A nice article by @anilananth.bsky.social on using AI to explore design spaces, find unexpected solutions, (re)discover symmetries, and propose new relationships featuring @yuqirose.bsky.social @mariokrenn.bsky.social & myself. Note AI ≠ LLMs in this piece. www.quantamagazine.org/ai-comes-up-...

"AI Comes Up with Bizarre Physics Experiments. But They Work." by @anilananth.bsky.social @quantamagazine.bsky.social: www.quantamagazine.org/ai-comes-up-... Covering our work with Rana Adhikari @ligo.org on discovering GW detectors & work by @yuqirose.bsky.social & @kylecranmer.bsky.social on ...

Thank you, David

Finished reading @anilananth.bsky.social's book, Why Machines Learn. This was an excellent read. I feel that the context and history for which science develops helps my understanding. His prose and explanations were better than anything else I have yet to encounter in my Computer Science education.

SFO has a nice collection of AI books @adambecker.bsky.social @anilananth.bsky.social @emilymbender.bsky.social @alexhanna.bsky.social @summerfieldlab.bsky.social @sayash.bsky.social‬ @randomwalker.bsky.social‬

1/4 These days most writers, including me, get asked: "Will you use AI to help you write?" My answer is: No. Not because I'm inherently against the idea, but because it undercuts the very reason I became a writer...

"Why machines learn" by @anilananth.bsky.social is an amazing book that teaches the fundamental math concepts behind machine learning and artificial intelligence. I lost count of the "aha!" moments I experienced while reading this masterpiece. I loved it! #AI #math

When I proposed WHY MACHINES LEARN in Oct 2020, to my then editor Stephen Morrow, @carpenter512.bsky.social, I was sure he'd say no to a book full of math & equations. But he saw something in the proposal that even I hadn't and said yes, and I'm grateful for that! Got to thank him today in person.

I have just started reading this fantastic book by @anilananth.bsky.social. I look forward to diving into the hardcore #math behind machine learning! It will be a challenging journey, but a rewarding one. 🤖

Looking forward to reading this @rowhoop.bsky.social ! Thanks...

Thank you @ganyet.bsky.social. I love this line about WHY MACHINES LEARN: "This book is like an invitation to enter Mago Pop's workshop to realize that magic doesn't exist: that it's all mathematics, engineering, and a lot, a lot of human intelligence." I had to look up Mago Pop and Sant Jordi :-)

The Centrality of Bayes's Theorem for Machine Learning. It’s hard to overstate just how important Bayes’s Theorem — something that Thomas Bayes came up with in the 1700s — is for machine learning. But the theorem challenges our intuitions. Here’s a brief intro: anilananthaswamy.com/why-machines...

Came out of my AL/ML bubble to write a 'crisis in cosmology' story, about a new study that uses TRGB stars to scale a new cosmic distance ladder to measure the Hubble constant; the tension persists...for @scientificamerican.bsky.social @leebillings.bsky.social scientificamerican.com/article/the-...

"For the first time, we can...get performant neural networks that mimic complex human & animal cognition," said @suryaganguli.bsky.social speaking on the symbiosis of AI & neuroscience at the Simons Institute. "That's remarkable and exciting. Caveats...to follow" simons.berkeley.edu/talks/surya-...

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...

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 😜

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!

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

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...

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).

tis excellent Thank you @anilananth.bsky.social

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

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.

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

In good company with @anilananth.bsky.social's book!

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...

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)

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).

@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!

My favorite books, 2024

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”.

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...

Have added you to my new list Anil, thanks for flagging go.bsky.app/AFSGdUQ

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

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

Why Machines Learn: The Elegant Math Behind Modern AI, by Anil Ananthaswamy @anilananth.bsky.social . Writing a book about math that is compelling and includes enough math to actually give a feel for the topic is hard. This book mixes history and math in a way that I found quite compelling.

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

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).

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

💯 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".

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...

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...

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

#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...

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

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...

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

Why Machines Learn - The Elegant Math Behind Modern AI By Anil Ananthaswamy @anilananth.bsky.social - a fantastic, accessible introduction to the math behind AI www.penguinrandomhouse.com/books/677608...

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...

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