Podcast appearances and mentions of dana mackenzie

The Book of Why by Judea Pearl & Dana Mackenzie reminds us that correlation is not causation. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. It lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence.  The New Science of Cause and Effect "The Book of Why" by Judea Pearl & Dana Mackenzie - Book PReview Book of the Week - BOTW - Season 7 Book 52 Buy the book on Amazon https://amzn.to/3BSL2YB GET IT. READ :) #why #causality #awareness  FIND OUT which HUMAN NEED is driving all of your behavior http://6-human-needs.sfwalker.com/ Human Needs Psychology + Emotional Intelligence + Universal Laws of Nature = MASTER OF LIFE AWARENESS https://www.sfwalker.com/master-life-awareness --- Support this podcast: https://podcasters.spotify.com/pod/show/sfwalker/support

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This week, we're casting our eyes towards the brightest and largest object in our night sky: the Moon. As India becomes the 4th nation to achieve a successful soft landing on our only natural sateillite, we saw a fantastic opportunity to chart the history of how the Moon was formed and the many billions worth of missions invested in finding out more about it... Like this podcast? Please help us by supporting the Naked Scientists

In this week's episode, host Kristin Hayes talks with Nafisa Lohawala, a fellow at Resources for the Future who researches the effects of government policies on the transportation sector. Lohawala discusses the findings of a recent report that explores efforts to electrify medium- and heavy-duty vehicle fleets, the opportunities and challenges of electrification as a pathway toward lower transportation-sector emissions, and policies that could aid electrification. References and recommendations: “Medium- and Heavy-Duty Vehicle Electrification: Challenges, Policy Solutions, and Open Research Questions” by Beia Spiller, Nafisa Lohawala, and Emma DeAngeli; https://www.rff.org/publications/reports/medium-and-heavy-duty-vehicle-electrification-challenges-policy-solutions-and-open-research-questions/ Special series on the Common Resources blog: Electrifying Large Vehicles by Emma DeAngeli, Nafisa Lohawala, and Beia Spiller; https://www.resources.org/special-series-electrifying-large-vehicles/ “The Book of Why: The New Science of Cause and Effect” by Judea Pearl and Dana Mackenzie; https://www.hachettebookgroup.com/titles/judea-pearl/the-book-of-why/9780465097616/

Alex Strouf and Dennis Semrau open this week's Prep Mania by reacting to the news of Dana Mackenzie being let go as the Waunakee boys basketball coach after 20 years. Then, Cassie Bonde, Stougton softball head coach, joins. Hillary and Addison Blomberg, twin stars at Verona softball, join. Wrapping up with a shot clock discussion.

Scott Cunningham is Professor of Public Policy at the University of Strathclyde and is the Editor-in-Chief of the journal, Technological Forecasting & Social Change. In this episode, we talked about technological forecasting and social change. Prof. Cunningham gave an overview of how technological forecasting, policy, and business are interwoven, and how a systematic view is important in predicting the long-term pattern in technology. He described the broader context of tech mining, and why it is important to have mid to long-term forecasts. Recommendations for books and papers: The Book of Why, by Dana Mackenzie and Judea Pearl(Paper) Vehicle Ownership and Income Growth, Worldwide: 1960-2030 by Joyce Dargay, Dermot Gately and Martin Sommer 

Alex Strouf and Dennis Semrau preview the boys and girls basketball state tournaments. Plus, McFarland girls head coach Sara Mallegni, Waunakee boys head coach Dana MacKenzie, and WIAA Executive Director Stephanie Hauser.

In this episode, we spoke to Prof Galit Shmueli, Tsing Hua Distinguished Professor at the Institute of Service Science, and Institute Director at the College of Technology Management, National Tsing Hua University. Galit talked with us about the multi-disciplinary work she has done over the years, as well as the differences between statistical models that are purposed for predicting as opposed to explaining. We also discussed causal inference and how it can be used to estimate behaviour modification by the tech giants. We continued and talked about the ethics and the complexity of that landscape.  Galit's recommended books:  1.    The age of surveillance capitalism, Shoshana Zuboff 2.     Books on causality:     • The book of Why, Dana Mackenzie and Judea Pearl      • Causal Inference in Statistics: A Primer, Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell      • Causality, Judea Pearl  3.     Mostly Harmless Econometrics: An Empiricist's Companion, Joshua D. Angrist, ‎Jörn-Steffen Pischke 

www.60EightBasketball.com December 16-17, 2022 - Madison College Freshman Basketball about Edgewood HS Connection with players clicking No control during games – Players need to make plays Organizing your teams situations Confidence in preparation 2022 Waunakee Warriors Wisconsin Basketball Coaches Association Website: www.wisbca.org Twitter: https://twitter.com/WisBCA Instagram: https://www.instagram.com/wisbca/ Facebook: https://www.facebook.com/Wisconsin-Basketball-Coaches-Association-188909991139925 Dr. Dish: Website: https://www.drdishbasketball.com/ JustAgame Fieldhouse: Website: www.Justagamefieldhouse.com --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/billy-kegler7/support

To say that event A causes event B is to not only make a claim about our actual world, but about other possible worlds — in worlds where A didn't happen but everything else was the same, B would not have happened. This leads to an obvious difficulty if we want to infer causes from sets of data — we generally only have data about the actual world. Happily, there are ways around this difficulty, and the study of causal relations is of central importance in modern social science and artificial intelligence research. Judea Pearl has been the leader of the “causal revolution,” and we talk about what that means and what questions remain unanswered.Support Mindscape on Patreon.Judea Pearl received a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn. He is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. He is a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science, The Allen Newell Award from the Association for Computing Machinery, the Benjamin Franklin Medal, the Rumelhart Prize from the Cognitive Science Society, the ACM Turing Award, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect.Web siteGoogle Scholar publicationsWikipediaAmazon author pageTwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!   Dr. Jerry is talking today about questions and answers in the world of data science machinery and artificial intelligence.   Key Takeaways: What are Dr. Jerry's favorite AI design tools? Dr, Jerry shares his four primary tools: MATLAB. Is a commercial product. It has a home, academic, and enterprise version. MATLAB has toolkits and applications. The Predictive Maintenance Toolbox at MATLAB, especially the preventive failure model is of great value when we want to know why things fail, also by measuring systems performance and predicting the useful life of a product. Mathematical Modeling with Symbolic Math Toolbox is useful for algorithm-based environments. It is built on solid mathematics. R Programming is Dr. Jerry's favorite free tool for programming with statistical and math perspectives. R is an open and free source and comes with a lot of applications. Python is a great tool for programming and is as capable as R programming to assist us in problem-solving. Python is very useful when you know your work is directed to an enterprise level. Does Dr. Jerry have any recommended books for causality? The Book of Why is foundational for both the businessperson and the data scientist. It provides a historical review of what causality is and why it is important. For a deeper understanding of causality, Dr. Jerry recommends Causal Inference in Statistics: A Primer.   Counterfactuals and Causal Inferences: Methods and Principles it is a great tool to think through the counterfactual analysis.   Behavioral Data Analysis with R and Python is an awesome book for the practitioner who wants to know what behaviors are, how they show up in data, the causal characteristics, and how to abstract behavioral aspects from data. Dr. Jerry recommends Designing for Behavior Change, it talks about the three main strategies that we use to help people change their behaviors. The seven rules of human behavior can be found in Eddie Rafii's latest book: Behaviology, New Science of Human Behavior. Dr. Jerry shares his favorite tools for casual analysis: Compellon allows us to do performance analysis, showing the fundamental causal chains in your target of interest. It can be used by analysts. It allows users to do “what-if” analysis. Compellon is a commercial product.   Causal Nexus is an open-source package in Python that has a much deeper look at causal models than Compellon. BayesiaLab is a commercial tool that is one of the higher-end tools an organization can have. It allows you to work on casual networks and counterfactual events. It is used in AI research.   What skills are needed for data science machinery and AI developers? Capabilities can be segmented into Data-oriented, Information-oriented, Knowledge, and Intelligence. These different capabilities are used in many roles according to several levels of maturity.   Stay Connected with AI Live and Unbiased: Visit our website AgileThought.com Email your thoughts or suggestions to Podcast@AgileThought.com or Tweet @AgileThought using #AgileThoughtPodcast!   Learn more about Dr. Jerry Smith   Mentioned in this episode: MATLAB MATLAB Mathematical Modeling Python Artificial Intelligence with R Compellon Causal Nex BayesiaLab   Dr. Jerry's Book Recommendations: The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie   Causal Inference in Statistics: A Primer, Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell   Counterfactuals and Causal Inferences: Methods and Principles,  Stephen L. Morgan and Christopher Winship   Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results, Florent Buisson   Designing for Behavior Change: Applying Psychology and Behavioral Economics, Stephen Wendel   Behaviology, New Science of Human Behavior, Eddie Rafii

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Dr. Dana MacKenzie, a mathematician turned science writer and co-author of The Book of Why (written with Turning Award winner, Judea Pearl), join us to talk about correlation versus causation and other key concepts that have relevance as we seek to create and understand the future of AI in education.

As humans, our instinct is to ask the questions “why” and “what if?” As you go about your day, you might ask yourself, “If I take this aspirin, will my headache go away?” or “What did I eat that made my stomach hurt?” You might even ask questions about the past too like, “What if I left my house just a few minutes earlier, would I have made my flight?” Whenever we ask questions like these, we are dealing with cause and effect relationships, or how certain factors lead to various results. In the scientific community, “Correlation is not causation” has been the mantra chanted by scientists for more than a century, prohibiting causal talk in many classrooms and scientific studies. Today, however, we have gone through a Causal Revolution instigated by author Judea Pearl and his colleagues. Through The Book of Why, Pearl shows us how his work in causal relationships will allow us to explore the world in more ways than one. It also shows us that the key to artificial intelligence is human thought and creating machines that can determine causes and effects. As you read, you’ll learn how the human brain is the most advanced tool in the world, how misunderstood data can lead to protests of the smallpox vaccine, and how controlled experiments have been around for as long as humans. Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

The New Science of Cause and Effect. As humans, our instinct is to ask the questions “why” and “what if?” As you go about your day, you might ask yourself, “If I take this aspirin, will my headache go away?” or “What did I eat that made my stomach hurt?” You might even ask questions about the past too like, “What if I left my house just a few minutes earlier, would I have made my flight?” Whenever we ask questions like these, we are dealing with cause and effect relationships, or how certain factors lead to various results. In the scientific community, “Correlation is not causation” has been the mantra chanted by scientists for more than a century, prohibiting causal talk in many classrooms and scientific studies. Today, however, we have gone through a Causal Revolution instigated by author Judea Pearl and his colleagues. Through The Book of Why, Pearl shows us how his work in causal relationships will allow us to explore the world in more ways than one. It also shows us that the key to artificial intelligence is human thought and creating machines that can determine causes and effects. As you read, you’ll learn how the human brain is the most advanced tool in the world, how misunderstood data can lead to protests of the smallpox vaccine, and how controlled experiments have been around for as long as humans. *** Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

Dana was the Senior User Interface Artist/Designer at Neversoft and worked on Tony Hawk's Pro Skater 4, Tony Hawk's Underground, Tony Hawk's Underground 2, and Tony Hawk's American Wasteland. He also has been commissioned for his amazing talent as an artist by Tony Hawk himself, doing artwork for Birdhouse skateboards as well as the Tony Hawk Huck Jam Series. He further has done artwork for the upcoming Pretending I'm A Superman documentary on the history of the THPS franchise. Dana currently works for Infinity Ward as the Senior UI Artist/UI Art Lead. Their most recently shipped game is Call of Duty: Warzone.

In this episode, I talk with Irineo Cabreros about causality. We discuss why causality matters, what does and does not imply causality, and two different mathematical formalizations of causality: potential outcomes and directed acyclic graphs (DAGs). Causal models are usually considered external to and separate from statistical models, whereas Irineo’s new paper shows how causality can be viewed as a relationship between particularly chosen random variables (potential outcomes). Links: Causal models on probability spaces (Irineo Cabreros, John D. Storey) The Book of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie)

Billions of years ago, Mars probably hosted many water features: streams, rivers, gullies, etc. But until recently, water detected on the Red Planet was either locked up in ice or flitting about as a gas in the atmosphere. Now, researchers analyzing radar data from the Mars Express mission have found evidence for an enormous salty lake under the southern polar ice cap of Mars. Daniel Clery joins host Sarah Crespi to discuss how the water was found and how it can still be liquid—despite temperatures and pressures typically inhospitable to water in its liquid form. Read the research. Sarah also talks with science journalist Katherine Kornei about her story on changing athletic performance after gender transition. The feature profiles researcher Joanna Harper on the work she has done to understand the impacts of hormone replacement therapy and testosterone levels in transgender women involved in running and other sports. It turns out within a year of beginning hormone replacement therapy, transgender women plateau at their new performance level and stay in a similar rank with respect to the top performers in the sport. Her work has influenced sports oversight bodies like the International Olympic Committee. In this month's book segment, Jen Golbeck interviews Andrew Lawler about his book The Secret Token: Myth, Obsession, and the Search for the Lost Colony of Roanoke. Next month's book will be The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie. Write us at sciencepodcast@aaas.org or tweet to us @sciencemagazine with your questions for the authors. This week's episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. [Image: Henry Howe; Music: Jeffrey Cook]

Billions of years ago, Mars probably hosted many water features: streams, rivers, gullies, etc. But until recently, water detected on the Red Planet was either locked up in ice or flitting about as a gas in the atmosphere. Now, researchers analyzing radar data from the Mars Express mission have found evidence for an enormous salty lake under the southern polar ice cap of Mars. Daniel Clery joins host Sarah Crespi to discuss how the water was found and how it can still be liquid—despite temperatures and pressures typically inhospitable to water in its liquid form. Read the research. Sarah also talks with science journalist Katherine Kornei about her story on changing athletic performance after gender transition. The feature profiles researcher Joanna Harper on the work she has done to understand the impacts of hormone replacement therapy and testosterone levels in transgender women involved in running and other sports. It turns out within a year of beginning hormone replacement therapy, transgender women plateau at their new performance level and stay in a similar rank with respect to the top performers in the sport. Her work has influenced sports oversight bodies like the International Olympic Committee. In this month’s book segment, Jen Golbeck interviews Andrew Lawler about his book The Secret Token: Myth, Obsession, and the Search for the Lost Colony of Roanoke. Next month’s book will be The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie. Write us at sciencepodcast@aaas.org or tweet to us @sciencemagazine with your questions for the authors. This week’s episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. [Image: Henry Howe; Music: Jeffrey Cook]

“Wow, that woman sounds incredibly sweet, and incredibly knowledgeable.” – My husband after I recorded this podcast. That about sums up this podcast. Dana Mackenzie of Hearthstone Aussies is a sometime breeder, longtime judge (for a number of different venues),… Read more › The post Session 7 – Dana Mackenzie appeared first on TheAustralianShepherd.net - Everything Aussie!.

Normally when creating a digital file, such as a picture, much more information is recorded than necessary-even before storing or sending. The image on the right was created with compressed (or compressive) sensing, a breakthrough technique based on probability and linear algebra. Rather than recording excess information and discarding what is not needed, sensors collect the most significant information at the time of creation, which saves power, time, and memory. The potential increase in efficiency has led researchers to investigate employing compressed sensing in applications ranging from missions in space, where minimizing power consumption is important, to MRIs, for which faster image creation would allow for better scans and happier patients. Just as a word has different representations in different languages, signals (such as images or audio) can be represented many different ways. Compressed sensing relies on using the representation for the given class of signals that requires the fewest bits. Linear programming applied to that representation finds the most likely candidate fitting the particular low-information signal. Mathematicians have proved that in all but the very rarest case that candidate-often constructed from less than a tiny fraction of the data traditionally collected-matches the original. The ability to locate and capture only the most important components without any loss of quality is so unexpected that even the mathematicians who discovered compressed sensing found it hard to believe. For More Information: "Compressed Sensing Makes Every Pixel Count," What's Happening in the Mathematical Sciences, Vol. 7, Dana Mackenzie.

What.s in store for our climate and us? It.s an extraordinarily complex question whose answer requires physics, chemistry, earth science, and mathematics (among other subjects) along with massive computing power. Mathematicians use partial differential equations to model the movement of the atmosphere; dynamical systems to describe the feedback between land, ocean, air, and ice; and statistics to quantify the uncertainty of current projections. Although there is some discrepancy among different climate forecasts, researchers all agree on the tremendous need for people to join this effort and create new approaches to help understand our climate. It.s impossible to predict the weather even two weeks in advance, because almost identical sets of temperature, pressure, etc. can in just a few days result in drastically different weather. So how can anyone make a prediction about long-term climate? The answer is that climate is an average of weather conditions. In the same way that good predictions about the average height of 100 people can be made without knowing the height of any one person, forecasts of climate years into the future are feasible without being able to predict the conditions on a particular day. The challenge now is to gather more data and use subjects such as fluid dynamics and numerical methods to extend today.s 20-year projections forward to the next 100 years. For More Information: Mathematics of Climate Change: A New Discipline for an Uncertain Century, Dana Mackenzie, 2007.

What.s in store for our climate and us? It.s an extraordinarily complex question whose answer requires physics, chemistry, earth science, and mathematics (among other subjects) along with massive computing power. Mathematicians use partial differential equations to model the movement of the atmosphere; dynamical systems to describe the feedback between land, ocean, air, and ice; and statistics to quantify the uncertainty of current projections. Although there is some discrepancy among different climate forecasts, researchers all agree on the tremendous need for people to join this effort and create new approaches to help understand our climate. It.s impossible to predict the weather even two weeks in advance, because almost identical sets of temperature, pressure, etc. can in just a few days result in drastically different weather. So how can anyone make a prediction about long-term climate? The answer is that climate is an average of weather conditions. In the same way that good predictions about the average height of 100 people can be made without knowing the height of any one person, forecasts of climate years into the future are feasible without being able to predict the conditions on a particular day. The challenge now is to gather more data and use subjects such as fluid dynamics and numerical methods to extend today.s 20-year projections forward to the next 100 years. For More Information: Mathematics of Climate Change: A New Discipline for an Uncertain Century, Dana Mackenzie, 2007.

A person needing a kidney transplant may have a friend or relative who volunteers to be a living donor, but whose kidney is incompatible, forcing the person to wait for a transplant from a deceased donor. In the U.S. alone, thousands of people die each year without ever finding a suitable kidney. A new technique applies graph theory to groups of incompatible patient-donor pairs to create the largest possible number of paired-donation exchanges. These exchanges, in which a donor paired with Patient A gives a kidney to Patient B while a donor paired with Patient B gives to Patient A, will dramatically increase transplants from living donors. Since transplantation is less expensive than dialysis, this mathematical algorithm, in addition to saving lives, will also save hundreds of millions of dollars annually. Naturally there can be more transplants if matches along longer patient-donor cycles are considered (e.g., A.s donor to B, B.s donor to C, and C.s donor to A). The problem is that the possible number of longer cycles grows so fast hundreds of millions of A >B>C>A matches in just 5000 donor-patient pairs that to search through all the possibilities is impossible. An ingenious use of random walks and integer programming now makes searching through all three-way matches feasible, even in a database large enough to include all incompatible patient-donor pairs. For More Information: Matchmaking for Kidneys, Dana Mackenzie, SIAM News, December 2008. Image of suboptimal two-way matching (in purple) and an optimal matching (in green), courtesy of Sommer Gentry.

The Moon Sees Me