anilananth.bsky.social - Profile | ThreadSky | a Reddit-style client for Bluesky

I had a great time talking with @anilananth.bsky.social as part of the Simons Institute Polylogues. We cover universal learning, generalization phenomena, how transformers are both surprisingly general but also limited, and the difference between statistics and ML! www.youtube.com/watch?v=Aja0...

submitted 3 days ago • 0 comments

I went through my RL bookmarks, because it seems like finally the rest of the world has caught up to my world, I rediscovered this gem 💎 mpatacchiola.github.io/blog/2016/12... although I suspect nobody wants to learn RL this way now 😜

submitted 35 days ago • 1 comment

Everyone is talking about DeepSeek's impact on industry. But another huge impact is the leveling of playing field between academia and industry: if these efficiency numbers bear out, then academia can both use LLMs and study/research them at scale!

submitted 35 days ago • 1 comment

These two books, by @anilananth.bsky.social and @tomchivers.bsky.social, are the first two books in a very long time that I read in their entirety without significant pause or other diversion along the way. I cannot recommend them enough! #booksky #dataSkyence

submitted 39 days ago • 2 comments

As always, @rao2z.bsky.social has a way with words and speaks his mind in this Machine Learning Street Talk episode: "We should be looking for secrets of Nature, because Nature won't tell us. But we are now looking for secrets of OpenAI." youtube.com/clip/UgkxCH1...

submitted 39 days ago • 0 comments

This lecture is primarily about how animal brains have evolved, but in so doing Paul Cisek helps illuminate why artificial intelligence based on large-language models is fundamentally insufficent in reaching anything resembling human intelligence (perhaps even lampray intelligence).

submitted 44 days ago • 4 comments

tis excellent Thank you @anilananth.bsky.social

submitted 45 days ago • 1 comment

Philosophy & Theory of Neuroscience Conference 🧠 next week @ Chapman & available via zoom.Talks by @drmichaellevin.bsky.social @meganakpeters.bsky.social Newsome, Yassa, Roskies, Carolyn Parkinson, Frederick Eberhardt & many others! www.chapman.edu/scst/confere... #philsky #philsci #neuroskyence

submitted 50 days ago • 3 comments

A gentle reminder that you should always resist the temptation to tag a creative person into a negative review of their work. It doesn't matter what your intentions are, it's kind of an asshole maneuver. You are free to say whatever you like, of course. Making it their problem, however, is not kind.

submitted 55 days ago • 162 comments

As of January 2025, Kai Polsterer is the new Scientific Director of HITS, taking over from Tilmann Gneiting who had been Scientific Director for the last 2 years. The new deputy Scientific Director will be Rebecca Wade (@rebecca-wade.bsky.social). Read more here: ow.ly/HMlS50UBKUg Photos ©HITS

submitted 54 days ago • 0 comments

The Many Moods of Machine Learning If you feel overwhelmed by the jargon of machine learning and wonder how it all ties together, you aren’t alone. These are numerous important axes along which one can analyze ML. Here's a blog post with some intuitions : anilananthaswamy.com/why-machines...

submitted 58 days ago • 0 comments

Two books I enjoyed in 2024 Speaking with Nature: the origins of Indian Environmentalism by Ramachandra Guha @Ram_Guha Why Machines Learn by @anilananth.bsky.social (still reading this)

submitted 62 days ago • 0 comments

Thanks @jslbutler.bsky.social. You had to pick out a page on which I found my first typo/error :-) In the case of both MLE and MAP, the derivate is taken with respect to "theta" (it says "x" in the case of MLE on the page).

submitted 62 days ago • 1 comment

@anilananth.bsky.social is rising up my ranks of those helping to make the complexity of AI intelligible to non-experts, as this post illustrates. I look forward to reading his book in 2025!

submitted 62 days ago • 0 comments

My favorite books, 2024

submitted 65 days ago • 34 comments

I’m not going to lie. Best side effect of @bsky.app is some of the best book recommendations in years! Now reading “Why Machines Learn”.

submitted 64 days ago • 0 comments

The Theoretical Minimum (for Machine Learning) Linear Algebra, Calculus, and Probability & Statistics often get mentioned as the minimum math you need to start on your machine learning journey. But why these disciplines? Here’s a blog post that explains why... anilananthaswamy.com/why-machines...

submitted 63 days ago • 1 comment

The #SciComm starter pack is almost full. Make sure to revisit it as l’ve added lots of folk this week. Do share it and flag up if you’d like added. go.bsky.app/VJQzace

submitted 104 days ago • 161 comments

Today's Book: Why Machines Learn: The Elegant Math Behind Modern AI - Anil Ananthaswamy @anilananth @anilananth.bsky.social @DuttonBooks @duttonbooks.bsky.social @MLStreetTalk #bookoftheday

submitted 65 days ago • 1 comment

A must read! Insightful, elegant, rigorous, yet always accessible.

submitted 73 days ago • 0 comments

1/5 For all the fuss about who invented backpropagation, it's worth noting that the Frank Rosenblatt--who designed the first single-layer artificial neural network, or the perceptron, back in the late 1950s--identified the problem of training multi-layer perceptrons (MLPs).

submitted 77 days ago • 2 comments

I loved the book, Why Machines Learn, by @anilananth.bsky.social I’d like everyone to understand how AI systems work to reduce misconceptions about their capabilities. To demystify artificial intelligence for my nontechnical students, I made this video youtu.be/kl0-xCP0ghE

submitted 79 days ago • 0 comments

💯 Hallucination is totally the wrong word, implying it is perceiving the world incorrectly. But it's generating false, plausible sounding statements. Confabulation is literally the perfect word. So, let's all please start referring to any junk that an LLM makes up as "confabulations".

submitted 82 days ago • 18 comments

The latest Science-in-Parallel episode dropped, in which I talk of this epochal moment in human history (the coming of LLMs), the 2024 NobelPrize for Hinton and Hopfield, and the history of neural networks, besides the writing of WHY MACHINES LEARN. scienceinparallel.org/2024/12/anil...

submitted 82 days ago • 0 comments

What? Linear algebra and calculus and machine learning for the holidays! Might a math-y book be a good gift for the holidays? I hope so :-) “A masterpiece.”-Geoff Hinton “A masterful work.”-Melanie Mitchell US www.penguinrandomhouse.com/books/677608... UK www.penguin.co.uk/books/446849...

submitted 86 days ago • 0 comments

Weekend read 📖🧪 Ananthaswamy, A., 2024. How close is AI to human-level intelligence? Nature 636, 22–25. doi.org/10.1038/d415... @anilananth.bsky.social

submitted 86 days ago • 1 comment

#blrlitfest on Dec 15th: I'll anchor a panel on "How Science Speaks" with science writers @anilananth.bsky.social and @simonsinghnerd.bsky.social, astrophysicist Annapurni Subramaniam, and structural biologist Venki Ramakrishnan. It's free, do attend! bangaloreliteraturefestival.org/year-2024/sc...

submitted 87 days ago • 2 comments

I wanted to make my first post about a project close to my heart. Linear algebra is an underappreciated foundation for machine learning. Our new framework CoLA (Compositional Linear Algebra) exploits algebraic structure arising from modelling assumptions for significant computational savings! 1/4

submitted 88 days ago • 3 comments

Recently answered @anilananth.bsky.social's questions for Nature. No matter when it arrives, AGI and the road to reach it will both help tackle thorny problems (e.g. climate change and diseases), and pose huge risks. Understanding and transparency are key. www.nature.com/articles/d41...

submitted 87 days ago • 0 comments

While I'm recommending books (starting with my own, of course 😋), here's a thread of some others I've enjoyed recently! 📚😊

submitted 88 days ago • 6 comments

Existing large language models are unlikely to gain human-level intelligence on their own What important features are missing? Is it possible to develop models that can guarantee the safety of their own behaviour? Great article by @anilananth.bsky.social www.nature.com/articles/d41...

submitted 89 days ago • 0 comments

A very nice article! Take a look if you need more examples to convince your friends that the current AI will not take over the world.* *unless some geniuses in power will give a model full control over sensitive systems and will be surprised that its various limitations caused a crisis

submitted 89 days ago • 0 comments

In case you want to follow some if the world's leading thinkers about thinking (human and artificial)...

submitted 89 days ago • 0 comments

Thank you Nature and @anilananth.bsky.social for this great feature on LLMs and AGI (and for highlighting our work arxiv.org/abs/2406.03689)

submitted 89 days ago • 0 comments

“Most of my life, I thought people talking about AGI are crackpots,” says Subbarao Kambhampati, a computer scientist at Arizona State University in Tempe. “Now, of course, everybody is talking about it. You can’t say everybody’s a crackpot.” 😅

submitted 90 days ago • 1 comment

I know, the market is saturated with everyone’s opinion on AI. But @anilananth.bsky.social voice is penetrating. A sharp write up.

submitted 90 days ago • 0 comments

Will simply scaling up LLMs get us to AGI? My feature for Nature, w/ inputs from @fchollet.bsky.social @rao2z.bsky.social @yoshuabengio.bsky.social @melaniemitchell.bsky.social @dileeplearning.bsky.social @andrewgwils.bsky.social, Raia Hadsell, Keyon Vafa, Karl Friston www.nature.com/articles/d41...

submitted 90 days ago • 5 comments

Characteristically excellent piece by @anilananth.bsky.social on how close LLMs/AI are to AGI. Here AGI is defined as "a machine capable of the whole range of cognitive tasks that human brains can handle." But AGI boosterists (not Anil!) never really mean that... www.nature.com/articles/d41...

submitted 90 days ago • 1 comment

"ChatGPT taught us something that I don't think we'd have learned any other way...which is that, in some sense, language is a closed system sort of in the same way that arithmetic is closed under integers." @tonyzador.bsky.social the BrainInspired podcast. Really worth a listen! youtu.be/9C0qkxu0UyE

submitted 95 days ago • 0 comments

"As far as I can tell, transformers are almost a counter-example to the successes of Neuro-AI" @tonyzador.bsky.social insights on the Brain Inspired podcast, in conversation with Paul Middlebrooks, are an absolute treat! youtu.be/9C0qkxu0UyE

submitted 95 days ago • 0 comments