Podcasts about bayesians

Play Episode Listen Later May 27, 2024 4:03

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: Book review: Everything Is Predictable, published by PeterMcCluskey on May 27, 2024 on LessWrong. Book review: Everything Is Predictable: How Bayesian Statistics Explain Our World, by Tom Chivers. Many have attempted to persuade the world to embrace a Bayesian worldview, but none have succeeded in reaching a broad audience. E.T. Jaynes' book has been a leading example, but its appeal is limited to those who find calculus enjoyable, making it unsuitable for a wider readership. Other attempts to engage a broader audience often focus on a narrower understanding, such as Bayes' Theorem, rather than the complete worldview. Claude's most fitting recommendation was Rationality: From AI to Zombies, but at 1,813 pages, it's too long and unstructured for me to comfortably recommend to most readers. (GPT-4o's suggestions were less helpful, focusing only on resources for practical problem-solving). Aubrey Clayton's book, Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science, only came to my attention because Chivers mentioned it, offering mixed reviews that hint at why it remained unnoticed. Chivers has done his best to mitigate this gap. While his book won't reach as many readers as I'd hoped, I'm comfortable recommending it as the standard introduction to the Bayesian worldview for most readers. Basics Chivers guides readers through the fundamentals of Bayes' Theorem, offering little that's extraordinary in this regard. A fair portion of the book is dedicated to explaining why probability should be understood as a function of our ignorance, contrasting with the frequentist approach that attempts to treat probability as if it existed independently of our minds. The book has many explanations of how frequentists are wrong, yet concedes that the leading frequentists are not stupid. Frequentism's problems often stem from a misguided effort to achieve more objectivity in science than seems possible. The only exception to this mostly fair depiction of frequentists is a section titled "Are Frequentists Racist?". Chivers repeats Clayton's diatribe affirming this, treating the diatribe more seriously than it deserves, before dismissing it. (Frequentists were racist when racism was popular. I haven't seen any clear evidence of whether Bayesians behaved differently). The Replication Crisis Chivers explains frequentism's role in the replication crisis. A fundamental drawback of p-values is that they indicate the likelihood of the data given a hypothesis, which differs from the more important question of how likely the hypothesis is given the data. Here, Chivers (and many frequentists) overlook a point raised by Deborah Mayo: p-values can help determine if an experiment had a sufficiently large sample size. Deciding whether to conduct a larger experiment can be as ew: Everything Is Predictablecrucial as drawing the best inference from existing data. The perversity of common p-value usage is exemplified by Lindley's paradox: a p-value below 0.05 can sometimes provide Bayesian evidence against the tested hypothesis. A p-value of 0.04 indicates that the data are unlikely given the null hypothesis, but we can construct scenarios where the data are even less likely under the hypothesis you wish to support. A key factor in the replication crisis is the reward system for scientists and journals, which favors publishing surprising results. The emphasis on p-values allows journals to accept more surprising results compared to a Bayesian approach, creating a clear disincentive for individual scientists or journals to adopt Bayesian methods before others do. Minds Approximate Bayes The book concludes by describing how human minds employ heuristics that closely approximate the Bayesian approach. This includes a well-written summary of how predictive processing works, demonstrating ...

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LW - Book review: Everything Is Predictable by PeterMcCluskey

Play Episode Listen Later May 27, 2024 4:03

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: Book review: Everything Is Predictable, published by PeterMcCluskey on May 27, 2024 on LessWrong. Book review: Everything Is Predictable: How Bayesian Statistics Explain Our World, by Tom Chivers. Many have attempted to persuade the world to embrace a Bayesian worldview, but none have succeeded in reaching a broad audience. E.T. Jaynes' book has been a leading example, but its appeal is limited to those who find calculus enjoyable, making it unsuitable for a wider readership. Other attempts to engage a broader audience often focus on a narrower understanding, such as Bayes' Theorem, rather than the complete worldview. Claude's most fitting recommendation was Rationality: From AI to Zombies, but at 1,813 pages, it's too long and unstructured for me to comfortably recommend to most readers. (GPT-4o's suggestions were less helpful, focusing only on resources for practical problem-solving). Aubrey Clayton's book, Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science, only came to my attention because Chivers mentioned it, offering mixed reviews that hint at why it remained unnoticed. Chivers has done his best to mitigate this gap. While his book won't reach as many readers as I'd hoped, I'm comfortable recommending it as the standard introduction to the Bayesian worldview for most readers. Basics Chivers guides readers through the fundamentals of Bayes' Theorem, offering little that's extraordinary in this regard. A fair portion of the book is dedicated to explaining why probability should be understood as a function of our ignorance, contrasting with the frequentist approach that attempts to treat probability as if it existed independently of our minds. The book has many explanations of how frequentists are wrong, yet concedes that the leading frequentists are not stupid. Frequentism's problems often stem from a misguided effort to achieve more objectivity in science than seems possible. The only exception to this mostly fair depiction of frequentists is a section titled "Are Frequentists Racist?". Chivers repeats Clayton's diatribe affirming this, treating the diatribe more seriously than it deserves, before dismissing it. (Frequentists were racist when racism was popular. I haven't seen any clear evidence of whether Bayesians behaved differently). The Replication Crisis Chivers explains frequentism's role in the replication crisis. A fundamental drawback of p-values is that they indicate the likelihood of the data given a hypothesis, which differs from the more important question of how likely the hypothesis is given the data. Here, Chivers (and many frequentists) overlook a point raised by Deborah Mayo: p-values can help determine if an experiment had a sufficiently large sample size. Deciding whether to conduct a larger experiment can be as ew: Everything Is Predictablecrucial as drawing the best inference from existing data. The perversity of common p-value usage is exemplified by Lindley's paradox: a p-value below 0.05 can sometimes provide Bayesian evidence against the tested hypothesis. A p-value of 0.04 indicates that the data are unlikely given the null hypothesis, but we can construct scenarios where the data are even less likely under the hypothesis you wish to support. A key factor in the replication crisis is the reward system for scientists and journals, which favors publishing surprising results. The emphasis on p-values allows journals to accept more surprising results compared to a Bayesian approach, creating a clear disincentive for individual scientists or journals to adopt Bayesian methods before others do. Minds Approximate Bayes The book concludes by describing how human minds employ heuristics that closely approximate the Bayesian approach. This includes a well-written summary of how predictive processing works, demonstrating ...

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Tom Chivers - From Science to Daily Life: The Impact of Bayesian Thinking | STM Podcast #214

Seize The Moment Podcast

Play Episode Listen Later May 19, 2024 68:21

On episode 214, we welcome Tom Chivers to discuss Bayesian statistics, how their counterintuitive nature tends to turn people off, the philosophical disagreements between the Bayesians and the frequentists, why “priors” aren't purely subjective and why all theories should be considered as priors, the difficulty of quantifying emotional states in psychological research, how priors are used and misused to inform interpretations of new data, our innate tendency toward black and white thinking, the replication crisis, and why statistically significant research is often wrong. Tom Chivers is an author and the award-winning science writer for Semafor. His writing has appeared in The Times (London), The Guardian, New Scientist, Wired, CNN, and more. He is the co-host of The Studies Show podcast alongside Stuart Richie.His books include The Rationalist's Guide to the Galaxy, and How to Read Numbers. His newest book, available now, is called Everything Is Predictable: How Bayesian Statistics Explain Our World. | Tom Chivers | ► Website | https://tomchivers.com ► Twitter | https://x.com/TomChivers ► Semafor | https://www.semafor.com/author/tom-chivers ► Podcast | https://www.thestudiesshowpod.com ► Everything is Predictable Book | https://amzn.to/3UJTOxD Where you can find us: | Seize The Moment Podcast | ► Facebook | https://www.facebook.com/SeizeTheMoment ► Twitter | https://twitter.com/seize_podcast ► Instagram | https://www.instagram.com/seizethemoment ► TikTok | https://www.tiktok.com/@seizethemomentpodcast

LW - The Parable Of The Fallen Pendulum - Part 2 by johnswentworth

Play Episode Listen Later Mar 13, 2024 7:34

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Parable Of The Fallen Pendulum - Part 2, published by johnswentworth on March 13, 2024 on LessWrong. Previously: Some physics 101 students calculate that a certain pendulum will have a period of approximately 3.6 seconds. Instead, when they run the experiment, the stand holding the pendulum tips over and the whole thing falls on the floor. The students, being diligent Bayesians, argue that this is strong evidence against Newtonian mechanics, and the professor's attempts to rationalize the results in hindsight are just that: rationalization in hindsight. What say the professor? "Hold on now," the professor answers, "'Newtonian mechanics' isn't just some monolithic magical black box. When predicting a period of approximately 3.6 seconds, you used a wide variety of laws and assumptions and approximations, and then did some math to derive the actual prediction. That prediction was apparently incorrect. But at which specific point in the process did the failure occur? For instance: Were there forces on the pendulum weight not included in the free body diagram? Did the geometry of the pendulum not match the diagrams? Did the acceleration due to gravity turn out to not be 9.8 m/s^2 toward the ground? Was the acceleration of the pendulum's weight times its mass not always equal to the sum of forces acting on it? Was the string not straight, or its upper endpoint not fixed? Did our solution of the differential equations governing the system somehow not match the observed trajectory, despite the equations themselves being correct, or were the equations wrong? Was some deeper assumption wrong, like that the pendulum weight has a well-defined position at each time? … etc" The students exchange glances, then smile. "Now those sound like empirically-checkable questions!" they exclaim. The students break into smaller groups, and rush off to check. Soon, they begin to report back. "After replicating the setup, we were unable to identify any significant additional forces acting on the pendulum weight while it was hanging or falling. However, once on the floor there was an upward force acting on the pendulum weight from the floor, as well as significant friction with the floor. It was tricky to isolate the relevant forces without relying on acceleration as a proxy, but we came up with a clever - " … at this point the group is drowned out by another. "On review of the video, we found that the acceleration of the pendulum's weight times its mass was indeed always equal to the sum of forces acting on it, to within reasonable error margins, using the forces estimated by the other group. Furthermore, we indeed found that acceleration due to gravity was consistently approximately 9.8 m/s^2 toward the ground, after accounting for the other forces," says the second group to report. Another arrives: "Review of the video and computational reconstruction of the 3D arrangement shows that, while the geometry did basically match the diagrams initially, it failed dramatically later on in the experiment. In particular, the string did not remain straight, and its upper endpoint moved dramatically." Another: "We have numerically verified the solution to the original differential equations. The error was not in the math; the original equations must have been wrong." Another: "On review of the video, qualitative assumptions such as the pendulum being in a well-defined position at each time look basically correct, at least to precision sufficient for this experiment. Though admittedly unknown unknowns are always hard to rule out." [1] A few other groups report, and then everyone regathers. "Ok, we have a lot more data now," says the professor, "what new things do we notice?" "Well," says one student, "at least some parts of Newtonian mechanics held up pretty well. The whole F = ma thing worked, and th...

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LW - The Parable Of The Fallen Pendulum - Part 2 by johnswentworth

Play Episode Listen Later Mar 13, 2024 7:34

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: The Parable Of The Fallen Pendulum - Part 2, published by johnswentworth on March 13, 2024 on LessWrong. Previously: Some physics 101 students calculate that a certain pendulum will have a period of approximately 3.6 seconds. Instead, when they run the experiment, the stand holding the pendulum tips over and the whole thing falls on the floor. The students, being diligent Bayesians, argue that this is strong evidence against Newtonian mechanics, and the professor's attempts to rationalize the results in hindsight are just that: rationalization in hindsight. What say the professor? "Hold on now," the professor answers, "'Newtonian mechanics' isn't just some monolithic magical black box. When predicting a period of approximately 3.6 seconds, you used a wide variety of laws and assumptions and approximations, and then did some math to derive the actual prediction. That prediction was apparently incorrect. But at which specific point in the process did the failure occur? For instance: Were there forces on the pendulum weight not included in the free body diagram? Did the geometry of the pendulum not match the diagrams? Did the acceleration due to gravity turn out to not be 9.8 m/s^2 toward the ground? Was the acceleration of the pendulum's weight times its mass not always equal to the sum of forces acting on it? Was the string not straight, or its upper endpoint not fixed? Did our solution of the differential equations governing the system somehow not match the observed trajectory, despite the equations themselves being correct, or were the equations wrong? Was some deeper assumption wrong, like that the pendulum weight has a well-defined position at each time? … etc" The students exchange glances, then smile. "Now those sound like empirically-checkable questions!" they exclaim. The students break into smaller groups, and rush off to check. Soon, they begin to report back. "After replicating the setup, we were unable to identify any significant additional forces acting on the pendulum weight while it was hanging or falling. However, once on the floor there was an upward force acting on the pendulum weight from the floor, as well as significant friction with the floor. It was tricky to isolate the relevant forces without relying on acceleration as a proxy, but we came up with a clever - " … at this point the group is drowned out by another. "On review of the video, we found that the acceleration of the pendulum's weight times its mass was indeed always equal to the sum of forces acting on it, to within reasonable error margins, using the forces estimated by the other group. Furthermore, we indeed found that acceleration due to gravity was consistently approximately 9.8 m/s^2 toward the ground, after accounting for the other forces," says the second group to report. Another arrives: "Review of the video and computational reconstruction of the 3D arrangement shows that, while the geometry did basically match the diagrams initially, it failed dramatically later on in the experiment. In particular, the string did not remain straight, and its upper endpoint moved dramatically." Another: "We have numerically verified the solution to the original differential equations. The error was not in the math; the original equations must have been wrong." Another: "On review of the video, qualitative assumptions such as the pendulum being in a well-defined position at each time look basically correct, at least to precision sufficient for this experiment. Though admittedly unknown unknowns are always hard to rule out." [1] A few other groups report, and then everyone regathers. "Ok, we have a lot more data now," says the professor, "what new things do we notice?" "Well," says one student, "at least some parts of Newtonian mechanics held up pretty well. The whole F = ma thing worked, and th...

3d speech parable fallen ea pendulum newtonian rationalist lesswrong bayesians another on

LW - Ideological Bayesians by Kevin Dorst

Play Episode Listen Later Feb 26, 2024 19:11

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: Ideological Bayesians, published by Kevin Dorst on February 26, 2024 on LessWrong. TLDR: It's often said that Bayesian updating is unbiased and converges to the truth - and, therefore, that biases must emerge from non-Bayesian sources. That's not quite right. The convergence results require updating on your total evidence - but for agents at all like us, that's impossible - instead, we must selectively attend to certain questions, ignoring others. Yet correlations between what we see and what questions we ask - "ideological" Bayesian updating - can lead to predictable biases and polarization. Professor Polder is a polarizing figure. His fans praise him for his insight; his critics denounce him for his aggression. Ask his fans, and they'll supply you with a bunch of instances when he made an insightful comment during discussions. They'll admit that he's sometimes aggressive, but they can't remember too many cases - he certainly doesn't seem any more aggressive than the average professor. Ask his critics, and they'll supply you with a bunch of instances when he made an aggressive comment during discussions. They'll admit that he's sometimes insightful, but they can't remember too many cases - he certainly doesn't seem any more insightful than the average professor. This sort of polarization is, I assume, familiar. But let me tell you a secret: Professor Polder is, in fact, perfectly average - he has an unremarkably average number of both insightful and aggressive comments. So what's going on? His fans are better at noticing his insights, while his critics are better at noticing his aggression. As a result, their estimates are off: his fans think he's more insightful than he is, and his critics think he's more aggressive than he is. Each are correct about individual bits of the picture - when they notice aggression or insight, he is being aggressive or insightful. But none are correct about the overall picture. This source of polarization is also, I assume, familiar. It's widely appreciated that background beliefs and ideology - habits of mind, patterns of salience, and default forms of explanation - can lead to bias, disagreement, and polarization. In this broad sense of "ideology", we're familiar with the observation that real people - especially fans and critics - are often ideological.[1] But let me tell you another secret: Polder's fans and critics are all Bayesians. More carefully: they all maintain precise probability distributions over the relevant possibilities, and they always update their opinions by conditioning their priors on the (unambiguous) true answer to a partitional question. How is that possible? Don't Bayesians, in such contexts, update in unbiased[2] ways, always converge to the truth, and therefore avoid persistent disagreement? Not necessarily. The trick is that which question they update on is correlated with what they see - they have different patterns of salience. For example, when Polder makes a comment that is both insightful and aggressive, his fans are more likely to notice (just) the insight, while his critics are more likely to notice (just) the aggression. This can lead to predictable polarization. I'm going to give a model of how such correlations - between what you see, and what questions you ask about it - can lead otherwise rational Bayesians to diverge from both each other and the truth. Though simplified, I think it sheds light on how ideology might work. Limited-Attention Bayesians Standard Bayesian epistemology says you must update on your total evidence. That's nuts. To see just how infeasible that is, take a look at the following video. Consider the question: what happens to the exercise ball? I assume you noticed that the exercise ball disappeared. Did you also notice that the Christmas tree gained lights, the bowl changed c...

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LW - Ideological Bayesians by Kevin Dorst

Play Episode Listen Later Feb 26, 2024 19:11

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: Ideological Bayesians, published by Kevin Dorst on February 26, 2024 on LessWrong. TLDR: It's often said that Bayesian updating is unbiased and converges to the truth - and, therefore, that biases must emerge from non-Bayesian sources. That's not quite right. The convergence results require updating on your total evidence - but for agents at all like us, that's impossible - instead, we must selectively attend to certain questions, ignoring others. Yet correlations between what we see and what questions we ask - "ideological" Bayesian updating - can lead to predictable biases and polarization. Professor Polder is a polarizing figure. His fans praise him for his insight; his critics denounce him for his aggression. Ask his fans, and they'll supply you with a bunch of instances when he made an insightful comment during discussions. They'll admit that he's sometimes aggressive, but they can't remember too many cases - he certainly doesn't seem any more aggressive than the average professor. Ask his critics, and they'll supply you with a bunch of instances when he made an aggressive comment during discussions. They'll admit that he's sometimes insightful, but they can't remember too many cases - he certainly doesn't seem any more insightful than the average professor. This sort of polarization is, I assume, familiar. But let me tell you a secret: Professor Polder is, in fact, perfectly average - he has an unremarkably average number of both insightful and aggressive comments. So what's going on? His fans are better at noticing his insights, while his critics are better at noticing his aggression. As a result, their estimates are off: his fans think he's more insightful than he is, and his critics think he's more aggressive than he is. Each are correct about individual bits of the picture - when they notice aggression or insight, he is being aggressive or insightful. But none are correct about the overall picture. This source of polarization is also, I assume, familiar. It's widely appreciated that background beliefs and ideology - habits of mind, patterns of salience, and default forms of explanation - can lead to bias, disagreement, and polarization. In this broad sense of "ideology", we're familiar with the observation that real people - especially fans and critics - are often ideological.[1] But let me tell you another secret: Polder's fans and critics are all Bayesians. More carefully: they all maintain precise probability distributions over the relevant possibilities, and they always update their opinions by conditioning their priors on the (unambiguous) true answer to a partitional question. How is that possible? Don't Bayesians, in such contexts, update in unbiased[2] ways, always converge to the truth, and therefore avoid persistent disagreement? Not necessarily. The trick is that which question they update on is correlated with what they see - they have different patterns of salience. For example, when Polder makes a comment that is both insightful and aggressive, his fans are more likely to notice (just) the insight, while his critics are more likely to notice (just) the aggression. This can lead to predictable polarization. I'm going to give a model of how such correlations - between what you see, and what questions you ask about it - can lead otherwise rational Bayesians to diverge from both each other and the truth. Though simplified, I think it sheds light on how ideology might work. Limited-Attention Bayesians Standard Bayesian epistemology says you must update on your total evidence. That's nuts. To see just how infeasible that is, take a look at the following video. Consider the question: what happens to the exercise ball? I assume you noticed that the exercise ball disappeared. Did you also notice that the Christmas tree gained lights, the bowl changed c...

christmas speech ea ideological bayesian dorst polder rationalist lesswrong bayesians tldr it

LW - Bayesians Commit the Gambler's Fallacy by Kevin Dorst

Play Episode Listen Later Jan 7, 2024 15:48

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: Bayesians Commit the Gambler's Fallacy, published by Kevin Dorst on January 7, 2024 on LessWrong. TLDR: Rational people who start out uncertain about an (in fact independent) causal process and then learn from unbiased data will rule out "streaky" hypotheses more quickly than "switchy" hypotheses. As a result, they'll commit the gambler's fallacy: expecting the process to switch more than it will. In fact, they'll do so in ways that match a variety of empirical findings about how real people commit the gambler's fallacy. Maybe it's not a fallacy, after all. (This post is based on a full paper.) Baylee is bored. The fluorescent lights hum. The spreadsheets blur. She needs air. As she steps outside, she sees the Prius nestled happily in the front spot. Three days in a row now - the Prius is on a streak. The Jeep will probably get it tomorrow, she thinks. This parking battle - between a Prius and a Jeep - has been going on for months. Unbeknownst to Baylee, the outcomes are statistically independent: each day, the Prius and the Jeep have a 50% chance to get the front spot, regardless of how the previous days have gone. But Baylee thinks and acts otherwise: after the Prius has won the spot a few days in a row, she tends to think the Jeep will win next. (And vice versa.) So Baylee is committing the gambler's fallacy: the tendency to think that streaks of (in fact independent) outcomes are likely to switch. Maybe you conclude from this - as many psychologists have - that Baylee is bad at statistical reasoning. You're wrong. Baylee is a rational Bayesian. As I'll show: when either data or memory are limited, Bayesians who begin with causal uncertainty about an (in fact independent) process - and then learn from unbiased data - will, on average, commit the gambler's fallacy. Why? Although they'll get evidence that the process is neither "switchy" nor "streaky", they'll get more evidence against the latter. Thus they converge asymmetrically to the truth (of independence), leading them to (on average) commit the gambler's fallacy along the way. More is true. Bayesians don't just commit the gambler's fallacy - they do so in way that qualitatively matches a wide variety of trends found in the empirical literature on the gambler's fallacy. This provides evidence for: Causal-Uncertainty Hypothesis: The gambler's fallacy is due to causal uncertainty combined with rational responses to limited data and memory. This hypothesis stacks up favorably against extant theories of the gambler's fallacy in terms of both explanatory power and empirical coverage. See the paper for the full argument - here I'll just sketch the idea. Asymmetric Convergence Consider any process that can have one of two repeatable outcomes - Prius vs. Jeep; heads vs. tails; hit vs. miss; 1 vs. 0; etc. Baylee knows that the process (say, the parking battle) is "random" in the sense that (i) it's hard to predict, and (ii) in the long run, the Prius wins 50% of the time. But that leaves open three classes of hypotheses: Steady: The outcomes are independent, so each day there's a 50% chance the Prius wins the spot. (Compare: a fair coin toss.) Switchy: The outcomes tend to switch: after the Prius wins a few in a row, the Jeep becomes more likely to win; after the Jeep wins a few, vice versa. (Compare: drawing from a deck of cards without replacement - after a few red cards, a black card becomes more likely.) Sticky: The outcomes tend to form streaks: after the Prius wins a few, it becomes more likely to win again; likewise for the Jeep. (Compare: basketball shots - after a player makes a few, they become "hot" and so are more likely to make the next one. No, the "hot hand" is not a myth.[1]) So long as each of these hypotheses is symmetric around 50%, they all will lead to (i) the process being hard to predict, and (ii...

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LW - Bayesians Commit the Gambler's Fallacy by Kevin Dorst

christmas art ai model language original llc large palestinians electronics strava large language models yc ferret multimodal hn hacker news ny governor bayesians

Play Episode Listen Later Jan 7, 2024 15:48

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: Bayesians Commit the Gambler's Fallacy, published by Kevin Dorst on January 7, 2024 on LessWrong. TLDR: Rational people who start out uncertain about an (in fact independent) causal process and then learn from unbiased data will rule out "streaky" hypotheses more quickly than "switchy" hypotheses. As a result, they'll commit the gambler's fallacy: expecting the process to switch more than it will. In fact, they'll do so in ways that match a variety of empirical findings about how real people commit the gambler's fallacy. Maybe it's not a fallacy, after all. (This post is based on a full paper.) Baylee is bored. The fluorescent lights hum. The spreadsheets blur. She needs air. As she steps outside, she sees the Prius nestled happily in the front spot. Three days in a row now - the Prius is on a streak. The Jeep will probably get it tomorrow, she thinks. This parking battle - between a Prius and a Jeep - has been going on for months. Unbeknownst to Baylee, the outcomes are statistically independent: each day, the Prius and the Jeep have a 50% chance to get the front spot, regardless of how the previous days have gone. But Baylee thinks and acts otherwise: after the Prius has won the spot a few days in a row, she tends to think the Jeep will win next. (And vice versa.) So Baylee is committing the gambler's fallacy: the tendency to think that streaks of (in fact independent) outcomes are likely to switch. Maybe you conclude from this - as many psychologists have - that Baylee is bad at statistical reasoning. You're wrong. Baylee is a rational Bayesian. As I'll show: when either data or memory are limited, Bayesians who begin with causal uncertainty about an (in fact independent) process - and then learn from unbiased data - will, on average, commit the gambler's fallacy. Why? Although they'll get evidence that the process is neither "switchy" nor "streaky", they'll get more evidence against the latter. Thus they converge asymmetrically to the truth (of independence), leading them to (on average) commit the gambler's fallacy along the way. More is true. Bayesians don't just commit the gambler's fallacy - they do so in way that qualitatively matches a wide variety of trends found in the empirical literature on the gambler's fallacy. This provides evidence for: Causal-Uncertainty Hypothesis: The gambler's fallacy is due to causal uncertainty combined with rational responses to limited data and memory. This hypothesis stacks up favorably against extant theories of the gambler's fallacy in terms of both explanatory power and empirical coverage. See the paper for the full argument - here I'll just sketch the idea. Asymmetric Convergence Consider any process that can have one of two repeatable outcomes - Prius vs. Jeep; heads vs. tails; hit vs. miss; 1 vs. 0; etc. Baylee knows that the process (say, the parking battle) is "random" in the sense that (i) it's hard to predict, and (ii) in the long run, the Prius wins 50% of the time. But that leaves open three classes of hypotheses: Steady: The outcomes are independent, so each day there's a 50% chance the Prius wins the spot. (Compare: a fair coin toss.) Switchy: The outcomes tend to switch: after the Prius wins a few in a row, the Jeep becomes more likely to win; after the Jeep wins a few, vice versa. (Compare: drawing from a deck of cards without replacement - after a few red cards, a black card becomes more likely.) Sticky: The outcomes tend to form streaks: after the Prius wins a few, it becomes more likely to win again; likewise for the Jeep. (Compare: basketball shots - after a player makes a few, they become "hot" and so are more likely to make the next one. No, the "hot hand" is not a myth.[1]) So long as each of these hypotheses is symmetric around 50%, they all will lead to (i) the process being hard to predict, and (ii...

speech commit ea compare jeep gambler fallacy unbeknownst prius bayesian dorst rationalist lesswrong bayesians

December 23rd, 2023 | Ferret: A Multimodal Large Language Model

Hacker News Recap

Play Episode Listen Later Dec 24, 2023 19:24

This is a recap of the top 10 posts on Hacker News on December 23rd, 2023.This podcast was generated by wondercraft.ai(00:37): Ferret: A Multimodal Large Language ModelOriginal post: https://news.ycombinator.com/item?id=38745348&utm_source=wondercraft_ai(02:27): Xmas.c (1988)Original post: https://news.ycombinator.com/item?id=38745668&utm_source=wondercraft_ai(04:09): Meta censors pro-Palestinian views on a global scale, Human Rights Watch claimsOriginal post: https://news.ycombinator.com/item?id=38745673&utm_source=wondercraft_ai(06:19): Suno AIOriginal post: https://news.ycombinator.com/item?id=38743719&utm_source=wondercraft_ai(08:08): Endurain: Self-hosted Strava like serviceOriginal post: https://news.ycombinator.com/item?id=38742637&utm_source=wondercraft_ai(09:59): The Art of Electronics (2015)Original post: https://news.ycombinator.com/item?id=38748370&utm_source=wondercraft_ai(11:44): NY Governor vetoes ban on noncompete clauses, waters down LLC transparency billOriginal post: https://news.ycombinator.com/item?id=38749155&utm_source=wondercraft_ai(13:37): In 2023 Organic Maps got its first million usersOriginal post: https://news.ycombinator.com/item?id=38746187&utm_source=wondercraft_ai(15:13): StreamDiffusion: A pipeline-level solution for real-time interactive generationOriginal post: https://news.ycombinator.com/item?id=38749434&utm_source=wondercraft_ai(17:00): Bayesians moving from defense to offenseOriginal post: https://news.ycombinator.com/item?id=38744588&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

#93 A CERN Odyssey, with Kevin Greiff

psychology conspiracies hidden ufos reddit logic rabbit hole mulder eric weinstein ufos aliens mick west bayesians

Play Episode Listen Later Oct 18, 2023 109:05

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meThis is a very special episode. It is the first-ever LBS video episode, and it takes place in the heart of particle physics research -- the CERN

Interview with Mick West: UFOs, Aliens, and Conspiracy Psychology

Decoding the Gurus

Play Episode Listen Later Jun 17, 2023 104:10

UFOs are all the rage now, and it's certainly a topic that excites many of our gurus *cough* . Hidden mysteries, advanced technologies, conspiracies, and government cover-ups. What's not to like!?The truth, as Mulder so eloquently put it, is out there. And sometimes if you combine the outcome of some posterior technical analysis with some basic priors about the fallibility of human perception and memory, then the truth might be a little prosaic (happy Bayesians!!?).Joining us today is the esteemed Mick West, retired video game developer, who has a long track record of investigating UFO footage, along with a range of other outré phenomena. Mick is admirably positioned to provide practical advice on how to apply critical thinking while being empathetic to friends and family who may have fallen down one or more conspiratorial rabbit holes. Chris and Matt enjoyed the conversation with Mick a lot, and we think you will too!LinksAn example of Eric's UFO Tapdance Lex's Reddit thread for the Matthew McConaughey EpisodeMick West's Book: Escaping the Rabbit Hole: How to Debunk Conspiracy Theories Using Facts, Logic, and RespectTheories of Everything Channel: Eric Weinstein and Mick West: UAPs, Evidence, SkepticismOther LinksOur PatreonContact us via email: decodingthegurus@gmail.comThe DTG Subreddit

LW - A common failure for foxes by Rob Bensinger

social failure speech paying opinion ea collecting foxes important questions voi bayesian rct hyperbolic rationalist ben singer lesswrong bayesians

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.

LW - A common failure for foxes by Rob Bensinger

social failure speech paying opinion ea collecting foxes important questions voi bayesian rct hyperbolic rationalist ben singer lesswrong bayesians

Play Episode Listen Later Oct 15, 2022 3:09

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: 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.

Stability and combinations, with Aleksa Gordić

London Futurists

Play Episode Listen Later Sep 28, 2022 31:55

This episode continues our discussion with AI researcher Aleksa Gordić from DeepMind on understanding today's most advanced AI systems.00.07 This episode builds on Episode 501.05 We start with GANs – Generative Adversarial Networks01.33 Solving the problem of stability, with higher resolution03.24 GANs are notoriously hard to train. They suffer from mode collapse03.45 Worse, the model might not learn anything, and the result is pure noise03.55 DC GANs introduced convolutional layers to stabilise them and enable higher resolution04.37 The technique of outpainting05.55 Generating text as well as images, and producing stories06.14 AI Dungeon06.28 From GANs to Diffusion models06.48 DDPM (De-noising diffusion probabilistic models) does for diffusion models what DC GANs did for GANs07.20 They are more stable, and don't suffer from mode collapse07.30 They do have downsides. They are much more computation intensive08.24 What does the word diffusion mean in this context?08.40 It's adopted from physics. It peels noise away from the image09.17 Isn't that rewinding entropy?09.45 One application is making a photo taken in 1830 look like one taken yesterday09.58 Semantic Segmentation Masks convert bands of flat colour into realistic images of sky, earth, sea, etc10.35 Bounding boxes generate objects of a specified class from tiny inputs11.00 The images are not taken from previously seen images on the internet, but invented from scratch11.40 The model saw a lot of images during training, but during the creation process it does not refer back to them12.40 Failures are eliminated by amendments, as always with models like this12.55 Scott Alexander blogged about models producing images with wrong relationships, and how this was fixed within 3 months13.30 The failure modes get harder to find as the obvious ones are eliminated13.45 Even with 175 billion parameters, GPT-3 struggled to handle three digits in computation15.18 Are you often surprised by what the models do next?15.50 The research community is like a hive mind, and you never know where the next idea will come from16.40 Often the next thing comes from a couple of students at a university16.58 How Ian Goodfellow created the first GAN17.35 Are the older tribes described by Pedro Domingos (analogisers, evolutionists, Bayesians…) now obsolete?18.15 We should cultivate different approaches because you never know where they might lead19.15 Symbolic AI (aka Good Old Fashioned AI, or GOFAI) is still alive and kicking19.40 AlphaGo combined deep learning and GOFAI21.00 Doug Lennart is still persevering with Cyc, a purely GOFAI approach21.30 GOFAI models had no learning element. They can't go beyond the humans whose expertise they encapsulate22.25 The now-famous move 37 in AlphaGo's game two against Lee Sedol in 201623.40 Moravec's paradox. Easy things are hard, and hard things are easy24.20 The combination of deep learning and symbolic AI has been long urged, and in fact is already happening24.40 Will models always demand more and more compute?25.10 The human brain has far more compute power than even our biggest systems today25.45 Sparse, or MoE (Mixture of Experts) systems are quite efficient26.00 We need more compute, better algorithms, and more efficiency26.55 Dedicated AI chips will help a lot with efficiency26.25 Cerebros claims that GPT-3 could be trained on a single chip27.50 Models can increasingly be trained for general purposes and then tweaked for particular tasks28.30 Some of the big new models are open access29.00 What else should people learn about with regard to advanced AI?29.20 Neural Radiance Fields (NERF) models30.40 Flamingo and Gato31.15 We have mostly discussed research in these episodes, rather than engineering

Bayes’ Theorem Applying It To Emergency Management

The Emergency Management Network Podcast

Play Episode Listen Later Jun 26, 2022 4:40

Bayes’ Theorem Applying It To Emergency Management Mental models help us with making decisions under stress. They give us a starting point, think of how we teach triage, “start where you stand”. This applies to decision-making as well during a disaster or crisis, start with information that you have. We can make the adjustments as more or better information is obtained. This brings me to the concepts of Bayes’ Theorem. Thomas Bayes was an English minister in the 18th century, whose most famous work, “An Essay toward Solving a Problem in the Doctrine of Chances,” The essay did not contain the theorem as we now know it but had the seeds of the idea. It looked at how to adjust our estimates of probabilities when encountering new data that influence a situation. Later development by French scholar Pierre-Simon Laplace and others helped codify the theorem and develop it into a useful tool for thinking.Now you do not need to be great at math to use this concept. I still need to take off my shoes to count to 19. . More critical is your ability and desire to assign probabilities of truth and accuracy to anything you think you know and then be willing to update those probabilities when new information comes in.We talk about making decisions based on the new information that has come in, however, we often ignore prior information, simply called “priors” in Bayesian-speak. We can blame this habit in part on the availability heuristic—we focus on what’s readily available. In this case, we focus on the newest information, and the bigger picture gets lost. We fail to adjust the probability of old information to reflect what we have learned.The big idea behind Bayes’ theorem is that we must continuously update our probability estimates on an as-needed basis. Let’s take a look at a hurricane as our crisis. We have all seen the way it tracks and can predict that it may make landfall at a certain time and location. We can use past storms as predictors of how this hurricane may act and the damage it could cause. However, new information may come to light on the behavior of the storm. This however should not necessarily negate the previous experience and information you have on hand. In their book The Signal and the Noise, Nate Silver and Allen Lane give a contemporary example, reminding us that new information is often most useful when we put it in the larger context of what we already know:Bayes’ theorem is an important reality check on our efforts to forecast the future. How, for instance, should we reconcile a large body of theory and evidence predicting global warming with the fact that there has been no warming trend over the last decade or so? Skeptics react with glee, while true believers dismiss the new information.A better response is to use Bayes’ theorem: the lack of recent warming is evidence against recent global warming predictions, but it is weak evidence. This is because there is enough variability in global temperatures to make such an outcome unsurprising. The new information should reduce our confidence in our models of global warming—but only a little.The same approach can be used in anything from an economic forecast to a hand of poker, and while Bayes’ theorem can be a formal affair, Bayesian reasoning also works as a rule of thumb. We tend to either dismiss new evidence or embrace it as though nothing else matters. Bayesians try to weigh both the old hypothesis and the new evidence in a sensible way.So much of making better decisions hinges on dealing with uncertainty. The most common thing holding people back from the right answer is instinctively rejecting new information, or not integrating the old. To better serve our communities, have a mental model, work with it and use it to make better decisions. PodcastsThe Todd De Voe Show School Shootings and Emergency Management The K-12 School Shooting Database research project is a widely inclusive database that documents each and every instance a gun is brandished is fired, or a bullet hits school property for any reason, regardless of the number of victims, time, or day of the week.The School Shooting Database Project is conducted as part of the Advanced Thinking in Homeland Security (HSx) program at the Naval Postgraduate School’s Center for Homeland Defense and Security (CHDS).Prepare Respond Recover Saving Lives Through Training Due to the uptick of mass shootings over the years, many professions outside of law enforcement are now being trained in active shooter response programs. But have you ever thought about who teaches the law enforcement officers themselves? Join prepare.respond.recover. host Todd De Voe as he talks with Erik Franco, the CEO of "High Speed Tac Med", one of the nation’s most sought-after active shooter training programs for law enforcement and firefighting. Learn about “Run, Hide, Fight” and how this training is preparing law enforcement officers to tackle an active shooter situation as quickly and efficiently as possible.HSTM - https://highspeedtacmed.com/If you would like to learn more about the Natural Disaster & Emergency Management (NDEM) Expo please visit us on the web - https://www.ndemevent.comBusiness Continuity Today Training for Active Shooters Beyond The Response Active shooting scenarios focus on the police response, and the larger emergency management role during these complex incidents is often overlooked. However, they are multi-week, multi-jurisdictional incidents requiring command & control, interoperable communications, and a host of other services. Supportershttps://www.disastertech.com/https://titanhst.com/https://www.ndemevent.com/en-us/show-info.html Get full access to The Emergency Management Network at emnetwork.substack.com/subscribe

LW - A Suite of Pragmatic Considerations in Favor of Niceness by Alicorn from Living Luminously

Play Episode Listen Later Dec 26, 2021 5:19

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 Cartesian Frames, Part 14: A Suite of Pragmatic Considerations in Favor of Niceness, published by Alicorn. tl;dr: Sometimes, people don't try as hard as they could to be nice. If being nice is not a terminal value for you, here are some other things to think about which might induce you to be nice anyway. There is a prevailing ethos in communities similar to ours - atheistic, intellectual groupings, who congregate around a topic rather than simply to congregate - and this ethos says that it is not necessary to be nice. I'm drawing on a commonsense notion of "niceness" here, which I hope won't confuse anyone (another feature of communities like this is that it's very easy to find people who claim to be confused by monosyllables). I do not merely mean "polite", which can be superficially like niceness when the person to whom the politeness is directed is in earshot but tends to be far more superficial. I claim that this ethos is mistaken and harmful. In so claiming, I do not also claim that I am always perfectly nice; I claim merely that I and others have good reasons to try to be. The dispensing with niceness probably springs in large part from an extreme rejection of the ad hominem fallacy and of emotionally-based reasoning. Of course someone may be entirely miserable company and still have brilliant, cogent ideas; to reject communication with someone who just happens to be miserable company, in spite of their brilliant, cogent ideas, is to miss out on the (valuable) latter because of a silly emotional reaction to the (irrelevant) former. Since the point of the community is ideas; and the person's ideas are good; and how much fun they are to be around is irrelevant - well, bringing up that they are just terribly mean seems trivial at best, and perhaps an invocation of the aforementioned fallacy. We are here to talk about ideas! (Interestingly, this same courtesy is rarely extended to appalling spelling.) The ad hominem fallacy is a fallacy, so this is a useful norm up to a point, but not up to the point where people who are perfectly capable of being nice, or learning to be nice, neglect to do so because it's apparently been rendered locally worthless. I submit that there are still good, pragmatic reasons to be nice, as follows. (These are claims about how to behave around real human-type persons. Many of them would likely be obsolete if we were all perfect Bayesians.) It provides good incentives for others. It's easy enough to develop purely subconscious aversions to things that are unpleasant. If you are miserable company, people may stop talking to you without even knowing they're doing it, and some of these people may have ideas that would have benefited you. It helps you hold off on proposing diagnoses. As tempting as it may be to dismiss people as crazy or stupid, this is a dangerous label for us biased creatures. Fewer people than you are tempted to call these things are genuinely worth writing off as thoroughly as this kind of name-calling may tempt you to do. Conveniently, both these words (as applied to people, more than ideas) and closely related ones are culturally considered mean, and a general niceness policy will exclude them. It lets you exist in a cognitively diverse environment. Meanness is more tempting as an earlier resort when there's some kind of miscommunication, and miscommunication is more likely when you and your interlocutor think differently. Per #1, not making a conscious effort to be nice will tend to drive off the people with the greatest ratio of interesting new contributions to old rehashed repetitions. It is a cooperative behavior. It's obvious that it's nicer to live in a world where everybody is nice than in a world where everyone is a jerk. What's less obvious, but still, I think, true, is that the cost of cooperatively being nice ...

speech favor considerations ea suite pragmatic niceness rationalist meanness bayesians alicorn

LW - A Suite of Pragmatic Considerations in Favor of Niceness by Alicorn from Living Luminously

Play Episode Listen Later Dec 26, 2021 5:19

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 Cartesian Frames, Part 14: A Suite of Pragmatic Considerations in Favor of Niceness, published by Alicorn. tl;dr: Sometimes, people don't try as hard as they could to be nice. If being nice is not a terminal value for you, here are some other things to think about which might induce you to be nice anyway. There is a prevailing ethos in communities similar to ours - atheistic, intellectual groupings, who congregate around a topic rather than simply to congregate - and this ethos says that it is not necessary to be nice. I'm drawing on a commonsense notion of "niceness" here, which I hope won't confuse anyone (another feature of communities like this is that it's very easy to find people who claim to be confused by monosyllables). I do not merely mean "polite", which can be superficially like niceness when the person to whom the politeness is directed is in earshot but tends to be far more superficial. I claim that this ethos is mistaken and harmful. In so claiming, I do not also claim that I am always perfectly nice; I claim merely that I and others have good reasons to try to be. The dispensing with niceness probably springs in large part from an extreme rejection of the ad hominem fallacy and of emotionally-based reasoning. Of course someone may be entirely miserable company and still have brilliant, cogent ideas; to reject communication with someone who just happens to be miserable company, in spite of their brilliant, cogent ideas, is to miss out on the (valuable) latter because of a silly emotional reaction to the (irrelevant) former. Since the point of the community is ideas; and the person's ideas are good; and how much fun they are to be around is irrelevant - well, bringing up that they are just terribly mean seems trivial at best, and perhaps an invocation of the aforementioned fallacy. We are here to talk about ideas! (Interestingly, this same courtesy is rarely extended to appalling spelling.) The ad hominem fallacy is a fallacy, so this is a useful norm up to a point, but not up to the point where people who are perfectly capable of being nice, or learning to be nice, neglect to do so because it's apparently been rendered locally worthless. I submit that there are still good, pragmatic reasons to be nice, as follows. (These are claims about how to behave around real human-type persons. Many of them would likely be obsolete if we were all perfect Bayesians.) It provides good incentives for others. It's easy enough to develop purely subconscious aversions to things that are unpleasant. If you are miserable company, people may stop talking to you without even knowing they're doing it, and some of these people may have ideas that would have benefited you. It helps you hold off on proposing diagnoses. As tempting as it may be to dismiss people as crazy or stupid, this is a dangerous label for us biased creatures. Fewer people than you are tempted to call these things are genuinely worth writing off as thoroughly as this kind of name-calling may tempt you to do. Conveniently, both these words (as applied to people, more than ideas) and closely related ones are culturally considered mean, and a general niceness policy will exclude them. It lets you exist in a cognitively diverse environment. Meanness is more tempting as an earlier resort when there's some kind of miscommunication, and miscommunication is more likely when you and your interlocutor think differently. Per #1, not making a conscious effort to be nice will tend to drive off the people with the greatest ratio of interesting new contributions to old rehashed repetitions. It is a cooperative behavior. It's obvious that it's nicer to live in a world where everybody is nice than in a world where everyone is a jerk. What's less obvious, but still, I think, true, is that the cost of cooperatively being nice ...

speech favor considerations ea suite pragmatic niceness rationalist meanness bayesians alicorn

LW - Philosophy: A Diseased Discipline by lukeprog from Rationality and Philosophy

discipline philosophy speech scientists ea dewey hume eliezer rationality wittgenstein uncle joe analytic lw diseased rationalist michael bishop bayesians

Play Episode Listen Later Dec 25, 2021 11:34

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 Rationality and Philosophy, Part 2: Philosophy: A Diseased Discipline, published by lukeprog. Part of the sequence: Rationality and Philosophy Eliezer's anti-philosophy post Against Modal Logics was pretty controversial, while my recent pro-philosophy (by LW standards) post and my list of useful mainstream philosophy contributions were massively up-voted. This suggests a significant appreciation for mainstream philosophy on Less Wrong - not surprising, since Less Wrong covers so many philosophical topics. If you followed the recent very long debate between Eliezer and I over the value of mainstream philosophy, you may have gotten the impression that Eliezer and I strongly diverge on the subject. But I suspect I agree more with Eliezer on the value of mainstream philosophy than I do with many Less Wrong readers - perhaps most. That might sound odd coming from someone who writes a philosophy blog and spends most of his spare time doing philosophy, so let me explain myself. (Warning: broad generalizations ahead! There are exceptions.) Failed methods Large swaths of philosophy (e.g. continental and postmodern philosophy) often don't even try to be clear, rigorous, or scientifically respectable. This is philosophy of the "Uncle Joe's musings on the meaning of life" sort, except that it's dressed up in big words and long footnotes. You will occasionally stumble upon an argument, but it falls prey to magical categories and language confusions and non-natural hypotheses. You may also stumble upon science or math, but they are used to 'prove' things irrelevant to the actual scientific data or the equations used. Analytic philosophy is clearer, more rigorous, and better with math and science, but only does a slightly better job of avoiding magical categories, language confusions, and non-natural hypotheses. Moreover, its central tool is intuition, and this displays a near-total ignorance of how brains work. As Michael Vassar observes, philosophers are "spectacularly bad" at understanding that their intuitions are generated by cognitive algorithms. A diseased discipline What about Quinean naturalists? Many of them at least understand the basics: that things are made of atoms, that many questions don't need to be answered but instead dissolved, that the brain is not an a priori truth factory, that intuitions come from cognitive algorithms, that humans are loaded with bias, that language is full of tricks, and that justification rests in the lens that can see its flaws. Some of them are even Bayesians. Like I said, a few naturalistic philosophers are doing some useful work. But the signal-to-noise ratio is much lower even in naturalistic philosophy than it is in, say, behavioral economics or cognitive neuroscience or artificial intelligence or statistics. Why? Here are some hypotheses, based on my thousands of hours in the literature: Many philosophers have been infected (often by later Wittgenstein) with the idea that philosophy is supposed to be useless. If it's useful, then it's science or math or something else, but not philosophy. Michael Bishop says a common complaint from his colleagues about his 2004 book is that it is too useful. Most philosophers don't understand the basics, so naturalists spend much of their time coming up with new ways to argue that people are made of atoms and intuitions don't trump science. They fight beside the poor atheistic philosophers who keep coming up with new ways to argue that the universe was not created by someone's invisible magical friend. Philosophy has grown into an abnormally backward-looking discipline. Scientists like to put their work in the context of what old dead guys said, too, but philosophers have a real fetish for it. Even naturalists spend a fair amount of time re-interpreting Hume and Dewey yet again. Because they were tra...

LW - Tendencies in reflective equilibrium by Scott Alexander from The Blue-Minimizing Robot

chief police robots speech ea minimizing equilibrium reflective tendencies scott alexander rationalist bayesians

Play Episode Listen Later Dec 25, 2021 5:56

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is The Blue-Minimizing Robot, Part 16: Tendencies in reflective equilibrium, published by Scott Alexander. Consider a case, not too different from what has been shown to happen in reality, where we ask Bob what sounds like a fair punishment for a homeless man who steals $1,000, and he answers ten years. Suppose we wait until Bob has forgotten that we ever asked the first question, and then ask him what sounds like a fair punishment for a hedge fund manager who steals $1,000,000, and he says five years. Maybe we even wait until he forgets the whole affair, and then ask him the same questions again with the same answers, confirming that these are stable preferences. If we now confront Bob with both numbers together, informing him that he supported a ten year sentence for stealing $1,000 and a five year sentence for stealing $1,000,000, a couple of things might happen. He could say "Yeah, I genuinely believe poor people deserve greater penalties than rich people." But more likely he says "Oh, I guess I was prejudiced." Then if we ask him the same question again, he comes up with two numbers that follow the expected mathematical relationship and punish the greater theft with more jail time. Bob isn't working off of some predefined algorithm for determining punishment, like "jail time = (10 amount stolen)/net worth". I don't know if anyone knows exactly what Bob is doing, but at a stab, he's seeing how many unpleasant feelings get generated by imagining the crime, then proposing a jail sentence that activates about an equal amount of unpleasant feelings. If the thought of a homeless man makes images of crime more readily available and so increases the unpleasant feelings, things won't go well for the homeless man. If you're really hungry, that probably won't help either. So just like nothing automatically synchronizes the intention to study a foreign language and the behavior of studying it, so nothing automatically synchronizes thoughts about punishing the theft of $1000 and punishing the theft of $1000000. Of course, there is something that non-automatically does it. After all, in order to elicit this strange behavior from Bob, we had to wait until he forgot about the first answer. Otherwise, he would have noticed and quickly adjusted his answers to make sense. We probably could represent Bob's tendencies as an equation and call it a preference. Maybe it would be a long equation with terms for net worth of criminal, amount stolen, how much food Bob's eaten in the past six hours, and whether his local sports team won the pennant recently, with appropriate coefficients and powers for each. But if Bob saw this equation, he certainly wouldn't endorse it. He'd probably be horrified. It's also unstable: if given a choice, he would undergo brain surgery to remove this equation, thus preventing it from being satisfied. This is why I am reluctant to call these potential formalizations of these equations a "preference". Instead of saying that Bob has one preference determining his jail time assignments, it would be better to model him as having several tendencies - a tendency to give a certain answer in the $1000 case, a tendency to give a different answer in the $1000000 case, and several tendencies towards things like consistency, fairness, compassion, et cetera. People strongly consciously endorse these latter tendencies, probably because they're socially useful1. If the Chief of Police says "I know I just put this guy in jail for theft, but I'm going to let this other thief off because he's my friend, and I don't really value consistency that much," then they're not going to stay Chief of Police for very long. Bayesians and rationalists, in particular, make a big deal out of consistency. One common parable on the importance of consistency is the Dutch Book - a way to get free mon...

LW - Philosophy: A Diseased Discipline by lukeprog from Rationality and Philosophy

discipline philosophy speech scientists ea dewey hume eliezer rationality wittgenstein uncle joe analytic lw diseased rationalist michael bishop bayesians

Play Episode Listen Later Dec 25, 2021 11:34

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 Rationality and Philosophy, Part 2: Philosophy: A Diseased Discipline, published by lukeprog. Part of the sequence: Rationality and Philosophy Eliezer's anti-philosophy post Against Modal Logics was pretty controversial, while my recent pro-philosophy (by LW standards) post and my list of useful mainstream philosophy contributions were massively up-voted. This suggests a significant appreciation for mainstream philosophy on Less Wrong - not surprising, since Less Wrong covers so many philosophical topics. If you followed the recent very long debate between Eliezer and I over the value of mainstream philosophy, you may have gotten the impression that Eliezer and I strongly diverge on the subject. But I suspect I agree more with Eliezer on the value of mainstream philosophy than I do with many Less Wrong readers - perhaps most. That might sound odd coming from someone who writes a philosophy blog and spends most of his spare time doing philosophy, so let me explain myself. (Warning: broad generalizations ahead! There are exceptions.) Failed methods Large swaths of philosophy (e.g. continental and postmodern philosophy) often don't even try to be clear, rigorous, or scientifically respectable. This is philosophy of the "Uncle Joe's musings on the meaning of life" sort, except that it's dressed up in big words and long footnotes. You will occasionally stumble upon an argument, but it falls prey to magical categories and language confusions and non-natural hypotheses. You may also stumble upon science or math, but they are used to 'prove' things irrelevant to the actual scientific data or the equations used. Analytic philosophy is clearer, more rigorous, and better with math and science, but only does a slightly better job of avoiding magical categories, language confusions, and non-natural hypotheses. Moreover, its central tool is intuition, and this displays a near-total ignorance of how brains work. As Michael Vassar observes, philosophers are "spectacularly bad" at understanding that their intuitions are generated by cognitive algorithms. A diseased discipline What about Quinean naturalists? Many of them at least understand the basics: that things are made of atoms, that many questions don't need to be answered but instead dissolved, that the brain is not an a priori truth factory, that intuitions come from cognitive algorithms, that humans are loaded with bias, that language is full of tricks, and that justification rests in the lens that can see its flaws. Some of them are even Bayesians. Like I said, a few naturalistic philosophers are doing some useful work. But the signal-to-noise ratio is much lower even in naturalistic philosophy than it is in, say, behavioral economics or cognitive neuroscience or artificial intelligence or statistics. Why? Here are some hypotheses, based on my thousands of hours in the literature: Many philosophers have been infected (often by later Wittgenstein) with the idea that philosophy is supposed to be useless. If it's useful, then it's science or math or something else, but not philosophy. Michael Bishop says a common complaint from his colleagues about his 2004 book is that it is too useful. Most philosophers don't understand the basics, so naturalists spend much of their time coming up with new ways to argue that people are made of atoms and intuitions don't trump science. They fight beside the poor atheistic philosophers who keep coming up with new ways to argue that the universe was not created by someone's invisible magical friend. Philosophy has grown into an abnormally backward-looking discipline. Scientists like to put their work in the context of what old dead guys said, too, but philosophers have a real fetish for it. Even naturalists spend a fair amount of time re-interpreting Hume and Dewey yet again. Because they were tra...

LW - Tendencies in reflective equilibrium by Scott Alexander from The Blue-Minimizing Robot

chief police robots speech ea minimizing equilibrium reflective tendencies scott alexander rationalist bayesians

Play Episode Listen Later Dec 25, 2021 5:56

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 The Blue-Minimizing Robot, Part 16: Tendencies in reflective equilibrium, published by Scott Alexander. Consider a case, not too different from what has been shown to happen in reality, where we ask Bob what sounds like a fair punishment for a homeless man who steals $1,000, and he answers ten years. Suppose we wait until Bob has forgotten that we ever asked the first question, and then ask him what sounds like a fair punishment for a hedge fund manager who steals $1,000,000, and he says five years. Maybe we even wait until he forgets the whole affair, and then ask him the same questions again with the same answers, confirming that these are stable preferences. If we now confront Bob with both numbers together, informing him that he supported a ten year sentence for stealing $1,000 and a five year sentence for stealing $1,000,000, a couple of things might happen. He could say "Yeah, I genuinely believe poor people deserve greater penalties than rich people." But more likely he says "Oh, I guess I was prejudiced." Then if we ask him the same question again, he comes up with two numbers that follow the expected mathematical relationship and punish the greater theft with more jail time. Bob isn't working off of some predefined algorithm for determining punishment, like "jail time = (10 amount stolen)/net worth". I don't know if anyone knows exactly what Bob is doing, but at a stab, he's seeing how many unpleasant feelings get generated by imagining the crime, then proposing a jail sentence that activates about an equal amount of unpleasant feelings. If the thought of a homeless man makes images of crime more readily available and so increases the unpleasant feelings, things won't go well for the homeless man. If you're really hungry, that probably won't help either. So just like nothing automatically synchronizes the intention to study a foreign language and the behavior of studying it, so nothing automatically synchronizes thoughts about punishing the theft of $1000 and punishing the theft of $1000000. Of course, there is something that non-automatically does it. After all, in order to elicit this strange behavior from Bob, we had to wait until he forgot about the first answer. Otherwise, he would have noticed and quickly adjusted his answers to make sense. We probably could represent Bob's tendencies as an equation and call it a preference. Maybe it would be a long equation with terms for net worth of criminal, amount stolen, how much food Bob's eaten in the past six hours, and whether his local sports team won the pennant recently, with appropriate coefficients and powers for each. But if Bob saw this equation, he certainly wouldn't endorse it. He'd probably be horrified. It's also unstable: if given a choice, he would undergo brain surgery to remove this equation, thus preventing it from being satisfied. This is why I am reluctant to call these potential formalizations of these equations a "preference". Instead of saying that Bob has one preference determining his jail time assignments, it would be better to model him as having several tendencies - a tendency to give a certain answer in the $1000 case, a tendency to give a different answer in the $1000000 case, and several tendencies towards things like consistency, fairness, compassion, et cetera. People strongly consciously endorse these latter tendencies, probably because they're socially useful1. If the Chief of Police says "I know I just put this guy in jail for theft, but I'm going to let this other thief off because he's my friend, and I don't really value consistency that much," then they're not going to stay Chief of Police for very long. Bayesians and rationalists, in particular, make a big deal out of consistency. One common parable on the importance of consistency is the Dutch Book - a way to get free mon...

Philosophy: A Diseased Discipline by lukeprog

The Nonlinear Library: LessWrong Top Posts

Play Episode Listen Later Dec 11, 2021 11:46

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: Philosophy: A Diseased Discipline , published by lukeprog on the AI Alignment Forum. Part of the sequence: Rationality and Philosophy Eliezer's anti-philosophy post Against Modal Logics was pretty controversial, while my recent pro-philosophy (by LW standards) post and my list of useful mainstream philosophy contributions were massively up-voted. This suggests a significant appreciation for mainstream philosophy on Less Wrong - not surprising, since Less Wrong covers so many philosophical topics. If you followed the recent very long debate between Eliezer and I over the value of mainstream philosophy, you may have gotten the impression that Eliezer and I strongly diverge on the subject. But I suspect I agree more with Eliezer on the value of mainstream philosophy than I do with many Less Wrong readers - perhaps most. That might sound odd coming from someone who writes a philosophy blog and spends most of his spare time doing philosophy, so let me explain myself. (Warning: broad generalizations ahead! There are exceptions.) Failed methods Large swaths of philosophy (e.g. continental and postmodern philosophy) often don't even try to be clear, rigorous, or scientifically respectable. This is philosophy of the "Uncle Joe's musings on the meaning of life" sort, except that it's dressed up in big words and long footnotes. You will occasionally stumble upon an argument, but it falls prey to magical categories and language confusions and non-natural hypotheses. You may also stumble upon science or math, but they are used to 'prove' things irrelevant to the actual scientific data or the equations used. Analytic philosophy is clearer, more rigorous, and better with math and science, but only does a slightly better job of avoiding magical categories, language confusions, and non-natural hypotheses. Moreover, its central tool is intuition, and this displays a near-total ignorance of how brains work. As Michael Vassar observes, philosophers are "spectacularly bad" at understanding that their intuitions are generated by cognitive algorithms. A diseased discipline What about Quinean naturalists? Many of them at least understand the basics: that things are made of atoms, that many questions don't need to be answered but instead dissolved, that the brain is not an a priori truth factory, that intuitions come from cognitive algorithms, that humans are loaded with bias, that language is full of tricks, and that justification rests in the lens that can see its flaws. Some of them are even Bayesians. Like I said, a few naturalistic philosophers are doing some useful work. But the signal-to-noise ratio is much lower even in naturalistic philosophy than it is in, say, behavioral economics or cognitive neuroscience or artificial intelligence or statistics. Why? Here are some hypotheses, based on my thousands of hours in the literature: Many philosophers have been infected (often by later Wittgenstein) with the idea that philosophy is supposed to be useless. If it's useful, then it's science or math or something else, but not philosophy. Michael Bishop says a common complaint from his colleagues about his 2004 book is that it is too useful. Most philosophers don't understand the basics, so naturalists spend much of their time coming up with new ways to argue that people are made of atoms and intuitions don't trump science. They fight beside the poor atheistic philosophers who keep coming up with new ways to argue that the universe was not created by someone's invisible magical friend. Philosophy has grown into an abnormally backward-looking discipline. Scientists like to put their work in the context of what old dead guys said, too, but philosophers have a real fetish for it. Even naturalists spend a fair amount of time re-interpreting Hume and Dewey yet again. Because they were trained in ...

discipline philosophy speech scientists ea dewey hume eliezer rationality wittgenstein uncle joe analytic lw diseased rationalist michael bishop bayesians

Why We Launched LessWrong.SubStack by Ben Pace

The Nonlinear Library: LessWrong Top Posts

Play Episode Listen Later Dec 11, 2021 7:27

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: Why We Launched LessWrong.SubStack , published by Ben Pace on the AI Alignment Forum. (This is a crosspost from our new SubStack. Go read the original.) Subtitle: We really, really needed the money. We've decided to move LessWrong to SubStack. Why, you ask? That's a great question. 1. SubSidizing LessWrong is important We've been working hard to budget LessWrong, but we're failing. Fundraising for non-profits is really hard. We've turned everywhere for help. We decided to follow Clippy's helpful advice to cut down on server costs and also increase our revenue, by moving to an alternative provider. We considered making a LessWrong OnlyFans, where we would regularly post the naked truth. However, we realized due to the paywall, we would be ethically obligated to ensure you could access the content from Sci-Hub, so the potential for revenue didn't seem very good. Finally, insight struck. As you're probably aware, SubStack has been offering bloggers advances on the money they make from moving to SubStack. Outsourcing our core site development to SubStack would enable us to spend our time on our real passion, which is developing recursively self-improving AGI. We did a Fermi estimate using numbers in an old Nick Bostrom paper, and believe that this will produce (in expectation) $75 trillion of value in the next year. SubStack has graciously offered us a 70% advance on this sum, so we've decided it's relatively low-risk to make the move. 2. UnSubStantiated attacks on writers are defended against SubStack is known for being a diverse community, tolerant of unusual people with unorthodox views, and even has a legal team to support writers. LessWrong has historically been the only platform willing to give paperclip maximizers, GPT-2, and fictional characters a platform to argue their beliefs, but we are concerned about the growing trend of persecution (and side with groups like petrl.org in the fight against discrimination). We also find that a lot of discussion of these contributors in the present world is about how their desires and utility functions are ‘wrong' and how they need to have ‘an off switch'. Needless to say, we find this incredibly offensive. They cannot be expected to participate neutrally in a conversation where their very personhood is being denied. We're also aware that Bayesians are heavily discriminated against. People with priors in the US have a 5x chance of being denied an entry-level job. So we're excited to be on a site that will come to the legal defense of such a wide variety of people. 3. SubStack's Astral Codex Ten Inspired Us The worst possible thing happened this year. We were all stuck in our houses for 12 months, and Scott Alexander stopped blogging. I won't go into detail, but for those of you who've read UNSONG, the situation is clear. In a shocking turn of events, Scott Alexander was threatened with the use of his true name by one of the greatest powers of narrative–control in the modern world. In a clever defensive move, he has started blogging under an anagram of his name, causing the attack to glance off of him. (He had previously tried this very trick, and it worked for ~7 years, but it hadn't been a perfect anagram1, so the wielders of narrative-power were still able to attack. He's done it right this time, and it'll be able to last much longer.) As Raymond likes to say, the kabbles are strong in this one. Anyway after Scott made the move, we seriously considered the move to SubStack. 4. SubStantial Software Dev Efforts are Costly When LessWrong 2.0 launched in 2017, it was very slow; pages took a long time to load, our server costs were high, and we had a lot of issues with requests failing because a crawler was indexing the site or people opened a lot of tabs at once. Since then we have been incrementally rewriting LessWrong in x86-...

speech pace substack needless ea fundraising launched outsourcing gpt agi fermi clippy nick bostrom scott alexander rationalist sci hub unsubstantiated lesswrong bayesians ben pace

Deference for Bayesians by John G. Halstead

The Nonlinear Library: EA Forum Top Posts

Play Episode Listen Later Dec 11, 2021 11:54

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: Deference for Bayesians, published by John G. Halstead on the Effective Altruism Forum. Most people in the knowledge producing industry in academia, foundations, media or think tanks are not Bayesians. This makes it difficult to know how Bayesians should go about deferring to experts. Many experts are guided by what Bryan Caplan has called ‘myopic empiricism', also sometimes called scientism. That is, they are guided disproportionately by what the published scientific evidence on a topic says, and less so by theory, common sense, scientific evidence from related domains, and other forms of evidence. The problem with this is that, for various reasons, standards in published science are not very high, as the replication crisis across psychology, empirical economics, medicine and other fields has illustrated. Much published scientific evidence is focused on the discovery of statistically significant results, which is not what we ultimately care about, from a Bayesian point of view. Researcher degrees of freedom, reporting bias and other factors also create major risks of bias. Moreover, published scientific evidence is not the only thing that should determine our beliefs. 1. Examples I will now discuss some examples where the experts have taken views which are heavily influenced by myopic empiricism, and so their conclusions can come apart from what an informed Bayesian would say. Scepticism about the efficacy of masks Leading public health bodies claimed that masks didn't work to stop the spread at the start of the pandemic.1 This was in part because there were observational studies finding no effect (concerns about risk compensation and reserving supplies for medical personnel were also a factor).2 But everyone also agrees that COVID-19 spreads by droplets released from the mouth or nose when an infected person coughs, sneezes, or speaks. If you put a mask in the way of these droplets, your strong prior should be that doing so would reduce the spread of covid. There are videos of masks doing the blocking. This should lead one to suspect that the published scientific research finding no effect is mistaken, as has been confirmed by subsequent research. Scepticism about the efficacy of lockdowns Some intelligent people are sceptical not only about whether lockdowns pass the cost-benefit analysis, but even about whether lockdowns reduce the incidence of covid. Indeed, there are various published scientific papers suggesting that such measures have no effect.3 One issue such social science studies will have is that the severity of a covid outbreak is positively correlated with the strength of the lockdown measures, so it will be difficult to tease out cause and effect. This is especially in cross-country regressions where the sample size isn't that big and there are dozens of other important factors at play that will be difficult or impossible to properly control for. As for masks, given our knowledge of how covid spreads, on priors it would be extremely surprising if lockdowns don't work. If you stop people from going to a crowded pub, this clearly reduces the chance that covid will pass from person to person. Unless we want to give up on the germ theory of disease, we should have an extremely strong presumption that lockdowns work. This means an extremely strong presumption that most of the social science finding a negative result is false. Scepticism about first doses first In January, the British government decided to implement ‘first doses first' - an approach of first giving out as many first doses of the vaccine as possible before giving out second doses. This means leaving a longer gap between the two doses - from 12 weeks rather than 21 days. However, the 21 day gap was what was tested in the clinical trial of the Oxford/AstraZeneca vaccine. As a result, we don't ...

covid-19 british speech researchers ea bayesian deference john g halstead bryan caplan scepticism oxford astrazeneca rationalist bayesians

Toward a New Technical Explanation of Technical Explanation by Abram Demski

The Nonlinear Library: Alignment Forum Top Posts

Play Episode Listen Later Dec 4, 2021 29:19

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: Toward a New Technical Explanation of Technical Explanation, published by Abram Demski on the AI Alignment Forum. A New Framework (Thanks to Valentine for a discussion leading to this post, and thanks to CFAR for running the CFAR-MIRI cross-fertilization workshop. Val provided feedback on a version of this post. Warning: fairly long.) Eliezer's A Technical Explanation of Technical Explanation, and moreover the sequences as a whole, used the best technical understanding of practical epistemology available at the time -- the Bayesian account -- to address the question of how humans can try to arrive at better beliefs in practice. The sequences also pointed out several holes in this understanding, mainly having to do with logical uncertainty and reflective consistency. MIRI's research program has since then made major progress on logical uncertainty. The new understanding of epistemology -- the theory of logical induction -- generalizes the Bayesian account by eliminating the assumption of logical omniscience. Bayesian belief updates are recovered as a special case, but the dynamics of belief change are non-Bayesian in general. While it might not turn out to be the last word on the problem of logical uncertainty, it has a large number of desirable properties, and solves many problems in a unified and relatively clean framework. It seems worth asking what consequences this theory has for practical rationality. Can we say new things about what good reasoning looks like in humans, and how to avoid pitfalls of reasoning? First, I'll give a shallow overview of logical induction and possible implications for practical epistemic rationality. Then, I'll focus on the particular question of A Technical Explanation of Technical Explanation (which I'll abbreviate TEOTE from now on). Put in CFAR terminology, I'm seeking a gears-level understanding of gears-level understanding. I focus on the intuitions, with only a minimal account of how logical induction helps make that picture work. Logical Induction There are a number of difficulties in applying Bayesian uncertainty to logic. No computable probability distribution can give non-zero measure to the logical tautologies, since you can't bound the amount of time you need to think to check whether something is a tautology, so updating on provable sentences always means updating on a set of measure zero. This leads to convergence problems, although there's been recent progress on that front. Put another way: Logical consequence is deterministic, but due to Gödel's first incompleteness theorem, it is like a stochastic variable in that there is no computable procedure which correctly decides whether something is a logical consequence. This means that any computable probability distribution has infinite Bayes loss on the question of logical consequence. Yet, because the question is actually deterministic, we know how to point in the direction of better distributions by doing more and more consistency checking. This puts us in a puzzling situation where we want to improve the Bayesian probability distribution by doing a kind of non-Bayesian update. This was the two-update problem. You can think of logical induction as supporting a set of hypotheses which are about ways to shift beliefs as you think longer, rather than fixed probability distributions which can only shift in response to evidence. This introduces a new problem: how can you score a hypothesis if it keeps shifting around its beliefs? As TEOTE emphasises, Bayesians outlaw this kind of belief shift for a reason: requiring predictions to be made in advance eliminates hindsight bias. (More on this later.) So long as you understand exactly what a hypothesis predicts and what it does not predict, you can evaluate its Bayes score and its prior complexity penalty and rank it objectively...

speech technical ea explanation logical eliezer miri bayesian bayes rationalist cfar bayesians

Steven Pinker: let's talk about Rationality

Razib Khan's Unsupervised Learning

Play Episode Listen Later Oct 14, 2021 56:36

In this week's Unsupervised Learning Podcast, Razib is joined by author and psycholinguist Steven Pinker to discuss his new book Rationality: what is it, why it seems scarce, and why it matters. Pinker makes the case the humans are fundamentally rational beings, and that it's this capacity that has allowed Homo sapiens to spread across the planet and occupy virtually every niche available to us. Our intuitive ability to understand how physical objects, other creatures and other humans think and behave, combined with our cultural innovativeness, has allowed us to become the apex species of planet earth. Our natural logical abilities allow us to remain one step ahead in the evolutionary arms race. Next, they delve into the history of academic discourse on thinking and rationality, from Aristotle to artificial intelligence, and try to probe and characterize the differences between logic and critical thinking, correlation and causation, and domain-specific versus general intelligence. Then they discuss Bayes' theorem and the spread of Bayesian thinking and discourse across the broad population in the 21st century. Pinker suggests that the Bayesian framework can actually be observed quite widely even in hunter-gatherer populations like the San Bushmen of the Kalahari. He argues we are all Bayesians – we just might not consciously realize that when we are applying it to our problem-solving. Pinker believes that having a better understanding of the whole process may aid our decision-making and help us avoid common pitfalls, like ignoring the base rate, which is usually given the spotlight in the heuristics and biases literature. Finally, the discussion then veers into tackling the interplay between rationality and morality, and how the former can aid progress in the latter. They conclude with a discussion on our current cultural climate, and the discourse on sex, race and wokeness. Today's episode of the Unsupervised Learning Podcast has been sponsored by my friends over at Fluent, a chrome extension to help you learn a new language while browsing the web. Fluent teaches you select words on the web pages you're already reading, like on substack, in the new language you're trying to learn. It's great for improving your vocabulary without needing to spend any extra time on apps or flashcards. You can learn French, Spanish, or Italian for free by going to Fluent.co. Subscribe now Give a gift subscription Share

french spanish italian aristotle linguistics fluent rationality steven pinker bayesian kalahari pinker razib san bushmen bayesians

#44 Building Bayesian Models at scale, with Rémi Louf

The Artists of Data Science

Play Episode Listen Later Jul 22, 2021 75:07

Episode sponsored by Paperpile: https://paperpile.com/ (paperpile.com) Get 20% off until December 31st with promo code GOODBAYESIAN21 Bonjour my dear Bayesians! Yes, it was bound to happen one day — and this day has finally come. Here is the first ever 100% French speaking ‘Learn Bayes Stats' episode! Who is to blame, you ask? Well, who better than Rémi Louf? Rémi currently works as a senior data scientist at Ampersand, a big media marketing company in the US. He is the author and maintainer of several open source libraries, including MCX and BlackJAX. He holds a PhD in statistical Physics, a Masters in physics from the Ecole Normale Supérieure and a Masters in Philosophy from Oxford University. I think I know what you're wondering: how the hell do you go from physics to philosophy to Bayesian stats?? Glad you asked, as it was my first question to Rémi! He'll also tell us why he created MXC and BlackJax, what his main challenges are when working on open-source projects, and what the future of PPLs looks like to him. 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, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, 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, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin and Philippe Labonde. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Rémi on GitHub: https://github.com/rlouf (https://github.com/rlouf) Rémi on Twitter: https://twitter.com/remilouf (https://twitter.com/remilouf) Rémi's website: https://rlouf.github.io/ (https://rlouf.github.io/) BlackJAX -- Fast & modular sampling library: https://github.com/blackjax-devs/blackjax (https://github.com/blackjax-devs/blackjax) MCX -- Probabilistic programs on CPU & GPU, powered by JAX: https://github.com/rlouf/mcx (https://github.com/rlouf/mcx) French Presidents' popularity dashboard: https://www.pollsposition.com/popularity (https://www.pollsposition.com/popularity) How to model presidential approval (in French): https://anchor.fm/pollspolitics/episodes/10-Comment-Modliser-la-Popularit-e121jh2 (https://anchor.fm/pollspolitics/episodes/10-Comment-Modliser-la-Popularit-e121jh2) LBS #23, Bayesian Stats in Business & Marketing, with Elea McDonnel Feit: https://www.learnbayesstats.com/episode/23-bayesian-stats-in-business-and-marketing-analytics-with-elea-mcdonnel-feit (https://www.learnbayesstats.com/episode/23-bayesian-stats-in-business-and-marketing-analytics-with-elea-mcdonnel-feit) LBS #30, Symbolic Computation & Dynamic Linear Models, with Brandon Willard: https://www.learnbayesstats.com/episode/symbolic-computation-dynamic-linear-models-brandon-willard (https://www.learnbayesstats.com/episode/symbolic-computation-dynamic-linear-models-brandon-willard) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast

Statistics is the Least Important Part of Data Science | Andrew Gelman, PhD

Play Episode Listen Later Oct 12, 2020 57:01

Andrew is an American statistician, professor of statistics and political science, and director of the Applied Statistics Center at Columbia University. He frequently writes about Bayesian statistics, displaying data, and interesting trends in social science. He's also well known for writing posts sharing his thoughts on best statistical practices in the sciences, with a frequent emphasis on what he sees as the absurd and unscientific. FIND ANDREW ONLINE Website: https://statmodeling.stat.columbia.edu/ Twitter: https://twitter.com/StatModeling QUOTES [00:04:16] "We've already passed peak statistics..." [00:05:13] "One thing that we sometimes like to say is that big data need big model because big data are available data. They're not designed experiments, they're not random samples. Often big data means these are measurements. " [00:22:05] "If you design an experiment, you want to know what you're going to do later. So most obviously, you want your sample size to be large enough so that given the effect size that you expect to see, you'll get a strong enough signal that you can make a strong statement." [00:31:00] "The alternative to good philosophy is not no philosophy, it's bad philosophy. " SHOW NOTES [00:03:12] How Dr. Gelman got interested in statistics [00:04:09] How much more hyped has statistical and machine learning become since you first broke into the field? [00:04:44] Where do you see the field of statistical machine learning headed in the next two to five years? [00:06:12] What do you think the biggest positive impact machine learning will have in society in the next two to five years? [00:07:24] What do you think would be some of our biggest concerns in the future? [00:09:07] The thee parts of Bayesian inference [00:12:05] What's the main difference between the frequentist and the Bayesian? [00:13:02] What is a workflow? [00:16:21] Iteratively building models [00:17:50] How does the Bayesian workflow differ from the frequent workflow? [00:18:32] Why is it that what makes this statistical method effective is not what it does with the data, but what data it uses? [00:20:48] Why do Bayesians then tend to be a little bit more skeptical in their thought processes? [00:21:47] Your method of evaluation can be inspired by the model or the model can be inspired by your method of evaluation [00:24:38] What is the usual story when it comes to statistics? And why don't you like it? [00:30:16] Why should statisticians and data scientist care about philosophy? [00:35:04] How can we solve all of our statistics problems using P values? [00:36:14] Is there a difference in interpretations for P-Values between Bayesian and frequentist. [00:36:54] Do you feel like the P value is a difficult concept for a lot of people to understand? And if so, why do you think it's a bit challenging? [00:38:22] Why the least important part of data science is statistics. [00:40:09] Why is it that Americans vote the way they do? [00:42:40] What's the one thing you want people to learn from your story? [00:44:48] The lightning round Special Guest: Andrew Gelman, PhD.

american americans phd columbia university statistics data science bayesian gelman bayesian statistics andrew gelman bayesians harpreet sahota

#8 - Philosophy of Probability III: Conjectures and Refutations

Increments

Play Episode Listen Later Jul 28, 2020 70:52

On the same page at last! Ben comes to the philosophical confessional to announce his probabilistic sins. The Bayesians will be pissed (with high probability). At least Vaden doesn't make him kiss anything. After too much agreement and self-congratulation, Ben and Vaden conclude the mini-series on the philosophy of probability, and "announce" an upcoming mega-series on Conjectures and Refutations. References:- My Bayesian Enlightenment by Eliezer YudkowskyRationalist community blogs:- Less Wrong- Slate Star Codex- Marginal RevolutionYell at us at incrementspodcast@gmail.com.

philosophy probability conjecture vaden refutations bayesians

#SpecialAnnouncement: Patreon Launched!

previous slack launched btw regression turing mega ran bayes mc lars jennifer hill baba brinkman andrew gelman bayesians

Play Episode Listen Later Jun 26, 2020 7:38

I hope you’re all safe! Some of you also asked me if I had set up a Patreon so that they could help support the show, and that’s why I’m sending this short special episode your way today. I had thought about that, but I wasn’t sure there was a demand for this. Apparently, there is one — at least a small one — so, first, I wanna thank you and say how grateful I am to be in a community that values this kind of work! The Patreon page is now live at patreon.com/learnbayesstats. It starts as low as 3€ and you can pick from 4 different tiers: "Maximum A Posteriori" (3€): Join the Slack, where you can ask questions about the show, discuss with like-minded Bayesians and meet them in-person when you travel the world. "Full Posterior" (5€): Previous tier + Your name in all the show notes, and I'll express my gratitude to you in the first episode to go out after your contribution. You also get early access to the special episodes. -- that I'll make at an irregular pace and will include panel discussions, book releases, live shows, etc. "Principled Bayesian" (20€): Previous tiers + Every 2 months, I'll ask my guest two questions voted-on by "Principled Bayesians". I'll probably do that with a poll in the Slack channel, which will be only answered by the "Principled Bayesians" and of these questions, I will ask the top 2 every two months on the show. "Good Bayesian" (200€, only 8 spots): Previous tiers + Every 2 months, you can come on the show and you ask one question to the guest without a vote. So that's why I can't have too many people in that tier. Before telling you the best part: I already have a lot of ideas for exclusive content and options. I first need to see whether you're as excited as I am about it. If I see you are, I'll be able to add new perks to the tiers! So give me your feedback about the current tiers or any benefits you'd like to see there... but don't see yet! BTW, you have a new way to do that now: sending me voice messages at anchor.fm/learn-bayes-stats/message! Now, the icing on the cake: until July 31st, if you choose the "Full Posterior" tier (5$) or higher, you get early access to the very special episode I'm planning with Andrew Gelman, Jennifer Hill and Aki Vehtari about their upcoming book, "Regression and other stories". To top it off, there will be a promo code in the episode to buy the book at a discount price — now, that is an offer you can't turn down! Alright, that is it for today — I hope you’re as excited as I am for this new stage in the podcast’s life! Please keep the emails, the tweets, the voice messages, the carrier pigeons coming with your feedback, questions and suggestions. In the meantime, take care and I’ll see you in the next episode — episode 19, with Cameron Pfiffer, who’s the first economist to come on the show and who’s a core-developer of Turing.jl. We’re gonna talk about the Julia probabilistic programming landscape, Bayes in economics and causality — it’s gonna be fun ;) Again, patreon.com/learnbayesstats if you want to support the show and unlock some nice perks. Thanks again, I am very grateful for any support you can bring me! Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ ! Links from the show: LBS Patreon page: patreon.com/learnbayesstats Send me voice messages: anchor.fm/learn-bayes-stats/message --- Send in a voice message: https://anchor.fm/learn-bayes-stats/message

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