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2 episodes with avi goldfarb
3 episodes with avi goldfarb
3 episodes with avi goldfarb
2 episodes with avi goldfarb
2 episodes with avi goldfarb
2 episodes with avi goldfarb
2 episodes with avi goldfarb
In this final and special episode of the AI in the Workplace mini-series, guest hosts Pauline James and David Creelman will distill key insights shared by leading experts they were fortunate to learn from. Expert insights are highlighted from Avi Goldfarb, Rotman Chair in Artificial Intelligence and Healthcare at the University of Toronto; Shingai Manjengwa, Head of AI Education at ChainML; Ben Zweig, CEO of Revelio Labs; Dr. Jarik Conrad, VP of Human Insights at UKG; Kate Bischoff, Founder of Thrive Law and Consulting; Jesslyn Dymond, Director of Data Ethics at TELUS; Frank Rudzicz, Associate Professor at Dalhousie University; and Vimal Sharma, VP of HR at WIS International.Tune in and Discover: • AI's Impact on the Workforce: Explore insights on AI's disruptive potential, its role in leveling the skills field by automating tasks, and HR's pivotal role in managing change and leading reskilling initiatives.• Streamlining HR Processes: Discover how AI automates labor-intensive HR tasks, enabling professionals to focus on strategic activities, and the importance of ethical AI use through human oversight and governance.• Advancing Organizational Competence: Learn about the critical need for AI literacy within organizations to responsibly leverage AI, and AI's potential to enhance empathy and reduce biases in decision-making.• Enhancing HR Practices with AI: Valuable resources for deepening AI knowledge and engaging in responsible AI use discussions are shared, emphasizing HR's evolving role in the age of AI.As we conclude this insightful journey, it's clear that AI's role in HR is both transformative and complex. By embracing AI with informed enthusiasm, HR leaders can steer their organizations towards innovative, efficient, and ethical futures. The insights shared in this series are just the beginning; the real work lies in applying these learnings to navigate the evolving landscape of AI in HR. Please keep us posted on your progress in this regard! Feature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events
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.
In this Snippets episode, David King, Senior Managing Director for Canada and South America at Robert Half—the world's first and largest specialized talent solutions and business consulting firm—takes us on a deep dive into the post-pandemic landscape of talent recruitment and labor markets. Don't miss this opportunity to stay ahead of the curve and make informed decisions for your organization.With an astounding tenure of 29 years at Robert Half, David shares his take on current labor market trends and predictions for 2024. He also shares valuable insights on talent decision-making factors and priorities, the dynamics of hybrid work environments, the impact of AI on the workforce, and strategies for achieving high levels of engagement in a workplace. Additionally, he highlights the advantages of adopting a six-quarter forecast strategy. If you're interested in our Snippets podcasts or The Way Forward live webcasts, please take a moment and visit us at peo-leadership.com. Guests have included Stephen Poloz, Avi Goldfarb, Dr. Michael Roizen, Morgan Housel, and Professor Janice Stein. We've covered such topics as growth, uncertainty, mental health, leadership, the new world, and a host of others. If you'd like to learn more about our leadership community, please feel free to contact lgoren@peo-leadership.com. If you enjoyed today's podcast, please subscribe and give us a review on Apple podcasts, or wherever you find your favourite podcasts.
Ready to enhance your investment strategy? Tune in as Leon sits down with Liam O'Sullivan, Principal Co-Head of Client Portfolio Management at RPIA. An expert in alternative investments and risk management, Liam shares insight on managing an investment portfolio, current market paradigm shifts, the crucial role of fixed income, and the importance of differentiating between high-grade and high-yield bonds. If you're interested in our Snippets podcasts or The Way Forward live webcasts, please take a moment and visit us at peo-leadership.com. Guests have included Stephen Poloz, Avi Goldfarb, Dr. Michael Roizen, Morgan Housel, and Professor Janice Stein. We've covered such topics as growth, uncertainty, mental health, leadership, the new world, and a host of others. If you'd like to learn more about our leadership community, please feel free to contact lgoren@peo-leadership.com. If you enjoyed today's podcast, please subscribe and give us a review on Apple podcasts, or wherever you find your favourite podcasts.
Welcome to this week's episode of the Mixtape with Scott! This week we have an outstanding guest named Avi Goldfarb of the University of Toronto. Avi is a PhD economist who graduated from Northwestern in the early 2000s specializing in the economics of the internet. He is now at the University of Toronto where he is a professor in the marketing department as well as chief data scientist with a very interesting lab called the Creative Destruction lab that among other things specializes in the economics of artificial intelligence. He is the author of two very popular and probably both best selling books aimed at a general audience on the economics of artificial intelligence: Power and Prediction and Prediction Machines (both with Joshua Gans and Ajay Agrawal). Given the popularity of AI, as well as the recent turn of events with AI giant, OpenAI, I think there couldn't be a better time to to have him on the show. I loved this interview and accidentally went over, but Avi graciously hung in there with me. I hope you love it too. Don't forget to like, share and comment! Happy Thanksgiving to all!Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Scott's Substack at causalinf.substack.com/subscribe
In the first of a special mini-series focused on AI in the workplace, guest hosts Pauline James and David Creelman ask "what exactly is AI all about and where is it heading?"Joining Pauline and David on the podcast is Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. He is also Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the bestselling books Prediction Machines and Power and Prediction.He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi has testified before the U.S. Senate Judiciary Committee on related work in competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere.Questions for Avi include:You have been studying AI for a long time; well before its advances garnered the attention of the general population. Is anything taking you by surprise with the current advances?You have talked about AI automating prediction in ways that we had only understood humans could do well/reasonably well previously. Can you explain this for our audience?You have an interesting (and hopeful) perspective that AI may become more of an equalizer across professions than past advances have been, can you tell us more about that?Can you provide your thoughts on how long it will be until we see broad sweeping changes in work and how it is organized?With your extensive work with entrepreneurs on the bleeding edge of new technology, what advice do you have for those on the other side of the equation: those who are making recommendations and purchasing tech for their organization. How should we balance not being left behind with appropriate diligence?We do our best to ensure editorial objectivity. The views andFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here and contact sales@hr-gazette.com. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events
In this episode, Leon chats with Dan Carmichael, the president of Whitecap. Dan tells us about his motivations for leaving a 20-year-long big corporate job at Dell Technologies to join the small business world. Leon and Dan discuss staying on the forefront of emerging technology and Dan gives advice to business leaders on getting involved with AI.If you're interested in our Snippets podcasts or The Way Forward live webcasts, please take a moment and visit us at peo-leadership.com. You'll find on our site various previous recorded webcasts with guests such as Stephen Poloz, Avi Goldfarb, Dr. Michael Roizen, Morgan Housel, Jason Selk, and Mitchell Goldhar. We've covered such topics as growth, uncertainty, mental health, leadership, the new world, and a host of others. Check out our upcoming PEO Leadership Conference: Adapting to a World of Opportunity, taking place on November 13th and 14th in Toronto. If you'd like to learn more about our leadership community, please feel free to contact lgoren@peo-leadership.com. If you enjoyed today's podcast, please subscribe and give us a review on Apple podcasts, or wherever you find your favourite podcasts.
Scheerer´s Impulse: female Unternehmerinnen, Leadership, enterpreneur, mindset
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
Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. He is also Chief Data Scientist at the Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, and a faculty affiliate at the Vector Institute for Artificial Intelligence. Avi has written extensively on a broad range of topics from marketing, statistics, law, management, medicine, political science, refugee studies, among many others. He has also conducted much deep work in the study of AI and machine learning and how businesses can wield and leverage the predictive capabilities of these technologies.In this podcast, he shares:Point-to-point changes v. the bigger, broader systemic changes that AI may introduceHow AI prediction changes judgement. Until now, most of the exploration of machine learning have been around prediction, but how will things change when AI starts getting good at judgement? The fascinating implications machine learning and AI will have on business decision-making, economics, and competitive advantage _________________________________________________________________________________________Episode Timeline:00:00—Highlight from today's episode00:44—Introducing Avi+ The topic of today's episode2:21—If you really know me, you know that...2:50—Could you talk about cost of prediction coming on?6:25—Can machine learning surpass human judgment, or where do they have their place?9:30—Could you explain the point-to-point solutions vs. systemic changes?11:40—How does technology change power?14:27—Could you give us an example of the impact of AI-powered predictions in a practical real-life case?17:01—Do you think we have a reason to worry?19:01—How can people continue learning from you?__________________________________________________________________________________________Additional Resources: Personal Page: https://www.avigoldfarb.com/Linkedin: https://ca.linkedin.com/in/avi-goldfarb-46a7473Twitter: https://twitter.com/avicgoldfarb?lang=en
Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. He is also Chief Data Scientist at the Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, and a faculty affiliate at the Vector Institute for Artificial Intelligence. Avi has written extensively on a broad range of topics from marketing, statistics, law, management, medicine, political science, refugee studies, among many others. He has also conducted much deep work in the study of AI and machine learning and how businesses can wield and leverage the predictive capabilities of these technologies.In this podcast, he shares:Point-to-point changes v. the bigger, broader systemic changes that AI may introduceHow AI prediction changes judgement. Until now, most of the exploration of machine learning have been around prediction, but how will things change when AI starts getting good at judgement? The fascinating implications machine learning and AI will have on business decision-making, economics, and competitive advantage _________________________________________________________________________________________Episode Timeline:00:00—Highlight from today's episode00:44—Introducing Avi+ The topic of today's episode2:21—If you really know me, you know that...2:50—Could you talk about cost of prediction coming on?6:25—Can machine learning surpass human judgment, or where do they have their place?9:30—Could you explain the point-to-point solutions vs. systemic changes?11:40—How does technology change power?14:27—Could you give us an example of the impact of AI-powered predictions in a practical real-life case?17:01—Do you think we have a reason to worry?19:01—How can people continue learning from you?__________________________________________________________________________________________Additional Resources: Personal Page: https://www.avigoldfarb.com/Linkedin: https://ca.linkedin.com/in/avi-goldfarb-46a7473Twitter: https://twitter.com/avicgoldfarb?lang=en
My guest today is Avi Goldfarb. Avi is a Professor at the University of Toronto's Rotman School of Management, the Rotman Chair in Artificial Intelligence and Healthcare, as well as the co-author of two bestselling books on AI and its economic impact. His most recent book, Power and Prediction, is probably the best piece of content I have read in explaining how AI may reshape business models, systems, and products. We recorded this before GPT-4's release last week which, if anything, makes Avi's ideas on AI's impact all the more poignant. Please enjoy my conversation with Avi Goldfarb. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus. Tegus is the modern research platform for leading investors. I'm a longtime user and advocate of Tegus, a company that I've been so consistently impressed with that last fall my firm, Positive Sum, invested $20M to support Tegus' mission to expand its product ecosystem. Whether it's quantitative analysis, company disclosures, management presentations, earnings calls - Tegus has tools for every step of your investment research. They even have over 4000 fully driveable financial models. Tegus' maniacal focus on quality, as well as its depth, breadth and recency of content makes it the one-stop, end-to-end research platform for investors. Move faster, gather deep research to build conviction and surface high-quality, alpha-driving insights to find your differentiated edge with Tegus. As a listener, you can take the Tegus platform for a free test drive by visiting tegus.co/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes. Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Show Notes (00:03:15) - [First question] - His initial reaction to chat GPT when it first launched (00:07:08) - Prediction Machines; The impact price has on how much something is used by humans (00:11:07) - The shift from steam powered factories to electric ones and the transition between the two in regards to systems and application solutions; Power and Prediction (00:17:06) - Midpoints between a point solution and a systems solution and applications that are being built in the middle of them (00:19:10) - What application, system, and point solutions feel like today in the world of AI (00:27:03) - The transition from a world governed by rules to one by decisions (00:30:58) - How the power of prediction moves us from a binary to a decimal framework (00:34:48) - Ways power disruption will occur as we navigate the emerging AI frontier (00:44:33) - Other functions like personalization that entrepreneurs should think about putting into their products and features (00:47:18) - How we should be thinking about the generation of information and data (00:51:32) - A future where technology either desimates or empowers specific industries (00:54:16) - What he's most excited and worried about given the emerging frontier of AI (00:55:41) - The kindest thing anyone has ever done for him
"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
In this episode: Lauren Hawker Zafer is joined by Avi Goldfarb Who Can Benefit From This Conversation? This conversation is for all of us who are interested in taking an economist lens of thinking about technology and the statistical nature of prediction. As prediction becomes cheaper, what are the cream and sugar to prediction in this space? Join this informative conversation with Avi and Lauren to learn more about power and prediction. Who is Avi Goldfarb? Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare, and Professor of Marketing, at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and a Research Associate at the National Bureau of Economic Research. A former Senior Editor at Marketing Science, his research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, computing, law, management, medicine, physics, political science, public health, statistics, and economics. He co-authored the bestselling book Prediction Machines: The Simple Economics of Artificial Intelligence. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award, and he testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. Avi received his Ph.D. in economics from Northwestern University. His new book, also a bestseller, Power and Prediction, was published in November by Harvard Business Review Press. REDEFINING AI is powered by The Squirro Academy - learn.squirro.com. Try our free courses on AI, ML, NLP and Cognitive Search at the Squirro Academy and find out more about Squirro here.
Spotlight One is a snippet from our upcoming episode: Power and Prediction with Avi Goldberg! Listen to the full episode as soon as it comes out, by following and subscribing to Redefining AI. Who is Avi Goldfarb? Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare, and Professor of Marketing, at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and a Research Associate at the National Bureau of Economic Research. A former Senior Editor at Marketing Science, his research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, computing, law, management, medicine, physics, political science, public health, statistics, and economics. He co-authored the bestselling book Prediction Machines: The Simple Economics of Artificial Intelligence. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award, and he testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. Avi received his Ph.D. in economics from Northwestern University. His new book, also a bestseller, Power and Prediction, was published in November 2022 by Harvard Business Review Press.
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 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
Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare? In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices
Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare? In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology
Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare? In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices
What if you could predict your revenue, workflow, and an ideal customer base using AI? Can you apply these predictions to stay ahead of your competitors and lead to better economic outcomes? In this episode, Avi Goldfarb joins Tom to discuss how AI offers value to any economic venture by decoupling prediction from decision-making, allowing business owners to make better calculated decisions. Learn more about your ad choices. Visit megaphone.fm/adchoices
Joining Cardiff for this episode is Avi Goldfarb, Rotman Chair In Artificial Intelligence and Healthcare At The Rotman School Of Management, University Of Toronto, and the co-author (with his fellow economists Ajay Agrawal and Joshua Gans) of an excellent new book, "Power and Prediction: The Disruptive Economics of Artificial Intelligence".In their chat, Avi and Cardiff discuss:Why AI is best understood as a "prediction technology"Examples of AI already in useWhich parts of the economy could be transformed by AI, and howHistorical analogies to previous eras of widespread technological disruptionHow AI will change the way people and companies make decisionsWhy this change will shift institutions away from blunt rules and towards individual discretionIn the labor market, who will gain and who will lose from the adoption of AIWhat the use of AI might teach us about what it means to be humanAnd all throughout the chat, they look at the fundamental question of whether artificial intelligence is about to make the economy—and the world—a whole lot weirder. And if so, just how far along that path to weirdness are we already?Related links: "Prediction and Power", by Ajay Agrawal, Joshua Gans, and Avi Goldfarb"The impact of AI on the future of workforces", The White House CEA and the European Commission“Before the Flood”, by Sam Hammond"The golden age of AI-generated art is here", by Tom Faber"Historical analogies for large language models", by Dynomight Internet Website Hosted on Acast. See acast.com/privacy for more information.
In this episode, Dr. Aaron Fritts interviews Dr. Danny Goel, orthopedic surgeon and CEO / co-founder of PrecisionOS, a virtual reality (VR) education company aimed at creating valuable opportunities for orthopedic surgery trainees to build their skillsets. The CE experience for this Podcast is powered by CMEfy - click here to reflect and earn credits: https://earnc.me/P2tgkL --- SHOW NOTES Dr. Goel shares his story, from his residency, to his MBA years, and the eventual idea for his company. PrecisionOS emerged as an idea when he was searching for training opportunities in shoulder reconstruction surgery. As a proceduralist, he realized that most new skills were acquired by traveling to courses and workshops hosted by professional societies and medical device companies. He wanted to find a way to make training more accessible by integrating virtual reality. Virtual reality is popular in the orthopedics space since there is a direct application for it. For example, understanding which implant to use in each clinical problem can be training via virtual reality. Dr. Goel emphasizes that the use of VR for mindless repetition will be fruitless. It is rather the deliberate, systematic, and data-driven approach to practice that allows for skill attainment and refinement. The doctors also speak about the evolution of VR hardware and how the headset must evolve to become sleeker and more user friendly. The headset cost has decreased, making it more scalable for distribution around the world. Additionally, tactile sensation has been integrated through manual controllers. These haptics are improving in the same way that visual optics did in the last iteration. Dr. Goel highlights data showing that haptics are extremely important for new trainees, while visual cues are more important to experienced surgeons. Finally, Dr. Goel describes how he met his co-founders, the different areas of expertise that they contribute to the company, and the team dynamic that allows him to practice medicine full time while also serving as CEO. This balance allows him to maintain perspective on clinical challenges and fit his product accordingly. --- RESOURCES PrecisionOS: https://www.precisionostech.com/ The Future of Surgery Training and Education: https://www.precisionostech.com/wp-content/uploads/2019/12/The-Future-of-Surgery-Training-and-Education-Whitepaper.pdf Immersive Virtual Reality for Surgical Training: A Systematic Review: https://www.journalofsurgicalresearch.com/article/S0022-4804(21)00416-9/fulltext The Metaverse by Matthew Ball: https://www.matthewball.vc/metaversebook Prediction Machines by Ajay Agarwal, Joshua Gans, Avi Goldfarb: https://www.predictionmachines.ai/
On the heels of launching my latest book, Our Book of Awesome, I'm enjoying the fellowship of two authors in my life — one of whom I met 22 years ago when I was in my final year at Queen's. Bounding into my life at the time came a young professor named Ajay Agrawal. And I mean bounding! He was cold calling left, right and center, dancing around the room, and extremely theatrical. As you listen to him you'll see why I found him so captivating and clairvoyant. Professor Ajay Agrawal has won Professor of the Year seven times! He's like Canada's Adam Grant. He is the co-author of the bestselling book, Prediction Machines: The Simple Economics of Artificial Intelligence, named one of the best tech books of the year by Forbes, The New York Times and The Economist. His latest book has just come out and it is called, Power and Prediction, also co-authored with Joshua Gans and Avi Goldfarb. Ajay is a tenured professor at Rotman, a research associate at The National Bureau of Economic Research in Cambridge, Massachusetts, founder of the Creative Destruction Lab, a not-for-profit program that helps start-ups launch, and co-founder of Next Canada dedicated to the development and training of young entrepreneurs. He is also a recent winner of the Order of Canada which is the highest civilian honor that Canada awards. Over the years I've gotten a chance to meet Ajay's truly lovely partner in life: Gina Buonaguro. And, guess what? She's a writer too! Ajay focuses on the future. Gina focuses on the past. Gina is originally from New Jersey but has been living in Toronto for many years. She started at Villanova University, all the way up to the University of British Columbia on a Fullbright Scholarship. Gina's written dozens of articles, won five writing grants and is the co-author of six historical fiction books, including her latest, The Virgins of Venice. You could not think of two books which are more different: The Virgins of Venice and Power and Prediction. One is a 500 year old historical fiction saga taking place in a convent with sexy nuns. And the other a deep dive into AI. I was intrigued by the relationship dynamics between them, what their books really say, and how their writing processes work. So I invited them, together, to come on 3 Books. I also asked Leslie to join the conversation. So the four of us sat down in Gina and Ajay's living room and we discussed questions like: what is the fate of girls in 16th century Venice, what does it mean for a city to be excommunicated, why has Uber been so revolutionary, what is the point vs systems solution in AI, how can books be shared and read together, what is an Untouchable Day, how can we think about living a little more intentionally, how does AI manipulate us today and it goes on and on and on. This is a wide ranging conversation that I think you will truly enjoy. Let's flip the page into Chapter 117 now… What You'll Learn: What does it mean for a city to be excommunicated? Why has Uber been so revolutionary? What is a point solution vs a system solution in AI? What is holding AI back? How can books be shared and read together? What is a writer's group? What is the power of reading aloud to our kids? How can we bring in more quiet into our busy urban lives? What is an untouchable day? How can we live intentionally? How does AI manipulate us? What are the challenges of raising kids in a tech centric world? Why are young people finding social interactions so awkward these days? What is the Chinese solution to screen time? What is the tension between ideology and critical thinking? How can we encourage more critical thinking? How can we temper cancel culture? What is the role of school today? You can find show notes and more information by clicking here: https://www.3books.co/chapters/117 Leave us a voicemail. Your message may be included in a future chapter: 1-833-READ-A-LOT. Sign up to receive podcast updates here: https://www.3books.co/email-list 3 Books is a completely insane and totally epic 15-year-long quest to uncover and discuss the 1000 most formative books in the world. Each chapter discusses the 3 most formative books of one of the world's most inspiring people. Sample guests include: Brené Brown, David Sedaris, Malcolm Gladwell, Angie Thomas, Cheryl Strayed, Rich Roll, Soyoung the Variety Store Owner, Derek the Hype Man, Kevin the Bookseller, Vishwas the Uber Driver, Roxane Gay, David Mitchell, Vivek Murthy, Mark Manson, Seth Godin, Judy Blume and Quentin Tarantino. 3 Books is published on the lunar calendar with each of the 333 chapters dropped on the exact minute of every single new moon and every single full moon all the way up to 5:21 am on September 1, 2031. 3 Books is an Apple "Best Of" award-winning show and is 100% non-profit with no ads, no sponsors, no commercials, and no interruptions. 3 Books has 3 clubs including the End of the Podcast Club, the Cover to Cover Club, and the Secret Club, which operates entirely through the mail and is only accessible by calling 1-833-READ-A-LOT. Each chapter is hosted by Neil Pasricha, New York Times bestselling author of The Book of Awesome, The Happiness Equation, Two-Minute Mornings, etc. For more info check out: https://www.3books.co
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.
Professors Ajay, Avi & Joshua, co-authors of “Power & Prediction: The Disruptive Economics of Artificial Intelligence” share a bit about how to bring together a team of three creative, imaginative people – how they work together, form ideas, and ultimately shape a vision of the future of technology. “Power & Prediction” is being released Tuesday, November 15th. Ajay Agrawal is Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management at the University of Toronto's Rotman School of Management. He is the founder of Creative Destruction Lab, cofounder of Next 36 and Next AI, and cofounder of Sanctuary, an AI/robotics company. Avi Goldfarb is the Rotman Chair in AI and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. Avi is also Chief Data Scientist at Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, a faculty affiliate at the Vector Institute for Artificial Intelligence, and a research associate at the National Bureau of Economic Research. Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Professor of Strategic Management at Toronto's Rotman School of Management. He is Chief Economist at Creative Destruction Lab, department editor (Strategy) at Management Science, and cofounder and managing director of Core Economic Research.
Guest Avi Goldfarb discusses AI as prediction technology likely to transform our systems over a long period of time. Goldfarb is the Rotman Chair of Artificial Intelligence and Healthcare and a Professor of Marketing at the Rotman School of Management at the University of Toronto. He's also Chief Data Scientist at the Creative Destruction Lab, a Faculty Affiliate at the Vector Institute and the Schwartzman 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. Additionally, he is co-author of a new book titled Power and Prediction, The Disruptive Economics of Artificial Intelligence, which will be coming out on November 15th.
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.
These are weird times. On the one hand, scientific and technological progress seem to be getting harder. Add to that slowing population growth, and it's possible economic growth over the next century or two might slow to a halt. On the other hand, one area where we seem to be observing rapid technological progress is in artificial intelligence. If that goes far enough, it's easy to imagine machines being able to do all the things human inventors and scientists do, possibly better than us. That would seem to pull in the opposite direction, leading to accelerating and possibly unbounded growth; a singularity.Are those the only options? Is there a middle way? Under what conditions? This is an area where some economic theory can be illuminating. This article is bit unusual for New Things Under the Sun in that I am going to focus on a small but I think important part of a single 2019 article: “Artificial Intelligence and Economic Growth” by Aghion, Jones, and Jones. There are other papers on what happens to growth if we can automate parts of economic activity,undefined but Aghion, Jones, and Jones (2019) is useful because (among other things) it focuses on what happens in economic growth models if we automate the process of invention itself.This podcast is an audio read through of the (initial draft of the) post What if we could automate invention?, originally published on New Things Under the Sun.Articles MentionedAghion, Philippe, Benjamin F. Jones, and Charles I. Jones. 2019. Artificial Intelligence and Economic Growth. In The Economics of Artificial Intelligence: An Agenda, ed. Ajay Agrawal, Joshua Gans, and Avi Goldfarb. National Bureau of Economic Research. ISBN 978-0-226-61333-8
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, 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 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, and the Economist.
So far, advances in AI are not bringing us real “intelligence.” Rather, these advances are bringing us a key part of intelligence: prediction. This enables businesses to make predictions faster and more precisely to improve their business models and marketplace advantage. In this episode of Mind the Gap, Avi Goldfarb, an economist at the University of Toronto's Rotman School of Management and one of the authors of “Prediction Machines: The Simple Economics of Artificial Intelligence,” will explain the economics of AI and how it can lead to better and cheaper predictions.
Georgia Tech associate professor Jon R. Lindsay discusses the role and ethics of AI in war, the risks and dangers in developing military and national security applications, and how AI applications will alter the nature of international conflict. Notes:Jon R. Lindsay bioJon R. Lindsay, Information Technology and Military Power (Ithaca, NY: Cornell University Press, 2020).Avi Goldfarb and Jon R. Lindsay, “Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War,” International Security 46, no. 3 (2022): pp. 7-50.Jon R. Lindsay, “Cyber Conflict vs. Cyber Command: Hidden Dangers in the American Military Solution to a Large-Scale Intelligence Problem,” Intelligence and National Security 36, no. 2 (2021): pp. 260-278. See acast.com/privacy for privacy and opt-out information.
Some of you that are old enough will remember when doctors, clinics and hospitals were complaining about implementing electronic medical records which we now call EMR. EMR then advanced and became Electronic Health records or EHR. EHR is actually more powerful than EMR. EHR is the term most of us use today. Then, if your product created a report or an image, your company was busy creating links to the electronic records so the report or image could be stored electronically. Now we take all much of this for granted. Our guest today says that even though EHRs were not intentionally designed to aid clinical informatics “without EHR we would have no AI in healthcare.” Today we dive into the mind of a clinician and researcher who is very involved in clinicial informatics and artificial intelligence. Our guest today is Ron Li, MD. Ron is a Clinical Assistant Professor Department of Medical and Hospital Medicine, Stanford University and he is the Medical Informatics Director for Digital Health and Artificial Intelligence Clinical Integration at Stanford Health Care. We learn about how a health care system is investing in efforts to design and implement programs and workflows that incorporate clinical informatics and AI to improve outcomes and reduce costs. And, we talk about how MedTech fits in. This is our 6th episode related to AI in MedTech. We have at least one more. If you have listened to most of these, you will have a good idea as to what is going on in the minds of clinicians, researchers, companies and providers. This knowledge can help guide you in your career and/or your company's strategies related to informatics, deep learning and its products. Do your products need an AI component to add more value or do they need to fit into a work flow that is being enhanced by AI? Thanks for listening in today. If you like this podcast, please refer it to a friend simply by using the share link on your podcast player. If you want to learn more about the MedTech Leaders community, go to MedTechLeaders.net. Now Go Win Your Week!! Ron's LinkedIn profile link Books Ron recommends: The Fifth Discipline by Peter M. Senge link Cloud Computing: Concepts, Technology & Architecture (The Pearson Service Technology Series from Thomas Erl) by Thomas Erl, Ricardo Puttini, Zaigham Mahmood link Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb link Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture by Jamshid Gharajedaghi link Ted Newill's LinkedIn Profile link More Medical Device Success podcasts link Medical Device Success website link MedTech Leaders Community link Link to Ted's contact page
L'intelligence artificielle va-t-elle faire disparaître mon job ? C'est l'une des questions que l'on entend le plus, autour de l'IA. Elle est légitime : à chaque révolution technique, certaines tâches deviennent obsolètes ou sont automatisées. Lorsqu'il s'agit de remplacer un cheval par une machine à vapeur, le cheval ainsi que l'ensemble des humains travaillant à le mettre à la tâche s'en voient impactés. Mais lorsqu'il s'agit d'automatiser l'intelligence même, serait-ce toute l'humanité qui serait impactée ? Et si je souhaite préparer mon avenir, anticiper ce grand remplacement de l'Homme par la machine, comment m'y prendre ? Quels métiers pourrais-je exercer ? Jean-Philippe Couturier est un grand spécialiste de l'intelligence artificielle, dont il décrypte les méandres devant les étudiants d'HEC, de Mines Telecom et de Paris Panthéon Assas et il est aussi le fondateur de la startup Whoz, qui se spécialise justement dans l'application de l'intelligence artificielle à la gestion des ressources humaines. Il est donc très bien placé pour répondre à ces questions du lien entre l'intelligence artificielle et le monde du travail et tente, à travers cet entretien, de nous en donner les clés, en nous permettant au passage de prendre du recul sur la vitesse à laquelle ces changements sont en train d'avoir lieu. Quelques références : Lorsque mon boss sera une intelligence artificielle : Manager et travailler à l'ère de l'intelligence artificielle, de Jean-Philippe Couturier : https://amzn.to/2vXWaB8 Factfullness, de Hans Rosling : https://amzn.to/38TzJf6 Prediction Machines, de A. Agrawal, Joshua Gans, Avi Goldfarb : https://amzn.to/2HOXlpq N'hésitez pas à vous abonner, à partager cet épisode et à en parler autour de vous ! Site web : www.anti-brouillard.fr Instagram : www.instagram.com/antibrouillard/ Twitter : www.twitter.com/Anti_brouillard Facebook : www.facebook.com/anti.brouillard.podcast/ LinkedIn : www.linkedin.com/in/fabienroques/ Email : anti.brouillard.podcast@gmail.com Crédit logo : Axel Delbrayère - http://delbrayere.com/
We can say very little about the long-run outlook of technological change, and even less about the exact form such change might take. But a certain class of models of innovation - models of combinatorial innovation - does provide some insight about how technological progress may look over very long time frames. Let's have a look.This podcast is an audio read through of the (initial version of the) article Combinatorial Innovation and Progress in the Very Long Run, published on New Things Under the Sun. Articles mentioned:Weitzman, Martin L. 1998. Recombinant Growth. Quarterly Journal of Economics 113(2): 331-360. https://doi.org/10.1162/003355398555595Koppl, Roger, Abigail Devereaux, James Herriot, and Stuart Kauffman. 2019. The Industrial Revolution as a Combinatorial Explosion. Working paper. (Earlier version - arXiv:1811.04502)Jones, Charles. 2021. Recipes and Economic Growth: A Combinatorial March Down an Exponential Tail. NBER Working Paper 28340. https://doi.org/10.3386/w28340Poincaré, Henri. 1910. Mathematical Creation. The Monist 321-335. https://doi.org/10.1093/monist/20.3.321Agrawal, Ajay, John McHale, and Alex Oettl. 2019. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth. Chapter in The Economics of Artificial Intelligence, eds. Ajay Agrawal, Joshua Gans, and Avi Goldfarb. Chicago: University of Chicago Press, pgs. 149-174. https://doi.org/10.7208/9780226613475-007
In the seventh episode of ReBootHealth, I speak with Avi Goldfarb. Avi is Professor of Marketing and the Rotman Chair of Artificial Intelligence and Healthcare at the University of Toronto. He is also co-author of the Book Prediction Machines: The Simple Economics of Artificial Intelligence. This interview with Avi was fascinating as he unpacked AI and healthcare through an economic lens. The content in this episode won't help you build a better AI but it will do one better—help set the context for the profound impact of AI on the business and structure of healthcare. We talked about AI's impact on the anatomy of a decision, the pace of adoption in healthcare, its potential role in clinical medicine and design implications, and a utopian/dystopian view of the future of health. I highly recommend Avi's book (linked below) for a novel way to view AI without all the hype and buzz. I must admit that this episode was quite enjoyable to research as it opened a whole new perspective for me. I promise it will do the same for you. As always, I hope you find the episode valuable. Prediction Machines: The Simple Economics of Artificial Intelligence Avi Goldfarb's publications Please write a review on Apple Podcast. For other episodes or to learn more you can visit us at ReBootHealth or follow us on Twitter: @Reboothealth1 Episode recorded on August 13, 2021. 05:00—AI and its role in decision-making and healthcare. 15:04—Estimating the pace of change for AI in medicine. 19:20—How to capture value with AI. 27:10—Applying lessons of the past and present to AI adoption. 39:40—Decision support versus automation. 44:15—How to design technology for the task. 47:31—Will 'ship-to-shop' apply to healthcare? 54:27—Canada's status in AI/healthcare.
I denne episoden av #LØRN snakker Silvija CEO i Gateway Digital AS, Alf Lande. Gateway Digital er en ny tidsalderteknologipartner som tilbyr digitale ingeniørtjenester, teknologiløsninger for bedrifter og organisasjoner over hele verden. I samtalen snakker Silvija og Alf om den norske mediebransjen, og hvor AI benyttes innen denne bransjen. Alf forklarer at mediebransjen henter stor internasjonal annerkjennelse for fremragende digital transformasjon, og han legger til at det hele norske næringslivet kan lære av denne sektoren. - We are all technology companies now Dette lørner du: AI Digital Transformasjon Ledelse Media Anbefalt litteratur: Prediction Machines: The Simple Economics of Artificial Intelligence av Ajay Agrawal, Avi Goldfarb og Joshua Gans See acast.com/privacy for privacy and opt-out information.
In Episode 3 we continue our mini-theme on Artificial Intelligence with award-winning economist and professor Joshua Gans, speaking about his book Prediction Machines: the simple economics of Artificial Intelligence, co-written with professors, Ajay Agrawal and Avi Goldfarb. This is an expansive career that features several books, numerous publications and awards. Aside from all the books and accolades, we think you'll find professor Gans, not only a great mind, but a gregarious one as well. This interview was conducted just before the COVID-19 pandemic gripped the world. On that note, you may want to check out professor Gans newest, short, but thoughtful and very timely book, Economics In the age of COVID-19 published by the MIT press, available on digital and audio. For more information on Josh or his books, visit https://www.joshuagans.com. He's also on Twitter @joshgans.
L’intelligence artificielle va-t-elle faire disparaître mon job ? C’est l’une des questions que l’on entend le plus, autour de l’IA. Elle est légitime : à chaque révolution technique, certaines tâches deviennent obsolètes ou sont automatisées. Lorsqu’il s’agit de remplacer un cheval par une machine à vapeur, le cheval ainsi que l’ensemble des humains travaillant à le mettre à la tâche s’en voient impactés. Mais lorsqu’il s’agit d’automatiser l’intelligence même, serait-ce toute l’humanité qui serait impactée ? Et si je souhaite préparer mon avenir, anticiper ce grand remplacement de l’Homme par la machine, comment m’y prendre ? Quels métiers pourrais-je exercer ? Jean-Philippe Couturier est un grand spécialiste de l’intelligence artificielle, dont il décrypte les méandres devant les étudiants d’HEC, de Mines Telecom et de Paris Panthéon Assas et il est aussi le fondateur de la startup Whoz, qui se spécialise justement dans l’application de l’intelligence artificielle à la gestion des ressources humaines. Il est donc très bien placé pour répondre à ces questions du lien entre l’intelligence artificielle et le monde du travail et tente, à travers cet entretien, de nous en donner les clés, en nous permettant au passage de prendre du recul sur la vitesse à laquelle ces changements sont en train d’avoir lieu. Quelques références : Lorsque mon boss sera une intelligence artificielle : Manager et travailler à l'ère de l'intelligence artificielle, de Jean-Philippe Couturier : https://amzn.to/2vXWaB8 Factfullness, de Hans Rosling : https://amzn.to/38TzJf6 Prediction Machines, de A. Agrawal, Joshua Gans, Avi Goldfarb : https://amzn.to/2HOXlpq N'hésitez pas à vous abonner, à partager cet épisode et à en parler autour de vous ! Site web : www.anti-brouillard.fr Instagram : www.instagram.com/antibrouillard/ Twitter : www.twitter.com/Anti_brouillard Facebook : www.facebook.com/anti.brouillard.podcast/ LinkedIn : www.linkedin.com/in/fabienroques/ Email : anti.brouillard.podcast@gmail.com Crédit logo : Axel Delbrayère - http://delbrayere.com/
You may not be aware, but artificial intelligence (AI) has already established itself in our daily lives. From Amazon to Alexa, sophisticated algorithms affect much of what we do. The next ten years will see advancements in electronic decision-making, facial recognition, language translation, and voice-to-text. Are you willing to accept the cost in loss of privacy due to AI’s insatiable thirst for data for the benefit in added productivity? What will be the new careers in AI world? Abhijeet Chavan and IV Ashton walk us through some of the inner workings of AI, some expectations in areas like Natural Language Processing, and give us advice on how to prepare for the future of this technology. This is a fascinating episode for listeners interested in court technology, Natural Language Processing, algorithms, individual privacy, language translation, and emerging technologies. There is a link to a short segment of the book Prediction Machines by Ajay Agrawal, Joshua Gans, Avi Goldfarb, in the Show Notes section on our website. https://nacmnet.org/podcasts Leave a comment or question about the podcast at clapodcast@nacmnet.org.
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. He is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, a Research Associate at the National Bureau of Economic Research, and President of Goldfarb Analytics Corporation. In his new book, “Prediction Machines,” Goldfarb explains how artificial intelligence (AI) will affect business, public policy, and society in terms that work for decision makers in virtually all fields. Perhaps his most important role is coming up this October in Boston, when he will be the featured speaker at the FP&A Luncheon at AFP 2019. Goldfarb recently joined us on the AFP Conversations podcast, and we discussed using AI to make predictions in all areas of life and business. AFP 2019, this October in Boston, is where treasury and finance professionals separate the hype from the reality. Visit www.afp2019.org/register to sign up and use discount code PODCASTAFP2019 at checkout to save $100.
Are you optimistic about our future with ARTIFICIAL INTELLIGENCE? Avi Goldfarb (professor and author of “Prediction Machines”) shares a brief history of AI, various AI applications that are being used in the marketplace, and specific reasons why we should be optimistic about our future with AI. SHOWNOTES Contents Key Learnings References & Links Andrea’s Commentary […] The post #15 (S2) ARTIFICIAL INTELLIGENCE: TALKING WITH SIRI & ALEXA with professor & author Avi Goldfarb appeared first on Talk About Talk.
On today's Global Exchange Podcast, we are joined by The Honourable John Manley to discuss Canada-U.S. economic and security relations in the age of Donald Trump. The Global Exchange is part of the CGAI Podcast Network. Subscribe to the CGAI Podcast Network on SoundCloud, iTunes, or wherever else you can find Podcasts! Bios: - Colin Robertson: A former Canadian diplomat, Colin Robertson is Vice President of the Canadian Global Affairs Institute. - The Hon. John Manley: John Manley is the former president and CEO of the Business Council of Canada. He is also a former federal cabinet minister who held the portfolios of Foreign Affairs, Industry and Finance. He is also the chair of the CGAI Advisory Council. Related Links: - "The Future of North American Trade: Assessing the USMCA" with Colin Robertson, Eric Miller, Sarah Goldfeder, Laura Dawson & Larry Herman [CGAI Podcast] (https://www.cgai.ca/the_future_of_north_american_trade_assessing_the_usmca) Book Recommendations: - The Hon. John Manley: "Prediction Machines: The Simple Economics of Artificial Intelligence" by Ajay Agrawal, Joshua Gans & Avi Goldfarb (https://www.amazon.ca/Prediction-Machines-Economics-Artificial-Intelligence/dp/1633695670/ref=sr_1_1?ie=UTF8&qid=1543874325&sr=8-1&keywords=prediction+machines) - Colin Robertson: "The Empty Throne: America's Abdication of Global Leadership" by Ivo H. Daalder & James M. Lindsay (https://www.amazon.ca/Empty-Throne-Americas-Abdication-Leadership/dp/1541773853/ref=asc_df_1541773853/?tag=googleshopc0c-20&linkCode=df0&hvadid=312366109380&hvpos=1o1&hvnetw=g&hvrand=9406841849723246295&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9000668&hvtargid=pla-546139235966&psc=1) Recording Date: November 26th, 2018 Follow the Canadian Global Affairs Institute on Facebook, Twitter (@CAGlobalAffairs), or on Linkedin. Head over to our website at www.cgai.ca for more commentary. Produced by Jared Maltais. Music credits to Drew Phillips.
How are we supposed to think about Machine Learning? How are businesses going to change? This week I interview Joshua Gans, Professor of Strategic Management at the Rotman School of Business at the University of Toronto and the Chief Economist at the University's Creative Destruction Lab. Joshua is the co-author, along with Ajay Agarwal and Avi Goldfarb, of Prediction Machines: The Simple Economics of Artificial Intelligence. Are you an actuary? Someone you know? Check out the Not Unprofessional Project, for the price of a CAS webinar you get unlimited access to content dedicated to Continuing Education Credits for Actuaries, especially Professionalism credits. CE On Your Commute! Subscribe to the Not Unreasonable Podcast in iTunes, stitcher, or by rss feed. Sign up for the mailing list at notunreasonable.com/signup. See older show notes at notunreasonable.com/podcast.
Will AI machines destroy humanity as we know it? Actually, quite the contrary. AI matters to the everyday business owner more than they might know. There’s a lot to be learned in prediction technology and today’s guest shares advice on how you can use this knowledge to grow your marketing and sales potential. Avi Goldfarb is the Ellison Professor of Marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, and a Research Associate at the National Bureau of Economic Research. Avi is also the author of Prediction Machines, which he will be discussing on today’s show! In 2012, there were only a couple of companies at the time that were calling themselves AI companies. However, that slowly grew and by 2015, Avi had seen a huge increase of at least 50 companies being created a year. Avi and his co-authors decided to investigate and research what this technology meant for our modern society. The Mosaic web browser made the internet accessible to the general population, but it took us about a decade to figure out how to really commercialize it. Avi believes the same will hold true with AI. We are going to see businesses use AI tools in various ways to solve key problems. Although AI will not improve the general intelligence of your company, prediction is still really, really valuable. Why? Because it cuts down on all the unknown variables in your decision-making process when you are faced with uncertainty. Through prediction, you can be much more confident (and you have the data to prove it) on what’s the correct direction to take your business in. Interview Links: Avigoldfarb.com Creativedestructionlab.com Resources: Scaling Up for Business Growth Workshops: Take the first step to mastering the Rockefeller Habits by attending one of our workshops. Scaling Up Website Gazelles Website Bill on YouTube
Avi Goldfarb, a professor at the University of Toronto’s Rotman School of Management, explains the economics of machine learning, a branch of artificial intelligence that makes predictions. He says as prediction gets cheaper and better, machines are going to be doing more of it. That means businesses — and individual workers — need to figure out how to take advantage of the technology to stay competitive. Goldfarb is the coauthor of the book “Prediction Machines: The Simple Economics of Artificial Intelligence.”
Nora on Avi Goldfarb and mix of online and in person Cathi on Gen Z and social shopping
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
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