, I got 25 in there, but you're trying to make you want to make if I want to make 10 This month, I put 10 up. And with the whole idea that I'm could lose all 10,000 of it. Allen: Okay so you're only using 10. Denny: Yeah, but I'm only using 10. If I lose, I lose the 10 then, you know, I'm a big boy. You know, we try again next month. Allen: So like, today's the 13th, you're only up 900. So you still got a ways to go before you get to the goal. Denny: No, no, I'm up 900 over how much I should be up. Allen: So you've already made the 10. And you made another 900? Denny: No, no, no, no. Oh, hold on a second. Okay. Okay, I started out, okay, with 10,000 in the account, okay. And I go to a future value calculator and I plug in, say three and a half percent. Okay. And I plug in 21 days, okay. Yeah. Well, that'll, at the end of the month, if I do that I shouldn't have around $21,000. Okay. And what the future value calculator says is that on day two, I should have 10,300 and some dollars on it. Okay, and then day three, I should have close to 10 Seven. Okay. So I go down what the day is what it says where I should be to achieve the deal. And I'm up 900 Okay, over that. Allen: I say okay, okay. Okay, so you're on pace. You're better you're better than doing on pace to double Denny: Yeah, right. I'm, yeah, I do what's called a phase and betting deal. Okay. Yeah. And so.. Allen: So that's what you're doing on the SPX on the RUT, and you're also doing oil. So how do you put in oil? Denny: I don't know oil, I buy maybe two to three contracts okay of the weeklies now, okay, and do a credit spread on them and try to make, you know, 4 or 500 bucks on the credit spreads and let them expire worthless. Okay. And, and then and the only and I'm only trying that because I know how to make money doing the monthlies and, and getting in at 45 days and, and monitoring it. So I'm a natural born tanker. Okay. Right. And, and, and it can cost me money at times. Okay. But, you know, I guess I'm fortunate that I'm not looking where my next meal is coming from. Allen: Right. Cool. So like today, you know, we have SPX is down 4.3% Today, big moves, they move down. So I'm assuming based on what you said, when you got in on SPX had already started moving down, so you sold calls today? Denny: Yeah, I sold calls I sold about 4090 and 4095. Allen: Okay, and then basically, you didn't have any trouble today? Denny: No and yesterday, yesterday went up. Okay. But when I went when I entered it, it was going sideways. And it was more advantageous on the calls yesterday. So I sold 4185 and 4190 yesterday, okay. And, you know, they they expired worthless okay. Allen: And is there any time you do both puts and calls? Denny: Yes. Yep. It looks like it's going absolutely sideways. Like I say, enter my trade between 1030 and 11. And I usually go to the golf course about one o'clock. But before I go to the golf course, I pull my account up and I look at it and the pit looks like it's going sideways. Then I create an iron condor and I go in and sell puts. Allen: And then what about a stoploss you have any? Denny: Yeah, I put stop losses in on everything. Allen: What percent? Like how do you know when to get out? Denny: I put 40% Okay. Allen: So 40% loss. Denny: Yeah. Allen: Okay. Cool. And so you're pretty happy with that? Denny: Yeah, you know, until it burns me I guess I will you know, I'm waiting. I'm waiting for it. I'm you know, I've done this long enough now that I know that nothing is failsafe. Allen: No, but you're doing this in a time that it is pretty volatile. You know? I mean vix today was at 27. But yeah, even so the VIX is kind of low for what's going on and all the stuff that's happening with the Fed. And, you know, we're still in a bear market. So we're still getting these wild bull market, not not a bull market rally, but a, like a whipsaw rally to go up, and then we, we hit back down on a dime. And so it still it has been very up in Downy and so well, having a you know, the strategy that you're just like, hey, I'm not gonna, I'm just gonna play day by day and not worry about at night. I think that makes a lot of sense. Denny: Yeah. You know, and, you know, I am a very, very avid reader. Okay, so I read Barron's, I read the bestsellers, Business Daily, and stuff like that, not because I think that they are going to enlighten me on anything. But what I have read is, there's a lot of guys in there that tell us about the history of the market. Okay. And for every bear market, you know, usually lasts nine to 18 months. And there's usually four to five mini rallies in there that everyone is calling the bottom of the bear market, and then it drops again, you know, and so, if we understand that, you don't get too overly enthused with the rising SPX or a Dow. Allen: Yeah, yeah. It's, I mean, that comes with experience or like you said, you know, learning and education. Cool. So what do you see going forward? Like, what's, what's next for you? Denny: Man? You know, I just enjoy doing this stuff. You know, I mean, you know, I'm in the twilight twilight of my life. You know, I'm 76 years old. Man. I'm a real young 76. I mean, I'm very mobile. I play, play golf every day. Right now, while we're speaking. I'm in Duncanville, Texas at my grandson's tennis match. He just, he just won his doubles match. And so about a half hour he'll start playing singles. So we'll watch that but.. Allen: Yeah it's a little how, I tell you that. Denny: Yeah, 95 right now here but you know, my normal week is yesterday was Monday I was in junior high volleyball and Flower Mound, which is 30 miles away from where we live. But today I'm at varsity tennis in Duncanville. That's not bad. That's close to where I live. Tomorrow. I got off then Thursday. I got junior varsity tennis. That's a home meet. And then Friday night, I've got got varsity football and Flower Mound. Okay. That's almost every day of the week. I'm doing something with the grandkids. Allen: You're going golfing every day and you're still trading every day? Denny: Yeah, and I'm trading every day. No, and you know, thanks to you. You've shown me ways that I don't have to sit there and stare at a computer. To make money. Allen: Yeah, yeah. Yeah. No, that's not the I really like what you're doing. I like your style. You know, it's like, okay, you know, put a trade on, let it work, and then go enjoy my life. Denny: Yeah. Doesn't work. So what, you know, there's another day. Allen: Yeah, but the return is good enough that, you know, you get compensated, even if there are losses, the you're, you're playing with bigger numbers. So it's like, hey, if I can make 100%, then yeah, I can lose 20, 30, 40%. That's okay. Yes. Because I can still make much more than that, you know, in the stock market. They're like, Oh, wait, you know, you shouldn't lose more than five or 10% of your account? Well, you're only making 10% a year. So obviously, you don't want to lose more than that. But if the numbers are bigger than you can take bigger, bigger, bigger bumps, so.. Denny: And I'll tell you, I'll tell you what I use I still I still use your option trading Google Spreadsheet. Allen: For the credit spreads, yeah. Denny: Yeah, I use it every day. Allen: Yep, makes it simple, right? Just calculate Yeah. Denny: The only thing is I went in and change changed the 25% to 40%. Allen: But I like it because it's like simple, you know, and I'm sure people listening to this. They're gonna be like, Okay, what do I do again? So it's like, just gonna recap. You know, you wake up in the morning, you see where the SPX and the RUT are opening, right? Yeah, take a look at the VIX. You divided by 16 and then you add that.. Denny: That's your that's your percentage movement in the ETL. Okay, that's Allen: A percentage move of the SPS. Okay. So you multiply that percentage by the open. By the Open, and then that you find your range. Denny: That will give you the that'll give you the movement, which, so say it's 1843 and say, say your your divide by say, say it's say VIX is 32. Okay, okay. Okay, you divide by 16. That's two to 2%. Okay, so say.. Allen: Okay that's percentage. Okay, yeah. Denny: 2%. So say right, opened at 1800. Today, you take 2%, that's $36. So then you take 36 off of 1800. Okay. And, you know, that puts you down to 1764. And then you add 36 to the 1800. And that gives you 1836 yeah. Allen: We have a 87% probability of this range working out for the day, it's not for the month, whatever it is for the day. And that works out to be about 1.5 standard deviations. So we've got the range, that's about one and a half standard deviations, that's 87% probability about that. And for you, it's been working pretty good. And you set it at a 40% stop loss. Oh, and then the other thing is that you get into the trade about an hour and a half an hour, hour and a half after the market opens. And so.. Denny: And the reason of the hour, hour and a half is it took me a while to realize this, the market tends to at times gap up or gap down. Okay. And then about an hour to an hour and a half later, it kind of self corrects itself. Allen: Sometimes that Yeah, yeah. But they say, you know, the opening bell is usually amateur hour. And so yeah, I mean, I could have told you that I don't trade the first hour of the day, you know, markets open markets open about 8:30 here Central time, so I don't trade before 10 o'clock, which is exactly an hour and a half. So I do that.. Denny: Yeah, that's when I'm looking at the momentum indicators and everything.  Allen: And then you let your trades expire? Denny: Yes. Allen: Okay. So you got that going on. And then.. Denny: Well the good thing about it is trades good, you can't get out of it anyway, because you've made all your money by about two o'clock and go in and try to close the trades. It says that say you get the message just some of the bid ask or zero. Allen: So, okay, so you got that going on. And you got the oil, weeklies gone. So that keeps you busy. That keeps you diversified. You're making decent amount. You're happy. That's awesome. I love it. That's that's what this is all about, you know, Denny: Keeps going to Hawaii. Yeah. You know, Allen: Yeah life is good, right? You're hanging out with grandkids you got you still have the house in Hawaii, you go on vacations, everyone, wherever you feel like it. So I like it.. Denny: In two weeks. I'll be in New York City. Allen: That's great. Cool.  Denny: Going to see Billy Joel at Madison Square Garden. Allen: Very nice. So did you do any kind of trading before you came across us? Denny: Yes. And I lost my rear end. Allen: Oh, no, that's not good. Yeah. Denny: I was way too aggressive. Okay, and not patient. And that's when I was gonna get out of the equity market completely. When I saw your oil deal, okay. And, you know, and I figured I had a better chance at oil, because it's something that we all need. And it's something that's not going out of style. Even if we go to all electric cars. What people don't understand is that two thirds of the pharmaceuticals and all of the plastic comes tomorrow. And that's none that's going away. Nope. There's going to be a demand. Allen: Yeah. In fact, you know, even with everything with the more solar and the more wind power they bring on, the world is still using more oil now than we have, like 10 years ago, the demand continues to increase, just goes up and up and up every year. So yeah, it's not going anywhere, anytime soon. So we're going to continue to trade even if demand starts going down. It's such a big market that we'll be trading oil for, you know, for the next 20-30 years.  Denny: Yeah Allen: That's, I mean, it's a different so basically, the you are trading equities but then when you found out and you learn about how we sell options, that kind of really flipped the switch?  Denny: Yeah that intrigued me. Okay. First of all well, my background before I got into the advertising thing was I owned a car dealership. Okay, I owned a Ford dealership. If you know anything about car, guys, we're super aggressive and we love leverage. And when I saw options, and I saw the leverage available, I said, this is my ticket. Allen: So then, why are we still at 25,000? Why don't we go more? Denny: You know, I've got a, I've got a wife. Okay, that funny story, okay? All donations came in and bought me out. I guess it's 28 years ago now. And I got a very sizable check. And the day I got that check, my wife reached over and she grabbed that check. And she said, seed money only comes once in a lifetime. And this is going for our old age and for fun. I go, Okay. Well, one of the ways that I've stayed married 52 years, is that I always get the last word. "Yes, dear". So, she, in the money, she basically watches it, okay. And, and she thinks that, you know, a lot of what I'm doing, although I'm making money and stuff like that, on on a basis is a little bit too risky for her, her deal. And so that, you know, that's what she has given me to play with. Okay. Consequently, I have pointed out to her recently, that because of that money, she's not had to buy any groceries out of her retirement account. For her Social Security check. I played for all the plane tickets wherever we go. This trip to New York. I've got $1,000 in Hamilton tickets invested. And she didn't have to pay for any of that. So don't you think it's about time that we started looking at adding more to that, you know, so that I think by the end of the year, she might, you know, lead me forward a little bit more. Allen: Do you have other investments and stuff elsewhere? Yeah, yeah, money's coming in. So it's not like you need this to live off of   Denny: No, no, no, no. Man, like, it's like I said that when my COVID that stopped an annual mid six figure income. I mean, on a normal week, before COVID. I was, well, on a normal month, I was doing 800,000 to 1 million pieces of direct mail a month. But that so you know, it's a good sized business, okay. With annual revenues, anywhere from two and a half to three $3 million. And, and I'm a one man show. I have no employees in that business. You know.   Allen: So it's still running, you still run that business? Yeah.   Denny: Yeah. In fact, I just got a job today. I mean, you know, they're, they're doing  infrequent, you know, I mean, you know, I might have made 30,000 bucks for the whole year doing that, you know, which, you know, that used to be a week sometimes, you know,   Allen: You know, so let me ask you this. Are we going to see below MSRP prices anytime soon?   Denny: No, no, no.   Allen: How about MSRC? Like, I'm seeing prices that are like way above like, double MSRP. Yeah, I'm not paying.   Denny: As soon as the chip shortage is alleviated, and they start to get inventory sometime in the next 18 to 24 months. They'll have inventory again. Oh, wow. But I don't know if you've seen what's happened to the used car market?   Allen: No, it's taken off like crazy.    Denny: Yeah, I mean, you know, my wife has macular degeneration now. And so, leasing a car is unless you have a business purpose. leasing a car is a bad investment. Okay. My wife had macular degeneration, we didn't know if she was going to, they were going to be able to get it stopped and whether she was going to be able to continue to drive. So the car that I'm sitting in right now is her car. Okay. And we leased it, and it had a $21,000 residual on it at the end of the lease period. And we were, you know, we were gonna turn it in. And then I pulled up what the value on it was, the retail value on this car was 31,000. So I went down to the Ford dealership, and broken but check for the car. And they can't want me to lease another one. I know. Thank you, you know, and so and that's happened all throughout the industry. And it's consequently forced the US car prices way up. And so what's going to happen two fold things going to happen. Matt, real quick, I know that you know, either way saw your day on this, but this is interesting. Once the inventory, get levels get up, all these car dealers that have these massive use car inventories are going to have so much water in their inventory. And water is excess pricing to what the current market book value on the vehicles is. In other words, if you can't sell it for what you own it for, you're gonna lose money. Right? And, and a lot of these big-- you live in Houston, I live in Dallas, a lot of these big dealerships that have two and 300 guards in the ground, are going to have a million and a half to $2 million in water in their inventory. And they're going to have to get rid of them. Okay. And so the rear end will fall out of the used car market. And you know, so right now consumers are getting screwed on automobiles. But the dealer has his day of reckoning coming due.   Allen: Yeah, but if you need a car now, you're screwed.   Denny: You need a car now you're in trouble. A buddy of mine went looked at a Subaru Outback with 19,000 miles on it, that it was a year and a half old. And they wanted $35,000 for it.  Allen: Yeah, yeah, don't get in a wreck. I mean, my car I've been thinking about my wife is like, can you just get a new car, please? I'm like, No, I like it. You know, I'm trying to get it up to 200,000. You know, miles on it. Yeah, trying to get there. I mean, it's fine. It works. You know? It's comfortable. It looks fine. From the outside. Everything is comfortable. It works. You know, it's nice Toyota keeps running. But she's like, can you get some bigger? I'm like, Alright, so we looked around, and I'm like, Man, I don't want to pay this stuff. You know, it's not even. It's not like we can't afford the payment or anything. It's just from where it used to be to where it is. Now. There's no difference. The car is the same. You just charged me a whole lot more for no reason. Just because yeah, there's a you can. So yeah, yeah, no, I don't want to play that. Denny: Yeah, their day of reckoning is coming.  Allen: We'll be alright. Well, do you have any advice for our listeners, people that are learning and trying to figure out like you found your way, right, you found your niche in trading, and it took you I don't know how many years you were trading for two years. But how many years? Were you looking before? Before that? Denny: Oh five years, I probably probably five years before I found you. Okay. And two years, two years of.. Allen: Learning and testing Denny: Not doing what you told me to do. And getting and getting burned, to realize, to realize that the things that you teach patients, you know, just the little thing and Think or Swim your standard deviation deal, you know, saying, Oh, you've got a red line there. That's not good. You know, just those little things, you know. So the biggest advice, the best advice I could give to an individual, be patient. Don't try to hit homeruns. You know, the age old adage, pigs get fat, hogs get slaughtered, is so true. It's like one of my rules on the SPX. You know, a $5 spread. Okay, a $5 spreads on the SPX is 500 bucks. Okay. So if I'm trying to make 4% to 5% a day, that means I'm looking to get 20 cents. On my credit spread. That's it 20 cents. Okay. And if you look at what the delta is on that, it's usually 12 to 13, which puts me in a real advantageous position. You know, so don't get greedy. Just let time be. let time be your friend. Allen: Right? Yep. And that actually might be a shortcut for you. So you don't even have to worry about the VIX. You just go in to get the 12 Delta.  Denny: I'm in the process of doing about a year study on this, okay. Because I back tested it using the Delta. Okay. And some wild market swings, it comes out that it doesn't work out. Right. Okay. Yeah. Allen: But the thing is, it's hard to back test it because you're saying that you go in after looking at it visually and being like, Okay, I want to be on this side or I want to be on that side. You can't do that. Unless you do it manually yourself with a like a software that I like the one I use where you got to go in day by day by day. If you're one of those programs where you just put in the numbers and you Just let it run, it doesn't work. Denny: You've got to plug them in yourself. Yeah. And it's time consuming. Especially if you're doing dailies. Yeah. Because you got you got 256 for every year. Allen: Yeah. And I mean, like, you know, when we when we back test a new strategy, it's like I want to I want you know, a good 10 years of data, you know, I want to see the the ups and the downs and the flats and the recessions and the bulls market and everything. I want to know that it's going to work long term, not just for a couple because I've been burned on that too. You know, I, I back tested different strategies like the butterfly on McDonald's and a butterfly on a Walmart and they worked great for five years. For five years, they made money. I went in there with guns blazing. You know, I took like every money out of money I had at the time at $25,000 on one trade, just want Dre put it all and boom, blew up. And I'm like, what happened? Oh, my God, man. It was a fluke. I'm gonna do it again. Next month, next month, boom, blew up again. You know.. Denny: Those butterflies and iron condors look great. You sit there and you look at the leverage you've got on that you go, Whoa, you know, but you know, you got to think, why isn't everyone doing it? There's a reason. Allen: So, there's lots of little tweaks behind it. Yeah, yeah. This has been fun. Denny, I'm gonna let you go. I appreciate you. And if there's anything you need, please reach out to us. We're always here for you. And thank you for sharing your wisdom. Denny: Okay, well, you know, I mean, I just want to tell you and your listeners that your program has definitely taught me a lot and made me a lot successful. Faster than I ever would have been. Allen: That's awesome. That's good to hear. Make my day. I love it. I love it. JOIN OUR FREE PRIVATE FACEBOOK GROUP: https://optiongenius.com/alliance  Like our show? Please leave us a review here - even one sentence helps. Thank you!

The Nonlinear Library
LW - Understanding Infra-Bayesianism: A Beginner-Friendly Video Series by Jack Parker

The Nonlinear Library

Play Episode Listen Later Sep 22, 2022 4:26


Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Understanding Infra-Bayesianism: A Beginner-Friendly Video Series, published by Jack Parker on September 22, 2022 on LessWrong. Click here to see the video series This video series was produced as part of a project through the 2022 SERI Summer Research Fellowship (SRF) under the mentorship of Diffractor. Epistemic effort: Before working on these videos, we spent ~400 collective hours working to understand infra-Bayesianism (IB) for ourselves. We built up our own understanding of IB primarily by working together on the original version of Infra-Exercises Part I and subsequently creating a polished version of the problem set in hopes of making it more user-friendly for others. We then spent ~320 hours writing, shooting, and editing this video series. Part 5 through Part 8 of the video series were checked for accuracy by Vanessa Kosoy, but any mistakes that remain in any of the videos are fully our own. Goals of this video series IB appears to have quite a bit of promise. It seems plausible that IB itself or some better framework that builds on and eventually replaces IB could end up playing a significant role in solving the alignment problem (although, as with every proposal in alignment, there is significant disagreement about this). But the original sequence of posts on IB appears to be accessible only to those with a graduate-level understanding of math. Even those with a graduate-level understanding of math would likely be well-served by first getting a gentle overview of IB before plunging into the technical details. When creating this video series, we had two audiences in mind. Some people just want to know what the heck infra-Bayesianism is at a high level and understand how it's supposed to help with alignment. We designed this video series to be a one-stop shop for accomplishing this goal. We hope that this will be the kind of video series where viewers won't ever have to pause a video and go do a search for some word or concept they didn't understand or that the video assumes knowledge of. To that end, the first four videos go over preliminary topics (which can definitely be skipped depending on how familiar the viewer already is with these topics). Here are the contents of the video series: Intro to Bayesianism Intro to Reinforcement Learning Intro to AIXI and Decision Theory Intro to Agent Foundations Vanessa Kosoy's Alignment Research Agenda Infra-Bayesianism Infra-Bayesian Physicalism Pre-DCA A Conversation with John Wentworth A Conversation with Diffractor A Conversation with Vanessa Kosoy We found that in order to explain IB effectively, we needed to show how IB is situated within Vanessa Kosoy's broader research agenda (which itself is situated within the agent foundations class of research agendas). We also wanted to give a concrete example of how IB could be applied to create a concrete protocol for alignment. Pre-DCA is such a protocol. It is very new and is changing quite rapidly as Vanessa tinkers with it more and more. By the time readers of this post watch the Pre-DCA video, it is likely that parts of it will already be out of date. That's perfectly fine. The purpose of the Pre-DCA video is purely to illustrate how one might go about leveraging IB to brainstorm a solution to alignment. Our second audience are those who want to gain mastery of the technical details behind IB so that they can apply it to their own alignment research. We hope that the video series will serve as a nice "base camp" for gaining a high-level understanding of IB before delving into more technical sources (such as Infra-Exercises Part I, the original sequence of posts on IB, or Vanessa's post on infra-Bayesian physicalism). Why videos? The primary reason that we chose to create videos instead of a written post is that video is a much more neglected medium for AI alignmen...

The Modern CFO
The Product Manager CFO with Marcum LLP's Jack Boyles

The Modern CFO

Play Episode Listen Later Sep 21, 2022 36:06


Financial management can make or break a business. Any business undertaking attempted without taking cost drivers, growth prospects, and value realization goals, among other critical factors, into account is leaving a big, wide door open to problems.Jack Boyles, Managing Director at Marcum LLP, understands this perfectly well. With his extensive experience in financial planning and modeling, valuations, and funding strategies, Jack keeps a trained eye on both the micro and macro factors that influence today's rapidly evolving financial services sector.In this episode of The Modern CFO, Jack talks with host Andrew Seski about critical factors to consider for growing companies, how he deals with the unexpected, and the valuable lessons he learned over his 25-year-long career as founder, investor, and CFO of several companies.‍‍Show Links Check out Marcum LLP Connect with Jack Boyles on LinkedIn or via email Check out Nth Round Connect with Andrew Seski on LinkedIn ‍TranscriptPlease note that the transcript is AI-generated and may contain errors. The content in the podcast is not intended as investment advice, and is meant for informational and entertainment purposes only.‍‍[00:00:00] Andrew Seski: Hello everyone and welcome back to The Modern CFO podcast. As always, I'm your host, Andrew Seski. Today, we're joined by Jack Boyles. Jack, thank you so much for being here. [00:00:19] Jack Boyles: Thank you. I'm looking forward to our conversation. I reviewed a number of your other podcasts. They're all great and I learned something in each one.[00:00:25] Andrew Seski: So today, Jack serves as CFO at Marcum. Jack's based in Boston and has been a CFO across a number of industries and is insatiable when it comes to learning new things, trying new industries. [00:00:38] But one of the things that we've been talking about, maybe ad nauseam, but between us is the idea that maybe there is a certain time and place where CFOs can have their biggest impact at, you know, either a type of financing, an industry, and maybe CFOs shouldn't necessarily grow across all stages and all different types of industries. Maybe they should be specialized and maybe there is a time and place for that CFO who can drive the most value. [00:01:05] So this is a topic I really want to dive into and really dig our teeth into because Jack has such a unique vantage point, serving his entire career really honing in on this idea. So Jack, I got to turn it over to you to tease out some of the value and insights here on sort of that topic and whatever else we can foray into across all of the experiences you had as a CFO.[00:01:26] Jack Boyles: Thanks for the great introduction. Yeah, I'm not CFO of Marcum — number one. Marcum has a group of consulting CFOs and so I now work with roughly a half-dozen small and medium-sized companies as a fractional CFO. Prior to that, I've been CFO of a number of companies in which I was founder, investor, angel, and always had a CFO title in a wide variety of verticals — distribution and logistics, software manufacturing, IT services, natural resources. [00:01:57] And right now my portfolio includes a SaaS company — a company working on carbon credits with blockchain — and another marketplace for health services. So, you know, it's a pretty broad spectrum and I've enjoyed it because there has been a number of learning opportunities. [00:02:14] But returning to your theme, I found I'm really good at the five million to 50 million-dollar service orientation companies. And I've realized that that's where I can add the most value. I'm not somebody who can take a company public, although I've sold a number of companies to Fortune 500 companies. But it's really recognizing there are different skill sets for those by both vertical and by size of company, if you will, the capital intensity and sort of the economic structure underlying the business.[00:02:45] So I can break down those and, you know, they're all interesting problems, but it's really a different skill set for each one of them. And you need to manage differently as that, you know, financially-oriented team member. [00:02:58] Andrew Seski: In terms of where some of this interest comes from from my end is the fundraising environment over the last few years dramatically changing in the last few months. So what may have been, you know, a company doing five to 10 million then that could have been valued, and maybe in the software land, maybe even at a hundred X multiples at one point, just an absolute crazy valuation and fundraising environment to, you know, a very, very immediate, almost shift in going from, you know, pure growth orientation to conservative cost cutting, you know, headcount reduction. And I think the question there stems not only just from where the CFO can be the most valuable in their niche and their competency, but also how to weather the volatility of different market cycles. [00:03:42] And there are a lot of variables to play with here so I really like your answer that the CFO can be really valuable by identifying their impact in a niche due to all of the other market environments and volatility in the markets that could, you know, shift strategy and financial strategies that a company may pursue.[00:03:58] Jack Boyles: Well, you're shining a spotlight on, you know, certainly what is the most critical thing for growing companies, which is, do they have access to capital? And is it the right capital on the right terms and in the right timing? You know, obviously, you progress from family and friends to seed rounds, to Series A and up. [00:04:17] But it's really more important, or the starting point for that analysis is really, what's driving the need for cash? Is it building your organization? Is it financing working capital? Is it plant and equipment expansion? Is it building relationships that you need to invest in? So really understanding from a, what I would call a fairly granular level, what are the cost and capital drivers in your business and really internalizing that, that economic, that, you know, the calculus of the business, because that's gonna tell you what kind of capital you need and where to go knocking on the door. It's seldom the case that you're gonna be the first guy knocking on that door, but making sure that they understand your economic model is critical.[00:04:59] And so to narrow your field down on who you're focusing on and what you're offering and making sure, I mean, whether you look at PitchBook or anything else, it's fairly easy to qualify those people and what their investment criteria are. Most firms are very upfront about what they invest in and there's nothing wrong with reaching. But there's also economy and wisdom and finding people who've done your deal before with like competitors because they understand it. They get it. Whether you consider that investor a bank or a venture capital or a family office, find people who have done it before. They're gonna bring more knowledge to the deal — in the one they do because they are always seeking to be better. Their due diligence will be a lot more efficient and helpful to you.[00:05:43] Andrew Seski: So I want to dive into something that comes up on most podcasts. When we talk about people's route to CFO roles, there's a very traditional background of accounting courses throughout undergrad and maybe a consulting job or a Big Four role. We've had a mix between a very traditional and maybe some nontraditional of serving in the Navy. And I want to go back in time to Dartmouth undergrad and leaving school. What was your, some of those first roles? Did you have sort of a traditional background? Because I want to then kind of hit on all the successes you've had because you have a pretty incredible track record as well. [00:06:19] Jack Boyles: Not at all. I got an MBA at Dartmouth and I was something of a quant jock having a mathematics degree and liking computers, which was kind of a new thing then. And, you know, took all the accounting courses. And when I got close to what the careers looked like with the Big Eight — and there were eight at that time — versus the other things that were out there, I chose consulting. [00:06:41] I joined a firm, Temple, Barker & Sloan, in Boston, worked with them for years. And candidly, they liked me because I spoke business and I could write Fortran. Those were the qualifications. And so I ended up doing most of the financial modeling on a broad range of projects and really, you know, got to be known as something of a guru in figuring out the economics in how to simplify them to the important details. I mean, that's an important notion. [00:07:07] Getting a level of detail right is sometimes the hardest thing to do right in making a projection. Too detailed — you can't maintain it, change it, and it's not useful as a policymaking tool. Too macro — it's not informing you on what the really important relationships are between the resources and their results in a business.[00:07:28] I did that for a number of years, worked across telecommunications, oil and gas, resource recovery, some consumer products, and then got tired of working for big companies because, you know, you were kind of siloed. And so when I looked over my years in consulting, the fun companies were all small and growing. That made the choice easy. So I went off on my own and one after another, you know, lived out that dream. [00:07:53] Andrew Seski: So you've mentioned early on that you are really passionate about continuous learning. And I think you probably identified consulting as one of those ways to be very, very oriented to try to be a value adder early on in your career but also across a lot of different industries so that you can continue to learn. It's very clear that you maintain that theme by being able to have a similar job title across all of these different types of firms.[00:08:18] But how are you thinking about that in terms of some of the risk profile of — I think there are a lot of CFOs who have probably fairly, just a pretty well-defined risk adversity — but going from big consulting shop to smaller firms to deploy some of that knowledge, did that phase you at all or were you pretty comfortable in those positions? [00:08:37] Jack Boyles: My wife didn't ask a lot of questions about what I was doing. So honestly, I was blessed with somebody who was very supportive and understanding and had confidence that I could make it work, whatever I chose to do. And she's, you know, she's been half-right.[00:08:52] Andrew Seski: Well, let's start talking about some of the consistent themes across these CFO roles because you do have a lot of experience in successful exits. Like I mentioned, your track record is incredible. So I want to dive into some of the themes and valuable lessons that we can share to the network of CFOs and listeners today.[00:09:11] And maybe it starts with the kind of continuous learning aspect of always trying to drive forward continuous learning. Maybe it's the definition of what a modern CFO is across being somebody who's really proficient in understanding and measuring the value of technology versus maybe opportunity cost. So were there any things that stood out really early in your career that were cemented later across some of the more successful exits that you've had?[00:09:40] Jack Boyles: I think one of the most important things to do is not overestimate your team's understanding of what the CFO is really supposed to do. And I think it's really helpful when engaging, you know, with a new team to lay out, you know, your assessment of what the roadmap is and what the principle projects are, the priorities, timing, and resource required for them. [00:10:02] Above all, we have to be good project managers. Yes, we have to have the financial disciplines and understand how to put financial statements together and make intelligent decisions about IT, infrastructure, and risk mitigation, and so forth. But really laying out that roadmap for your team members and really saying, "These are the things I own," "These are the things I need your support with." And don't assume that they really understand what the role is and how integrating it needs to be in how the business develops. [00:10:33] You know, the CFO should really take responsibility for building the infrastructure to support the vision of the people who are creating the products and services and the technologists in this day and age that are driving it forward. But to really confirm their understanding of your role, the need for detail, the need to measure what they're doing and provide regular feedback in particular that monitors their progress against their objectives. So to me, that's a lesson I learned over and over again and every time I skip it, it's like, how did I miss that? It's just, I thought I had learned that lesson the last time. And that's critical whether it's, you know, regardless of what industry you're in. [00:11:12] You mentioned the other thing about the thing that keeps me motivated. You know, one of the things that happens at business school and when you're a math major is you acquire all these analytical techniques and tools. You know, I'm really in the business of, you know, old tools for new problems. And so when somebody talks to me about security policy — huge issue for most companies today in the security, you know, whether it's compliance with GDPR or SOX to any of those issues — you know, you don't hear anybody talking about applying Bayesian analysis to that, which is, we all know the technique, but use that framework to structure the decision, to add quantitative data and substance where you can, but also understand, you know, what you're not gonna know and is undiscoverable and be able to make decisions. [00:11:59] You know, the role of a CFO if they're effective with not only the preparation of financials but can adapt that data to the decision making that's in front of them — that's critical. That's a valuable, valuable partner in your decision-making process. Not that they don't get a vote — they do and should have a vote — but the reality is making sure we've chosen the right analytical framework and context for the problem, understand what we know, what we don't know, what's worth researching, and how much time and resources are we willing to spend to improve the decision. Critical thing. And it cuts through a lot of the maxims you hear from one CFEO or, you know, one entrepreneur or the other, speed is everything in one case, fail fast. You hear all these things, but putting it in structure and putting numbers to it really helps you apply those lessons in a very focused and constructive way.[00:12:54] Andrew Seski: I want to continue to talk about this just for a moment because we've had now the pandemic. It looks like we already have a looming recession. When we talk about constructing sort of traditional models with a little bit of leeway and communicating out, you know, exactly what the role of the CFO is, how do you create and think, or how do you personally think about how to create some sort of, you know, configurability around circumstances changing and some sort of flexibility in terms of, you know, creating the models that would be able to handle, you know, some of the maybe more unforeseen types of events that we've had in the last few years?[00:13:29] Jack Boyles: Oh. [00:13:30] Andrew Seski: It's a complex question. [00:13:32] Jack Boyles: Well, I mean, you know, there's great literature on that over the past 10 years, starting with The Black Swan and the work of The Undoing Project, which is about people, you know, two psychologists won the Nobel Prize in economy and economics for really undoing capital markets theory, is what they did, and sort of challenge some of the basics of, you know, thinking fast and thinking slow, which is Daniel Kahneman's famous book. [00:13:59] Andrew Seski: Is Undoing, is that a Michael Lewis? [00:14:01] Jack Boyles: Yes. The Undoing Project is the story of Kahneman and his partner that led to the Nobel Prize. Kahneman, you know, his partner died in this research, but Kahneman continues to write and is still very influential about thinking about how decisions are made and what we, what we just assume and make decisions on every day, which needs to be tested, which is sort of at the root of these unforeseen things that nobody saw coming. [00:14:29] I'll segue back to something I raised earlier: security issues today. You know, when you ask Amazon and you've moved all your stuff to their cloud services, you know, what are you gonna do to make sure we never fail? And they say, you're making an assumption that we're not gonna fail sometime. Assume that the network's gonna go down at some point. That's a real risk. How are you gonna handle it? We can't provide that guarantee. I think about risk in that way, which is I really do carefully consider obsolescence risk of products and services. That's particularly relevant today given the pace of technological innovation and disruption going on. [00:15:05] I think, you know, we have to think very carefully in most businesses. The current clients that I have are not really geared in doing flexible planning regarding the likely wage expectations of, you know, anybody they're hiring. You know, it's not just the commission you pay a recruiter. It's the fact that the basic wages are gonna be 10% higher. So really working through at a fairly, you know, a mid-granular level, which is wages, resources, regulation can change and fundamentally alter the nature of competition in your vertical competitors themselves as well as new products and services. And I think you just have to be structured about that and really be honest. [00:15:47] People wave a hand at it by saying we've got very strong customer relationships. Well, yeah, maybe you do. I can look back and see what the recurring revenue is per customer and I'm not sure what that tells me, you know, given the threats to their business, the threats of competitors, you know, this is a free market capital society. They're gonna earn money for their shareholders and do what they think is right for them. You really have to be very circumspect about placing too much reliance on those strong customer relationships that you've had forever and even the legal contracts underneath them. I tend to be a skeptic when it comes to that.[00:16:26] Andrew Seski: Right. Having a really, really specific understanding of stakeholders, you know, not just your stakeholders but their stakeholders and, you know, whether that's their investors, the shareholders, employee owners, you know, the things that affect their businesses and your clients' businesses as well.[00:16:40] Jack Boyles: Everybody at the table.[00:16:42] Andrew Seski: Everyone at the table.[00:16:43] Jack Boyles: Everybody at the table has alternatives and it's important to understand that you can't, you know, neglect any of them and because whether it's your circumstances or their circumstances that changes dramatically, you both have to re-examine the relationship and be prepared for it.[00:16:59] Andrew Seski: One of the things we were talking about just before we started recording were some big shifts that have taken place in terms of where financial data is stored, maybe the, like sort of the future of the CFO role. And I want to touch on some of that because I think it'll reframe some of the conversation into what we can think about in terms of strategic planning in the next three to five years or even zooming out further with more innovative technologies. You mentioned you had a blockchain company that you're working with doing carbon credit so you're hitting two major themes that, even in the news right now around climate change and government funding, some new climate initiatives.[00:17:35] So I want to zoom out a little bit and talk about some of the macro things that have happened in terms of where technology and financial services have intersected, especially in the role of the CFO. [00:17:45] Jack Boyles: My perspective is if you look back over 50 years, there have been three or four major events that wholly changed the way finance was supported within companies, starting with the creation of ADP. When Frank Wattenberg created that company back in the sixties, nobody dreamed that you'd ever have the confidence to outsource the most confidential data you had, which is the compensation of your employees. You know, 10 years later, you were considered inefficient and backwards if you weren't using an outsourcer to manage the payroll processing problem. They did it better. They did it more competently. They were well-equipped to keep pace with a compliance requirements that constantly changed. Looking back, it was like, why didn't we do that earlier? [00:18:29] A couple years later, we moved from big, secure IBM mainframes to running our financials on little local area networks everywhere that rolled up. It was a revolution from having to have a mini computer, a mainframe to process your financial data or, worse yet, do a lot of it manually. That happened, you know, overnight. We all changed again with the year 2000 worries and upgraded all of our technology. [00:18:58] The last thing that happened was the move to the cloud. In 2015, I remember talking to financial partners about, you know, was anybody else contemplating moving their accounting onto these crazy platforms, NetSuite and Intacct? Not a one. I talked to a dozen companies. Not a one. Three years later, they were behind the eight ball if they weren't in that project. And now you have to have a very stable, very small business if you haven't moved your financials to the cloud, whether it's on Oracle or SAP or Intacct or NetSuite or QuickBooks Online. [00:19:34] And I predict the next, you know, role to change is the CFO. I think that the reality is the breadth of skills that a CFO had to bring 20 years ago is irrelevant today, largely. You know, the person you want in that role has great familiarity with the vertical, has great familiarity and comfort with the size of company — how many people, what's the size of the management team. You work entirely different if you're in a C-suite of a Fortune 500 than if you're one of three people running a 50-million-dollar company and you have very intimate and intense relationships with the other members of that C-suite. [00:20:13] So I think that's going to change and you're going to find, you know, CFOs, particularly for growing companies, change more often. Somebody who's really good from startup to 10 million. Somebody else has a different skillset from 10 to a hundred million, and you need somebody else for the IPO. They're different skillsets. You know, the lower you go, the broader range of skills you have to marshal and more hats you have to wear as you go up the chain, you become more of a manager and in public relations role. [00:20:46] So within the sectors that I serve, I find that it's as important for me to be able to source critical services, whether it's in IT, professional services, legal accounting, insurance, or other specialty services, whether it's R&D tax credits, 401(k) advisory work, issues of that nature. So I'm, you know, a third sourcing agent for all the professional services, a third, you know, controller, whatever accounting hat I have to wear. And third really business planner partner to the other executives. [00:21:20] Andrew Seski: So that's really helpful in terms of contextualizing all of the dynamic requirements of the CFO today. And I think it's really helpful to look backwards before looking forward. One of the things I want to segue slightly into — maybe it's more consistent or maybe it's even changing now because of everything that is more standardized and in the cloud — but I want to talk about liquidity and exits and relationship with CEOs. [00:21:45] You've had a number of exits and I'm trying to decide if I have an opinion whether or not transactions will always be complicated. You're always gonna need to bring all of the stakeholders we've mentioned into the same room to hash through details and figure out what's best for buyers and sellers. And while there might be some standardization, there's still a ton of human-level emotion behind, you know, exits. [00:22:09] So I want to know if there's been any sort of intersection between the efficiency of due diligence and exit planning. Has technology influenced all of that or is it still highly manual? A little emotional as always in building great companies and maybe having an exit, but it'd be a fun thing to think through and talk about because it's been a hard few years. I think the number of transactions that happened in the last few years have probably been off the charts. In the early 2020, I think 2020, there was record number of IPOs, first half of the year. So just thinking through that, I would love to hear either stories or lessons learned or, you know, your perspective on whether or not you think technology's gonna impact liquidity and exits. [00:22:50] Jack Boyles: Well, I think two things. In terms of the mechanics of it, you know, the progress in deal rooms and standard terms and analytical tools to look and value companies is extraordinary today. The tools at our disposal to do financial analysis have never been better. I think the hidden value of the technology isn't just the deal room and the ability to communicate better. I think you also find that people who've done a number of transactions are starting to put more and more emphasis on what are the fundamental infrastructure systems that are in place. [00:23:25] If I'm buying a company that's using the same systems I do, hallelujah. My transaction implementation cost have been cut by two-thirds. I'm not retraining their staff. I'm not reinventing the wheel. I'm doing some data cleanup at consolidation. So if you're a small company or mid-size company with a view towards being bought or buying others, choosing an industry standard platform for your ERP is critical, you know, that's not customized. It greatly simplifies and ensures the success of a transaction because it means you spend, you know, two months integrating operations rather than a year. Time is of the essence in these transactions. [00:24:07] And I think we're gonna go into a phase, particularly with, if we are in fact in recession and are likely to see a number of quarters and the capital pools are gonna dry up or be constraints fundamental, I think you're gonna see a wave of consolidations among these companies and that's gonna be their choice, either sell their IP and their customer lists if they're just technologists or go out of business because I don't think the subsequent rounds that were readily available two years ago are gonna be coming as quick or be as favorable in terms of valuations. [00:24:40] So when you look at the, you know, how the worm's turning, I would urge mid-size companies, who are revenue, you know, have profitability, positive cash flow, to really think about who are the comparable and natural acquirers for them. Chances are those companies, if they need to exit or thinking about it, they probably know who their acquirer is. And I would in some cases that, you know, urge them to have those conversations before they engage in investment banker because we're all looking at the same two-year outlook, which is highly uncertain in terms of both economic environment, as well as the availability of capital. And I'd plan for that. [00:25:20] In most cases, you know, companies that are consolidating in some form, they already know who the players are. And they know, and they're very thoughtful and intentional about what they're gonna look like to facilitate that and remove obstacles to combinations. [00:25:35] Andrew Seski: So just thinking from an investor's standpoint and from a founder's standpoint, I think in the next three to five years, there's kind of a double-edged sword here. I think on one hand, there's some excitement around if there is a downturn and money is being spent more strategically and maybe a little less out of fear of missing out on opportunities than there is that shakeup where really there could be some market dominators, if they can survive a downturn and really capture a big part of the market share in their industries.[00:26:07] So I think that is somewhat exciting to see the shakeup. It's probably nerve-racking as well for both investors and founders in the same vein. But I was gonna ask if you were really excited about anything on that kind of time horizon. I know we just mentioned the next two years feel very uncertain. But just from all these different perspectives, I was thinking it might be unique to hear what you might be excited about in the next three to five.[00:26:30] Jack Boyles: Personally, I think, you know, the whole promise of blockchain technology, in particular smart contracts, is really going to change finance in very fundamental ways that most people don't grasp yet. When I consider simple things that we had, you know, trade finance, importing goods from another country where it used to be a long, drawn-out procedure with very strict guidelines for the documentation and a very globally revered process for clearing payments and managing the transport of goods. That's a blockchain transaction. That's a smart contract today and it's collapsing.[00:27:05] Well, you know, that's, those same technologies are gonna influence lots of things in the finance world. And so I honestly see financial organizations changing dramatically. So individually as somebody who's working with small companies as a finance guy, I find that very exciting to anticipate those changes because it'll be as important as outsourcing payroll and moving your financials to the cloud and fractionalizing your CFO. It's really gonna change the way things work. [00:27:34] And the, to me, the biggest question is, it's not "if," it's "when." Is it, it could be two years. It could be five years. It could be seven. I'm not smart enough to know what the obstacles to adoption are. Oh, maybe I do. Yeah, I'm guessing it'll be government.[00:27:48] Andrew Seski: Well, I think there are a ton of regulatory pushes being made like, as we speak, basically. But I'm glad to see that a lot of the blockchain applications that are catching some traction are around decentralized finance. It's a really hard problem to solve. But there are a lot of people trying to put certain blockchain applications out there where it's sort of a square peg in a round hole. It's a more natural fit, I think, in a lot of the legalese of smart contracts being digitized. So I'm also looking forward to that. [00:28:17] I always ask whether or not you feel something is, you know, maybe undervalued or underestimated in the world from your vantage point. I know we've touched on a lot of big themes across innovative technology, across the changing role of the CFO. But just wanted to give you the opportunity if you wanted to take the conversation in really any direction where you just feel that people may not fully appreciate something that's more clear to you given all of your industry experience. [00:28:45] Jack Boyles: This is hard for somebody who's a numbers guy to say, but the proper functioning of teams is more important than I ever wanted to admit, you know, as I chose to be a math major and then went, you know, focused on quantitative things in my consulting career. And I think COVID and virtualization of so many organizations, I think there'll be another library filled with the books consultants write in three to five years about what separates those companies that did that well and knew how to bring back and re-engage their workforce. [00:29:18] The successful company that, you know, that we write about five years from now is not the one that said, well, you know, starting 2023, you've gotta spend two days a week in the office. They're gonna be a lot more sensitive to it. They're gonna be a lot more, they'll learn a lot more from how the teams functioned during COVID and immediately thereafter and they'll figure it out. And that's gonna separate the real winners and the teams that have, you know, long-term, excess profitability, and market valuations, and all of those other good things from the rest. Because once you can do that, you're accessing a global workforce, which means you can, you know, do a much better job optimizing, you know, targeted recruiting at the best cost. You'll find centers of excellence and be able to tap into them much more rapidly than a firm that's constrained and tied into some old HR, you know, notions of how this should work. [00:30:11] So I can't predict who those companies are, but that's what I'm watching very carefully. What are the innovative companies doing when it comes to how they manage their workforce, how they reward their workforce now that we've broken the model that says you show up in the same place every day. [00:30:27] And you know, certain industries are, certain companies, those that process medical claims, for example, have led in sort of, well, we don't have to do this in New York City; we can do it in Upstate New York. Or, you know, there are lots of examples of people that have taken a function and done it well, but it tends to be a very routine function and it tends to be easily supported remotely.[00:30:50] You know, the last two years gave us an opportunity to blow everything up and try new models. As somebody who's enjoyed a business career and continues to enjoy seeing what's coming, I'm really looking forward to seeing who the winners are in that race. [00:31:04] Andrew Seski: Yeah, absolutely. I was curious if you, I know you've been somebody over the course of your career who's continuously pushing the envelope on trying to find whatever is on the horizon. I'm curious as to if there are any unique sources that you look to. I mean, I've mentioned on other podcasts, I still get a physical Wall Street Journal. I'm very careful on how I curate social media and how I get news. And it's, you can just so easily be bombarded. I'm curious as to how you curate what you receive or if there are any kind of unique ways that you go seek out information or book recommendations. [00:31:38] And I only ask because Nth Round just launched a newsfeed because we are the same way. Everyone on our team has such unique access to really different types of news and we consolidate it and try to, you know, just showcase what we're thinking about that we think is interesting. It's always kind of a really unique niche between finance, technology, regulation, but it's important to us. And it's just a really interesting mix of news. So I'm just kind of curious as to, you know, as you look to your next revolution of Web3 and blockchain and everything that's happening in the world of technology and finance and regulation, kind of how you're sifting through, you know, the huge amount of content.[00:32:16] Jack Boyles: You know, honestly, we're drinking from a fire hydrant right now.[00:32:20] Andrew Seski: Absolutely. [00:32:21] Jack Boyles: I mean, just, you know, there's so much new technology and I've never prided myself as someone who can create technology. But I've always thought I was pretty good at seeing its applications and where I could really have a role. So having said that, you know, I do scan, I love to listen to a16z podcast. They always seem to be ahead of the curve in terms of identifying a technology and sort of what the fundamental economics are that are gonna, you know, lead to mass adoption. So I find that to be a great source of ideas in thinking about what's coming next. [00:32:54] Myself, I tend to go to raw data. Who is the ex-CEO of Microsoft, not Bill Gates' successor. Who's created a, you know, an American facts database. So I'll open the phone book, essentially, of facts — the Census Bureau, the tax rolls, you know, Bureau of Labor and Statistics — and look at something that may, you know, based on the idea that there's a new technology, say, well, if this applies to plumbers, how many plumbers are there in the world? You know, where are they, what do they do? Really understanding, sort of not trying to solve a global, you know, moonshot problem, but is there a problem everybody has in their household every day that this widget, this service might address? [00:33:37] To me, I am a low-hanging fruit guy. So if there's a problem that says, you know, there was really a better mouse trap, I'd be all over it because I can estimate how many mice there are and think about the problems of addressing that problem. So that's kind of how I think about things. [00:33:54] I do have an example. I ran into a company that was doing field service in electronic repairs. I looked at it and said, well, there's 300 or 400 companies you have to maintain relationships with for warranties. And there's four to 5,000 of you guys across the nation. And there's only one national player? That doesn't seem right. There's an arbitrage. There's a roll up here. [00:34:14] So to me, that was an interesting problem. I worked on it. We merged a couple companies, interesting things. But I'll look at the existing situation in an industry. I think I'm pretty good at looking at the macro forces of how an industry works, how a business works, see where there's a real arbitrage and next opportunity to exploit, you know, not trying to reinvent the wheel, but make it work better, consolidate where possible. [00:34:40] Andrew Seski: Well, stay on after the recording. I've got a very funny story. I'll have to confirm, but I believe it's told on the podcast, it's a Steve Ballmer story about early Microsoft days. But one of our podcast guests had to report to Ballmer and got some very implicit advice in his early career about efficiency and modeling, you know, assumptions after data. So we'll talk about that as we wrap up. [00:35:03] But how would you recommend people get in touch if they'd like to talk to you about any of these concepts that we've covered today or get in touch with Marcum about maybe utilizing some of the services that you're currently serving? [00:35:16] Jack Boyles: The easiest thing. I'm on LinkedIn and very visible, Jack Boyles. There aren't that many of them. So you should be able to find me. There's also a jack.boyles@marcumllp and msn.com as well. So, happy to take all calls and look forward to chatting with anybody who found this an interesting conversation. [00:35:34] Andrew Seski: Excellent. Well, thank you so much for joining The Modern CFO podcast. And I hope to talk again soon.[00:35:38] Jack Boyles: Great. Thanks, Andrew. Take care.‍

The Nonlinear Library
LW - Twitter Polls: Evidence is Evidence by Zvi

The Nonlinear Library

Play Episode Listen Later Sep 21, 2022 11:28


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: Twitter Polls: Evidence is Evidence, published by Zvi on September 20, 2022 on LessWrong. Follow-up to: Law of No Evidence Recently, there was some debate about a few Twitter polls, which led into a dispute over the usefulness of Twitter polls in general and how to deal with biased and potentially misleading evidence. Agnus Callard is explicitly asking the same question I asked, which is the opposite of ignoring sample bias: What is accounting for the difference? Sample selection is definitely one of the explanations here. One can also point to several other key differences. My poll asks about you, Patrick asks about how others seem. My poll asks about struggle, Patrick asks about stability. My poll asks about a year versus a point in time, a potential flaw. My poll asks about now, Patrick asks about since pandemic onset. None of this is well-controlled or ‘scientific' in the Science sense. No one is saying any of this is conclusive or precise. What is ‘bad' evidence if it isn't weak evidence? Adam's theory here is that it is misleading evidence. That makes sense as a potential distinction. Under this model: Weak evidence induces a small Bayesian update in the correct direction. Bad evidence can induce an update in the wrong direction. Usually, people with such taxonomies will also think that strong evidence by default trumps weak evidence, allowing you to entirely ignore it. That is not how that works. Either something has a likelihood ratio, or it doesn't. The question is, what to do about the danger that someone might misinterpret the data and update ‘wrong'? I love that the account is called ‘Deconstruction Guide.' Thanks, kind sir. Whether or not this ‘depends on the poll' depends on what level of technically correct we are on, and one can go back and forth on that several times. The fully correct answer is: Yes, some info. You always know that the person chose to make the poll, and how many people chose to respond given the level of exposure, and the responses always tell you something, even if the choices were ‘Grune' and ‘Mlue,' ‘Yes' and ‘Absolutely,' or ‘Maybe' and ‘Maybe Not.' Remember that if any other result would have told you something, then this result also tells you something, because it means the result that would have told you something did not happen. That doesn't mean it helps you with any particular question. Anyway, back to main thread. Getting into a Socratic dialog with a Socratic philosopher, and letting them play the role of Socrates. Classic blunder. I certainly want to know the extent to which the world is full of lunatics. Adam Gurri's new claim has now narrowed to something more reasonable, that citing a Twitter poll as representative even of some subgroup marks you as foolish. We can agree that taking a Twitter poll, not adjusting for sample bias, and drawing conclusions is foolish. Saying it equates to a subgroup that is similar to the group polled still requires dealing with response bias and all that, but mostly seems fine. Adjusting for the nature of your sample should render the whole thing fine in any case. You can also find good information in a Twitter poll by comparing its results to another Twitter poll using the same account (and same retweets, ideally). The difference between the two is meaningful. This can be a difference between questions or wordings, or a difference over time, or something else. Rules of Evidence Aristotle is indeed wise. He points to the important distinction between evidence, as in Bayesian evidence or a reason one might change one's mind or one's probabilities, and the rules of evidence in a given format of debate or discourse. In a court of law, some forms of Bayesian evidence are considered irrelevant or, even more extremely, prejudicial, exactly because they should cause one to update their probabi...

The Nonlinear Library
EA - Prize and fast track to alignment research at ALTER by Vanessa

The Nonlinear Library

Play Episode Listen Later Sep 19, 2022 5:32


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: Prize and fast track to alignment research at ALTER, published by Vanessa on September 18, 2022 on The Effective Altruism Forum. Cross-posted from the AI Alignment Forum. On behalf of ALTER and Superlinear, I am pleased to announce a prize of at least 50,000 USD, to be awarded for the best substantial contribution to the learning-theoretic AI alignment research agenda among those submitted before October 1, 2023. Depending on the quality of submissions, the winner(s) may be offered a position as a researcher in ALTER (similar to this one), to continue work on the agenda, if they so desire. Submit here. Topics The research topics eligible for the prize are: Studying the mathematical properties of the algorithmic information-theoretic definition of intelligence. Building and analyzing formal models of value learning based on the above. Pursuing any of the future research directions listed in the article on infra-Bayesian physicalism. Studying infra-Bayesian logic in general, and its applications to infra-Bayesian reinforcement learning in particular. Theoretical study of the behavior of RL agents in population games. In particular, understand to what extent infra-Bayesianism helps to avoid the grain-of-truth problem. Studying the conjectures relating superrationality to thermodynamic Nash equilibria. Studying the theoretical properties of the infra-Bayesian Turing reinforcement learning setting. Developing a theory of reinforcement learning with traps, i.e. irreversible state transitions. Possible research directions include studying the computational complexity of Bayes-optimality for finite state policies (in order to avoid the NP-hardness for arbitrary policies) and bootstrapping from a safe baseline policy. New topics might be added to this list over the year. Requirements The format of the submission can be either a LessWrong post/sequence or an arXiv paper. The submission is allowed to have one or more authors. In the latter case, the authors will be considered for the prize as a team, and if they win, the prize money will be split between them either equally or according to their own internal agreement. For the submission to be eligible, its authors must not include: Anyone employed or supported by ALTER. Members of the board of directors of ALTER. Members of the panel of the judges. First-degree relatives or romantic partners of judges. In order to win, the submission must be a substantial contribution to the mathematical theory of one of the topics above. For this, it must include at least one of: A novel theorem, relevant to the topic, which is difficult to prove. A novel unexpected mathematical definition, relevant to the topic, with an array of natural properties. Some examples of known results which would be considered substantial at the time: Theorems 1 and 2 in "RL with imperceptible rewards". Definition 1.1 in "infra-Bayesian physicalism", with the various theorems proved about it. Theorem 1 in "Forecasting using incomplete models". Definition 7 in "Basic Inframeasure Theory", with the various theorems proved about it. Evaluation The evaluation will consist of two phases. In the first phase, I will select 3 finalists. In the second phase, each of the finalists will be evaluated by a panel of judges comprising of: Adam Shimi Alexander Appel Daniel Filan Vanessa Kosoy (me) Each judge will score the submission on a scale of 0 to 4. These scores will be added to produce a total score between 0 and 16. If no submission achieves a score of 12 or more, the main prize will not be awarded. If at least one submission achieves a score of 12 or more, the submission with the highest score will be the winner. In case of a tie, the money will be split between the front runners. The final winner will be announced publicly, but the scores received by various submissions...

Surfing the Nash Tsunami
S3-E45.3 - Paris Review: Session 5 on Clinical Trial Innovation

Surfing the Nash Tsunami

Play Episode Listen Later Sep 19, 2022 13:48


This episode addresses the other talks in Session 5 focusing on clinical trial innovation, after Frank Anania's opener.Roy Sabo presented a cogent, comprehensible discussion of the value of adaptive trial strategies. Roger relates the presentation to a conversation on the NASH Tsunami last month with Stephen Harrison. Stephen introduced an example of adjusting FibroScan thresholds to boost the number of patients who could enter clinical trials. Roger commends Sabo on the “wizardry Bayesian statistics.”The following two talks discussed organizing patient databases in ways that simplified trial recruitment and had the potential to reduce screen fail rates. Lastly, the group explores the pros and cons of educating patients on improving self-care. The benefit is that it creates a larger, ready-made patient pool and improves overall health. The drawback is that it increases placebo rates in clinical trials, thereby requiring drugs to perform better to demonstrate statistical difference. However, in a world of well-informed patients practicing aggressive health self-management, there should be the expectation that drugs offer better performance.The conversation finishes with covering the rest of the day one program.

The Delingpod: The James Delingpole Podcast

Support the Delingpod's existence! by joining James' Locals: https://jamesdelingpole.locals.com/ Norman Fenton is Professor of Risk Information Management at Queen Mary London University and is also a Director of Agena, a company that specialises in risk management for critical systems.  Norman is a mathematician by training whose current research focuses on critical decision-making and, in particular, on quantifying uncertainty.  This typically involves analysing and predicting the probabilities of unknown events using causal, probabilistic models (Bayesian networks).  This type of reasoning enables improved assessment by taking account of both statistical data where available and also expert judgment,  providing more powerful insights and better decision making than is possible from purely data-driven solutions. Website: http://www.eecs.qmul.ac.uk/~norman/    Freedom isn't free - James needs your support to continue creating The Delingpod. There are many ways you can show your support to James: Join the James Delingpole Community as a paid supporter at: jamesdelingpole.locals.com Support James monthly at: subscribestar.com/jamesdelingpole Support James' Writing at: substack.com/jamesdelingpole www.delingpoleworld.com Buy James a Coffee at: buymeacoffee.com/jamesdelingpole   Find full episodes of The Delingpod for free (and leave a 5-star rating) on: Apple Podcasts: https://podcasts.apple.com/gb/podcast/the-delingpod-the-james-delingpole-podcast/id1449753062 Spotify: https://open.spotify.com/show/7bdfnyRzzeQsAZQ6OT9e7G?si=a21dc71c7a144f48 Podbean: delingpole.podbean.com Odysee: https://odysee.com/@JamesDelingpoleChannel:0 Rumble: https://rumble.com/user/JamesDelingpole BitChute: https://www.bitchute.com/channel/Zxu5yMwNWTbs/ YouTube: https://www.youtube.com/c/TheJamesDelingpoleChannel   Follow James on Social Media: Twitter: twitter.com/jamesdelingpole Instagram: instagram.com/delingpodclips GETTR: gettr.com/jamesdelingpole Telegram: https://t.me/+dAx_7JX7WQlwYzVk  

The Nonlinear Library
AF - Prize and fast track to alignment research at ALTER by Vanessa Kosoy

The Nonlinear Library

Play Episode Listen Later Sep 17, 2022 5:29


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: Prize and fast track to alignment research at ALTER, published by Vanessa Kosoy on September 17, 2022 on The AI Alignment Forum. On behalf of ALTER and Superlinear, I am pleased to announce a prize of at least 50,000 USD, to be awarded for the best substantial contribution to the learning-theoretic AI alignment research agenda among those submitted before October 1, 2023. Depending on the quality of submissions, the winner(s) may be offered a position as a researcher in ALTER (similar to this one), to continue work on the agenda, if they so desire. Submit here. Topics The research topics eligible for the prize are: Studying the mathematical properties of the algorithmic information-theoretic definition of intelligence. Building and analyzing formal models of value learning based on the above. Pursuing any of the future research directions listed in the article on infra-Bayesian physicalism. Studying infra-Bayesian logic in general, and its applications to infra-Bayesian reinforcement learning in particular. Theoretical study of the behavior of RL agents in population games. In particular, understand to what extent infra-Bayesianism helps to avoid the grain-of-truth problem. Studying the conjectures relating superrationality to thermodynamic Nash equilibria. Studying the theoretical properties of the infra-Bayesian Turing reinforcement learning setting. Developing a theory of reinforcement learning with traps, i.e. irreversible state transitions. Possible research directions include studying the computational complexity of Bayes-optimality for finite state policies (in order to avoid the NP-hardness for arbitrary policies) and bootstrapping from a safe baseline policy. New topics might be added to this list over the year. Requirements The format of the submission can be either a LessWrong post/sequence or an arXiv paper. The submission is allowed to have one or more authors. In the latter case, the authors will be considered for the prize as a team, and if they win, the prize money will be split between them either equally or according to their own internal agreement. For the submission to be eligible, its authors must not include: Anyone employed or supported by ALTER. Members of the board of directors of ALTER. Members of the panel of the judges. First-degree relatives or romantic partners of judges. In order to win, the submission must be a substantial contribution to the mathematical theory of one of the topics above. For this, it must include at least one of: A novel theorem, relevant to the topic, which is difficult to prove. A novel unexpected mathematical definition, relevant to the topic, with an array of natural properties. Some examples of known results which would be considered substantial at the time: Theorems 1 and 2 in "RL with imperceptible rewards". Definition 1.1 in "infra-Bayesian physicalism", with the various theorems proved about it. Theorem 1 in "Forecasting using incomplete models". Definition 7 in "Basic Inframeasure Theory", with the various theorems proved about it. Evaluation The evaluation will consist of two phases. In the first phase, I will select 3 finalists. In the second phase, each of the finalists will be evaluated by a panel of judges comprising of: Adam Shimi Alexander Appel Daniel Filan Vanessa Kosoy (me) Each judge will score the submission on a scale of 0 to 4. These scores will be added to produce a total score between 0 and 16. If no submission achieves a score of 12 or more, the main prize will not be awarded. If at least one submission achieves a score of 12 or more, the submission with the highest score will be the winner. In case of a tie, the money will be split between the front runners. The final winner will be announced publicly, but the scores received by various submissions will not. Fast Track If the prize is awar...

The PicPod
PicPod 65 @PCCS22: Bayesian vs Frequentist statistics with David Harrison

The PicPod

Play Episode Listen Later Sep 16, 2022 46:21


Without statistics, studies are just words. We need some way of translating data in to messages. More and more we read about studies which are using the mysterious Bayesian approach. How does this compare to the standard “frequentist” approach? What are the differences in data collection, analysis, messaging, and interpretation? […]

British Ecological Society Journals
MEEin3: Identifying latent behavioral states in animal movement

British Ecological Society Journals

Play Episode Listen Later Sep 15, 2022 3:09


The latest Methods in Ecology and Evolution brought to you in 3 minutes… more or less! In this episode we interview Dr Josh Cullen about their recent publication titled "Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method". Read the full article here: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13745 For more exclusive content check out the MEE blog and social media. Music: “You're no help” by Silent Partner (CC BY-SA 3.0).

Surfing the Nash Tsunami
S3-E45 - A Review of Paris NASH 2022 with Jeff McIntyre

Surfing the Nash Tsunami

Play Episode Listen Later Sep 15, 2022 62:28


The importance of integrating the NAFLD and NASH field within a wider scope of metabolic health was emphasized at the 8th Paris NASH Meeting. Last week, Surfers Roger Green, Louise Campbell and Jörn Schattenberg were in attendance alongside patient advocate and episode guest, Jeff McIntyre.The conversation starts with Roger asking the group about one thing each found particularly striking from the meeting. Jörn responds first, noting his many appearances across the history of the event. He thinks this year's dominating theme to be the engagement of regulatory questions that address moving beyond biopsies and conditional drug approval. Jeff joins to echo this takeaway, adding that he is intrigued by the multinational dynamics of the meeting. Next, Louise recalls a more specific crux. In response to an industry roundtable, she suggests that with the wealth of data provided from clinical trials it is now time to consider changing endpoints. Lastly, Roger offers his general thoughts on witnessing the shifting tensions between the scientific and patient advocate positions and the regulatory and payer responses. The group then compares the American and European positions on moving the field toward a more metabolic perspective. They consider where this pressure comes from in terms of regulators, patients, payers, politicians and employers.Moving on, Roger asks the group for an example of one particular talk or panel that grabbed their imagination and why. Louise returns to the statistics surrounding NASH. She reminds that while the field searches for more data, an expected rise of 110-125% in advanced liver disease and mortality by 2040 is underway. Given the influence of obesity on this rise, Louise highlights the role of allied health professionals in providing lifestyle guidance to mitigate disease progression from an earlier stage.In the closing session, both Jörn and Jeff reflect on participating in discussions surrounding the role of the patient voice and developing a global strategy for NASH. This leads the group to explore the potential for public advocacy in the field of NAFLD and NASH. Afterwards, Roger brings focus to educating physicians and other healthcare professionals on the nature of metabolic disease. By developing an understanding of the multifaceted application of many drugs in a metabolic context, the wait for an F1 or NAFLD drug approval can be possibly eliminated altogether. Louise adds that a pivot is required to move away from organ-centric thinking.Next, the group provides their thoughts on Session 5, starting with response adaptive trial design to pick the best dose. Roger and Jörn share ideas on clinical trial criteria and the use of Bayesian priors in enrichment strategies. Jeff also revisits the role of patient involvement in this topic.Roger then suggests the four go through and connect missing dots between sessions covered in the conversation thus far. After that, the final response:Louise looks toward a bright future with metabolic coordination. She believes patients are key in developing person-centric approaches. Jörn believes that addressing multiple organ systems is the way forward and that it is necessary to partner with other disciplines. Jeff feels grateful for his participation as a patient advocate. He says he is looking for the experience of the science, which in the end feels positive to him. Roger reiterates the importance of investigating these discussions as Paris NASH does. “One thing that became clearer to me in this meeting than it's ever been before, is that this is all about metabolic disease.” He is hopeful. Surf on for the full review.

The Nonlinear Library
EA - Roodman's Thoughts on Biological Anchors by lukeprog

The Nonlinear Library

Play Episode Listen Later Sep 14, 2022 2:51


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: Roodman's Thoughts on Biological Anchors, published by lukeprog on September 14, 2022 on The Effective Altruism Forum. A new review of Ajeya Cotra's Forecasting TAI with biological anchors (see also update here), written by David Roodman in April 2020, has been added to the folder of public reviews for Cotra's report. Roodman's summary: I think my main critical reaction is about the draft report's ecumenical approach. It puts non-zero weight on several different frameworks which, conditional on the various parameter choices favored in the report, contradict one another. This mixing of distributions expresses a kind of radical uncertainty: not only are we unsure about the parameter values within each framework; we're also unsure about which framework is most right. This set-up is pragmatic and humble, but. I think in principle the ecumenism discards useful information, by not imposing the restriction that the various frameworks agree. In principle, they are all measuring the same thing. In pure Bayesian reasoning, if one has several uncertain measurements of the same value, each represented by a probability distribution, then one combines these primary measurements by multiplying them pointwise and rescaling the result to have total integral one. This contrasts with the pointwise averaging performed in the draft report, which is the mathematical expression of ecumenism. In Bayesian reasoning, if two distributions for the same parameter are normal, then their combination is too; its mean is the average of the two primary means, weighting by the respective precisions (inverse variances). Weirdly, if the two primary means are far apart, so that the two distributions hardly overlap, then their combination can pop up in the no-man's-land between them. The intuition is that the combined distribution centers on the least unlikely estimate given what we know. I make that mathematical point less to argue for a mechanical implementation of Bayesian mixing of different perspectives than to advocate for an informal didactic that aims at unification. What is the least implausible way to reconcile the large disagreements between different frameworks? Could answers to that question help us settle on a single, favored framework, perhaps one that synthesizes ideas from more than one? That impulse ultimately led me to favor a single framework that fuses elements from several in the draft report. The idea is to model two training levels at once, of parameters and hyperparameters. Training of parameters corresponds to the training of a single neural network, or the learning a sentient organism undergoes during maturation. Hyperparameter training corresponds to the design space exploration that AI researchers engage in and, in the biological realm, to evolution. Each parameter training run may involve huge numbers of small parameter updates; each in turn serves a single hyperparameter training step. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Learning Bayesian Statistics
#68 Probabilistic Machine Learning & Generative Models, with Kevin Murphy

Learning Bayesian Statistics

Play Episode Listen Later Sep 14, 2022 65:35


Proudly sponsored by https://www.pymc-labs.io/ (PyMC Labs), the Bayesian Consultancy. https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf (Book a call), or get in touch! Hosting someone like Kevin Murphy on your podcast is… complicated. Not because Kevin himself is complicated (he's delightful, don't make me say what I didn't say!), but because all the questions I had for him amounted to a 12-hour show. Sooooo, brace yourselves folks! No, I'm kidding. Of course I didn't do that folks, Kevin has a life! This life started in Ireland, where he was born. He grew up in England and got his BA from the University of Cambridge. After his PhD at UC Berkeley, he did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California in 2011 on his sabbatical and then ended up staying.  He currently runs a team of about 8 researchers inside of Google Brain working on generative models, optimization, and other, as Kevin puts it, “basic” research topics in AI/ML. He has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and the last one coming in 2023. You may be familiar with his 2012 book, as it was awarded the DeGroot Prize for best book in the field of statistical science. Outside of work, Kevin enjoys traveling, outdoor sports (especially tennis, snowboarding and scuba diving), as well as reading, cooking, and spending time with his family. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Kevin's website: https://www.cs.ubc.ca/~murphyk/ (https://www.cs.ubc.ca/~murphyk/) Kevin on Twitter: https://mobile.twitter.com/sirbayes (https://mobile.twitter.com/sirbayes) Kevin's books (free pdf) on GitHub (includes a link to places where you can buy the hard copy): https://probml.github.io/pml-book/ (https://probml.github.io/pml-book/) Book that inspired Kevin to get into AI: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567 (https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567) State-space models library in JAX (WIP): https://github.com/probml/ssm-jax (https://github.com/probml/ssm-jax) Other software for the book (also in JAX): https://github.com/probml/pyprobml (https://github.com/probml/pyprobml) Fun photo of...

The Data Scientist Show
Bayesian thinking in work and life, ad attribution models and A/B testing, machine learning@Foursquare - Max Sklar - the data scientist show050

The Data Scientist Show

Play Episode Listen Later Sep 13, 2022 90:25


Max Sklar is an independent engineer and researcher. Previously, he was an engineering and Innovation Labs Advisor at Foursquare after 7 years at the company as a machine learning engineer. Previously, he has worked on Ad Attribution, recommendation engine, ratings. He is the host of The Local Maximum podcast. Max studied CS from Yale, and holds a Master degree in information systems from New York university. If you like the show, subscribe to the channel and give us a 5-star review :) Follow Daliana on https://twitter.com/DalianaLiu for more on data science and this podcast. Max's Linkedin: https://www.linkedin.com/in/max-sklar-b638464/ Max's website: localmaxradio.com/about Interviews he mentioned during the podcast: Andrew Gelman, Statistics at Columbia University Shirin Mojarad on Causality Johnny Nelson on Free Speech and Moderation online Stephanie Yang talking about Foursquare's Venue Rating System Dennis Crowley: on Labs, on Innovation Sophie Carr (Bayesian Mathematician) Will Kurt (Bayesian) Marsbot for Airpods Other Episodes Mentioned Bayesian Thinking P-Hacking Interview on Learn Bayesian Statistics

SuperDataScience
SDS 607: Inferring Causality

SuperDataScience

Play Episode Listen Later Sep 9, 2022 73:12


Dr. Jennifer Hill, Professor of Applied Statistics at New York University, joins Jon this week for a discussion that covers causality, correlation, and inference in data science. In this episode you will learn: • How causality is central to all applications of data science [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer's favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer's new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607

SuperDataScience
SDS 607: Inferring Causality

SuperDataScience

Play Episode Listen Later Sep 8, 2022 73:12


We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. In this episode you will learn: • How causality is central to all applications of data science [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer's favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer's new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607

The Nonlinear Library
EA - Marketing Messages Trial for GWWC Giving Guide Campaign by Erin Morrissey

The Nonlinear Library

Play Episode Listen Later Sep 8, 2022 24:14


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: Marketing Messages Trial for GWWC Giving Guide Campaign, published by Erin Morrissey on September 8, 2022 on The Effective Altruism Forum. The trial was run in conjunction with Josh Lewis (NYU). Thanks to David Moss and others for feedback on this post, and to Jamie Elsey for support with the Bayesian analysis. TL;DR Giving What We Can together with the EA Market Testing Team (EAMT) tested marketing and messaging themes on Facebook in their Effective Giving Guide Facebook Lead campaigns which ran from late November 2021 - January 2022. GWWC's Giving Guide answers key questions about effective giving and includes the latest effective giving recommendations to teach donors how to do the most good with their donations. These were exploratory trials to identify promising strategies to recruit people for GWWC and engage people with EA more broadly. We report the most interesting patterns from these trials to provide insight into which hypotheses might be worth exploring more rigorously in future (‘confirmatory analysis') work. Across four trials we compared the effectiveness of different types of (1) messages, (2) videos, and (3) targeted audiences. The key outcomes were (i) email addresses per dollar (when a Facebook user provides an email lead) and (ii) link clicks per dollar. Based on our analysis of 682,577 unique Facebook ‘impressions', we found: The cost of an email address was as low as $8.00 across campaigns, but it seemed to vary substantially across audiences, videos, and messages. The message "Only 3% of donors give based on charity effectiveness, yet the best charities can be 100x more impactful" generated more link clicks and email addresses per dollar than other messages. In contrast, the message "Giving What We Can has helped 6,000+ people make a bigger impact on the causes they care about most" was less cost-effective than the other messages. A ‘short video with facts about effective giving' generated more email addresses per dollar than either (1) a long video with facts about effective giving or (2) a long video that explained how GWWC can help maximize charitable impact, the GWWC 'brand video.' On a per-dollar basis ‘Animal' audiences that were given animal-related cause videos performed among the best, both overall and in the most comparable trials. ‘Lookalike' audiences (those with a similar profile as current people engaging with GWWC) performed best overall, for both cause and non-cause videos. However, ‘Climate' and ‘Global Poverty' audiences basically underperformed the ‘Philanthropy' audience when presented videos ‘for their own causes.' The Animal-related cause video performed particularly poorly on the ‘Philanthropy' audience. Demographics were mostly not predictive of email addresses per dollar nor link clicks per dollar See our Quarto dynamic document linked here for more details, and ongoing analyses. Purpose and Interpretation of this Report One of the primary goals of the EAMT is to identify the most effective, scalable strategies for marketing EA. Our main approach is to test marketing and messaging themes in naturally-occurring settings (such as advertising campaigns on Facebook, YouTube, etc.), targeting large audiences, to determine which specific strategies work best in the most relevant contexts. In this report, we share key patterns and insights about the effectiveness of different marketing and messaging approaches used in GWWC's Effective Giving Guide Facebook Lead campaigns. The patterns we share here serve as a starting point to consider themes and hypotheses to test more rigorously in our ongoing research project. We are hoping for feedback and suggestions from the EA community on these trials and their implementation and analysis. We continue to conduct detailed analyses of this data. We'd like to get ideas from the community ...

SuperDataScience
SDS 607: Inferring Causality

SuperDataScience

Play Episode Listen Later Sep 6, 2022 73:12


We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. In this episode you will learn: • How causality is central to all applications of data science [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer's favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer's new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607

The Nonlinear Library
LW - Bugs or Features? by qbolec

The Nonlinear Library

Play Episode Listen Later Sep 3, 2022 3:41


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: Bugs or Features?, published by qbolec on September 3, 2022 on LessWrong. We often laugh over human-specific "bugs" in reasoning, comparing it to a gold standard of some utility-maximizing perfect Bayesian reasoner. We often fear that a very capable AI following strict rules of optimization would reach some repugnant conclusions, and struggle to find "features" to add to guard against it. What if some of the "bugs" we are looking at, are actually the "features" we are looking for? We seem to distinguish "sacred" and "non-sacred" values, refusing to mix the two in calculations (for example human life vs money). What if this "tainted bit", "NaN-propagation", is a security feature guarding against Goodharting leading to genocide or dissolution of social trust? What if utility is not a single real number, but instead a pair? What if the ordering is not even lexicographic, but partial? What if it's a much longer tuple? Which brings me to next point: We often experience decision paralysis apparently unable to compare two actions. What if this is simply because the order must be partial for security reasons? An alternative explanation of this phenomenon is that we implicitly treat "wait for more data to arrive and/or situation to change in tie-braking way" as an action available to us - is that bad? We often decide which of the two end-states A vs B we prefer based on the path leading to them, amusingly favoring A in some scenarios and B in others. What if this is because we implicitly assume that end-state contains our brain with the memory of the path leading there? Isn't this a cool feature to treat the agent as part of its environment? Or what if this is because we implicitly factor in considerations of "what if other society members would follow this kind of path, or decision-making algorithm?". Isn't this a cool feature to think about second-order effects, about "acausal trades", and to treat own software as perhaps shared with other agents? At least some long-term stable cultures have norms requiring children to follow adults' advice even if it conflicts their own judgment and more importantly said children apparently follow along instead of revolting and doing what (seems) good for them. Isn't that corrigibility a feature we want from AIs we plan to rear? Shouldn't there be safe-guards against child-knowing-better-than-parent in any self-modifying system spawning new generations of itself? The whole sunken cost fallacy/heuristic. Isn't it actually a good thing to associate cost with each deviation from the original plan? Do we really want to zig-zag between more and more shiny objects with no meta-level realization that there's something wrong with this whole algorithm in general if it can't keep its trajectory predictable to itself? Yeah, sunken cost is more than that - it's not just fixed additional cost of decision - it's more like the guilt for not caring about your past self being invested into something. But again, isn't that a good thing from a security perspective? I anticipate that each of these examples can be laughed at using some toy problem simple enough to calculate on the napkin. Sure, but we are talking about producing agents with partial information about very fuzzy world around them with lots of other agents embedded in them, some of them sharing goals or even parts of source code - we will rarely meet spherical cows on our way and overfitting to these learning examples is the very problem we want to solve. Do we really plan to solve all of that with a single simple elegant formula (AIXI style), or the plan always was to throw in some safety heuristics to the mix? If it's the later, then perhaps we can take a hint from parents raising children, societies avoiding dissolving, and people avoiding mania? Thus, what I propose is to take a look at the l...

Learning Bayesian Statistics
#67 Exoplanets, Cool Worlds & Life in the Universe, with David Kipping

Learning Bayesian Statistics

Play Episode Listen Later Aug 31, 2022 60:42


Proudly sponsored by https://www.pymc-labs.io/ (PyMC Labs), the Bayesian Consultancy. https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf (Book a call), or get in touch! Is there life in the Universe? It doesn't get deeper than this, does it? And yet, why do we care about that? In the very small chance that there is other life in the Universe, we have even less chance to discover it, talk to it and meet it. So, why do we care? Well, it may surprise you but Bayesian statistics helps us think about these astronomical and — dare I say? — philosophical topics, as my guest, David Kipping, will brilliantly explain in this episode. David is an Associate Professor of Astronomy at Columbia University, where he leads the Cool Worlds Lab — I know, the name is awesome. His team's research spans exoplanet discovery and characterization, the search for life in the Universe and developing novel approaches to our exploration of the cosmos. David also teaches astrostatistics, and his contributions to Bayesian statistics span astrobiology to exoplanet detection. He also hosts the Cool Worlds YouTube channel, with over half a million subscribers, that discusses his team's work and broader topics within the field. Cool worlds, cool guest, cool episode. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: David's website: http://user.astro.columbia.edu/~dkipping/ (http://user.astro.columbia.edu/~dkipping/) David on Twitter: https://twitter.com/david_kipping (https://twitter.com/david_kipping) David's YouTube channel: https://www.youtube.com/c/coolworldslab (https://www.youtube.com/c/coolworldslab) David's research group: https://www.coolworldslab.com/ (https://www.coolworldslab.com/) Bayesian analysis of the astrobiological implications of life's early emergence on Earth : https://www.pnas.org/doi/10.1073/pnas.1111694108 (https://www.pnas.org/doi/10.1073/pnas.1111694108) We Have No Idea – A Guide to the Unknown Universe : https://www.goodreads.com/book/show/31625636-we-have-no-idea (https://www.goodreads.com/book/show/31625636-we-have-no-idea) Leonardo da Vinci's biography by Walter Isaacson: https://www.amazon.com/Leonardo-Vinci-Walter-Isaacson/dp/1501139169/ref=sr_1_1?keywords=leonardo+da+vinci+book&qid=1660142880&sprefix=leonardo+%2Caps%2C219&sr=8-1...

Shaping Opinion
Neuroscientist Karl Friston on Intelligence and Free Energy

Shaping Opinion

Play Episode Listen Later Aug 29, 2022 67:55


Pioneering neuroscientist Karl Friston joins Tim to talk about a concept he's developed called the free-energy principle, which may hold the key to advancing the understanding human intelligence as we know it. Karl is a theoretical neuroscientist. He's an authority on brain imaging. His work has advanced mankind's understanding of schizophrenia, among other things. At the moment, he's becoming better known as the originator of the free-energy principle for human action and perception. In this episode, we'll talk with Karl about that free-energy principle, what it is, what it means and what it can mean for the future. https://traffic.libsyn.com/secure/shapingopinion/Karl_Friston_Final_auphonic.mp3 I hope you have your coffee and are sitting in a comfortable place, because this conversation is going to introduce you to some entirely new thinking from one of the world's most unique scientific thinkers, Karl Friston. Before we get started, you need to know a little about Karl, and you will need an explanation of some of the words we will use here. Karl Friston is a theoretical neuroscientist. As mentioned, he is an authority on brain imaging.  1990, he invented something called statistical parametric mapping or (SPM).  invented SPM, a computational technique that helps create brain images in a consistent shape so researchers can make consistent comparisons. He then invented Voxel-based morphometry or (VBM). An example of this is when he studied London taxi drivers to measure the rear side of the brain's hippocampus to watch it grow as their knowledge of the streets grew. After that, he invented something called dynamic causal modeling (DCM) for brain imaging, to determine if people who have severe brain damage or minimally conscious or vegetative. He is one of the most frequently cited neuroscientists in the world.  Each one of these inventions centered on schizophrenia research and theoretical studies of value-learning – formulated as the dysconnection hypothesis of schizophrenia. To try to simplify, it's the hypothesis that when the so-called wiring in your brain isn't all connecting properly. Karl currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference).  That's what we cover in this episode. Karl received the first Young Investigators Award in Human Brain Mapping in 1996. He was elected a Fellow of the Academy of Medical Sciences in 1999. Since then, he has received numerous other honors and recognition for his work. Links The Genius Neuroscientist Who Might Hold the Key to True AI, Wired Karl Friston, The Helix Center Karl Friston and the Free Energy Principle, ExploringYourMind.com About this Episode's Guest Karl Friston Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of th...

The Saad Truth with Dr. Saad
My Chat with Computer Scientist Dr. Judea Pearl, Co-Author of The Book of Why (The Saad Truth with Dr. Saad_441)

The Saad Truth with Dr. Saad

Play Episode Listen Later Aug 19, 2022 73:45


Topics covered include causality (causal inferencing), heuristics, artificial intelligence, Bayesian statistics, operations research, optimization, Alan Turing, Daniel Pearl, purpose and meaning in life, and regret. Judea's website: http://bayes.cs.ucla.edu/jp_home.html Note: Apologies for the low audio stemming from my guest. It is tough to always ensure maximal production quality. _______________________________________ If you appreciate my work and would like to support it: https://subscribestar.com/the-saad-truth https://patreon.com/GadSaad https://paypal.me/GadSaad _______________________________________ This clip was posted earlier today (August 19, 2022) on my YouTube channel as THE SAAD TRUTH_1444: https://youtu.be/SVEAZDWQ_lc _______________________________________ The Parasitic Mind: How Infectious Ideas Are Killing Common Sense (paperback edition) was released on October 5, 2021. Order your copy now. https://www.amazon.com/Parasitic-Mind-Infectious-Killing-Common/dp/162157959X/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= https://www.amazon.ca/Parasitic-Mind-Infectious-Killing-Common/dp/162157959X https://www.amazon.co.uk/Parasitic-Mind-Infectious-Killing-Common/dp/162157959X _______________________________________ Please visit my website gadsaad.com, and sign up for alerts. If you appreciate my content, click on the "Support My Work" button. I count on my fans to support my efforts. You can donate via Patreon, PayPal, and/or SubscribeStar. _______________________________________ Dr. Gad Saad is a professor, evolutionary behavioral scientist, and author who pioneered the use of evolutionary psychology in marketing and consumer behavior. In addition to his scientific work, Dr. Saad is a leading public intellectual who often writes and speaks about idea pathogens that are destroying logic, science, reason, and common sense. _______________________________________

The Nonlinear Library
LW - Conditioning, Prompts, and Fine-Tuning by Adam Jermyn

The Nonlinear Library

Play Episode Listen Later Aug 19, 2022 6:46


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: Conditioning, Prompts, and Fine-Tuning, published by Adam Jermyn on August 17, 2022 on LessWrong. (Thanks to Evan Hubinger and Nicholas Schiefer for comments on these ideas.) These are some notes on the relation between conditioning language models, prompting, and fine-tuning. The key takeaways are: Prompting and fine-tuning can both be used to condition language models. Prompting is quite restricted in the kinds of conditionals it can achieve. Fine-tuning can implement arbitrary conditionals in principle, though not in practice. In practice fine-tuning can still implement more kinds of conditionals than prompting. We don't understand how fine-tuning conditionals generalize, which seems dangerous. Conditioning We can think of a language model as specifying a probability distribution π(x), where x is a sequence of tokens of fixed length N (the length of the context window). We generate text by sampling sequences from π. Sometimes we don't want to just sample from a language model. Instead, we want to condition the model on some facts about the sequence x. We can write the conditioned distribution as where c(x) encodes some constraints on x. For instance c(x) might require that the first token is “Apple”, or that the 7th and 12th tokens are the same, etc. Some c