Podcasts about bayes theorem

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence. Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google's ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges. The conversation shifts to deep learning's limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI. We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all. From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what's next for machine intelligence. Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of innovation!    Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction (02:55) The Five Tribes of Machine Learning Explained   (06:34) Bayesian vs. Frequentist: The Probability Debate   (08:27) What is Bayes' Theorem & How AI Uses It   (12:46) The Power & Limitations of Bayesian Networks   (16:43) How Bayesian Inference Works in AI   (18:56) The Rise & Fall of Bayesian Machine Learning   (20:31) Bayesian AI in Medical Diagnosis & Search and Rescue   (25:07) How Google Used Bayesian Networks for Ads   (28:56) The Role of Uncertainty in AI Decision-Making   (30:34) Why Bayesian Learning is Computationally Hard   (34:18) Analogical Learning – The Overlooked AI Paradigm   (38:09) Support Vector Machines vs. Neural Networks   (41:29) How SVMs Once Dominated Machine Learning   (45:30) The Future of AI – Bayesian, Neural, or Hybrid?   (50:38) Where AI is Heading Next  

The future can be scary, but what if there was a way for us to understand it a little better? Tom Chivers believes there is. His new book Everything Is Predictable explains how Bayes Theorem, a statistical model, can explain the world around us and, in some cases, help us predict the future. Learn more about your ad choices. Visit podcastchoices.com/adchoices

✍︎: The Curious Worldview Newsletter - the ultimate compliment to the podcast...Other episode of the podcast that suit this episode...Brian Klaas – Fluke & RandomnessRuss Roberts – EconTalkLuca Dellanna – Ergodicity All The Way DownScott Patterson – Chaos KingsNassim Taleb & Incerto PodcastFollow me on Instagram – @ryanfhoggEverything Is Predictable – Tom ChiversTom Chivers is a prolific science writer whose written for Buzzfeed, The Telegraph, Unherd, published books, written for loads of other publications as well and now writes for Semafor's daily flagship email (something I read everyday)… but here Tom is today to discuss his book about Bayes called… EVERYTHING IS PREDICTABLE: How Bayes' Remarkable Theorem Explains the World and, the lead is not buried in this case, it is a book about Bayes Throerom which to put it simply… is an equation to calculate probability.Now, my Talebian listeners will recognise a contradiction to our worldview in the title here… everything is predictable? how often has Taleb's quotes, how can we predict a future of infinite possibilities based off a finite experience of the past appeared on this podcast? We get into Chivers differences with that Talebian worldview, but as well, there is top to bottom what is Bayes theorem, why does it matter, the role of this theorem at the foundation of all of these LLM's and therefore much of AI. a neat little anecdote of Chivers family member, Sir John Maynard Keynes and plenty more as well!00:00 – Who Is Tom Chivers01:34 – Great Great Uncle John Maynard Keynes08:44 – What's The Point Of Bayes?19:14 – What Is Bayes Theorem?39:34 – Disagreeing With Nassim Taleb 52:24 – Counterintuitive Aspects Of Bayes56:28 – Bayes & LLM's & AI1:15:12 – Serendipity In Tom's Life

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 101 Space You Will Always Have With You, published by Screwtape on November 29, 2023 on LessWrong. Any community which ever adds new people will need to either routinely teach the new and (to established members) blindingly obvious information to those who genuinely haven't heard it before, or accept that over time community members will only know the simplest basics by accident of osmosis or selection bias. There isn't another way out of that. You don't get to stop doing it. If you have a vibrant and popular group full of people really interested in the subject of the group, and you run it for ten years straight, you will still sometimes run across people who have only fuzzy and incorrect ideas about the subject dauntless you are making an active effort to make Something Which Is Not That happen. Or in other words; I have run into people at Effective Altruism meetups who were aghast at the idea of putting a dollar price on a human life, people at LessWrong meetups who did not know what Bayes Theorem was, and people at Magic: The Gathering meetups who thought the old lands tapped for two mana. (Because, you see, new lands don't have a "T: Add [Mana Symbol] to your mana pool" ability, maybe the cards that do say that do something extra when you tap them?) Laughter and incredulity can come across as insulting and push people away. Instead, consider how to make sure the information you care about transmitting is regularly conveyed. It can happen to you! I. As I understand it, the standard Jewish Synagogue service includes a reading from the Five Books Of Moses such that at the end of a year the books have been read in their entirety. Anyone attending every week for a year will have at least heard all of those words once, and if someone has been around for a couple of years it's a reasonable assumption that if they missed a week here or a week there, they'd have heard it the next year. You can't go to synagogue for years and accidentally not know about the slavery in Egypt. I'm not Jewish, so my synagogue knowledge is mostly second hand. I was raised Christian, and while my family branch of Protestantism doesn't have such an organized sequence as the Five Books Of Moses I can confirm that it would have been practically impossible to somehow attend three months of church services and not have been told Jesus loved you. If you skipped a week, that's fine, it came up in other sermons too. If you zoned out at that bit, the first thing I remember being told about writing sermons was to repeat things about three times at different points in the speech. If you showed up with earplugs in, it was written in the program and sometimes in bright colours on the walls. I have on occasion been tempted to put that kind of redundant and overlapping effort into making people aware of such rationalist lessons such as "Zero And One Are Not Probabilities" or "Your Enemies Are Not Innately Evil." Linear education systems play by an entirely different set of rules. A standard American student will go through first grade, second grade, third grade, and so on up to the end of high school. Many will then go to university, and the university can assume that new students already know how to write essays and do algebra. (Though they can't safely assume this is true of every student! There was a college professor at my dinner table growing up, and overheard complaints about how college freshmen were unable to do things such as, without loss of generality, reliably remember the difference between "their" or "there" in a written essay.) Society as a whole does not get to make this assumption. The overt purpose of the entire education edifice is to deal with the fact that civilization has a constant influx of people who don't know how the government works, how written language works, or how we wound...

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 101 Space You Will Always Have With You, published by Screwtape on November 29, 2023 on LessWrong. Any community which ever adds new people will need to either routinely teach the new and (to established members) blindingly obvious information to those who genuinely haven't heard it before, or accept that over time community members will only know the simplest basics by accident of osmosis or selection bias. There isn't another way out of that. You don't get to stop doing it. If you have a vibrant and popular group full of people really interested in the subject of the group, and you run it for ten years straight, you will still sometimes run across people who have only fuzzy and incorrect ideas about the subject dauntless you are making an active effort to make Something Which Is Not That happen. Or in other words; I have run into people at Effective Altruism meetups who were aghast at the idea of putting a dollar price on a human life, people at LessWrong meetups who did not know what Bayes Theorem was, and people at Magic: The Gathering meetups who thought the old lands tapped for two mana. (Because, you see, new lands don't have a "T: Add [Mana Symbol] to your mana pool" ability, maybe the cards that do say that do something extra when you tap them?) Laughter and incredulity can come across as insulting and push people away. Instead, consider how to make sure the information you care about transmitting is regularly conveyed. It can happen to you! I. As I understand it, the standard Jewish Synagogue service includes a reading from the Five Books Of Moses such that at the end of a year the books have been read in their entirety. Anyone attending every week for a year will have at least heard all of those words once, and if someone has been around for a couple of years it's a reasonable assumption that if they missed a week here or a week there, they'd have heard it the next year. You can't go to synagogue for years and accidentally not know about the slavery in Egypt. I'm not Jewish, so my synagogue knowledge is mostly second hand. I was raised Christian, and while my family branch of Protestantism doesn't have such an organized sequence as the Five Books Of Moses I can confirm that it would have been practically impossible to somehow attend three months of church services and not have been told Jesus loved you. If you skipped a week, that's fine, it came up in other sermons too. If you zoned out at that bit, the first thing I remember being told about writing sermons was to repeat things about three times at different points in the speech. If you showed up with earplugs in, it was written in the program and sometimes in bright colours on the walls. I have on occasion been tempted to put that kind of redundant and overlapping effort into making people aware of such rationalist lessons such as "Zero And One Are Not Probabilities" or "Your Enemies Are Not Innately Evil." Linear education systems play by an entirely different set of rules. A standard American student will go through first grade, second grade, third grade, and so on up to the end of high school. Many will then go to university, and the university can assume that new students already know how to write essays and do algebra. (Though they can't safely assume this is true of every student! There was a college professor at my dinner table growing up, and overheard complaints about how college freshmen were unable to do things such as, without loss of generality, reliably remember the difference between "their" or "there" in a written essay.) Society as a whole does not get to make this assumption. The overt purpose of the entire education edifice is to deal with the fact that civilization has a constant influx of people who don't know how the government works, how written language works, or how we wound...

Prepare for an enlightening journey as we engage in a thought-provoking conversation with Dale Glover, a Real Seeker on a quest for truth. Dale's relentless pursuit of understanding our purpose in creation led him through a decade of research into the myriad of positive and negative evidences surrounding various religions.On May 5th, 2018, a pivotal moment arrived when Dale, guided by Bayes Theorem and a confidence to discover the truth of Christianity. On that very day, he repented of his sins and placed his unwavering faith in Christ.As the former co-host of the Skeptics and Seekers podcast and now the driving force behind the Real Seekers podcast, Dale has fearlessly shared his faith and explored complex philosophical questions. His recent achievement of a Master's in Philosophy from Ryerson University attests to his deep intellectual prowess.In this episode, we dive into Dale's multifaceted interests, ranging from the existence of God to the intriguing Evil God Challenge and, notably, the enigmatic Shroud of Turin. Join us for an engaging discussion that promises to shed new light on age-old mysteries and inspire your own quest for truth.Subscribe to our podcast for more insightful interviews and engaging discussions on faith, history, and the intriguing mysteries of the Shroud of Turin.Want to learn more about author Guy R. Powell? Check out the socials below:Website: www.guypowell.comInstagram: @guy.r.powellFacebook: @AHistoryOfTheShroudOfTurinBook Link: https://www.amazon.com/Only-Witness-History-Shroud-Turin-ebook/dp/B0C5TWVVMT/ref=tmm_kin_swatch_0?_encoding=UTF8&qid=1689797458&sr=1-1Connect today to unlock the mysteries of the Shroud of Turin.

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Probabilistic Thinking Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. Probabilistic thinking is making an estimate using math or logic to determine the likelihood that an outcome is going to happen. This often involves statistics and historical data. If the revenue in a company has grown by 10% for each of the past five years, then probabilistic thinking will point to a growth rate of 10% for the coming year. Founders can use probabilistic thinking also for uncertain situations where there is little historical data.  In our example, for estimating the revenue in the first year of a company without the benefit of a track record, we can use probabilistic reasoning. In this case, we can use logic to estimate the revenue. For example, we could look at similar companies to see what revenue they generated in their first year.  In applying probabilistic thinking, consider all the options.   Expand your focus on what is probable to include what is possible. Gather additional information to tune the probabilistic estimation.   This is called Bayes Theorem which incorporates new and relevant information into the decision-making process. Apply probabilistic thinking to your startup decisions.   Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding.Let's go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

In probability theory and statistics, Bayes' theorem, named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

Dale Glover is a Real Seeker, I'm someone who sincerely wants to know the truth about what our ultimate purpose is in creation. For about 10 years he researched the various positive and negative evidences for and against various religions. As of May 5th 2018, he finally discovered the truth of Christianity (using Bayes Theorem he was 53.14% confident) and so he repented of his sins and placed his faith in Christ that very day. Subsequent to his coming to faith, he shared his faith as the Christian xo-host of the Skeptics and Seekers podcast for 2 years before branching out on his own to host his new Podcast Real Seekers. Outside of podcasting, Dale is a philosopher, having just graduated with a Masters in Philosophy from Ryerson University in June of 2022, his interests range from everything to existence of God to Evil God Challenge to the Shroud of Turin.To learn more about Minimal Relevant Features check out: https://www.youtube.com/watch?v=hoYMn...Subscribe on Spotify or Apple Podcasts to listen to each week. New episodes uploaded every Thursday-Friday.Want to learn more? Check out the socials below:Website: www.guypowell.comInstagram: @guy.r.powellFacebook: @AHistoryOfTheShroudOfTurinJoin the email list for the latest news on the podcast: https://guypowell.us6.list-manage.com...Connect Today

Zombies and Bayes Theorem. What?! Okay, bear with me. Sometimes, our intuition leads us into perilous situations. Our brains try to be helpful...but because of mental shortcuts...it doesn't always work out so well for us... So...we have to pause...think and rationalise. Although we cannot predict the outcomes of a bad event. We can increase the probability of a favourable outcome if we learn how to obtain good reliable data and information so we stand a better chance of avoiding bad outcomes... it's not guaranteed to help us do that (such is life) but we can improve our odds of survival. Intrigued? .... tune in and let's huddle. Music by Infraction By the way: Here's the link to some other good mental models to help you survive! www.huddlewisdom.com/gettools

One of the most important parts of reasoning well involves understanding how to change your beliefs in response to new information. Unfortunately, this is something almost none of us does well without training. In this workshop, Anna will walk you through the odds formulation of Bayes' Theorem in order to identify the three independent components we need to update properly. Since Bayesian updating on the fly can be hard, you'll practice implementing a bundle of trigger-action plans that studies have shown to be successful in helping people respond more accurately to new evidence.This talk was taken from EA Global London 2021. Click here to watch the talk with the PowerPoint presentation. 

Support the show: https://www.buymeacoffee.com/datascienceharp Find David online: https://twitter.com/d_spiegel Read David's article "Will I live longer than my cat?": https://www.bbc.co.uk/news/magazine-19467491 Watch the video of this episode: https://youtu.be/pCWH97vBFmU Memorable Quotes from the show: [00:23:36] "...essentially what probability theory allows us to do is to make assumptions about how the world works, how the data is generated, and turn it and flip it around after we observe some data into statements about our uncertainty about underlying features of the world. We can do that, which of course is very explicit based on work indeed, where after processing data or uncertainty and it turns into uncertainty about the underlying quantities." Hightlights of the show: [00:01:29] Guest Introduction [00:03:08] Talk to us about how you first got interested in statistics and what was it that drew you to this field? [00:04:55] Why is it that it seems like mathematicians tend to dislike teaching statistics? [00:08:27] What is statistical science and what is it all about? [00:09:46] You talk about in your book, The Art of Statistics, how to handle problems and approach problems in statistics. You call the P, p, b, a C cycle. Tell us about that framework. [00:15:03] You mentioned in the book that statistics is to blame for the reproducibility and replication crises in science. Why? Why is that? [00:18:23] When we talk about induction and inductive inference, should the philosopher in us get worried at all about the problem of induction in statistics? [00:19:40] Tell our audience about the 'normal distribution'. [00:20:34] Do you have any examples of when inductive inference has failed in statistics that you could share with us? [00:22:15] Why do we need probability theory when we're doing statistics? [00:26:25] I think pouring into the Bayesian stuff is kind of taking a step back here, maybe first principles. But what is probability? How do we measure it? It seems like such a strange epistemological concept. [00:28:27] Can we say there's a at least some type of difference between epistemic probability and some physical or I believe you say aleatory? [00:30:03] Would there be a difference in the way that a philosopher or a statistician would interpret probability? [00:38:32] What's the Bayesian approach all about and why is it that courts in the UK are banning it or have banned it? [00:40:16] How is this (Bayesian approach) different from the frequentist approach to viewing probability? What's the central difference? [00:44:55] It seems like the prior distribution is something that makes base them so controversial. Why is that? [00:46:18] It seems like Bayes Theorem is the scientifically correct way to change your mind when you get new evidence, right? [00:48:18] David Deutsch mentioned lately about the Bayesian-ism, and he's having some qualms with Bayesian ism. He says that Bayesian-ism becomes controversial when you try to use it as a way to generate new ideas or judge one explanation against another. How do we reconcile that when we're faced with some epistemic. [00:49:51] About using it to help us in our everyday lives to make better decisions. How can we use Bayes in that context? [00:53:15] It is 100 years in the future. What do you want to be remembered for? Random Round [00:54:17] What do you believe that other people think is crazy? [00:55:02] What are you most curious about right now? [00:55:55] What are you currently reading? [00:58:33] What do you like most about your family? [00:58:53] What was your best birthday? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Our newest team member, Nick Rigler, put together an amazing case and leads a discussion that starts off with a complaint of back pain. M3 duo, Aaron Park and Jesus 'Berto' Varela, walk us through their reasoning in real-time and naturally turn to Bayes Theorem as things get challenging. We highlight back pain red flags, cardiac physiology, synovial fluid analysis, and plenty more along the way.Test your reasoning with each aliquot alongside Aaron and Berto!Follow us on Twitter:A&ORich AbramsA fantastic resource, by learners, for learners in Internal Medicine, Family Medicine, Pediatrics, Primary Care, Emergency Medicine, and Hospital Medicine.

In today's episode, Patrick and Greg use the context of COVID rapid tests to discuss issues of sensitivity, specificity, positive and negative predicted values, and the generally questionable utility of test accuracy information. Along the way they also mention escape rooms, C4, Embassy Suites, palak paneer, 93% accurate, astragali, SAT prep courses, the volume of a cone, risk and burden, and digging up the Rev. 

Guest speaker Dr. Micah Green provides an overview of the contemporary philosophical problem of miracles and “Hume’s Abject Failure”. This video is from our 2021 Spring lecture series covering Natural Theology & the Truth of Christianity. Each week, we evaluate the classical arguments for and against the existence of God and truth of Christianity. For […]

Extract taken from Live Q & A with Pat Flynn and James Madden, discussing the importance of mathematical reasoning in philosophy and why is Bayes theorem so important. About Me: http://linktr.ee/gattopanceri666

“Making good decisions means bringing consciousness to your bias and separating your ability to read signals from noise.” In our modern world, data is the new king. While the world is evolving and adapting to Artificial Intelligence and automation in each sector, the insurance industry is now managing and assessing risks with the help of data too. Tune in to this episode to find out how data science is helping evolve the insurance sector? The Brand Called You brings you Murli Buluswar, the Head of U.S. Consumer Analytics at Citi Bank leading 400+ analytics professionals. Murli is an analytic and strategic Financial Services Leader with a passion for science and analytics. He talks to us about his illustrious career in financial services and explains he derives data-driven decision-making in his organization. Insurance companies evaluate their customers by checking their background, health, etc, and Murli explains how the variability of this data affects decision-making. An alumnus of the University of Chicago and Auburn University, he explains why everyone should learn Bayes Theorem to make quicker and better decisions in life. Tune in to learn how this Global Financial Leader takes decisions in life! Find us on: Facebook - http://facebook.com/followtbcy/ Twitter - http://twitter.com/followtbcy/ Instagram - http://instagram.com/followtbcy/ --- Support this podcast: https://anchor.fm/tbcy/support

Help us meet our quarterly Radio Pledge Drive goal by donating here! Questions Covered: 05:22 – What do you think of using Bayes’ Theorem to predict the probability of the Resurrection of Jesus?  14:22 – Does the Church teach that people are innately good but commit evil acts, or that people are evil when they’ve committed evil acts?  23:42 – I am not Catholic yet. What happens if I die before I can go to my first Confession?  31:50 – How do I know if I am hearing God’s voice?  42:10 – If Mary had stepchildren by Joseph, would they have had a responsibility to care for Mary after Jesus died?  49:23 – When the Final Judgement happens, will people experience purgatory any longer?  …

More information about Brain Lenses at brainlenses.com.BL supporters receive an additional episode of the show each week. Info about becoming a supporter at the above address.Read the written version of this episode: brainlenses.substack.com/p/bayes-theorem This is a public episode. Get access to private episodes at brainlenses.substack.com/subscribe

In this episode we talk about all things Bayesian. What is Bayesian inference and why is it the cornerstone of Data Science?Bayesian statistics embodies the Data Scientist and their role in the data modelling process. A Data Scientist starts with an idea of how to capture a particular phenomena in a mathematical model - maybe derived from talking to experts in the company. This represents the prior belief about the model. Then the model consumes data around the problem - historical data, real-time data, it doesn't matter. This data is used to update the model and the result is called the posterior.Why is this Data Science? Because models that react to data and refine their representation of the world in response to the data they see are what the Data Scientist is all about.We talk with Dr Joseph Walmswell, Principal Data Scientist at life sciences company Abcam, about his experience with Bayesian modelling. Further ReadingPublication list for Dr. Joseph Walmswell (https://bit.ly/3s8xluH via researchgate.net)Blog on Bayesian Inference for parameter estimation (https://bit.ly/2OX46fV via towardsdatascience.com)Book Chapter on Bayesian Inference (https://bit.ly/2Pi9Ct9 via cmu.edu)Article on The Monty Hall problem (https://bit.ly/3f1pefr via Wikipedia)Podcast on  "The truth about obesity and Covid-19", More or Less: Behind the Stats podcast (https://bbc.in/3lBqCGS via bbc.co.uk)Gov.uk guidance:Article on "Understanding lateral flow antigen testing for people without symptoms" (https://bit.ly/313JDs9)Article on "Households and bubbles of pupils, students and staff of schools, nurseries and colleges: get rapid lateral flow tests" (https://bit.ly/3c5ZXih)Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.Recording date: 16 March 2021Interview date: 26 February 2021

Faith is typically based on “belief” and science is based on objective research and analysis. In this address, written for the Malvern (United Kingdom) Science and Faith conference, Dr. Ray discusses the concept of “evidence based faith,” attempting to rank our beliefs based on Bayes’ Theorem of probability analysis. This invites communities of faith to put the majority of their energy into what they can know (indeed, what they can hardly avoid knowing from the news!) rather than stressing the unknowable truth claims of traditional religion. We have no real evidence of heaven and hell but we have lots of evidence of racism, poverty, violence, and illness.

Patrick and Greg impulsively launch the inaugural segment of "Quantitude Wake Up Call" where they forget how longitude works and call Bayesian expert Roy Levy at 5:40 in the morning. Although somewhat rattled, Roy helps the Quantidudes better understand Bayesian inference and describes the many ways that this approach can help move our science forward. Along the way they discuss Jedi mind tricks, the birds and the bees, time zones, Virgos, the Dark Side, cowards, subjectivity, lecturing bus drivers, sideways fish, and statistical pop-up books.

On this episode of The Artists of Data Science, we get a chance to hear from Camilla Pang, a scientist specializing in translational bioinformatics. At the age of eight, she was diagnosed with autism spectrum disorder and struggled to understand the world around her and the way people were. Her book, “Explaining Humans:What science can teach us about life, love, and relationships” is an original and incisive exploration of human nature and the strangeness of our social norms. Camilla shares with us her journey into science, and her mission to understand human behavior at a young age. She also discusses the potential impacts of machine learning and A.I within the next few years, and the importance of understanding the nuances in data scientists that create individuality. WHAT YOU'LL LEARN [7:18] Potential negative impacts of A.I [17:00] Learning to embrace errors [38:11] Getting over the perfectionist mindset [39:30] Important soft skills you need to cultivate [44:17] Advice for women in STEM QUOTES [6:59] “...before we get on to making the most of A.I we first need to make the most out of human minds.” [17:20] “an error in one context is a solution in the next” [47:10] Don't judge yourself for thinking outside the box. Stay true to yourself and your vision. [55:23] “...just because you don't fit in a system, doesn't mean you weren't born to make a new one.” FIND CAMILLA ONLINE LinkedIn: https://www.linkedin.com/in/camilla-pang-8b177b69/ Instagram: https://www.instagram.com/millie_moonface/ Twitter: https://twitter.com/millzymai SHOW NOTES [00:01:32] Introduction for our guest [00:02:59] A large, open-ended question. [00:04:32] What you think the next big thing in machine learning is going to be in the next two to five years, [00:06:08] What do you think would be the biggest positive impact on society? [00:07:04] What do you think would be scariest applications of machine learning in the next two to five years? [00:07:51] What do you think separates the great Data scientists from the merely good ones? [00:09:47] Talk to us about the terms neurotypical and neurodiverse. Would you mind defining these terms for our audience? [00:11:29] What does it mean to think in boxes and what does it mean to think in trees? [00:14:59] Why are most people stuck in box thinking? [00:15:49] How to be a tree thinker [00:16:50] What can we do to start embracing errors in our own lives? [00:19:27] What do proteins have to do with personality and interpersonal relationships? [00:20:50] How could we use this understanding of proteins to be better colleagues and better teammates at work? [00:23:09] Never let your fear define your fate [00:25:16] Gradient descent in layman's terms [00:26:47] How to use gradient descent to find our path to prioritize and identify our goals? [00:28:37] How can we use Bayes Theorem for empathy and managing the relationships that we have with ourselves? [00:31:02] What neural nets can teach us about ourselves [00:32:17] Is data science an art? Or is it a science? [00:33:30] How does the creative process manifest itself in Data science? [00:35:11] How to take better notes [00:37:26] How to stop being a perfectionist [00:39:10] Why soft skills are hard work [00:42:54] We're both INFJ's! [00:44:26] Advice for women in STEM [00:46:21] What can the Data community do to foster the inclusion of women in Data science and AI and STEM? [00:47:00] What's the one thing you want people to learn from this story? [00:48:37] The lightning round Special Guest: Camilla Pang, PhD.

Jimmy and CC discuss 1:42 Drudge turns Left, 3:09 The Pandemic, 5:45 Bayes Theorem predicts  better odds, multiple tests, 8:30 Mask enforcement, 14:00 the 4th of  July, California, NY, Italy, 21:45 Lab rats too hardy and all male,  25:01 Actors can't act, 27:26 Give an inch, they'll take an ell: apologies, 31:46 Cultural Appropriation, 33:33 Parler, Gab, 42:27  Russia, NYT, Bounties, 47:47 Coin shortage, 52:52 Armed St. Louis  homeowners, 1:01:11 Guillotine for Bezos,  1:02:57 CHAZ CHOPED. 

Bayesian statistics help decide what email you get is spam. It can assess security and medical risks, decode DNA, enhance blurry pictures, help explain stock market volatility, predict the spread of an infectious disease, and its methods were even used by Alan Turing in World War 2 to crack the secret of the Nazi enigma code. In this episode, we explore the history of Bayes' theorem, the ideas behind it, and why it's really becoming a powerful statistical tool in the 21st century. The Random Sample is a podcast by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers. In this show, we share stories about mathematics, statistics and the people involved. To learn more about ACEMS, visit https://acems.org.au.See omnystudio.com/listener for privacy information.

The first of a few quick episodes where I continue going into coronavirus therapy by ranting on to you about all of the things I've learned about the current epidemic... with references to scientific papers so you can read them and find out how wrong I was. We discuss: Estimates for the rate of disease progression in different countries Results from the early serology (antibody) tests of the virus (sadly still thin on the ground) Why Bayes' theorem means that the accuracy of antibody tests is so important

Episode 119 - Andrew Mack Andrew joins the podcast to talk about modeling sports, betting using statistical methods and bayes theorem. Link to Andrew's book - https://www.amazon.com/Statistical-Sports-Models-Excel-Andrew/dp/1079013458 Season 5 is proudly sponsored by Betfair Pty Limited. Betfair operates a betting exchange and is licensed in the Northern Territory, Australia.

We go through Bayes Theorem of Probability and try to see how the brain uses actions to learn better. Research Paper: https://doi.org/10.1093/nc/niz012

This is part 1 of a new series on Mental Models - tips, tricks, and tools to add to your mental toolbox. In this episode we introduce the concept of a mental model but then quickly dive in to explanations of the most powerful models we've encountered. Join us and learn how to make better decisions (or know when a decision is not worth making), how to have more original and impactful ideas (and how to find the most promising ideas to work on out of the thousands you'll soon have), and when tidying up your messy desk is just plain wrong (sorry, Marie Kondo!) --------------- Shownotes: --------------- The Great Mental Models: General Thinking Concepts by Shane Parrish - https://www.goodreads.com/book/show/44245196-the-great-mental-models  Super Thinking: The Big Book of Mental Models by Gabriel Weinberg - https://www.goodreads.com/book/show/41181911-super-thinking  Feynman technique - https://fs.blog/2012/04/feynman-technique/  More Dakka by The Zvi - https://www.lesswrong.com/posts/z8usYeKX7dtTWsEnk/more-dakka  Least recently used idea: read the book Algorithms To Live By by Brian Christian - https://www.goodreads.com/book/show/25666050-algorithms-to-live-by  Eisenhower matrix - https://jamesclear.com/eisenhower-box  Josh Wolfe on Shane Parrish's podcast - https://fs.blog/josh-wolfe/  Eliezer Yudkowsky's marvelous introduction to Bayes Theorem. Seriously, read this: http://yudkowsky.net/rational/bayes  Tim Urban's WaitButWhy post on thinking from first principles like Elon Musk: https://waitbutwhy.com/2015/11/the-cook-and-the-chef-musks-secret-sauce.html  Deep Work by Cal Newport - https://www.goodreads.com/book/show/25744928-deep-work  So Good They Can't Ignore You by Cal Newport - https://www.goodreads.com/book/show/13525945-so-good-they-can-t-ignore-you  Keep Your Identity Small by Paul Graham - http://www.paulgraham.com/identity.html  Joscha Bach on the Singularity podcast: https://www.singularityweblog.com/joscha-bach/

Germs are regarded today with a combination of fear and disgust. But mankind’s first introduction to the microbial world started off on a very different foot. In this episode, as part of a larger series contextualizing germ theory, we’ll talk about the discovery of animalcules and how they forever changed our conception of the natural world -- and what causes disease. Plus, a new #AdamAnswers about the influence of Bayes Theorem on medicine!   Sources: Albury WR, Marie-Francois-Xavier Bichat, Encyclopedia of Life Science, 2001.  Ball CS, The Early History of the Compound Microscope, Bios, Vol 37, No2 (May 1966). Findlen P, Athanasius Kircher: The Last Man Who Knew Everything.  Feinstein AR, “An Analysis of Diagnostic Reasoning,” Yale Journal of Biology and Medicine, 1973. Forsberg L.Nature's Invisibilia: The Victorian Microscope and the Miniature Fairy, Victorian Studies 2015. Gest H. The discovery of microorganisms by Robert Hooke and Antoni van Leeuwenhoek, Fellows of The Royal Society. Notes and Records of the Royal Society of Lond, 2004.  Hall, GH, The Clinical Application of Bayes Theorem, The Lancet, September 9, 1967.  Howard-Jones N, Fracastoro and Henle: A Re-Appraisal of their Contribution to the Concept of Communicable Diseases,” Medical History, 1977, 21: 61-68. Lane N, The unseen world: reflections on Leeuwenhoek (1677) ‘Concerning little animals’. Philosophical Transactions of the Royal Society, 19 April 2015.  Lawson I, Crafting the microworld: how Robert Hooke constructed knowledge about small things, Notes and Records of the Royal Society of Lond, 2015. McLeMee S, Athanasius Kirchehr, Dude of Wonders, The Chronicle of Higher Education, May 28, 2002.  Van Leeuwenhoek A, Observations, communicated to the publisher by Mr. Antony van Leewenhoeck, in a dutch letter of the 9th Octob. 1676. here English'd: concerning little animals by him observed in rain-well-sea- and snow water; as also in water wherein pepper had lain infused (https://royalsocietypublishing.org/doi/10.1098/rstl.1677.0003) “Little worms which propagate plague,” J R Coll Physicians Edinb, 2008.  Van Zuylen J, “The microscopes of Antoni van Leeuwenhoek,” Journal of Microscopy., 1981. Music from https://filmmusic.io, "Wholesome," “Pookatori and Friends,” and  by Kevin MacLeod (https://incompetech.com). License: CC BY

In this episode, Dr. Licona discusses various ways we can approach ancient texts when studying history. Is history classified as "science"? How does probability factor into our view of history?The Risen Jesus podcast with Dr. Mike Licona equips people to have a deeper understanding of the Gospel, history, and New Testament studies. The program is hosted by Kurt Jaros and produced in partnership with Defenders Media.[0:00] Intro[0:44] How to Approach a Text[6:06] Neutrality Toward Text[8:06] Methodological Credulity[10:07] Other Historical Claims of Divinity[12:07] History as a Science[15:47] Arguments from Statistical Inference [16:49] Bayes’ Theorem [20:17] Viewer Question: Matthew’s Little Apocalypse [24:01] Outrowebsite | http://risenjesus.comfacebook | http://www.fb.me/michael.r.licona/twitter | http://www.twitter.com/michaelliconaDonate: If you enjoy the RJ Podcast and want to keep the content coming, please join our team of supporters at http://bit.ly/SupportRisenJesus

In a fairly technical episode, Matthew and Andrew chat with Dale Glover from Skeptics and Seekers on a variety of topics having to do with the burden of proof. When we talk in a semiformal or formal setting, there are issues of burden of proof, who has the responsibility of proof, and how it changes hands. Along with this burden, we should consider what constitutes evidence, and who should provide it. To take burden of proof seriously, we must consider the ways we establish that some bit of evidence is reliable and how we might come to think of some aspect of it as unconvincing. To do this, we talk about Bayes Theorem, Molinism, deduction, induction, and much much more. Come along with us for a satisfying exploration of how skeptics and Christians differ and where they agree on the burden of proof and how to think about evidence. --- Send in a voice message: https://anchor.fm/reasonpress/message

Dale is a guest on the "Ask an Atheist Anything" Podcast with hosts Andrew and Matt to probe when does the Atheist bear the burden of proof; topics range from discussing Bayes Theorem to the shifting of the burden of proof and the nature of defeaters (includes back and forth on Dale's critically acclaimed Molinistic Defeater).

In episode 19, Dr. Calum Miller and I complete our discussion of the Prior Probability of the Resurrection. Episode 18 laid a lot of groundwork for 19. Go listen to it if you haven't! In episode 19, we get a real understanding of what it takes to form an accurate prior probability for Jesus' Resurrection. We address several objections like, "dead people tend to stay dead, so in all likelihood, Jesus stayed dead too, " "miracles almost never happen, so the Resurrection probably didn’t happen," and "any naturalistic theory is going to be more probable than God raising Jesus from the dead." All are shown to be lacking. Toward the end we address some of the concerns that historians like Mike Licona have against using Bayes Theorem in historical arguments. Website: www.capturingchristianity.com Patreon: www.patreon.com/capturingchristianity

I take a dive into Decrypto, comparing how clues are given in it versus in Codenames. Both the similarities and differences shed light into our cognitive processes and how items are stored in our memories. Spoiler alert: Bayes’ Theorem is … Continue reading →

What situation in your daily life do you think is another example of Bayes Theorem? If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/96

Statistics, probability, and exegesis? Travis talks to Mark Giacobbe, Teaching Fellow and Ph.D. Candidate at Westminster Theological Seminary, about how something called Bayes' Theorem is informing his dissertation. Mark's unique path to scholarship includes a musical career, post-9/11 missions to Afghanistan, and now teaching Greek at WTS. Now that Mark's working on a dissertation about the potential literary context of Luke-Acts, statistics and probability are playing an important role in his argument. Check out ExegeticalTools.com for more great content, subscribe to the podcast, and follow us on social media @exegeticaltools! View this episode on our website for links to featured resources.

What are conditional probabilities and how can they be used to help you draft and set you weekly lineup for a fantasy football team? More About the Analysis: http://perceptionaction.com/bayesintro   More information: http://perceptionaction.com/ My Research Gate Page (pdfs of my articles) My ASU Web page Podcast Facebook page (videos, pics, etc)   Credits: The Flamin' Groovies - Shake Some Action Lame Drivers –Let Me Get Those Numbers Down White Hillls – Condition of Nothing Mark Lanegan - Saint Louis Elegy via freemusicarchive.org and jamendo.com

We don’t treat all of our beliefs equally. For some, we see them as either true or false, correct or incorrect. For others, we see them as probabilities, chances, odds. In one world, certainty, in the other, uncertainty. In this episode you will learn from two experts in reasoning how to apply a rule from the 1700s that makes it possible to see all of your beliefs as being in “grayscale,” as neither black nor white, neither 0 nor 100 percent, but always somewhere in between, as a shade of gray reflecting your confidence in just how wrong you might be...given the evidence at hand. • Show notes: http://bit.ly/1Nfby8T • Patreon: https://www.patreon.com/youarenotsosmart • Donate Directly through PayPal: https://www.paypal.me/DavidMcRaney SPONSORS • MIT Press: https://mitpress.mit.edu/smart • Casper Mattresses: https://casper.com/sosmart • The Great Courses Plus: https://www.thegreatcoursesplus.com/smart See omnystudio.com/listener for privacy information.

This week the topic was Bayesian statistics. We interviewed Emma Rixon from Nottingham Trent University about her work as a crime scene investigator and how forensic science uses Bayesian probabilities.Show notes and more episodes via www.furthermaths.org.uk/podcasts

How can probability be used to help clarify the God question? A quick (and easy) summary of Bayes' Theorem will provide a useful tool in weighing competing claims. (For the podcast transcript, search for the title at www.patheos.com/blogs/crossexamined)

Unsupported Operation 77General NewsJRuby smokes normal RubyApache Camel seems to be getting strong enough so that Red Hat purchased Fuse SourceMule 3.3 releasedIntelliJ IDEA 12 “Leda” EAP openedNew compiler mode brings eclipse like continuous compilation, and other improvements.Eclipse 4.2 releasedEclipse Xtend 1.0 releasedCeylon Milestone 3 was released, with a new JS compiler - discussed on the latest JBoss Asylum podcast.Weka is a new data mining tool from the University of Waikato - It includes an extensive series of pre-implemented machine learning algorithms, including well known classification and clustering algorithms. If you’ve ever been curious how Bayes Theorem works, this is a great tool to get up and running.Google NewsIO obviously happened this week:New JellyBean, but not sure what new APIsCloud messaging grows upGoogle Now - event reminders with estimated travel times to destination, advance Siri beating voice search etc. etc.New Google TV APIsNew YouTube APIsWeb IntentsGoogle Compute EngineAdobe has killed Flash for Android 4.1Mark and I used Party Mode on a trip today, worked great! Remember to turn your calendar from “yes accept any and all invitations” to “no” if thats what its setting is and you want to be more careful.Oddly - the photo I took when in Party Mode never uploaded...Google did not gloat at IO about Oracle case as far as I sawGWT 2.5 RC/beta?GWT control seeded to external standards group/organisation/steering committee - Red Hat coming on board, Vaadin, and othersVaadin 7 Alpha 3 also releasednow includes full GWT for client side JS development, new navigation apis, js execution apis, JS based components, Groovy2.0 releaseda static type checker to let the compiler tell you about the correctness of your code,static compilation for the performance of the critical parts of your application,modularity, splitting the Groovy JAR into smaller feature-oriented JARs and letting you create your own extension modules,JDK 7 Project Coin syntax enhancements, so that Groovy is still as friendly as possible with its Java cousin,and JDK 7 Invoke Dynamic integration to benefit from the support of the JVM for dynamic languages.Grails 2.1.0-RC3Greame Rocher happy with Grails smoking Play.Did we say Gradle went 1.0?Apache NewsApache Tomcat 7.0.28Phonegap 1.9.0 released ( now Apache Cordova I believe ) OtherAmazon EC2 down and took out Instagram, Netflix, and lots of others, followed up by a leap second bug taking out java apps everywhere - YAY.