Machine Learning Street Talk
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Videos
Michael I. Jordan on the new MLST. Four things: > AGI is a PR term. It confuses young people. > Discourse is bipolar, either alarmist or exuberant, this is in his words "so demoralizing" for 20- and 25-year-old researchers. > ML's methods came from statistics and operations research, NOT the AI tradition. > Data markets are Stackelberg games, not optimisation problems. A lot of ML researchers have never computed an equilibrium. Michael I. Jordan is a no-nonsense original gangster of the field and was described by Science magazine, back in 2016 as the most influential living computer scientist.
Machine Learning Street Talk59,431 просмотров • 16 дней назад
A masterclass from Jeremy Howard on why AI coding tools can be a trap -- and what 45 years of programming taught him that most vibe coders will never learn. - AI coding tools exploit gambling psychology - The difference between typing code and software engineering - Enterprise coding AND prompt-only vibe coding are "inhumane" i.e. disconnecting humans from understanding-building - AI tools remove the "desirable difficulty" you need to build deep mental models. Out on MLST now!
Machine Learning Street Talk169,625 просмотров • 3 месяцев назад
One of the most exciting new AI companies is Sakana AI in our opinion. Their CTO and their Co-founder Llion Jones Llion Jones is now saying it's time to move beyond transformers even though he was one of the 8 original inventors at Google. They are now investigating the next significant step forwards, and have a NeurIPS 2025 spotlight paper called "continuous thought machines" (CTM) which may well be just that (first author Luke Darlow). They just landed their Series B, with the legendary hardmaru as their CEO. They are actively investigating neuroevolution approaches which I strongly believe will be a big part of the future of AI -- and they publish their research in the open. Keep an eye on Sakana! 👌 Interview with Llion and Luke dropping today on MLST.
Machine Learning Street Talk186,883 просмотров • 6 месяцев назад
Professor Yi Ma (Yi Ma) discusses his new open-source book "Learning Deep Representations of Data Distributions" The thesis: Intelligence -- natural or artificial -- can be derived from just TWO principles: Parsimony and Self-Consistency.
Machine Learning Street Talk58,270 просмотров • 5 месяцев назад
AI is so smart, why are its internals 'spaghetti'? We spoke with Kenneth Stanley and Akarsh Kumar (MIT) about their new paper: Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis. Co-authors: Jeff Clune Joel Lehman
Machine Learning Street Talk84,353 просмотров • 11 месяцев назад
The Kaleidoscope Hypothesis by François Chollet
Machine Learning Street Talk36,756 просмотров • 4 месяцев назад
Iman Mirzadeh from Apple Iman Mirzadeh wrote the famous GSM-Symbolic paper a couple of months back which argued that LLMs are learning surface statistics and not genuinely reasoning due to their sensitivity to distractors and out of distribution examples.
Machine Learning Street Talk87,646 просмотров • 1 год назад
We just released our interview with the father of Generative AI - Jürgen Schmidhuber! The G, P, and T in "ChatGPT" (GPT means "Generative Pre-Trained Transformer") go back to Juergen's work of 1990-91 when he published what's now called "Unnormalised Linear Transformers," "Self-Supervised Pre-Training" for deep learning with long texts, and "Generative Adversarial Networks" for Artificial Curiosity. Remarkably, principles of both Transformers and LSTMs date back to 1991, the only palindromic year of the 20th century! Transformers are easier to parallelise, but LSTMs can solve problems which are unsolvable by Transformers. In this first part of our two part show, we discuss the history and the future of the field, with a focus on abstract planning, reasoning, and "learning to think." It's just dropped on MLST!
Machine Learning Street Talk100,051 просмотров • 1 год назад
Today Google DeepMind released AlphaEvolve: a Gemini coding agent for algorithm discovery. It beat the famous Strassen algorithm for matrix multiplication set 56 years ago. Google has been killing it recently. We had early access to the paper and interviewed the researchers.
Machine Learning Street Talk58,495 просмотров • 1 год назад
I finally got to meet François Chollet in person recently to interview him about ARC Prize, intelligence vs memorization, human cognitive development, learning abstractions, limits of pattern recognition and consciousness development. These are the best bits. Full show released tomorrow
Machine Learning Street Talk79,058 просмотров • 1 год назад
This is life arising from non-living matter ("abiogenesis") in a computer program and it looks just like a phase transition in statistical mechanics. Some argue grounding and special properties of chemstry are required, but what if life is an "inevitability of computation"? Blaise Agüera (@blaiseaguera.bsky.social)'s BFF experiments suggest "life emerges" from randomness via gelation dynamics 🧬
Machine Learning Street Talk31,660 просмотров • 6 месяцев назад
Exciting to see folks talking about Yann LeCun's Energy Based Models again! Here is a mini-master class from Dr. Jeff Beck on our recent show. Also: - PCA throws away low variance data, but in brains, low variance often holds the most important signals - Did the complex combinatorics of olfaction evolve into our frontal cortex's planning ability? - AGI is a misnomer, we want collective specialised intelligences - A robot shouldn't just look at a beach ball; it should poke and prod like a child to learn physics - Can we identify an agent by just looking at its behaviour? Hint: No! (pre-calculated lookup table can look like deep planning from the outside)
Machine Learning Street Talk20,901 просмотров • 4 месяцев назад
The most epic AI showdown ever? With Gary Marcus Daniel Kokotajlo and Dan Hendrycks - dropping now on MLST.
Machine Learning Street Talk41,668 просмотров • 11 месяцев назад
"Superintelligence vs Extinction" - those are your two options" Professor Michael I. Jordan, a pioneer in the field says the AI discourse in 2025 is really hurting young researchers. He argues that it's demoralising, that bright futures are being snuffed out and that there is "zero" economic thinking behind it.
Machine Learning Street Talk24,340 просмотров • 6 месяцев назад
