This post contains a list of the AI-related seminars that are scheduled to take place between 10 November and 31 December 2023. All events detailed here are free and open for anyone to attend virtually. Instrumental Time Series and Effect-Invariance for Policy Generalization Speaker: Jonas Peters (ETHZ) Organised by: UCL ELLIS Zoom link is here. Empowering Africa's Health Research Through Data Sharing and Governance Speaker: Lukman Enegi Ismaila Organised by: Lanfrica The Zoom link is here. Title to be confirmed Speaker: William Fedus (OpenAI) Organised by: Stanford MLSys Check the website nearer the time for the livestream link.

The Pulitzer Prize-winning book Gödel, Escher, Bach inspired legions of computer scientists in 1979, but few were as inspired as Melanie Mitchell. After reading the 777-page tome, Mitchell, a high school math teacher in New York, decided she "needed to be" in artificial intelligence. She soon tracked down the book's author, AI researcher Douglas Hofstadter, and talked him into giving her an internship. She had only taken a handful of computer science courses at the time, but he seemed impressed with her chutzpah and unconcerned about her academic credentials. Mitchell prepared a "last-minute" graduate school application and joined Hofstadter's new lab at the University of Michigan in Ann Arbor.

In March of 2021, the Santa Fe Institute hosted a workshop as part of its Foundations of Intelligence in Natural and Artificial Systems project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. During the workshop, speakers from diverse disciplines gathered to develop a taxonomy of intelligence, articulating their own understanding of intelligence and how their research has furthered that understanding. In this report, we summarize the insights offered by each speaker and identify the themes that emerged during the talks and subsequent discussions.

Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science at Portland State University. Prof. Mitchell is the author of a number of interesting books such as Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans. One interesting detail of her academic bio is that Douglas Hofstadter was her Ph.D. supervisor. During this 90 min interview with Melanie Mitchell, we cover a variety of interesting topics such as: how she started in physics, went into math, and ended up in Computer Science; how Douglas Hofstadter became her Ph.D. supervisor; the biggest issues that humanity is facing today; my predictions of the biggest challenges of the next 100 days of the COVID19 pandemic; how to remain hopeful when it is hard to be optimistic; the problems in defining AI, thinking and human; the Turing Test and Ray Kurzweil's bet with Mitchell Kapor; the Technological Singularity and its possible timeline; the Fallacy of First Steps and the Collapse of AI; Marvin Minsky's denial of progress towards AGI; Hofstadter's fear that intelligence may turn out to be a set of "cheap tricks"; the importance of learning and interacting with the world; the [hard] problem of consciousness; why it is us who need to sort ourselves out and not rely on God or AI; complexity, the future and why living in "Uncertain Times" is an unprecented opportunity. Intelligence is a very complex phenomenon and we should study it as such.

Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science at Portland State University. Prof. Mitchell is the author of a number of interesting books such as Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans. One interesting detail of her academic bio is that Douglas Hofstadter was her Ph.D. supervisor. During this 90 min interview with Melanie Mitchell, we cover a variety of interesting topics such as: how she started in physics, went into math, and ended up in Computer Science; how Douglas Hofstadter became her Ph.D. supervisor; the biggest issues that humanity is facing today; my predictions of the biggest challenges of the next 100 days of the COVID19 pandemic; how to remain hopeful when it is hard to be optimistic; the problems in defining AI, thinking and human; the Turing Test and Ray Kurzweil's bet with Mitchell Kapor; the Technological Singularity and its possible timeline; the Fallacy of First Steps and the Collapse of AI; Marvin Minsky's denial of progress towards AGI; Hofstadter's fear that intelligence may turn out to be a set of "cheap tricks"; the importance of learning and interacting with the world; the [hard] problem of consciousness; why it is us who need to sort ourselves out and not rely on God or AI; complexity, the future and why living in "Uncertain Times" is an unprecented opportunity. Intelligence is a very complex phenomenon and we should study it as such.

The tech industry pays programmers handsomely to tap the right keys in the right order, but earlier this month entrepreneur Sharif Shameem tested an alternative way to write code. First he wrote a short description of a simple app to add items to a to-do list and check them off once completed. Then he submitted it to an artificial intelligence system called GPT-3 that has digested large swaths of the web, including coding tutorials. "I got chills down my spine," says Shameem. "I was like, 'Woah something is different.'" GPT-3, created by research lab OpenAI, is provoking chills across Silicon Valley.

Artificial intelligence is better than humans at playing chess or go, but still has trouble holding a conversation or driving a car. A simple way to think about the discrepancy is through the lens of "common sense" -- there are features of the world, from the fact that tables are solid to the prediction that a tree won't walk across the street, that humans take for granted but that machines have difficulty learning. Melanie Mitchell is a computer scientist and complexity researcher who has written a new book about the prospects of modern AI. We talk about deep learning and other AI strategies, why they currently fall short at equipping computers with a functional "folk physics" understanding of the world, and how we might move forward. Melanie Mitchell received her Ph.D. in computer science from the University of Michigan.

Being a voracious reader is a prerequisite for academics in the humanities, but even the most dedicated bookworm needs to eat, sleep, and socialize. Not so for computers, which are known for being tireless, thorough, and very fast. And, when asked the right kinds of questions, these electronic speed-readers can grasp patterns that would otherwise lie beyond the reach of human scholars. That's exactly what happened when a team of researchers used machine-learning techniques to plow through transcripts of 40,000 speeches in a parliamentary assembly during the first two years of the French Revolution, according to a paper published in the Proceedings of the National Academy of Sciences last month. By quantifying the novelty of speech patterns and the extent to which those patterns were copied by subsequent speakers, the researchers illustrated how much of the important intellectual work of the revolution was initially carried out in committees, rather than in the whole assembly.

There are many patterns of collective behavior in biology that are easy to see because they occur along the familiar dimensions of space and time. Think of the murmuration of starlings. Loose groups of shoaling fish that snap into tight schools when a predator shows up. Then there are less obvious patterns, like those that the evolutionary biologist Jessica Flack tries to understand. In 2006, her graduate work at Emory University showed how just a few formidable-looking fighters could stabilize an entire group of macaques by intervening in scuffles between weaker monkeys, who would submit with teeth-baring grins rather than risk a fight they thought they would lose.

Cormac McCarthy is best known to the world as a writer of novels. These include Blood Meridian, All the Pretty Horses, No Country for Old Men, and The Road. At the Santa Fe Institute (SFI) he is a research colleague and thought of in complementary terms. At SFI we have been searching for the expression of these scientific interests in his novels and we maintain a furtive tally of their covert manifestations and demonstrations in his prose. Over the last two decades Cormac and I have been discussing the puzzles and paradoxes of the unconscious mind. Foremost among them, the fact that the very recent and "uniquely" human capability of near infinite expressive power arising through a combinatorial grammar is built on the foundations of a far more ancient animal brain. How have these two evolutionary systems become reconciled? Cormac expresses this tension as the deep suspicion, perhaps even contempt, that the primeval unconscious feels toward the upstart, conscious language. In this article Cormac explores this idea through processes of dream and infection. It is a discerning and wide-ranging exploration of ideas and challenges that our research community has only recently dared to start addressing through complexity science. I call it the Kekulé Problem because among the myriad instances of scientific problems solved in the sleep of the inquirer Kekulé's is probably the best known.