Podcast appearances and mentions of Joshua Gans

This week the Honorable Dr Andrew Leigh MP, and philosopher Peter Singer, join host Lloyd Vogelman on the couch for an unfiltered conversation that digs into the personal side of the Principle of Charity.Peter Singer - BioPeter Singer is emeritus professor of bioethics at Princeton University. He has a background in philosophy and works mostly in practical ethics. He is best known for Animal Liberation and for his writings about global poverty.In 2021, Peter received the Berggruen Prize for Philosophy and Culture. The prize comes with $1 million, which Peter donated to the most effective organizations working to assist people in extreme poverty and to reduce the suffering of animals in factory farms.Peter is the founder of The Life You Can Save, an organization based on his book of the same name.His writings in this area include the 1972 essay “Famine, Affluence, and Morality”, in which Peter argues for donating to help the global poor, and two books that make the case for effective giving, The Life You Can Save (2009, 2nd edition 2019) and The Most Good You Can Do (2015).Andrew LeighAndrew Leigh is the Assistant Minister for Competition, Charities, Treasury and Employment, and Federal Member for Fenner in the ACT. Prior to being elected in 2010, Andrew was a professor of economics at the Australian National University. He holds a PhD in Public Policy from Harvard, having graduated from the University of Sydney with first class honours in Arts and Law. Andrew is a past recipient of the Economic Society of Australia's Young Economist Award and a Fellow of the Australian Academy of Social Sciences.His books include Innovation + Equality: How to Create a Future That Is More Star Trek Than Terminator (with Joshua Gans) (2019), Reconnected: A Community Builder's Handbook (with Nick Terrell) (2020), What's the Worst That Could Happen? Existential Risk and Extreme Politics (2021), Fair Game: Lessons From Sport for a Fairer Society and a Stronger Economy (2022) and The Shortest History of Economics (2024).Andrew is a keen Ironman triathlete and marathon runner, and hosts a podcast called The Good Life: Andrew Leigh in Conversation, about living a happier, healthier and more ethical life.CREDITSYour hosts are Lloyd Vogelman and Emile Sherman This podcast is proud to partner with The Ethics CentreFind Lloyd @LloydVogelman on Linked inFind Emile @EmileSherman on Linked In and XThis podcast is produced by Jonah Primo and Sabrina OrganoFind Jonah at jonahprimo.com or @JonahPrimo on Instagram Hosted on Acast. See acast.com/privacy for more information.

In this episode we're joined by Federal Member for Fenner, the Honorable Dr Andrew Leigh MP, and philosopher and emeritus professor of bioethics at Princeton University, Peter Singer, to consider if we should value the lives of unborn future generations, more than we value those of us alive today. The consideration of lives unborn sits at the heart of ‘existential risk'. It asks us to take seriously all the future generations who, if humanity gets it right, could end up far far more numerous than every life lived to date. We could in fact, be just at the beginning of our beautiful journey as a species. But we do face a number of very real risks that could literally destroy us all - biowarfare, climate change and AI to name but a few.So, should we spend our limited resources helping the poorest and most in need today, wherever they live? Or should we divert resources to reduce the sorts of risks which, if left unchecked, could prevent countless generations from coming into existence at all?Peter Singer - BioPeter Singer is emeritus professor of bioethics at Princeton University. He has a background in philosophy and works mostly in practical ethics. He is best known for Animal Liberation and for his writings about global poverty. In 2021, Peter received the Berggruen Prize for Philosophy and Culture. The prize comes with $1 million, which Peter donated to the most effective organizations working to assist people in extreme poverty and to reduce the suffering of animals in factory farms.Peter is the founder of The Life You Can Save, an organization based on his book of the same name. His writings in this area include the 1972 essay “Famine, Affluence, and Morality”, in which Peter argues for donating to help the global poor, and two books that make the case for effective giving, The Life You Can Save (2009, 2nd edition 2019) and The Most Good You Can Do (2015).Andrew LeighAndrew Leigh is the Assistant Minister for Competition, Charities, Treasury and Employment, and Federal Member for Fenner in the ACT. Prior to being elected in 2010, Andrew was a professor of economics at the Australian National University. He holds a PhD in Public Policy from Harvard, having graduated from the University of Sydney with first class honours in Arts and Law. Andrew is a past recipient of the Economic Society of Australia's Young Economist Award and a Fellow of the Australian Academy of Social Sciences.His books include Innovation + Equality: How to Create a Future That Is More Star Trek Than Terminator (with Joshua Gans) (2019), Reconnected: A Community Builder's Handbook (with Nick Terrell) (2020), What's the Worst That Could Happen? Existential Risk and Extreme Politics (2021), Fair Game: Lessons From Sport for a Fairer Society and a Stronger Economy (2022) and The Shortest History of Economics (2024).Andrew is a keen Ironman triathlete and marathon runner, and hosts a podcast called The Good Life: Andrew Leigh in Conversation, about living a happier, healthier and more ethical life. CREDITSYour hosts are Lloyd Vogelman and Emile Sherman This podcast is proud to partner with The Ethics CentreFind Lloyd @LloydVogelman on Linked inFind Emile @EmileSherman on Linked In and XThis podcast is produced by Jonah Primo and Sabrina OrganoFind Jonah at jonahprimo.com or @JonahPrimo on Instagram Hosted on Acast. See acast.com/privacy for more information.

In this episode of TribePod – The Proactive Talent Podcast, host Matt Staney dives into the future of recruiting in an AI-driven world. Matt outlines six critical skills every modern recruiting team needs to thrive, from leveraging AI beyond automation to building talent ecosystems. Along the way, Matt offers practical tips, personal anecdotes, and recommended readings that will help you elevate your recruiting game and lead the charge in transforming talent acquisition. Whether you're a talent leader or a recruiter looking to stay ahead of the curve, this episode is packed with actionable insights that can't be missed. Tune in now to learn how to future-proof your recruiting strategy and build teams ready for the modern world! Recommended books and their authors mentioned in the episode: Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb The Culture Code: The Secrets of Highly Successful Groups by Daniel Coyle Talent Magnet: How to Attract and Keep the Best People by Mark Miller Work Without Jobs: How to Reboot Your Organization's Work Operating System by Ravin Jesuthasan and John Boudreau Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth by Amy C. Edmondson

This month on the Future in Sound, Jaxson Khan- former senior policy advisor to Canada's Minister of Innovation, Science, and Industry- explores the evolving role of AI in business. With a background in accessibility, AI policy, safety, innovation, and ethical frameworks, few people are better placed to help us think through the connections between tech and society. Jenn and Jaxson discuss the delicate balance between innovation and caution when it comes to AI. How do companies manage the risks of AI whilst also pursuing the opportunities it offers? Jaxson highlights the potential for AI both to enhance accessibility and to create new barriers if not implemented thoughtfully. They also tackle global approaches to AI policy—from the flexible frameworks seen in the U.S. to the more structured EU AI Act—emphasising the need for practical, citizen-focused regulations that build public trust.Useful links:Book: Prediction Machines by AJ Agrawal, Joshua Gans, and Avi GoldfarbClick here for the episode web page.For more insights straight to your inbox subscribe to the Future in Sight newsletter, and follow us on LinkedIn and Instagram This podcast is brought to you by Re:Co, a tech-powered advisory company helping private market investors pursue sustainability objectives and value creation in tandem. Produced by Chris AttawayArtwork by Harriet RichardsonMusic by Cody Martin

Andrew Leigh is a minister in the Australian parliament with a doctorate in economics from Harvard. Unlike many academic economists, however, Leigh has the gift of simplifying economics for all of us. His new book, How Economics Explains the World, presents economics as the prism to understand the human story. From the dawn of agriculture to AI, Leigh tells the story of how ingenuity, greed, and desire for betterment have, to an astonishing degree, determined humanity's past, present, and future. Andrew Leigh is the Assistant Minister for Competition, Charities, Treasury and Employment, and Federal Member for Fenner in the Australian Parliament. Prior to being elected in 2010, Andrew was a professor of economics at the Australian National University. He holds a PhD in Public Policy from Harvard, having graduated from the University of Sydney with first class honours in Arts and Law. Andrew is a past recipient of the Economic Society of Australia's Young Economist Award and a Fellow of the Australian Academy of Social Sciences. His books include Disconnected (2010), Battlers and Billionaires: The Story of Inequality in Australia (2013), The Economics of Just About Everything (2014), The Luck of Politics (2015), Choosing Openness: Why Global Engagement is Best for Australia (2017), Randomistas: How Radical Researchers Changed Our World (2018), Innovation + Equality: How to Create a Future That Is More Star Trek Than Terminator (with Joshua Gans) (2019), Reconnected: A Community Builder's Handbook (with Nick Terrell) (2020), What's the Worst That Could Happen? Existential Risk and Extreme Politics (2021) and Fair Game: Lessons From Sport for a Fairer Society & a Stronger Economy (2022). Andrew is a keen triathlete and marathon runner, and hosts a podcast called The Good Life: Andrew Leigh in Conversation, about living a happier, healthier and more ethical life. Andrew is the father of three sons - Sebastian, Theodore and Zachary, and lives with his wife Gweneth in Canberra.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

Welcome to Growthmates with Kate Syuma — Growth advisor, previously Head of Growth Design at Miro. I'm building Growthmates as a place to connect with inspiring leaders to help you grow yourself and your product. Here you can learn how companies like Dropbox, Adobe, Canva, Loom, and many more are building excellent products and growth culture. Get all episodes and a free playbook for Growth teams on our brand-new website — growthamtes.club, and press follow to support us on your favorite platforms. Listen now and subscribe on your favorite platforms — Apple, Spotify, or watch on YouTube (new!).—In this episode, I chat with Amy Bucher, Chief Behavioral Officer at Lirio and author of Engaged. We delve into the world of behavioral design, exploring how understanding human behavior can lead to more ethical and effective product development. Amy shares her journey from academia to leading behavioral design teams, and how frameworks like the COMBEE model and Behavior Change Wheel are essential tools for influencing user behavior.By the end of this episode, you'll learn how to apply behavioral science principles to your product, understand the importance of ethical design, and gain insights into leveraging AI for personalized user experiences

In March 2023, many experts supported an open letter that called for a six-month pause in giant AI experiments, and that development of these AIs should go ahead “only once we are confident that their effects will be positive, and their risks will be manageable”. In the second of our podcasts recorded at the 79th EP Panel, Tim Phillips asks Joshua Gans of the University of Toronto what might happen if we did pause AI adoption, and whether we should instead accelerate adoption of AI so that we can more quickly learn about its benefits and harms, and design better regulation as a result.

Episode 232: When AI Shifts the Balance of Power to Consumers: Preparing for a New Business Reality Matt Harris, Partner at Bain Capital Ventures, argues that generative AI will soon empower customers in unexpected ways. Consumers, he predicts, will soon use AI tools to continuously discover lower loan rates, higher-yield savings accounts, or more attractive insurance policies. As a result, Matt anticipates a shift in customer-company power dynamics. Imagine, for example, an AI agent able to continuously move your deposits to the highest-yielding savings accounts or refinance your loans to the best available rates with little intervention required from you. Imagine consumer tools for interpreting medical scans and providing a diagnosis to compare to your doctor's assessment. Generative AI could even transform the future of mental health therapy to be more effective, efficient, and streamlined. Join us as we discuss the present and future of consumers' newfound AI-driven power and analyze where organizations and their leaders should be pointed next to remain competitive. Guest: Matt Harris, Partner, Bain Capital Ventures Host: Rob Markey, Partner, Bain & Company Give Us Feedback: We'd love to hear from you. Help us enhance your podcast experience by providing feedback here in our listener survey. Want to get in touch? Send a note to host Rob Markey: https://www.robmarkey.com/contact-rob Time-stamped list of topics covered: [02:53] How businesses use generative AI for productivity and future innovation [09:23] Discussion on the Gartner Hype Cycle for generative AI [17:09] What it means for customers to be “superpowered” and an overview of a few generative AI applications in finance and medicine [22:28] AI's impact on society and the future of work [27:44] Adapting to a world with superpowered customers who now demand more from businesses, emphasizing the need for deepened customer relationships and greater levels of innovation Time-stamped list of notable quotes: [10:55] "Every new technology experiences [influx and they] disillusion the rest of the market because they have these spectacular failures.” [14:20] “[Generative AI] is a much more powerful tool for customers than it is for companies. And an equal or larger part of energy should be going in these boardrooms to imagining a world where customers have the power of generative AI and the implications for your entire business model.” [18:00] “I revere doctors. But how could we expect that a single human being would have all of the medical knowledge in their head? This is something computers will be better at.” [22:16] "I think I could make an argument that generative AI, combined with predictive machine learning, and being moved forward as fast as these technologies are advancing, will be better at almost everything.” [27:53] “We should be spending as much energy thinking and worrying and planning for a ‘superpowered' customer world as we are thinking about how we use [technology] ourselves, and I'm just not seeing people doing that.” Additional Resources: Read Matt's article for BCV, The Age of the Superpowered Customer The Age of AI and Our Human Future by Henry A. Kissinger, Eric Schmidt, and Daniel Huttenlocher The AI Advantage by Thomas H. Davenport Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb Learn about Bain's spin-off, Bain Capital, which has operated as a separate company since 1984 Learn More About Our Guest and Host: Guest: Matt Harris LinkedIn Profile Host: Rob Markey LinkedIn Profile

Is our future with generative AI terrifying, exciting, or fascinating? In this episode, we talk about the adoption of artificial intelligence in the workplace, the opportunities to implement AI in healthcare delivery, and privacy in a world enabled by generative AI. Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is the Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the bestselling books Prediction Machines: The Simple Economics of Artificial Intelligence and Power and Prediction: The Disruptive Economics of Artificial Intelligence. In this episode, we talk about the adoption of artificial intelligence in the workplace, the opportunities to implement AI in healthcare delivery, and privacy in a world enabled by generative AI.

Conforme avançamos na direção de 2030, uma nova frase ganha força: "toda empresa será uma empresa de IA". Será mesmo? Chamamos Evandro Barros, fundador e CEO da DATA H, para conversar e aí apareceu uma ideia melhor: toda empresa que souber combinar IA com dados e tirar o máximo da inteligência do seu time de especialistas, será uma Smart Company, criando soluções digitais do tipo "como pude viver até hoje sem isso?". Aí a receita é outra. Dá o play para entender, que a conversa está ótima. Insights do episódioO livro "The Coming Wave", de Mustafa SuleymanO livro "Power and Prediction: The Disruptive Economics of Artificial Intelligence", de Ajay Agrawal, Joshua Gans e Avi GoldfarbTambém vale ler "Máquinas Preditivas: A simples economia da inteligência artificial", de Ajay Agrawal, Joshua Gans e Avi GoldfarbO filme "Dumb Money" ("Dinheiro Fácil"), sobre o caso da GameStopO filme "O Jogo da Imitação", sobre a vida de Alan Turing A série "Betinho, no fio da navalha", sobre a vida do sociólogo Herbert de Souza 

The conversation this week is with Bradley Canham. Brad is the VP of Research for Transforma Insights, a UK based high tech analyst firm, corporate fellow and mentor at the University of St. Thomas Opus School of Business and Entrepreneur Program. He's also a member of the learning community at St. Thomas on generative AI and education. He's the founder and original editor of Eden Prairie Local News, a thriving news publication and example of social entrepreneurship.If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future AppliedAI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events!Emerging Technologies NorthAppliedAI MeetupResources and Topics Mentioned in this EpisodeTransforma InsightsEden Prairie Local NewsNatural language processingCustomer relationship managementGitHub CopilotTeaching What Can't Be Taught: The Shaman's Strategy by David RigoniCarlos CastanedaRadical Uncertainty: Decision-Making Beyond the Numbers by John Kay and Mervyn KingPrediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi GoldfarbEnjoy!Your host,Justin Grammens

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: My techno-optimism [By Vitalik Buterin], published by habryka on November 28, 2023 on LessWrong. Vitalik wrote a post trying to make the case for his own take on techno-optimism summarizing it as an ideology he calls "d/acc". I resonate with a lot of it, though also have conflicting feelings about trying to create social movements and ideologies like this. Below some quotes and the table of contents. Last month, Marc Andreessen published his "techno-optimist manifesto", arguing for a renewed enthusiasm about technology, and for markets and capitalism as a means of building that technology and propelling humanity toward a much brighter future. The manifesto unambiguously rejects what it describes as an ideology of stagnation, that fears advancements and prioritizes preserving the world as it exists today. This manifesto has received a lot of attention, including response articles from Noah Smith, Robin Hanson, Joshua Gans (more positive), and Dave Karpf, Luca Ropek, Ezra Klein (more negative) and many others. Not connected to this manifesto, but along similar themes, are James Pethokoukis's "The Conservative Futurist" and Palladium's "It's Time To Build for Good". This month, we saw a similar debate enacted through the OpenAI dispute, which involved many discussions centering around the dangers of superintelligent AI and the possibility that OpenAI is moving too fast. My own feelings about techno-optimism are warm, but nuanced. I believe in a future that is vastly brighter than the present thanks to radically transformative technology, and I believe in humans and humanity. I reject the mentality that the best we should try to do is to keep the world roughly the same as today but with less greed and more public healthcare. However, I think that not just magnitude but also direction matters. There are certain types of technology that much more reliably make the world better than other types of technology. There are certain types of technlogy that could, if developed, mitigate the negative impacts of other types of technology. The world over-indexes on some directions of tech development, and under-indexes on others. We need active human intention to choose the directions that we want, as the formula of "maximize profit" will not arrive at them automatically. In this post, I will talk about what techno-optimism means to me. This includes the broader worldview that motivates my work on certain types of blockchain and cryptography applications and social technology, as well as other areas of science in which I have expressed an interest. But perspectives on this broader question also have implications for AI, and for many other fields. Our rapid advances in technology are likely going to be the most important social issue in the twenty first century, and so it's important to think about them carefully. Table of contents Technology is amazing, and there are very high costs to delaying it The environment, and the importance of coordinated intention AI is fundamentally different from other tech, and it is worth being uniquely careful Existential risk is a big deal Even if we survive, is a superintelligent AI future a world we want to live in? The sky is near, the emperor is everywhere Other problems I worry about d/acc: Defensive (or decentralization, or differential) acceleration Macro physical defense Micro physical defense (aka bio) Cyber defense, blockchains and cryptography Info defense Social technology beyond the "defense" framing So what are the paths forward for superintelligence? A happy path: merge with the AIs? Is d/acc compatible with your existing philosophy? We are the brightest star Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to this week's episode of the Mixtape with Scott! This week we have an outstanding guest named Avi Goldfarb of the University of Toronto. Avi is a PhD economist who graduated from Northwestern in the early 2000s specializing in the economics of the internet. He is now at the University of Toronto where he is a professor in the marketing department as well as chief data scientist with a very interesting lab called the Creative Destruction lab that among other things specializes in the economics of artificial intelligence. He is the author of two very popular and probably both best selling books aimed at a general audience on the economics of artificial intelligence: Power and Prediction and Prediction Machines (both with Joshua Gans and Ajay Agrawal). Given the popularity of AI, as well as the recent turn of events with AI giant, OpenAI, I think there couldn't be a better time to to have him on the show. I loved this interview and accidentally went over, but Avi graciously hung in there with me. I hope you love it too. Don't forget to like, share and comment! Happy Thanksgiving to all!Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Scott's Substack at causalinf.substack.com/subscribe

In the first of a special mini-series focused on AI in the workplace, guest hosts Pauline James and David Creelman ask "what exactly is AI all about and where is it heading?"Joining Pauline and David on the podcast is Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. He is also Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the bestselling books Prediction Machines and Power and Prediction.He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi has testified before the U.S. Senate Judiciary Committee on related work in competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere.Questions for Avi include:You have been studying AI for a long time; well before its advances garnered the attention of the general population. Is anything taking you by surprise with the current advances?You have talked about AI automating prediction in ways that we had only understood humans could do well/reasonably well previously. Can you explain this for our audience?You have an interesting (and hopeful) perspective that AI may become more of an equalizer across professions than past advances have been, can you tell us more about that?Can you provide your thoughts on how long it will be until we see broad sweeping changes in work and how it is organized?With your extensive work with entrepreneurs on the bleeding edge of new technology, what advice do you have for those on the other side of the equation: those who are making recommendations and purchasing tech for their organization. How should we balance not being left behind with appropriate diligence?We do our best to ensure editorial objectivity. The views andFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here and contact sales@hr-gazette.com. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

When Joshua Gans and his co-authors released their book Prediction Machines in 2018, they were writing about a topic that seemed quite niche. At this time, machine learning was just starting out. In the last year, the speed at which artificial intelligence has advanced has surprised almost everyone.In this conversation, we hear how the analytical framework that he and his colleagues developed helps to sort through the hype. He argues artificial intelligence is best thought of as a prediction machine. You'll hear why he's optimistic that artificial intelligence will be able to help people remove some of the drudgery from some jobs, but at this time, doesn't seem likely to take over full jobs. He'll share how understanding artificial intelligence as an advance in predictive statistics will help leaders assess how artificial intelligence may or may not be useful. About our guest:Joshua Gans is a Professor of Strategic Management and holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management, the University of Toronto (with a cross-appointment in the Department of Economics). Joshua is also Chief Economist of the University of Toronto's Creative Destruction Lab. Prior to 2011, he was the foundation Professor of Management (Information Economics) at the Melbourne Business School, University of Melbourne and before that, he was at the School of Economics, University of New South Wales. At Rotman, he teaches MBA students entrepreneurial strategy. He has also co-authored (with Stephen King and Robin Stonecash) the Australasian edition of Greg Mankiw's Principles of Economics (published by Cengage), Core Economics for Managers (Cengage), Finishing the Job (MUP), Parentonomics (New South/MIT Press) and Information Wants to be Shared (Harvard Business Review Press) and The Disruption Dilemma (MIT Press, 2016);

This week's podcast episode features our special guest harrison.ai, CEO and Co-Founder, Aengus Tran.   harrison.ai started with the problem statement of healthcare capacity. According to Aengus, one of the biggest problems of our time is how to look after such a huge population with increased health needs. harrison.ai is looking to scale the global capacity of healthcare by building automation with AI systems and providing clinicians with superpowers of scale. Aengus says, "of all the things being done in in healthcare, scale will be the solution of quality healthcare globally."   After moving to Australia from Ho Chi Minh in Vietnam, Aengus was one of very few Vietnamese students at an English-speaking Australian school so quite quickly had to learn the English language. After school Aengus went on to study medicine at university after advice from none other than Paul Ramsay of Ramsay Healthcare - a company that has gone on to invest in harrison.ai.   While working through his medical degree, Aengus realised how much in terms of scale was missing from medicine. During his final years of training, Aengus was involved in a research project building technology along with Vertex Health to select human embryo; opening his eyes to the capabilities of AI systems and how they can scale.   After starting out in Australia and New Zealand, harrison.ai's radiology arm is regulated with FDA clearance in 38 countries with 1 million live touches and one in four radiologists using their technology in Australia alone. Their north star is to touch 1 million lives per day with their technology. harrison.ai has gone on to raise $US120M in series A and series B with investments from Skip Capital, Blackbird, Horizon Ventures and specialist, strategic, corporate investors Sonic Healthcare and Ramsay Healthcare.   Quickfire Round: Book: Prediction Machines, The Simple Economics of Artificial Intelligence, Ajay Agrawal, Joshua Gans, Avi Goldfarb Podcast: Lex Fridman on YouTube News Source: Medium, mainstream news App: VS Code, Rise Productivity Tool: very basic reminders app and keeping notes, Slack Favourite CEO: Mark Zuckerberg TV Show: House TEDTalk Topic: The health equality and impact that is possible through the scalability of AI   harrison.ai are continuously hiring in their AI teams. If you'd like to learn more or are you're curious about the tool, reach out to harrison.ai. See omnystudio.com/listener for privacy information.

On this episode Tricia responds to feedback from listeners like you who requested a few more 'solo shows.' Discussed in this episode: Using ChatGPT as a thought partner with your SOGI/GSA group, get the free guide: https://shiftingschools.lpages.co/chatgpt-and-your-gsasogi-group/ Want to try out the Equity and Generative AI course free as a listener? Learn more about the course: https://www.shiftingschools.com/store-2/p/5-day-ai-challenge-83rw3 Email me: tricia(at) shiftingschools (dotcom) to request your free pass. Learn more about the books I recommended for updating your professional development library: Deepfakes: The Coming Infocalypse by Nina Schick Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble Power and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agrawal , Joshua Gans ,Avi Goldfarb Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place by Janelle Shane Explore my free guide on teaching students about social media campaigns: https://tinyurl.com/onlinecampaignallyed "Apple and Google are not enforcing their stated terms of service as the Daily Wire App spreads violent anti-LGBTQ hate" from Media Matters: https://www.mediamatters.org/daily-wire/apple-and-google-are-not-enforcing-their-stated-terms-service-daily-wire-app-spreads What impact will and should artificial intelligence have on assessment? @GOAlearning is hosting a free online event Join us on March 22! Register here: https://goacademy.zoom.us/meeting/register/tJErdu6rrDoiGtHAiEd8gwnozz1H9shUaLSr Check out the podcast we will be talking about next week: https://www.beyond6seconds.com/

A Inteligência Artificial é, com certeza, a tecnologia disruptiva que mais gera entusiasmo e polêmica ultimamente. Mas é preciso olhar além do hype. Achar que a mera adoção da tecnologia “faz mágica” é o maior risco para as empresas. Esse é um dos motivos para 80% dos projetos de IA corporativa terem falhado em 2022. Por que deu errado? E como fazer dar certo? Ricardo Taborda, Matheus Ferreira e Eduardo Abbud, especialistas em ciência de dados e machine learning, e fundadores da 7D Analytics, contam tudo.Links do episódioPara conhecer: O site da 7D AnalyticsO filme “Tudo em Todo Lugar ao Mesmo Tempo” (Everything Everywhere All at Once), que pode ser assistido no Amazon Prime.O livro “O poder do pensamento matemático”, de Jordan Ellenberg.O livro Grokking Machine Learning, de Luis SerranoO canal do YouTube, Serrano.Academy, sobre machine learningO livro Rápido e devagar: Duas formas de pensar, de Daniel KahnemanO livro Influence: The Psychology of Persuasion, de Robert CialdiniO livro Os Números Não Mentem: 71 Histórias Para Entender o Mundo, de Vaclav Smil O livro Power and Prediction: The Disruptive Economics of Artificial Intelligence, de Ajay Agrawal, Joshua Gans  e Avi Goldfarb_____FALE CONOSCOEmail: news@theshift.info_____ASSINE A THE SHIFTwww.theshift.info

"You can see the computer age everywhere but in the productivity statistics," said Nobel laureate economic Robert Solow in 1987. A decade later, the '90s productivity boom was in full swing. Likewise, it took decades for electrification to have an impact on productivity growth in the early 20th century. Today, artificial intelligence can write a coherent paragraph or generate an image from a simple prompt. But when will AI show up in the statistics, boosting productivity and then economic growth? Avi Goldfarb joins Faster, Please! — The Podcast to discuss that question and more.Avi holds the Rotman Chair in Artificial Intelligence and Healthcare at the University of Toronto's Rotman School Of Management. He's also co-author, along with Ajay Agrawal and Joshua Gans, of 2022's Power and Prediction: The Disruptive Economics of Artificial Intelligence.In This Episode* Prediction at scale (1:34)* How AI has transformed ride hailing and marketing (5:37)* The potential for “system-level” changes (11:26)* When will AI show up in the statistics? (16:12)* The impact of ChatGPT and DALL-E (19:46)Below is an edited transcript of our conversation.Prediction at scaleJames Pethokoukis: What this book is about—and then you can tell me if I've gotten it horribly wrong—this is a book about machines making predictions using advanced statistical techniques. 1) Is that more or less right? And 2) why is that an important capability?Avi Goldfarb: That's more or less right. The only place where I [would offer] a little correction there is, the reason we're talking about artificial intelligence today is almost entirely due to advances in computational statistics. Yes, it is just stats and that sounds kind of unexciting. But once we have prediction at scale, it can be really transformative to all aspects of business in the economy. There's a reason why we're calling computational stats “artificial intelligence” and we didn't use to.Prediction at scale. That's a great three-word description. Probably why you used it. To what extent is that now happening? The name of the book is Power and Prediction: The Disruptive Economics of Artificial Intelligence. Is this prediction at scale already disruptive to some degree or is it, will be disruptive?The technology, for the most part, is pretty close to there, in the sense that we can do prediction at scale because we have the data and we have computational power to do all sorts of amazing things. For the most part, it hasn't been disruptive yet. And it hasn't been disruptive yet, just because we have the technology doesn't mean we know how to use it well and we know how to use it productively in our processes and systems in order to get the most out of it.Are there sectors currently doing this, but they're not doing it well yet? It's in a variety of sectors, but not enough companies doing it? Lots of companies are already using these machine learning tools, but they tend to be using them for things they were already doing before. If you had some prediction process to predict, if you're a bank, whether somebody's going to pay back a loan. In the very old days you'd have some human, the loan officer, look the customer up and down and go with their gut. And then, starting in the 1960s and especially in the ‘90s and beyond, we started to use scoring rules, partly your credit score and partly other things, to get a sense of whether people are going to pay them back. And so we were already doing a prediction task done by a machine. And now increasingly we're using these machine learning tools. We're using what we're calling AI, over the past five to 10 years, to predict whether people are going to pay back a loan. We're seeing those kinds of things all over the place, which is: You had some prediction, maybe you've used even a machine prediction before, and now we're using machine learning. We're using AI to make those predictions a little bit better. Lots of companies are using that.That sounds incremental. That sounds like an incremental advance.It's absolutely an incremental advance. We call these point solutions, which is, you look at your workflow, you identify something that a human is doing. You take out that human; you drop in a machine. You don't mess with a workflow because it's always easier to do things when you don't mess with a workflow. The problem is, when you don't mess with a workflow, there's only so much gain you can get. We've seen AI-based point solutions, prediction point solutions, all over the place. We haven't seen real transformation in very many industries. We've seen it in a couple. We haven't seen it in very many industries because real transformation requires doing things differently.How AI has transformed ride hailing and marketingDo you think that it has happened in one or two industries that you think would actually meet that bar of transformational? Can you give me an example?Absolutely. If you wanted to be a cab driver in the city of London 20 years ago, or even today, it takes three years of schooling. Learning to navigate those streets is really, really hard. And especially learning to navigate and predict where the traffic is going to be is really, really hard. And so there is a really rigorous process to screen people to be taxi drivers. In the US 30 years ago, there was something like 200 or 300 taxi drivers in the whole country. About 15 years ago, two technologies came about. The first one being digital dispatch, which is essentially tools for drivers to find riders, sometimes through prediction and sometimes through other tools. And then the second part was what's been disruptive with respect to that three years of schooling in the city of London, which is prediction tools for navigating a city. This is your GPS system.In the early days, many people selling digital dispatch and navigational predictions were selling them into professional driving companies, into taxi companies. “Hey, your taxi drivers can be 15 percent more efficient if they know the best route at this time.” That's what we call a point solution. You're already doing this, you take out some part of the human process, you drop in a machine, and you do it a little bit better. A couple of companies realized that digital dispatch combined with navigational prediction could create an entirely new type of industry. And this is the ride-hailing industry led by Uber and Lyft and others. That's a totally new kind of way to do personal transportation that made millions of amateur drivers as good as professional because they could navigate the city and find riders.Example number one is the taxi industry. Personal ride-hailing, for lack of a better word, has been transformed partly through digital and really those maps are important—and a big part of those maps is machine learning tools and figuring out where the traffic is, etc. So industry number one.Industry number two is advertising. I don't know if you've seen the TV show Mad Men. That was really how the advertising industry operated well into the ‘90s. Maybe not the soap opera aspect of it. Maybe, maybe not. I don't know. But the idea that there's a lot of wining and dining and charming people to convince them to spend millions of dollars on an ad campaign. And whether a campaign worked or not was largely based on gut feel. And which kinds of customers you targeted and which TV show and which magazine, all of that was priced based on intuition and not much else.Digital advertising came along in the late 1990s, and the first ways we thought about digital advertising was that it was like the magazine industry. So instead of advertising in People magazine, you're going to advertise on Yahoo using the exact same processes you did in People magazine. There was a rate card and it was going to be so many dollars per thousand users. And if you were doing general search, it might be $10, and if you're looking for real estate, it might be $50. And that's exactly how the magazine industry was priced. Some magazines were more than others based on readership and topic. And it was all based on personal selling, intuition, deals, etc.Then people realize that digital advertising created an opportunity to predict who the user was, who might see your ad. A user arrives at a publisher and an ad needs to be served, and you can predict who that user is and what they might want and when they might want it. Based on those predictions, rather than just do the magazine industry old way of doing things, you can now serve the right ad to the right person at the right time. Starting around 2000, there were all these innovations in online advertising that led to an industry that today looks almost nothing like the industry that you saw in Mad Men. Every time a user goes to a website, there is a real-time auction, in fractions of a second, between, in effect, thousands of advertisers for that user's attention. And there are all these intermediary steps, lots and lots of intermediaries—largely led by Google, but some other players that complement Google in that process—to create an entirely new kind of ad industry. The ad industry has had a system-level change because we can now predict, for a given impression or given user who's looking at a page, what they might want and when they might want it. Predictions changed the industry.The potential for “system-level” changesHow confident are you that this technology is powerful enough that we'll see system-level changes across the economy? That this is a general-purpose technology that will be significant? And do we have any idea what those changes will be, or is it, “They'll be big, but we don't know exactly what they are.”The technology itself is pretty extraordinary. And so in lots and lots of contexts, I'm pretty confident the technology's going to get there. There are some constraints on it, which is that you need data on the thing you're trying to predict in order for the predictions to work. But there are lots and lots of industries where we have great data. The technology barriers, I think, are being overcome. In some industries faster than others, but they're being overcome in lots and lots of places.That's not the only barrier. The technology is barrier number one. Think of an industry that I'm particularly excited about the potential of the technology, which is healthcare. Why is it so exciting for healthcare? Because diagnosis is at the center of how healthcare operates. If you know what's wrong with somebody, it's much easier to treat them, it's much less costly to treat them, and you can deliver the right treatment to the right person at the right time. Diagnosis, by the way, is prediction. It wasn't obvious, the way we thought about that in the past. But really, what it is, it can be solved [with] statistical prediction by using the information you have, the data on your symptoms, to fill in the information you don't have, which is what's actually causing your symptoms. If you do a Google Scholar search for something like “artificial intelligence healthcare,” you'll get a few million hits. There are lots of people who've done research producing AI for diagnosis. The technology, in many cases, is there. And in lots of other cases, it's pretty close.That doesn't mean it's going to transform healthcare. Why not? What's an AI doing diagnosis? They're doing a thing that makes doctors special. Yes, a good doctor in their workflow does all sorts of other things — they help patients navigate the stress of the healthcare system, they provide some treatments, etc. — but the thing that they went to school for all those years for, and for many of them the thing that they have that nurses and pharmacists and other medical professionals don't, is the ability to diagnose. When you bring in machine diagnosis into the healthcare system, that's going to be very disruptive to doctors. There are lots of reasons why, then, doctors might resist. First, they might be worried about their own jobs. Second, they might just not trust the machines and believe they're good enough. Because [in] the medical system doctors are a core source of power—they help determine how things work—they're going to resist many of the biggest system-level changes from AI-based diagnosis.And so you may have regulatory barriers, you may have organizational incentive barriers, and you may have barriers from the individual people on the ground who sabotage the machines that are trying to replace them. All of these are reasons — even if the technology is good enough — that AI in healthcare may be a long way away, even though we can see what that vision looks like. In other industries, it might be closer. In lots of retail contexts, you're trying to figure out who wants what and when — Amazon's pretty good at that in lots of ways — and in-store retailers can do that too. And so there are reasons to think that disruption in many retail industries will come faster.I just want to be a little careful here. I see the technology is there. There are some barriers on the technology side. If the payoff is big enough, I think most of the technology-related barriers can be overcome. To give you a sense of this: We hear a lot something like, “We don't want to do AI in our company because it's just so difficult to get the data organized and get the right data to build those predictions.” Well, yeah, it's difficult. But if the payoff is going to be transformative to the company and make the company millions or billions of dollars, then they'll spend thousands or millions in order to make it happen. And so a lot of the challenges aren't tech specific. They're incentives and organization based.When will AI show up in the statistics?I think of the classic Paul David paper about the dynamo. It took a while before factories used electricity, and they actually had to redo how the factory was designed to get full productivity value. And you say that we are sort of in the “between times.” And that makes me think of a classic Solow paradox: We see computers everywhere but in the statistics. He said that in '87. Are we, like, in the 1987 period with this technology? Or are we now in the late ‘90s where it's starting to happen and the boom is about to begin?I think we're in the early ‘80s.Not even the late ‘80s?He said that in 1987. By 1990 it was showing up in the data. So he just missed it.[We're in the early 1980s] in the sense that we don't quite know what the organization of the future looks like. There are reasons to think for many industries it might take a long time, like many years or decades, for it to show up in the productivity stats. While I do say we're in the early ‘80s because we haven't figured it out yet, I'm a little more optimistic that maybe it won't be 30 years to really have the impact. Mostly because we just have the lessons of history. We know from past technologies, and business leaders know from past technologies, electricity and the internet and the steam engine and others, that it requires some system-level change. And we now have the toolkits to think through, how do you build system-level change without destroying your company?When electricity was diffusing in the 1890s, there wasn't really any idea that this might take 40 years to figure out what the factory of the future looks like. It just wasn't on anybody's mind. The management challenges of redesign were unstudied, and there was no easily accessible knowledge to figure that out. Jump forward to the ‘80s and computing: Again, we hadn't even learned the lessons of electricity back then. Paul David's paper came out in 1990. It was a solution to the Solow paradox.But since then, we have a much better understanding of what's required for technological change. There has been decades of economics literature Erik Brynjolfsson, Tim Bresnahan, Paul David, and others. And there's been decades of management literature taking a lot of those ideas from econ and trying to communicate them to a broader audience to say, “Yes, it's hard. But doing nothing can also be a disaster. So being proactive is useful.” Then there's another piece about optimism here, which is that the entrepreneurial ecosystem is different than it used to be. And we have lots and lots of very smart people building tech companies, trying to make the system-level change happen. And that gives us more effectively more kicks at the can to actually figure out what the right system looks like.The impact of ChatGPT and DALL-EChatGPT and these text-to-image generators like DALL-E, are these significant innovations that can cause system change? Or are they toys that can't figure out how many arms people have and are able to produce B-level middle school essays?They're both. What do I mean by that? The technology is incredible. What ChatGPT can do and DALL-E can do is really, at least to me, it's amazing. Especially what ChatGPT can do. It's much better than I… That came much faster than at least I thought it was going to come. When I first saw it, I was blown away. So far it's a toy. So far, most applications have been “Hey, isn't this cool? I can do this kind of thing.” In a handful of places, it's moved beyond a toy to a point solution. Joshua [Gans], Ajay [Agrawal], and I wrote a piece in HBR. We drafted it out, and rather than reread it and edit it 60 times like we normally do, we sent it into ChatGPT and said, “Write this in a way that's easy to read.” And it did. We had to do some final edits afterwards. But like, we are already doing the same thing. It made a piece of our workflow a little bit more efficient. Point solution.A lot of the talk here in universities, “Uh-oh, we have to change the way we do final exams because ChatGPT can write those exams for our students.” Sure. But that's really not thinking through the potential of what the technology can do. What we've seen so far are toys and point solutions, but I do see extraordinary potential for system solutions in both. Both DALL-E and ChatGPT, and all these generative models. ChatGPT, if you think about it, what does it do? One thing it does is it allows anybody to write well. Like I told my students, you no longer have an excuse to write a bad essay with terrible grammar and punctuation that's not structured like a five-paragraph essay. No excuse anymore. It used to be, okay, maybe there's an excuse because there was some time crunch and you had other things due. Or your language skills — you're a math person, not an English person. No excuse anymore. ChatGPT upskills all those people who are good at other things but whose opportunities were constrained by their ability to write. So what's that new system? I don't know. But there are a lot more people around the world who are bad at writing English than are good at writing English. And if now everybody is a B high school-level student, able to write an essay or able to write well in English, an email or whatever it might be, that's going to be amazing. We just have to figure out how to harness that. We haven't yet.You've sort of given us a potential timeframe, broadly, for when we might see this in the data. When we see it in the data, how significant do you think this technology can be? What is, do you think, the potential impact once you can find it in the data, the productivity growth, which is kind of the end goal is here?That's a great question. Let me reframe it and say, the thing I'm worried about is that it won't reach its potential. A lot of people are worried about the impact of AI on jobs and what are people going to do if machines are intelligent? Jason Furman attended our first Economics of AI conference. This was in 2017. He was formerly chair of Obama's Council of Economic Advisors. And the thing I'm worried about is that there's not going to be enough AI. The productivity booms that we've had in history from way back to the steam engine and then electricity and then the computer age and the internet have been driven by system-level change, where we've figured out how to reinvent the economy. And that's led to sustained productivity growth: first the steam engine at 0.5 percent and then maybe 1 percent with electricity and then 2 percent after the war or more. I don't know what the number is going to be. I know you wanted me to give you a number. I don't know what the number's going to be. But this technology has potential to be like all those others, assuming we figure out what that system-level change looks like and we overcome the various sources of resistance.To sum it up, your concern is less about, can we solve the technical problems, versus, will society accept the results?Exactly. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/medicine

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/finance

In this special edition podcast Greg, Warren and Wolfgang with Dan and John look at #ChatGPT3, its impact, what it might mean for schools and learning, and a chance to share some ideas on ways to juggle GPT3 , support staff, students and school to cohabitate with #ai in an International School setting.   Guests: Greg Clinton, Ph.D Director of Technology at American International School Chennai   Warren Apel Director of Technology at The American School in Japan   Wolfgang Soeldner ICT Campus Partner international School of Geneva ·   About Greg Clinton   Greg Clinton is the Director of Technologies and R&D at American International School Chennai, India. He has also worked at Colegio Roosevelt in Lima, Peru, the American Embassy School in New Delhi, India, and Khartoum American School, Sudan. He holds a PhD in Comparative Literary and Cultural Studies from Stony Brook University. As part of his international school upbringing, he graduated with an IB Diploma from Cairo American College, Egypt.   LinkedIn: https://www.linkedin.com/in/greg-clinton/ Twitter:https://twitter.com/gswclinton AI and Education Collaborative: Request to Join Here https://forms.gle/7ZfbcmYYnjbrnTqt7   About Warren Apel   Warren Apel is the Director of Technology at The American School in Japan.  He has worked in international education for over 20 years, including positions at Cairo American College, the American Embassy School in New Delhi, India and the International School of Amsterdam.   LinkedIn: https://www.linkedin.com/in/warrenapel/ Twitter: https://twitter.com/warrena    About Wolfgang Soeldner   Teacher, edtech coordinator, and administrator with over 18 years of experience in international schools. Global nomad, traveler, and lifelong learner, always looking for ways to better myself.   LinkedIn: https://www.linkedin.com/in/wsoeldner/ Twitter: https://twitter.com/wsoeldner    Resources   Articles:   What Is Machine Learning? – A Visual Explanation How Machine Learning Works Huge “foundation models” are turbo-charging AI progress  What are foundation models? Some Moral and Technical Consequences of Automation Teachers Weigh In on How to Manage the New AI Chatbot ChatGPT for Educators: An Introduction Update Your Course Syllabus for ChatGPT by Ryan Watkins (19 Dec 2022, Medium) The End of High School English by Daniel Herman (9 Dec 2022, The Atlantic) “Language Game (philosophy)” (Wikipedia)    Books:  Brian Christian: The Alignment Problem Laura Major: What To Expect When You're Expecting Robots  Noise (2021) by Daniel Kahneman, Olivier Sibony, and Cass Sunstein Human Compatible: Artificial Intelligence and the Problem of Control (2020) by Stuart Russell Prediction Machines: The Simple Economics of Artificial Intelligence (2022) by Ajay Agrawal, Joshua Gans, and Avi Goldfarb Failure to Disrupt: Why Technology Alone Can't Transform Education (2020) by Justin Reich   Short Story:  The Gentle Seduction Marc Stiegler  Podcast:  Babbage: The tech behind ChatGPT A Skeptical Take on the AI Revolution (Ezra Klein Show, Jan. 6, 2023)   Literature/Film Klara and the Sun (2021) by Kazuo Ishiguro The Midas Plague (1954) by Frederick Pohl The Star Wars narrative universe Ex Machina (2014, dir. Alex Garland) Finch (2021, dir. Miguel Sapochnik) “Liberation Day” (short story, 2022) by George Saunders   Presentation:  Christina DiMicelli: AI - A Discussion for Education   Sites: https://sjtylr.net/2022/12/10/useme-ai-a-draft-model-for-adapting-to-ai-in-schools/ https://padlet.com/omedalion1/prz8aevxlr4g https://www.iste.org/areas-of-focus/AI-in-education   John Mikton on Social Media LinkedIn: https://www.linkedin.com/in/jmikton/ Twitter: https://twitter.com/jmikton Web: beyonddigital.org   Dan Taylor on social media: LinkedIn: https://www.linkedin.com/in/dantcz/ Twitter: https://twitter.com/DanTaylorAE Web: www.appsevents.com   Listen on: iTunes / Podbean / Stitcher / Spotify / YouTube   Would you like to have a free 1 month trial of the new Google Workspace Plus (formerly G Suite Enterprise for Education)? Just fill out this form and we'll get you set up bit.ly/GSEFE-Trial

Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Professor of Strategic Management at Toronto's Rotman School of Management. Joshua is a brilliant academic, an entertaining speaker, and an avid Star Wars fan. He is Chief Economist of Creative Destruction Lab, department editor (Strategy) at Management Science, and cofounder and managing director of Core Economic Research. Joshua has published numerous books on innovation, disruption, entrepreneurship, and most recently, pandemic economics. He is a research associate at the National Bureau of Economic Research, a research affiliate at MIT, a senior academic fellow at the e61 Institute, a distinguished fellow of the Luohan Academy, and a fellow of the Academy of Social Sciences in Australia.

Between ChatGPT generating limericks in the style of George Costanza and Lensa turning your profile picture into a cartoon, AI seems to have finally broken into mainstream awareness in the past few months. But what's going on below the surface? How did the technology advance to this point? Who has been funding its development, and how does it actually work? We dig into all of those issues (and other very basic questions we had about the technology) in this conversation with Ryan Khurana, Chief of Staff at WOMBO [www.w.ai], a Generative AI for entertainment company whose app Dream [www.dream.ai] won the Play Store App of the Year in 2022. ----- Book recommendations: Prediction Machines by Ajay Agrawal, Joshua Gans, Avi Goldfarb Power and Prediction by Ajay Agrawal, Joshua Gans, Avi Goldfarb Architects of Intelligence by Martin Ford Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville ----- Links: More episodes of Free Lunch by The Peak: https://readthepeak.com/shows/free-lunch Follow Taylor on Twitter: @taylorscollon Follow Sarah on Twitter: @sarahbartnicka Subscribe to The Peak's daily business newsletter: https://readthepeak.com/b/the-peak/subscribe

Welcome to episode #859 of Six Pixels of Separation - The ThinkersOne Podcast. Here it is: Six Pixels of Separation - The ThinkersOne Podcast - Episode #859. How does artificial intelligence affect the structure and dynamics of the global economy? What are the potential benefits and risks associated with artificial intelligence on the future of humanity? Joshua Gans is the co-author of the recently published book, Power and Prediction - The Disruptive Economics of Artificial Intelligence. He is also widely known as the co-author of Prediction Machines and over ten other books at the intersection of technology, disruption and economics. Joshua is a Professor of Strategic Management and holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management at the University of Toronto. He is also the Chief Economist at the Creative Destruction Lab and a Research Associate at the National Bureau of Economic Research. He is a leading expert in the field of economics, particularly in the areas of innovation, technology, and entrepreneurship. Along with book writing, he is a regular contributor to The New York Times, The Atlantic, and The Wall Street Journal. He has also done extensive work on entrepreneurship, the digital economy, and the management of intellectual property. Joshua is a recipient of the John Kenneth Galbraith Prize for his work on the economics of the digital economy and was recently named one of the world's top 25 most influential economists by Bloomberg. Enjoy the conversation... Running time: 47:06. Hello from beautiful Montreal. Subscribe over at Apple Podcasts. Please visit and leave comments on the blog - Six Pixels of Separation. Feel free to connect to me directly on Facebook here: Mitch Joel on Facebook. Check out ThinkersOne. or you can connect on LinkedIn. ...or on Twitter. Here is my conversation with Joshua Gans. Power and Prediction - The Disruptive Economics of Artificial Intelligence. Prediction Machines. Rotman School of Management. Creative Destruction Lab. Follow Joshua on Twitter. Follow Joshua on LinkedIn. This week's music: David Usher 'St. Lawrence River'.

Joining Cardiff for this episode is Avi Goldfarb, Rotman Chair In Artificial Intelligence and Healthcare At The Rotman School Of Management, University Of Toronto, and the co-author (with his fellow economists Ajay Agrawal and Joshua Gans) of an excellent new book, "Power and Prediction: The Disruptive Economics of Artificial Intelligence".In their chat, Avi and Cardiff discuss:Why AI is best understood as a "prediction technology"Examples of AI already in useWhich parts of the economy could be transformed by AI, and howHistorical analogies to previous eras of widespread technological disruptionHow AI will change the way people and companies make decisionsWhy this change will shift institutions away from blunt rules and towards individual discretionIn the labor market, who will gain and who will lose from the adoption of AIWhat the use of AI might teach us about what it means to be humanAnd all throughout the chat, they look at the fundamental question of whether artificial intelligence is about to make the economy—and the world—a whole lot weirder. And if so, just how far along that path to weirdness are we already?Related links: "Prediction and Power", by Ajay Agrawal, Joshua Gans, and Avi Goldfarb"The impact of AI on the future of workforces", The White House CEA and the European Commission“Before the Flood”, by Sam Hammond"The golden age of AI-generated art is here", by Tom Faber"Historical analogies for large language models", by Dynomight Internet Website Hosted on Acast. See acast.com/privacy for more information.

In this episode, Dr. Aaron Fritts interviews Dr. Danny Goel, orthopedic surgeon and CEO / co-founder of PrecisionOS, a virtual reality (VR) education company aimed at creating valuable opportunities for orthopedic surgery trainees to build their skillsets. The CE experience for this Podcast is powered by CMEfy - click here to reflect and earn credits: https://earnc.me/P2tgkL --- SHOW NOTES Dr. Goel shares his story, from his residency, to his MBA years, and the eventual idea for his company. PrecisionOS emerged as an idea when he was searching for training opportunities in shoulder reconstruction surgery. As a proceduralist, he realized that most new skills were acquired by traveling to courses and workshops hosted by professional societies and medical device companies. He wanted to find a way to make training more accessible by integrating virtual reality. Virtual reality is popular in the orthopedics space since there is a direct application for it. For example, understanding which implant to use in each clinical problem can be training via virtual reality. Dr. Goel emphasizes that the use of VR for mindless repetition will be fruitless. It is rather the deliberate, systematic, and data-driven approach to practice that allows for skill attainment and refinement. The doctors also speak about the evolution of VR hardware and how the headset must evolve to become sleeker and more user friendly. The headset cost has decreased, making it more scalable for distribution around the world. Additionally, tactile sensation has been integrated through manual controllers. These haptics are improving in the same way that visual optics did in the last iteration. Dr. Goel highlights data showing that haptics are extremely important for new trainees, while visual cues are more important to experienced surgeons. Finally, Dr. Goel describes how he met his co-founders, the different areas of expertise that they contribute to the company, and the team dynamic that allows him to practice medicine full time while also serving as CEO. This balance allows him to maintain perspective on clinical challenges and fit his product accordingly. --- RESOURCES PrecisionOS: https://www.precisionostech.com/ The Future of Surgery Training and Education: https://www.precisionostech.com/wp-content/uploads/2019/12/The-Future-of-Surgery-Training-and-Education-Whitepaper.pdf Immersive Virtual Reality for Surgical Training: A Systematic Review: https://www.journalofsurgicalresearch.com/article/S0022-4804(21)00416-9/fulltext The Metaverse by Matthew Ball: https://www.matthewball.vc/metaversebook Prediction Machines by Ajay Agarwal, Joshua Gans, Avi Goldfarb: https://www.predictionmachines.ai/

On the heels of launching my latest book, Our Book of Awesome, I'm enjoying the fellowship of two authors in my life — one of whom I met 22 years ago when I was in my final year at Queen's.   Bounding into my life at the time came a young professor named Ajay Agrawal. And I mean bounding! He was cold calling left, right and center,  dancing around the room, and extremely theatrical. As you listen to him you'll see why I found him so captivating and clairvoyant.    Professor Ajay Agrawal has won Professor of the Year seven times! He's like Canada's Adam Grant. He is the co-author of the bestselling book, Prediction Machines: The Simple Economics of Artificial Intelligence, named one of the best tech books of the year by Forbes, The New York Times and The Economist. His latest book has just come out and it is called, Power and Prediction, also co-authored with Joshua Gans and Avi Goldfarb.   Ajay is a tenured professor at Rotman, a research associate at The National Bureau of Economic Research in Cambridge, Massachusetts, founder of the Creative Destruction Lab, a not-for-profit program that helps start-ups launch, and co-founder of Next Canada dedicated to the development and training of young entrepreneurs. He is also a recent winner of the Order of Canada which is the highest civilian honor that Canada awards.   Over the years I've gotten a chance to meet Ajay's truly lovely partner in life: Gina Buonaguro. And, guess what? She's a writer too! Ajay focuses on the future. Gina focuses on the past. Gina is originally from New Jersey but has been living in Toronto for many years. She started at Villanova University, all the way up to the University of British Columbia on a Fullbright Scholarship.   Gina's written dozens of articles, won five writing grants and is the co-author of six historical fiction books, including her latest, The Virgins of Venice.   You could not think of two books which are more different: The Virgins of Venice and Power and Prediction. One is a 500 year old historical fiction saga taking place in a convent with sexy nuns. And the other a deep dive into AI.   I was intrigued by the relationship dynamics between them, what their books really say, and how their writing processes work. So I invited them, together, to come on 3 Books. I also asked Leslie to join the conversation.    So the four of us sat down in Gina and Ajay's living room and we discussed questions like: what is the fate of girls in 16th century Venice, what does it mean for a city to be excommunicated, why has Uber been so revolutionary, what is the point vs systems solution in AI, how can books be shared and read together, what is an Untouchable Day, how can we think about living a little more intentionally, how does AI manipulate us today and it goes on and on and on.   This is a wide ranging conversation that I think you will truly enjoy.   Let's flip the page into Chapter 117 now…   What You'll Learn: What does it mean for a city to be excommunicated? Why has Uber been so revolutionary? What is a point solution vs a system solution in AI? What is holding AI back? How can books be shared and read together? What is a writer's group? What is the power of reading aloud to our kids? How can we bring in more quiet into our busy urban lives? What is an untouchable day? How can we live intentionally? How does AI manipulate us? What are the challenges of raising kids in a tech centric world? Why are young people finding social interactions so awkward these days? What is the Chinese solution to screen time? What is the tension between ideology and critical thinking? How can we encourage more critical thinking?  How can we temper cancel culture? What is the role of school today?   You can find show notes and more information by clicking here: https://www.3books.co/chapters/117   Leave us a voicemail. Your message may be included in a future chapter: 1-833-READ-A-LOT.     Sign up to receive podcast updates here: https://www.3books.co/email-list    3 Books is a completely insane and totally epic 15-year-long quest to uncover and discuss the 1000 most formative books in the world. Each chapter discusses the 3 most formative books of one of the world's most inspiring people. Sample guests include: Brené Brown, David Sedaris, Malcolm Gladwell, Angie Thomas, Cheryl Strayed, Rich Roll, Soyoung the Variety Store Owner, Derek the Hype Man, Kevin the Bookseller, Vishwas the Uber Driver, Roxane Gay, David Mitchell, Vivek Murthy, Mark Manson, Seth Godin, Judy Blume and Quentin Tarantino. 3 Books is published on the lunar calendar with each of the 333 chapters dropped on the exact minute of every single new moon and every single full moon all the way up to 5:21 am on September 1, 2031. 3 Books is an Apple "Best Of" award-winning show and is 100% non-profit with no ads, no sponsors, no commercials, and no interruptions. 3 Books has 3 clubs including the End of the Podcast Club, the Cover to Cover Club, and the Secret Club, which operates entirely through the mail and is only accessible by calling 1-833-READ-A-LOT. Each chapter is hosted by Neil Pasricha, New York Times bestselling author of The Book of Awesome, The Happiness Equation, Two-Minute Mornings, etc. For more info check out: https://www.3books.co

There is plenty of hype about AI, but most organisations are still using old precesses to make decisions. We are  in the Between Times: "after AI's clear promise and before its transformational impact," as described in the book Power and Prediction: the disruptive economics of Artificial Intelligence.  In this episode, Professor Joshua Gans, one of the book's co-authors explains why organisations are not yet adapting the full power of AI and what will happen when they do. Learning notes from this episode: Artificial Intelligence is a prediction machine, which supports decision making. Today businesses often use AI for one or two processes, but most decisions are still made by humans. Technology first companies and start-ups often have more AI-based decision making, because they do not have to replace legacy processes. Business leaders should not accept AI as just a black box. In fact, Professor Gans argues that business brains might be better equipped to question AI and its benefits, than the engineers who build the algorithms. Here are three questions business leaders should ask to understand an AI system: What is this AI predicting? How will that prediction affect decision making? What is the decision maker's tolerance for errors?  ---- If you like learning about how tech products and profits get made, you'll like our newsletter. It's funny too. Sign up here. -----   Tech for Non-Techies clients  Reach senior leadership positions in Big Tech firms Lead digital transformation in established businesses Create tech businesses as non-technical founders Pivot into careers in venture capital If you want to have a great career in the Digital Age, then APPLY FOR A CONSULTATION CALL.   What happens when you apply for a consultation call: Sophia and her team will look through your application. If they genuinely think Sophia could help you, you will get a link to her calendar.. You will have a 20 – 30 minute call to discuss your goals and see if you are a good fit for each other.  If we establish that Tech for Non-Techies courses + coaching could help you and believe we would enjoy working together, we will discuss a relevant approach to suit you. The aim of the call is not to sell you on anything that is not right for you. We both win if you get results, but we both lose if you don't. We love hearing from our readers and listeners. So if you have questions about the content or working with us, just get in touch on info@techfornontechies.co   Say hi to Sophia on Twitter and follow her on LinkedIn. Following us on Facebook, Instagram and TikTok will make you smarter.    (Photo credit: Joshua Gans photographed by JOHN HRYNIUK PHOTOGRAPHY)

AI was expected to revolutionize the way we do just about everything, but the changes that were promised haven't materialized as quickly as expected. What's holding AI back?On this episode of Disruptors, an RBC podcast, host John Stackhouse sits down with Ajay Agrawal to dig into this question and more. Ajay is a professor at the University of Toronto's Rotman School of Management; he was named to The Order of Canada this year for his contributions to enhance Canada's productivity, competitiveness, and prosperity through innovation and entrepreneurship, and he's the founder of the Creative Destruction Lab, an early proponent of AI ingenuity.Ajay is also the author of two books about AI. His latest, Power and Prediction: The Disruptive Economics of Artificial Intelligence, co-written with fellow Rotman professors Joshua Gans and Avi Goldfarb, focuses on the fact that AI hasn't lived up to the excitement that he himself helped create. When he looked back at the predictions made in his 2018 bestseller, Prediction Machines: The Simple Economics of Artificial Intelligence, he realized it was time to shift focus away from AI as a technology and instead look at the economics of the systems in which it operates.  This episode also features an exciting new AI technology called GPT-3, which uses deep learning to produce text that reads like it was written by a human. It was created by Open AI, an organization founded in San Francisco in 2015. Ilya Sutskever, their chief scientist, is Canadian and a U of T alum. GPT-3 even provided a brief summary of John and Ajay's conversation: “Creative Destruction Lab was designed to address the market failure of commercializing early stage science. The program helps entrepreneurs with the judgment they need to turn their scientific innovation into a business. AI is characterized as a drop in the cost of prediction.AI is not going to figure out the complexities of health care. There are many barriers to deploying AI in health care, including system frictions that are not aligned with the incentives of hospitals, doctors, and insurers. It is difficult to experiment with AI in health care because of the need for a system-level overhaul.AI has the potential to help reduce discrimination by making it easier to detect and then fix. However, too much regulation of AI has the potential to stifle innovation. Canada is doing well on the research side of AI, but there is room for improvement on the application side.”Amazingly concise! This episode also features an AI-generated John Stackhouse, so listen in and see if you can tell the difference. To read Ajay Agrawal's newest book, “Power and Prediction: The Disruptive Economics of Artificial Intelligence”, co-written with fellow Rotman School of Management professors Joshua Gans and Avi Goldfarb click here. Follow this link to the University of Toronto's article about testing out GPT-3 and this one for more about Open AI, GPT-3 and Dall-E2.  Some background on IBM Watson can be found here. 

Peggy and Joshua Gans, professor of strategic management, University of Toronto, Rotman School of Management, joins the show to talk about his book Power and Prediction. He shares how long it took for electricity to have rapid adoption—and compares it to AI (artificial intelligence). They also discuss: Three stages of really innovative technology. Why the human is going to be a critical part of artificial intelligence. Old systems being displaced by new ones in industries like manufacturing. predictionmachines.ai (11/15/22 - 797) IoT, Internet of Things, Peggy Smedley, artificial intelligence, machine learning, big data, digital transformation, cybersecurity, blockchain, 5G, cloud, sustainability, future of work, podcast, Joshua Gans, University of Toronto This episode is available on all major streaming platforms. If you enjoyed this segment, please consider leaving a review on Apple Podcasts.

Joshua Gans is a Professor of Strategic Management and the holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management at the University of Toronto. He is also the Chief Economist of the University's Creative Destruction Lab. In 2018, together with Ajay Agrawal and Avi Goldfarb, he published Prediction Machines, an exploration of how basic tools from economics provide clarity about the AI revolution and a basis for action by leaders. The trio's latest book, “Power and Prediction: The Disruptive Economics of Artificial Intelligence” explains the economics of A.I. through the lens of decision systems. In conversation with Martin Reeves, Chairman of BCG Henderson Institute, Joshua discusses how the transformational potential of A.I. is only unlocked if decision systems are reconsidered holistically, mirroring the pattern observed in previous technological revolutions like the application of steam power, electricity, or digital communication. *** About the BCG Henderson Institute The BCG Henderson Institute is the Boston Consulting Group's think tank, dedicated to exploring and developing valuable new insights from business, technology, economics, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration, sign up to receive BHI INSIGHTS, our monthly newsletter, and follow us on LinkedIn and Twitter.

I am joined by Professor Joshua Gans, co-author of the new book Power and Prediction - The Disruptive Economics of Artificial Intelligence. Artificial Intelligence is promising to disrupt many businesses and industries. While there is huge potential, we are currently in the “between times”, just before the true, system-wide disruption is going to occur.

Professors Ajay, Avi & Joshua, co-authors of “Power & Prediction: The Disruptive Economics of Artificial Intelligence” share a bit about how to bring together a team of three creative, imaginative people – how they work together, form ideas, and ultimately shape a vision of the future of technology. “Power & Prediction” is being released Tuesday, November 15th. Ajay Agrawal is Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management at the University of Toronto's Rotman School of Management. He is the founder of Creative Destruction Lab, cofounder of Next 36 and Next AI, and cofounder of Sanctuary, an AI/robotics company. Avi Goldfarb is the Rotman Chair in AI and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. Avi is also Chief Data Scientist at Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, a faculty affiliate at the Vector Institute for Artificial Intelligence, and a research associate at the National Bureau of Economic Research. Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Professor of Strategic Management at Toronto's Rotman School of Management. He is Chief Economist at Creative Destruction Lab, department editor (Strategy) at Management Science, and cofounder and managing director of Core Economic Research.

What if Artificial Intelligence got so good at predicting what consumers want, that a virtual "Santa" could just automatically deliver Christmas toys, without parents even needing to order them in advance? That was just one of the many intriguing questions I explored this week with Joshua Ganz, who recently co-authored the new book,  Power and Prediction: The Disruptive Economics of Artificial Intelligence, along with Ajay Agrawal, and Avi Goldfarb. Ganz, who is a Professor of Strategic Management, at the University of Toronto concludes that while AI can be extremely useful in business; much of its promise may not be fully realized for years to come.  Find out why, listen now.

What if Artificial Intelligence got so good at predicting what consumers want, that a virtual "Santa" could just automatically deliver Christmas toys, without parents even having to order them in advance? That was just one of many intriguing questions I explored this week with Joshua Gans, who recently co-authored the new book, Power and Prediction: The Disruptive Economics of Artificial Intelligence, along with Ajay Agrawal, and Avi Goldfarb. Gans, who is a Professor of Strategic Management, at the University of Toronto, concludes that while AI can be extremely useful in business; much of its promise may not be fully realized for years to come.  Find out why, listen now.

What if Artificial Intelligence got so good at predicting what consumers want, that a virtual "Santa" could just automatically deliver Christmas toys, without parents even needing to order them in advance? That was just one of the many intriguing questions I explored this week with Joshua Ganz, who recently co-authored the new book,  Power and Prediction: The Disruptive Economics of Artificial Intelligence, along with Ajay Agrawal, and Avi Goldfarb. Ganz, who is a Professor of Strategic Management, at the University of Toronto concludes that while AI can be extremely useful in business; much of its promise may not be fully realized for years to come.  Find out why, listen now.

What if Artificial Intelligence got so good at predicting what consumers want, that a virtual "Santa" could just automatically deliver Christmas toys, without parents even having to order them in advance? That was just one of many intriguing questions I explored this week with Joshua Gans, who recently co-authored the new book, Power and Prediction: The Disruptive Economics of Artificial Intelligence, along with Ajay Agrawal, and Avi Goldfarb. Gans, who is a Professor of Strategic Management, at the University of Toronto, concludes that while AI can be extremely useful in business; much of its promise may not be fully realized for years to come.  Find out why, listen now.

Artificial intelligence technology has been advancing, and businesses have been putting it into action. But too many companies are just gathering a bunch of data to kick out insights and not really using AI to its fullest potential. Joshua Gans, professor at Rotman School of Management, says businesses need to apply AI more systemically. Because decision-making based on AI usually has ripple effects throughout the organization. Gans cowrote the HBR article “From Prediction to Transformation" and the new book "Power and Prediction: The Disruptive Economics of Artificial Intelligence."

Think like a Virus! Viewing the Pandemic through the Lens of Game Theory | with Joshua Gans In this episode Joshua Gans shares his insights on the ongoing pandemic from a game theoretic perspective. He explains how he first got into the topic, why being deadly is generally not the best strategy for any virus, and how hidden information can help a virus thrive. Joshua then walks us through ways to counter a virus and how to prepare for the future.   Joshua Gans is professor of Strategic Management and holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management of the University of Toronto. His research interests are in the areas of technological competition and innovation, industrial organization and regulatory economics, among others. He is also author of several books such as "The pandemic information solution".

Some of you that are old enough will remember when doctors, clinics and hospitals were complaining about implementing electronic medical records which we now call EMR.  EMR then advanced and became Electronic Health records or EHR.  EHR is actually more powerful than EMR.  EHR is the term most of us use today. Then, if your product created a report or an image, your company was busy creating links to the electronic records so the report or image could be stored electronically.  Now we take all much of this for granted. Our guest today says that even though EHRs were not intentionally designed to aid clinical informatics “without EHR we would have no AI in healthcare.” Today we dive into the mind of a clinician and researcher who is very involved in clinicial informatics and artificial intelligence.  Our guest today is Ron Li, MD.  Ron is a Clinical Assistant Professor Department of Medical and Hospital Medicine, Stanford University and he is the Medical Informatics Director for Digital Health and Artificial Intelligence Clinical Integration at Stanford Health Care. We learn about how a health care system is investing in efforts to design and implement programs and workflows that incorporate clinical informatics and AI to improve outcomes and reduce costs.  And, we talk about how MedTech fits in. This is our 6th episode related to AI in MedTech.  We have at least one more.  If you have listened to most of these, you will have a good idea as to what is going on in the minds of clinicians, researchers, companies and providers.  This knowledge can help guide you in your career and/or your company's strategies related to informatics, deep learning and its products.  Do your products need an AI component to add more value or do they need to fit into a work flow that is being enhanced by AI? Thanks for listening in today.  If you like this podcast, please refer it to a friend simply by using the share link on your podcast player.  If you want to learn more about the MedTech Leaders community, go to MedTechLeaders.net. Now Go Win Your Week!! Ron's LinkedIn profile link Books Ron recommends: The Fifth Discipline by Peter M. Senge link Cloud Computing: Concepts, Technology & Architecture (The Pearson Service Technology Series from Thomas Erl) by Thomas Erl, Ricardo Puttini, Zaigham Mahmood link Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb link Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture by Jamshid Gharajedaghi link Ted Newill's LinkedIn Profile link  More Medical Device Success podcasts link Medical Device Success website link  MedTech Leaders Community link Link to Ted's contact page 

Stanford health economics professor Jay Bhattacharya has said that the coronavirus is one of the biggest failures of the economics profession. And whether or not that's an exaggeration, it's true many economics have stayed quiet throughout the course of this virus. But not Joshua Gans.Joshua Gans is a Professor of Strategic Management and holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management at the University of Toronto (with a cross-appointment in the Department of Economics). He is also Chief Economist of the University of Toronto's Creative Destruction Lab.Listen as we dive into all things pandemic today: COVID-19 as a disease vs a function of behavior, gauging risk, privacy laws, and the range of COVID testing options. “Epidemiology in many respects is closer to a social science than it is to a natural science.”Episode Quotes:On pandemic research within Economics:"Economists had looked at infectious diseases, most notably AIDS. There was some work done, but it was, to say it was a fringe would be almost an understatement. There was no work done on if we have a global pandemic, what is the optimal macroeconomic response? There was nothing there."On creating public policy during a pandemic:"Here's where it gets frustrating. Here are the scientists doing their business, coming up with various studies and, you know, critical facts. But at the same time, they've got to take that and it's got to go all the way to public policy. And in between, there is this lump of stuff they don't know about: social science. And so they make guesses."On the fundamental information problem of pandemics:"It took me a little while to come around to realize that this calamity was more solvable than people were saying. And it wasn't going to require necessarily vaccine and other things like that. If we could just get our head around it. But that turned out to be a non-obvious view to a lot of people. And it certainly wasn't the way in which health people that I was speaking to had talked about it. So it was more a uniquely economic strategy type approach. What is the fundamental problem here? And can we solve that?"Show Links:Joshua Gans WebsiteJoshua Gans Substack: Plugging The GapOrder Book:The Pandemic Information SolutionOrder Book:The Pandemic Information GapOrder Book: Prediction MachinesOrder Book: The Disruption Dilemma