Avi Goldfarb, a Professor at the University of Toronto and an expert in AI, discusses the economic impact of AI and how it may reshape business models and systems. Topics include the impact of AI on writing skills and job opportunities, the historical adoption of transformative technologies like electricity, transitioning from rules to decisions with AI, the failure of companies like Blockbuster to adapt to new business models, the unpredicted impact of AI on job sectors, the effects of technology on various professions, and the value of reading specific books for business insights.

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.

Avi Goldfarb and Ajay Agrawal visit Google to discuss their book “Prediction Machines: The Simple Economics of AI.” The idea of artificial intelligence--job-killing robots, self-driving cars, and self-managing organizations--captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it’s not quite so complicated? The real job of artificial intelligence is to lower the cost of prediction. And once you start talking about costs, you can use some well-established economics to cut through the hype. The constant challenge for all managers is to make decisions under uncertainty. And AI contributes by making knowing what's coming in the future cheaper and more certain. But decision making has another component: judgment, which is firmly in the realm of humans, not machines. Making prediction cheaper means that we can make more predictions more accurately and then assess them with human judgment. Once managers can separate tasks into components of prediction and judgment, we can begin to understand how to optimize the interface between humans and machines. More than just an account of AI's powerful capabilities, “Prediction Machines” shows managers how they can most effectively leverage AI, disrupting business as usual only where required, and provides businesses with a toolkit to navigate the coming wave of challenges and opportunities. Originally published in May of 2018. Visit YouTube.com/TalksatGoogle to watch the video.  

Leading economists discuss the impact of AI on labor markets, debating augmentation versus automation. They explore worker surveillance, bargaining power, and policy recommendations to manage AI's influence on employment.

Avi Goldfarb, an economist specializing in the economics of the internet, discusses topics such as the history of AI in Toronto, creating lasting memories through vacations, the influence of family on interest in social science, challenges of obtaining data, luck and collaboration in research, methods in econometrics, analyzing the impact of ambulance chaser laws on online advertising, interdisciplinary research, the commercial potential of AI, and the downward sloping demand curve.