Economics of AGI episode w Alex Imas and Phil Trammell.
There’s a bunch of important questions about how we deal with AI that only economics can answer.
What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn’t explode?
It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong.
It was very helpful to chat through these things with Alex and Phil.
Watch on YouTube; read the transcript.
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Timestamps
(00:00:00) – Will capital share increase?
(00:19:36) – Messy Middle scenario
(00:25:57) – How to tax and redistribute AI wealth
(00:30:02) – Why demand collapse is unlikely
(00:39:26) – Human employees would be hard to integrate into the machine economy
(00:43:08) – What if some humans (or AIs) value wealth accumulation intrinsically?
(01:01:28) – What should developing countries do?
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Everyone knows that new technologies can be really disruptive to the labor market, but eventually new jobs emerge and things come back into balance. And there is a sense in which many view AI with the same lens. Yes, there will be pain in some sectors, but then there will be productivity gains and new sources of demand and new opportunities for labor that we can't conceive of yet. But could it be different this time? Could AI be disruptive in a manner that, say, the steam engine was not? On this episode we speak with Alex Imas, a professor at the University of Chicago focusing on economics and applied AI. We talk about his work on the AI and labor question, how to think about which jobs may be most at risk, and why the sheer speed of AI development could make it categorically different than prior general purpose technologies that came before it.
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When the computer scientist Ben Zhao learned that artists were having their work stolen by A.I. models, he invented a tool to thwart the machines. He also knows how to foil an eavesdropping Alexa and how to guard your online footprint. The big news, he says, is that the A.I. bubble is bursting.
SOURCES:Erik Brynjolfsson, professor of economics at Stanford University
Ben Zhao, professor of computer science at the University of Chicago
RESOURCES:"The AI lab waging a guerrilla war over exploitative AI," by Melissa Heikkilä (MIT Technology Review, 2024)
"Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models," by Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y. Zhao (Cornell University, 2023)
"Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models," by Shawn Shan, Wenxin Ding, Josephine Passananti, Stanley Wu, Haitao Zheng, and Ben Y. Zhao (Cornell University, 2023)
"A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going," by Michael Woodridge (2021)
EXTRAS:"Nuclear Power Isn’t Perfect. Is It Good Enough?" by Freakonomics Radio (2022)
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(0:00) Announcement from Friedberg!
(0:21) Sacks intros John Mearsheimer and Jeffrey Sachs
(1:32) What is the Deep State Party, and what are their goals?
(13:56) Should America leverage its power against dictators?
(22:07) The China threat: avoiding the escalatory path to nuclear war
(36:08) India's growing role; are China's wounds self-inflicted?
(47:07) Conflict in the Middle East and the path to peace
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Today we’re joined by Ben Zhao, a Neubauer professor of computer science at the University of Chicago. In our conversation, we explore his research at the intersection of security and generative AI. We focus on Ben’s recent Fawkes, Glaze, and Nightshade projects, which use “poisoning” approaches to provide users with security and protection against AI encroachments. The first tool we discuss, Fawkes, imperceptibly “cloaks” images in such a way that models perceive them as highly distorted, effectively shielding individuals from recognition by facial recognition models. We then dig into Glaze, a tool that employs machine learning algorithms to compute subtle alterations that are indiscernible to human eyes but adept at tricking the models into perceiving a significant shift in art style, giving artists a unique defense against style mimicry. Lastly, we cover Nightshade, a strategic defense tool for artists akin to a 'poison pill' which allows artists to apply imperceptible changes to their images that effectively “breaks” generative AI models that are trained on them.
The complete show notes for this episode can be found at twimlai.com/go/668.
Chris Blattman is a professor at the University of Chicago studying the causes and consequences of violence and war. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(06:49) – What is war?
(17:59) – Justification for war
(40:47) – War in Ukraine
(1:24:16) – Nuclear war
(1:35:36) – Drug cartels
(1:52:19) – Joseph Kony
(1:58:23) – World Wars
(2:05:31) – Civil wars
(2:12:05) – Israeli–Palestinian conflict
(2:20:49) – China vs USA
(2:26:58) – Love
(2:33:23) – Hard data
(2:40:58) – Mortality
(2:46:04) – Advice for young people
(2:50:45) – Tyler Cowen
Today we continue our ICLR ‘21 series joined by Allyson Ettinger, an Assistant Professor at the University of Chicago.
One of our favorite recurring conversations on the podcast is the two-way street that lies between machine learning and neuroscience, which Allyson explores through the modeling of cognitive processes that pertain to language. In our conversation, we discuss how she approaches assessing the competencies of AI, the value of control of confounding variables in AI research, and how the pattern matching traits of Ml/DL models are not necessarily exclusive to these systems.
Allyson also participated in a recent panel discussion at the ICLR workshop How Can Findings About The Brain Improve AI Systems?, centered around the utility of brain inspiration for developing AI models. We discuss ways in which we can try to more closely simulate the functioning of a brain, where her work fits into the analysis and interpretability area of NLP, and much more!
The complete show notes for this episode can be found at twimlai.com/go/483.
Today we’re joined by Rayid Ghani, Director of the Center for Data Science and Public Policy at the University of Chicago. Drawing on his range of experience, Rayid saw that while automated predictions can be helpful, they don’t always paint a full picture. The key is the relevant context when making tough decisions involving humans and their lives. We delve into the world of explainability methods, necessary human involvement, machine feedback loop and more.