We analyze the meta-learning of the initialization and step-size of learning algorithms for piecewise-Lipschitz functions, a non-convex setting with applications to both machine learning and algorithms. Starting from recent regret bounds for the exponential forecaster on losses with dispersed discontinuities, we generalize them to be initialization-dependent and then use this result to propose a practical meta-learning procedure that learns both the initialization and the step-size of the algorithm from multiple online learning tasks. Asymptotically, we guarantee that the average regret across tasks scales with a natural notion of task-similarity that measures the amount of overlap between near-optimal regions of different tasks.

When I was a child, computers were a fixture in my home, from the giant Atari on which I learned my ABCs, to the Commodore Amiga that my dad used for his videography business, to the PC towers that facilitated my first forays onto the internet. But tech was still a niche hobby back then. Even in college in the late 1990s and early 2000s, many of my friends got by just fine without computers. For people in college now--namely, my students--things are decidedly different. Gadgets are everywhere, and are increasingly designed to insert themselves into every aspect of our consciousness, colonizing every spare moment of our time and attention.

Exp. Psychology, Oxford ELSC, HebrewU Department of Computing Brain Mind Institute, EPFL Gatsby Unit, UCL Imperial College London Andrew M. Saxe Christopher Summerfield Ali Hummos

Good Job is Slate's advice column on work. Have a workplace problem big or small? I am a hard-line hater of generative AI (ChatGPT, Midjourney, etc.). I think it's bad for the environment and bad for society. It burns water resources, exploits workers in the global south, plagiarizes art and writing, and eliminates badly needed entry-level jobs.

There's always a catch: These free courses do not come with a shareable certificate of completion or graded assignments/exams. But you can start learning at a pace that suits you, so there's really nothing stopping you from enrolling. Find the best free online courses from MIT on edX.

Hannah, a former teacher, joins'Fox & Friends' to explain why she left the classroom, saying AI tools are making it difficult to teach. A former high school English teacher went viral this week after posting a candid video on social media announcing she was quitting the teaching profession because of how technology was "ruining" education. In her video, which reached over 1 million views on TikTok, Hannah explained how AI tools have made teaching more difficult because students rely on technology to do the work for them and are unmotivated to put in effort themselves. She said that kids do not know how to read because of read-aloud tools, and have short attention spans because of the "high stimulation" of social media. "They want to use [technology] for entertainment. They don't want to use it for education," she said in a TikTok video which reached over 1 million views.

This essay was adapted from Phil Christman's newsletter, the Tourist. Before 2023, my teaching year used to follow a predictable emotional arc. In September, I was always excited, not only about meeting a new crop of first-year writing students but even about the prep work. My lesson-planning sessions would take longer than intended and yet leave me feeling energized. I'd look forward to conference week--the one-on-one meetings I try to hold with every student, every term, at least once--and even to the first stack of papers.

These free online courses don't include certificates of completion or direct instructor messaging, but you still get unrestricted access to all the video content. You can learn at a pace that suits you, so there are no stressful deadlines. Find the best free AI courses on Udemy.

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This post contains a list of the AI-related seminars that are scheduled to take place between 5 May and 30 June 2025. All events detailed here are free and open for anyone to attend virtually. Gurobi Machine Learning Speaker: Roland Wunderling (Gurobi Optimisation) Organised by: Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list. Beyond Returns: A Candlestick-Based Approach to Covariance Estimation Speaker: Yasin Simsek (Duke University) Organised by: Statistics and Machine Learning in Finance, University of Oxford Join the mailing list to receive notifications about the seminar series. Robust and Conjugate Gaussian Processes Regression Speaker: François-Xavier Briol (University College London) Organised by: Finnish Center for Artificial Intelligence Zoom link is here.