Podcasts about prediction the disruptive economics

Newt talks with Professor Ajay Agrawal, a key player in the world of Artificial Intelligence and author of Power and Prediction: The Disruptive Economics of Artificial Intelligence. Agrawal discusses the rapid evolution of AI, highlighting the significant advancements made in the last decade. He explains that AI's core function is to improve prediction, but it also requires human clarity for value judgments within those predictions. Agrawal also discusses the potential for AI to dramatically increase productivity and trigger a large reallocation of capital. He predicts that AI will force society to be more explicit about value judgments and trade-offs, leading to increased transparency.See omnystudio.com/listener for privacy information.

2024. március 25., hétfő 9-10 óra MIHÁLOVITS GAZDA: Vágóhídon a magyar húsipar 2023 olyan volt a magyar húsiparnak, mintha a bibliai tíz csapás érkezett volna meg. Nem ment a hazai piac, nem ment az export, jött a külföldi dömpingáru, szétzilálta a piacot az árstop, a kiskereskedelmi adó, de még az árfigyelő rendszerre is fújnak a szakmában. A húsipar tragikus éve: amikor jól megy nekünk, magyar húst eszünk, amikor rosszul, akkor németet. Éder Tamás, Magyar Húsiparosok Szövetségének társadalmi elnöke TŐZSDENYITÁS:  Árokszállási Zoltán, az Equilor Befektetési Zrt. vezető elemzője HEURÉKA-ÉLMÉNY: Agent Smith(s): Mi az az AI "ágens"? "Aki az nagy nyelvi modellt (Large Language Model - LLM) szövegírásra használja, olyan, mintha csak számolna a számítógéppel". Mi köze a nyelvi képességeknek az autnóm rendszerekhez, mi az az ágens, hogyan fog hatni a vállalati folyamatokra? Szabados Levente, a Neuron Solutions vezető tanácsadója, a Frankfurt School docense, adattudós-teológus. A könyv, amit Levente javasolt: Power and Prediction: The Disruptive Economics of Artificial Intelligence / Ajay Agrawal - Joshua Gans - Avi Goldfarb.

In this Partnering Leadership conversation, Mahan Tavakoli speaks with Professor Ajay Agrawal, the Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management at the University of Toronto's Rotman School of Management. Ajay Agrawal is also the founder of the Creative Destruction Lab (CDL), a not-for-profit program for early-stage, science-based companies, and coauthor of two outstanding books on Artificial Intelligence: Prediction Machines: The Simple Economics of Artificial Intelligence and Power and Prediction: The Disruptive Economics of Artificial Intelligence. In the conversation, Professor Agrawal shared the origin of his passion for studying the intersection of technology and economics and the increasing importance of artificial intelligence for organizations as a decision-making tool that brings down the cost of prediction. Ajay Agrawal then explained why this shift would significantly impact individuals, organizations, and industries as machines enable better predictions while humans focus on the judgment required for decision-making. Finally, Ajay Agrawal shared examples of the transformative impact of the reduction in the cost of prediction and how leaders can help guide their organizations through the significant changes ahead.Some highlights:- Three people that helped shape Ajay Agrawal's career path- Why looking at AI from an economic perspective clarifies its potential to transform organizations and industries- Ajay Agrawal on the role of predictions in artificial intelligence - The importance of human judgment in decision making- How AI decision-making will redefine roles in the workplace- The disruptive economics of artificial intelligence - How generative AI such as ChatGPT works and what causes mistakes and misstatements- Ajay Agrawal on the importance of upskilling professionals - How organizations can redesign their structures and processes to take into account the new predictive world- The impact of AI on systems-level change- How organizations can leverage AI for business success- Why we're on the brink of a set of transformations that none of us have seen in our lifetimes- How AI can help create a better futureConnect with Professor Ajay Agrawal:Ajay Agrawal website Ajay Agrawal at Rotman School of Management Ajay Agrawal on LinkedIn Prediction Machines: The Simple Economics of Artificial Intelligence on Amazon Power and Prediction: The Disruptive Economics of Artificial Intelligence on Amazon Connect with Mahan Tavakoli: Mahan Tavakoli Website Mahan Tavakoli on LinkedIn Partnering Leadership Website

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 

Joel Passen might not be your go-to guest for a Customer Success podcast as he has spent the majority of his career leading revenue teams. But after just a few minutes of discussion, you quickly realize that Joel intimately understands what it takes to deliver on the customer journey and value outcomes for customers post-sale.In this episode, we discuss how he and his team at Sturdy.ai are helping their customers prevent churn with data and AI driven intelligence. We also get into the weeds about data in and between systems, CS' position within organizations and delivering customer value digitally.Enjoy, I know I sure did!Joel's LinkedIn: https://www.linkedin.com/in/joelpassen/Sturdy: https://www.sturdy.ai/Resources Mentioned in this Episode:Kenn So - Writer at Generational: https://www.generational.pub/G2 - B2B Buyer Behavior Trends Article: https://learn.g2.com/software-buyer-behavior-trendsTomasz Tunguz - https://tomtunguz.com/Book: Power and Prediction: The Disruptive Economics of Artificial IntelligenceBook: The Checklist ManifestoTool: Gamma - https://gamma.app/ - build presentations with AISupport the show+++++++++++++++++Listener Submissions:If you'd like to call in with commentary or a question to be addressed in a future episode, call our submission line at +1 (512) 222-7381. Leave us a 2-3 minute message with your comment or question using either your real name or a pseudonym, and we'll feature your clip on the show!Like/Subscribe/Review:If you are getting value from the show, please follow/subscribe so that you don't miss an episode and consider leaving us a review. Website:For more information about the show or to get in touch, visit DigitalCustomerSuccess.com. Buy Alex a Cup of Coffee:This show runs exclusively on caffeine - and lots of it. If you like what we're, consider supporting our habit by buying us a cup of coffee: https://bmc.link/dcspThank you for all of your support!The Digital Customer Success Podcast is hosted by Alex Turkovic

Few things have been more disruptive and hotly debated this year than AI as 2023 marked the potential tipping point in its mass adoption thanks to the public release of generative AI platforms like ChatGPT. From healthcare, to cybersecurity, to journalism and, now, Hollywood, AI is sending shockwaves through virtually every sector of our economy. So, it's a perfect time to highlight an episode of Disruptors, an RBC Podcast, where host John Stackhouse talks with one of Canada's preeminent AI voices,  Professor Ajay Agrawal. Ajay is a professor at the University of Toronto's Rotman School of Management and co-author of “Power and Prediction: The Disruptive Economics of Artificial Intelligence”. To demonstrate how quickly AI will evolve, many of the predictions on this episode have already come to pass. But some of the most compelling questions about the future of artificial intelligence and its impact on our world have yet to be answered.

ChatGPT reached 100 million users in just 2 months after launch, and generative AI has already changed how many professionals do their jobs today. But, how has it already changed business and how will it impact the economy? In this episode, you will hear from Professor Avi Goldfarb, co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence.  In this episode, you will learn: which industries have already been completely changed by AI how to think about using AI in your work (and when not to pass it off as your own work) about three waves of technological change: point solutions, applications solutions and systems solutions why one University of Chicago professor said Sophia Matveeva was a new technology ----- Apply for Sophia Matveeva's 1:1 Coaching Program Power in the Digital Age   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 YouTube, Facebook, Instagram and TikTok will make you smarter. 

211: Is Philanthropy in a State of Crisis? (Nathan Chappell & Brain Crimmins)SUMMARYIs philanthropy in a state of crisis? In episode #211 of Your Path to Nonprofit Leadership, Nathan Chappell and Brian Crimmins, co-authors of the book, The Generosity Crisis: The Case for Radical Connection to Solve Humanity's Greatest Challenges, offer a wake-up call to nonprofit leaders everywhere, but also suggest solutions to the downturn they see, and will help you re-establish the interconnection that drives generosity. Learn from the fascinating research they did, and implement their recommendations to spark the generosity of your organization's donors.  ABOUT BRIANBrian Crimmins is a global leader in social impact, a popular public speaker with the world's foremost speaking agency, the Washington Speakers Bureau, and Chief Executive Officer of Changing Our World. He is a frequent contributor to publications covering the sector and has been invited to speak around the world on topics touching corporate social responsibility, purpose, and social impact, emphasizing not just what mission-driven organizations can and should do in the nonprofit and CSR spaces, but how leaders might organize their inner and personal lives to expand the edges of their own potential. His expertise is tapped in service to some of the world's largest and most influential corporations as they define their core reason for being and translate theory into action. Brian holds a Bachelor of Science from St. John's University and an MBA in Marketing Management from St. John's Tobin School of Business.ABOUT NATHANNathan Chappell, MBA, MNA, CFRE is an entrepreneur, inventor, thought leader, author, and considered one of the world's foremost experts on the intersection between Artificial Intelligence and philanthropy. As a pioneer in the philanthropy sector, he has launched multiple start-ups that have revolutionized fundraising practices. Nathan's subject expertise has been nationally recognized by organizations including Fast Company, Forbes, Citi Bank Global Insights, AWS, Microsoft, SalesForce, The Chronicle of Philanthropy and the Association of Healthcare Philanthropy. Nathan is a member of the Forbes Technology Council and holds a Masters in Nonprofit Administration from the University of Notre Dame, an MBA from the University of Redlands, a certificate in International Economics from the University of Cambridge and a certificate in Artificial Intelligence from MIT.EPISODE TOPICS & RESOURCESTrillion Dollar Coach: The Leadership Playbook of Silicon Valley's Bill Campbell by Eric SchmidtPower and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agarwal

In this Partnering Leadership conversation, Mahan Tavakoli speaks with Professor Ajay Agrawal, the Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management at the University of Toronto's Rotman School of Management. Ajay Agrawal is also the founder of the Creative Destruction Lab (CDL), a not-for-profit program for early-stage, science-based companies, and coauthor of two outstanding books on Artificial Intelligence: Prediction Machines: The Simple Economics of Artificial Intelligence and Power and Prediction: The Disruptive Economics of Artificial Intelligence. In the conversation, Professor Agrawal shared the origin of his passion for studying the intersection of technology and economics and the increasing importance of artificial intelligence for organizations as a decision-making tool that brings down the cost of prediction. Ajay Agrawal then explained why this shift would significantly impact individuals, organizations, and industries as machines enable better predictions while humans focus on the judgment required for decision-making. Finally, Ajay Agrawal shared examples of the transformative impact of the reduction in the cost of prediction and how leaders can help guide their organizations through the significant changes ahead.Some highlights:- Three people that helped shape Ajay Agrawal's career path- Why looking at AI from an economic perspective clarifies its potential to transform organizations and industries- Ajay Agrawal on the role of predictions in artificial intelligence - The importance of human judgment in decision making- How AI decision-making will redefine roles in the workplace- The disruptive economics of artificial intelligence - How generative AI such as ChatGPT works and what causes mistakes and misstatements- Ajay Agrawal on the importance of upskilling professionals - How organizations can redesign their structures and processes to take into account the new predictive world- The impact of AI on systems-level change- How organizations can leverage AI for business success- Why we're on the brink of a set of transformations that none of us have seen in our lifetimes- How AI can help create a better futureConnect with Professor Ajay Agrawal:Ajay Agrawal website Ajay Agrawal at Rotman School of Management Ajay Agrawal on LinkedIn Prediction Machines: The Simple Economics of Artificial Intelligence on Amazon Power and Prediction: The Disruptive Economics of Artificial Intelligence on Amazon Connect with Mahan Tavakoli: Mahan Tavakoli Website Mahan Tavakoli on LinkedIn Partnering Leadership Website

Ich habe den Artikel in der Harvard Business Review gelesen:  From Prediction to Transformation   Ajay Agrawal, Joshua Gans, Avi Goldfarb   To realize their potential, AI technologies need new systems that leverage them.    Der Artikel beruht auf dem Buch Power and Prediction: The Disruptive Economics of Artificial Intelligence  der gleichen Autoren.  Der Text zeigt auf wie KI die Welt nach und nach verändert - so wie es der Strom und die Dampfkraft tat. Er ist von den gleichen Autoren des Bestsellers Prediction Machines.  Ich finde das Buch sehr gut und auch den Artikel sehr gut und ich habe das was ich besonders wichtig finde in meinem Podcast zusammengefasst.  Künstliche Intelligenz (KI) hat Branchen auf der ganzen Welt verändert Aber es hat gerade erst seine Transformationsleistung zu besseren und schnelleren Vorhersagen begonnen, die strategische Entscheidungen bestimmen werden. Wenn die Möglichkeiten für Vorhersagen ausgeschöpft werden, verändern sich Wertschöpfungsketten aller Branchen: Disruptionen quer über alle Kontingente.  Der Text zeigt auf wie KI die Welt verändert - so wie es der Strom und die Dampfkraft tat. Es geht mir vor allem um die folgenden Inhalte:  KI: Nicht Erkenntnisse sind der entscheidende Vorteil.  Der Wert von KI liegt in der Qualität der Entscheidungen.  “Wir wissen nicht, was wir mit den Vorhersagen unserer KI anfangen sollen”  “Wie KI den America's Cup gewann...”  KI kann Segeln lernen Und Segeln.  KI kann Boote bauen.  KI kann beides 24 Stunden am Tag. 365 Tage.  KI kann das mit mehreren Booten parallel.  2 Jahrzehnte nachdem Edison die Glühbirne eingeschaltet hatte, verbrauchten nur 3% der US Unternehmen Strom.  KI funktioniert in komplexen Umgebungen und segelt besser als Menschen.  KI hat systemische Auswirkungen, die noch nicht wirken.  Wie bei Strom und Dampfkraft:  Zögerliche Verbreitung    Entscheidungsfindungen  werden sich ändern:  Wer? Wo? Wie?  KI wird in Wertschöpfungsketten wirken: Unsicherheit verlagern.  Wie ein Steinwurf in den Teich.  Wie ein Dominoeffekt.  KI wird die Wirtschaft verändern wie Elektrizität und Dampfkraft.  Synchronisation und Ressourcenmanagement als Herausforderung.  Koordination und Modularität verbinden.  Beispiel Amazon:  Modularität und Koordination   Viel Erfolg

On this episode of The Intentional Fundraiser Podcast, I'm so excited to be talking with Nathan Chappell and Brian Crimmins, two brilliant minds in the nonprofit space, respectively.As a thought leader, public speaker, author and inventor, Nathan is one of the world's foremost experts on the intersection between Artificial Intelligence and philanthropy. Nathan is Senior Vice President of DonorSearch AI, leading artificial Intelligence deployments for some of the nation's largest nonprofit organizations.Nathan is a member of the Forbes Technology Council and holds a Master's in Nonprofit Administration from the University of Notre Dame, an MBA from the University of Redlands, a certificate in International Economics from the University of Cambridge and a certificate in Artificial Intelligence from MIT.Brian is a global leader in social impact, a popular public speaker with the world's foremost speaking agency, the Washington Speakers Bureau, and CEO of Changing Our World. Through service lines that include strategic planning, fundraising, corporate social engagement (CSR & ESG), research and analytics, and communications, Changing Our World raises billions in support of client causes.And counseling leading companies and brands to design and implement strategic corporate responsibility programs that deliver social impact while driving business strategy.Together, they (along with Michael Ashley) are co-authors of The Generosity Crisis: The Case for Radical Connection to Solve Humanity's Greatest Challenges, a thought-provoking exploration of the generosity decline in the U.S. and its impact on nonprofits, individuals and communities.Through their insightful analysis, Brian and Nathan present a compelling case for adopting a Radical Connection approach, which emphasizes building lasting connections rather than transactional interactions, to reverse this trend.Take a listen.RESOURCES MENTIONED IN THIS EPISODEThe Generosity Crisis WebsiteChanging Our World, Inc.DonorSearch AIGiving USAPower and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi GoldfarbStart with Why: How Great Leaders Inspire Everyone to Take Action by Simon SinekCONNECT WITH OUR GUESTS ONLINEConnect with Nathan Chappell onlineLinkedin / Website / Email Connect with Brian Crimmins onlineTwitter / Linkedin / EmailTHANK YOU TO OUR SPONSORThank you to our friends at Bloomerang for being a sponsor...

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

Exchange-traded funds, or ETFs, have become increasingly popular as investors look for a simple, diversified, and cost-effective approach to investing. While ETFs provide a convenient way for investors to gain exposure to a wide range of assets, in what direction is the ETF market headed in the future? In this episode, Rusty and Robyn talk with Eric Biegeleisen, Partner and Deputy Chief Investment Officer at 3EDGE Asset Management. In his role, Eric is responsible for the research operations at 3EDGE. His research focuses on discovering and exploring the interconnectedness of global capital markets through quantitative and qualitative analyses. Under Eric's leadership, 3EDGE has significantly enhanced its proprietary global capital markets model. Having a goal to steward 3EDGE's relationships with ETF managers to help bring new products to market, Eric talks with Rusty and Robyn about the ins and outs of exchange-traded funds (ETFs), the projected growth, and how they stand up against direct indexing. Eric also shares some of the opportunities ETF offers in the investment industry. Key Takeaways [02:59] - Eric's path to becoming 3EDGE Asset Management's Deputy Chief Investment Officer. [04:29] - How 3EDGE Asset Management serves private and institutional clients. [07:15] - How 3EDGE Asset Management constructs portfolios. [09:14] - What makes 3EDGE different from its competitors and peers. [10:28] - Eric's outlook on exchange-traded funds (ETFs). [11:32] - The pros and cons of investing in ETFs. [12:32] - How 3EDGE can compete against the big proprietary ETF providers. [15:01] - Eric's take on direct indexing being a threat to ETFs. [16:05] - The 3EDGE view on the stock market for 2023. [19:10] - Eric's tactical strategies for international equity. [21:57] - The strategy Eric uses to diversify assets. [25:11] - One of Eric's favorite investment strategies. [26:11] - How Eric maintains his energy and ability to perform at a high level. [27:11] - The people Eric is grateful for professionally. [28:36] - Eric's recommendation for content. Quotes [07:42] - "Behaviorally, investors can act as a herd, whether in a virtuous or vicious way. We need these behavioral factors to identify the real short-term potential changes." ~ Eric Biegeleisen [10:49] - "The ETF space continues to grow as it siphons off assets from other wrappers and large mutual funds. As new money folds in as it's gotten even larger, institutional dollars roll in." ~ Eric Biegeleisen [11:39] - "The pros (of ETFs) are a long list: transparent, low cost, tax efficient. There are over 1,700 listed ETFs in the U.S. alone, so there are exposures to nearly everything one would want." ~ Eric Biegeleisen Links  Eric Biegeleisen on LinkedIn Eric Biegeleisen on Twitter 3EDGE Asset Management Thunderstruck by AC/DC Stephen Cucchiaro Charles Schwab Windhaven Investment Management BlackRock Vanguard State Street Global Advisors Bob Phillips The Peter Attia Drive Podcast The Dawn of Everything: A New History of Humanity Never Finished: Unshackle Your Mind and Win the War Within Think Again: The Power of Knowing What You Don't Know Power and Prediction: The Disruptive Economics of Artificial Intelligence The Origin of Wealth Connect with our hosts Rusty Vanneman Robyn Murray Subscribe and stay in touch Apple Podcasts Spotify Google Podcasts 0141-OPS-1/19/2023

"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/finance

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

In the Sunday Book Review, I consider books that would interest the compliance professional, the business executive or anyone who might be curious. It could be books about business, compliance, history, leadership, current events or anything else that might interest me. In today's edition of the Sunday Book Review, we consider some of the top AI and machine learning books which every compliance professional should read in 2023: ·       Future Ready: The Four Pathways to Capturing Digital Value by Stephanie L. Woerner, Peter Weill, and Ina M. Sebastian ·        Digitalization of Financial Services in the Age of Cloud by Jamil Mina, Armin Warda, Rafael Marins, and Russ Miles ·       Power and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb ·        Practicing Trustworthy Machine Learning by Yada Pruksachatkun, Matthew Mcateer, and Subho Majumdar Resource The Enterpriser's Project- 10 must-read tech books for 2023 Learn more about your ad choices. Visit megaphone.fm/adchoices

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.

What is ChatGPT and what is behind its massive hype? How is it answering existential questions, writing love poems, articles, reports and even code? Is it a disruptor to the biggest disruptor Google? Will it and other AI-chatbots replace millions of jobs?What are its relevant use cases in a market like India? Host Dia Rekhi tries to answer these questions about the latest rage in the tech world, by talking to Ajay Agrawal, Professor at Rotman School of Management, University of Toronto,  Author of Power and Prediction: The Disruptive Economics of Artificial Intelligence (Ranked among 10 best books for 2022 by Forbes) and Aakrit Vaish, Co-Founder & CEO of Haptik.   Credits:  Wall Street Journal,  Adrian Twarog, CNBCTV18,  The Daily Show with Trevor Noah, Yahoo Finance    You can follow our host Dia Rekhi on his social media:Twitter - https://twitter.com/diarekhiETLinkedin - https://www.linkedin.com/in/dia-rekhi-702438104/   Catch the latest episode of ‘The Morning Brief' on ET Play, The Economic Times Online, Spotify, Apple Podcasts, JioSaavn, Amazon Music and Google Podcasts.See omnystudio.com/listener for privacy information.

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. 

What does "AI" really look like in businesses today? What is a Prediction Machine, and what is AI Economics? This week, we chat with Dr. Avi Goldfarb, author of Prediction Machines and Power and Prediction: The Disruptive Economics of Artificial Intelligence. Through a sequence of fascinating stories, Avi helps us demystify the stigma around artificial intelligence, and he helps us understand how AI has transformed and can transform business and industry the way electricity transformed steam-powered factories. Avi is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto, Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium.

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.

We've seen remarkable gains in artificial intelligence – but only in specific, narrow domains, like fraud prevention or navigation. One reason for that is the way AI innovations get adopted. Another is our poor ability to distinguish between real progress and so-called AI snake oil. This week, we demystify AI with guests Ajay Agrawal, professor in University of Toronto's Rotman School of Management, founder of the Creative Destruction Lab, and co-author of a new book, Power and Prediction: The Disruptive Economics of Artificial Intelligence; and Arvind Narayanan, professor of computer science at Princeton University and co-author of the newsletter and forthcoming book called AI Snake Oil.

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.

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.

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."

Have you ever wondered about the most promising industries in Machine Learning? Today we will learn from Avi Goldfarb, the chair of AI at the University of Toronto, about...The most promising AI industriesPotential problems with powerful AIThe economics behind innovationOn YouTubeThe Disruptive Power of Artificial Intelligence - ML 100SponsorsChuck's Resume TemplateDeveloper Book Club starting with Clean Architecture by Robert C. MartinBecome a Top 1% Dev with a Top End Devs MembershipLinksPower and Prediction: The Disruptive Economics of Artificial IntelligenceAvi GoldfarbLinkedIn: Avi Goldfarb Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy