Show Notes
Autonomous vehicles hardly live up to their name. The goal of true “driverlessness” was originally hyped in the 1930s but keeps getting kicked further and further into the future as the true complexity of driving comes into ever-sharper and more daunting focus. In 2022, even the most capable robotic cars aren’t self-determining agents but linked into swarms and acting as the tips of a vast and hidden web of design, programming, legislation, and commercial interest. Infrastructure is more than the streets and signs but includes licensing requirements, road rules, principles of product liability, and many other features that form the landscape to which driverless cars continue to adapt, and which they will increasingly alter. While most ethical debates about them seem to focus on the so-called “Trolley Problem” of how to teach machines to make decisions that minimize human casualties, there are many other wicked problems to consider: Is automated driving a technological solution or a policy solution? Should policymakers have the same expectations for automated and conventional driving? How safe must an automated vehicle be for deployment? Should humans or computers have ultimate authority over a given action? Should harm that a human could have prevented somehow outweigh harm that a human caused? Given that a hacker could infect entire fleets, maps, or real-time communication between cars, how much new risk are we willing to take to reduce the more traditional safety hazards with which we are familiar? And, perhaps most surreally: How do you ticket a robot, and who should pay? Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe. This week on complexity, we speak to Bryant Walker Smith (Twitter) at the University of South Carolina School of Law and The Center for Internet and Society at Stanford, whose work centers on the ethics of autonomous vehicles. We link up to explore the myriad complexities — technological, regulatory, and sociocultural — surrounding the development and roll-out of new mobility platforms that challenge conventional understanding of the boundaries between person, vehicle, institution, and infrastructure. Buckle up and lean back for a dynamic discussion on the ever-shifting locii of agency, privacy and data protection, the relationship between individuals, communities, and corporations… If you value our research and communication efforts, please subscribe to Complexity Podcast wherever you prefer to listen, rate and review us at Apple Podcasts, and/or consider making a donation at santafe.edu/give. Thank you for listening! Join our Facebook discussion group to meet like minds and talk about each episode. Podcast theme music by Mitch Mignano. Follow us on social media: Twitter • YouTube • Facebook • Instagram • LinkedIn Discussed: • Ethics of Artificial Intelligence in Transport • Who is driving driverless cars? • From driverless dilemmas to more practical commonsense tests for automated vehicles • Who’s Responsible When A Self-Driving Car Crashes? • How Do You Ticket a Driverless Car? • Controlling Humans and Machines • Regulation and the Risk of Inaction • Government Assessment of Innovation Shouldn’t Differ for Tech Companies • New Technologies and Old Treaties • It’s Not The Robot’s Fault! Russian and American Perspectives on Responsibility for Robot Harms Mentioned: Melanie Mitchell - A.I.: A Guide for Thinking People + Complexity ep. 21 Kathy Powers & Melanie Moses on The Complexity of Harm, Complexity ep. 75 Cris Moore on Algorithmic Injustice, Complexity ep. 51 Luis Bettencourt on Urban Networks, Complexity ep. 4 Sabine Hauert on Swarming Robots, Complexity ep. 3 Kevin Kelly - Out of Control Emergent Engineering Cory Doctorow Jake Harper (formerly of Zo
More from COMPLEXITY
In the final episode of the season, Abha sits down with Melanie to hear her perspective. They chat about Melanie’s career and research with Douglas Hofstadter, the author of Gödel, Escher, Bach. They also discuss her opinions on LLMs’ current capabilities, what she thinks of existential questions like the alignment problem, how sustainable the industry is, the difficulty of making claims about concepts like “intelligence” and “understanding,” and what she thinks future technological development should focus on.
When it comes to assessing intelligence, people have all kinds of tests — the SAT, IQ tests, and so on. There’s controversy over how fairly these tests really measure human intelligence, but at the very least, we know that they correlate with some general reasoning skills when people take them. That assumption breaks down when we try to assess intelligence in non-humans. What does it mean when a large language model passes an intelligence test meant for humans? Does it actually have the same reasoning skills that a human does, or is it doing something else? In today’s episode, with guests Erica Cartmill and Ellie Pavlick, we investigate the best ways to assess intelligence in non-humans, whether animals or machines.
There’s an argument to be made that if we train AI systems to learn the way babies do, we’ll get them closer to human-like intelligence. But how our own learning development functions in babyhood is still a mystery that researchers are untangling. We know that the information babies absorb is very different from how an LLM learns, and in today’s episode, with guests Linda Smith and Michael Frank we’ll attempt to look at the world through an infant’s eyes and examine why they’re able to do more with, seemingly, less information.
Large language models, like ChatGPT and Claude, have remarkably coherent communication skills. Yet, what this says about their “intelligence” isn’t clear. Is it possible that they could arrive at the same level of intelligence as humans without taking the same evolutionary or learning path to get there? Or, if they’re not on a path to human-level intelligence, where are they now and where will they end up? In this episode, with guests Tomer Ullman and Murray Shanahan, we look at how large language models function and examine differing views on how sophisticated they are and where they might be going.
Complex language is unique to the human species. It’s part of how we evolved, the backbone of our societies, and one of the primary ways we judge others’ intellect. Is it our intelligence that leads to our language abilities, or conversely, does our ability for language enhance our intelligence, or both? How do language and thinking interact? And can one exist without the other? Guests: Evelina Federenko, Steve Piantadosi, and Gary Lupyan.