Ramanan Sekar @_ramanans

Scientific terms should have precision. If we use the terms VLM, VLA, WAM in an indiscriminate fashion, as is becoming common in robotics, we are not helping clarity in communication. Let's keep the historical origins of these terms in mind. VLMs arose as multimodal extensions of LLMs-the training was for tasks like VQA (VIsual Question Answering). These capture the static semantics of the scene behind an image. No dynamics. World Models (e.g. @ylecun , Ha & Schmidhuber 2018) on the other hand are primarily dynamics models, which go back to control theory -1960 (Bellman, Kalman etc.) This makes them natural for robotics planning / policies- I am in a state s, what action a should I perform to get to state s'. In classical control, these models were written down a priori by modeling the physics of the system; today we think of them as learned neural networks trained from temporal data e.g. video, robot trajectories. But the concept is the same. We shouldn't mix this concept with VLMs.

San Francisco in 1849

Sam D'Amico @sdamico

a year ago

City that entirely runs on random gold rushes, gets yet another one

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@real_vaishak Congrats, Vaishak!

RL is not all you need, nor attention nor Bayesianism nor free energy minimisation, nor an age of first person experience. Such statements are propaganda. You need thousands of people working hard on data pipelines, scaling infrastructure, HPC, apps with feedback to drive benchmarks and data, tons of research and engineering on generative models, data mixtures, ablations, RL/selftraining, etc etc and we will probably need lots of people working hard to figure out safety, causal world models, awareness, models that create abstractions comparable to infinity and zero and use these to predict the existence of things like black holes and suggest experiments to verify such hypothesis, or come up with novel engineering designs to generate energy more efficiently, robotics, etc etc. It takes thousands of people and many ideas. In the end some simple ideas might become obvious but such obviousness only happens in retrospect. Yes, there is a bitter lesson but if we had followed it, we’d still be doing linear regression with RL. Let’s not oversimplify, but rather honour the research and engineering of thousands of people. Also, people keep rewriting history. When our language understanding start up (darkbluelabs) was acquired by Google about 10 years ago, we joined DeepMind, where the AGI documents were all about concepts, RL, episodic memories and made it clear that there was no room for language. To be honest, back then such a position wasn’t so crazy. Now it seems silly, but only because of the benefit of hindsight. There’s no 1 or 10 heroes in the history of AI. There’s many 1000s of hard working students, profs, engineers, operations and support people, product folks, managers, even hedge funds among others. Let’s honour the whole community and not just ceos or the philosophers of Bayes, RL, deep learning, etc. I look forward to learning from the next generation and seeing what they will achieve. To them: Don’t buy the existing narratives blindly, innovate. Remember that just like mathematics, AI will advance one grave at the time.

every time I open Spotify more tracks are grayed out, the songs are no longer available... music slowly became the least "file over app" part of my digital life my teenage self understood this better — nothing beats your own collection of MP3s

@mariofilhoml it is up to all of us to bring. it. back. say no to professional sponsored influencers with hyper-optimized content. say yes to boutique anons from the internet speaking their mind on their little corner of the internet.

We've completely lost the plot. The pursuit of a career in the arts is supposed to take time. It's supposed to require endless practice. Anything that comes quickly often goes away just as fast. If you don't enjoy making music, you should question why you're doing it.

Michael Patti @mpatti

2 years ago

“It’s not really enjoyable to make music now… it takes a lot of time, it takes a lot of practice, you have to get really good at an instrument or really good at a piece of production software. I think the majority of people don’t enjoy the majority of time they spend making

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this bit from the joe rogan episode with quentin tarantino and roger avary really resonated with me tarantino shares how he lost sight of his dream in his 20s, and seeing a colleague hitting 30 made him realize his mistake. sharing for anyone who is feeling lost in their 20s:

I would like to think about this as an invitation that physics extends towards AI/ML to think like physicists: -"Let's figure out how the world works". This is unlike computer scientists, who often see worst cases and adversaries. Nor mathematicians who need theorems everywhere.

The Nobel Prize @NobelPrize

2 years ago

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

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ML is so attractive to young physicists because many physics grads aren't really looking for physics, but for our times' great challenge. My generation still believed LHC was about to give us the secrets of the universe. We then found our own quantum mechanics in ML

Sad to see so many promising AI startup throwing the towel. Are we all back to getting rich fast? Or did those founders all believe that hype is business success? Sad.

Everyone is going crazy about this work. It is thought information theory is the answer to everything. Shannon gets fed up and writes a bulletin to curb that. Can’t think of any other examples in science like this. Do you?

"Perfection is impossible. In the 1,526 singles matches I played in my career, I won almost 80% of those matches. But what percentage of points did I win? 54% In other words, even top ranked tennis players win barely more than half the points they play. When you lose ever second point on average, you learn not to dwell on every shot. You teach yourself to think: 'Okay, I double faulted...it's only a point.' 'Okay, I came to the net and I got passed again...it's only a point.' Even a great shot, an overhead backhand smash that ends up on ESPN's top 10 playlist – that too is just a point. Here's why I'm telling you this. When you're playing a point, it has to be the most important thing in the world. And it is. But when it's behind you, it's behind you. This mindset is crucial – because it frees you to fully commit to the next point with intensity, clarity, and focus." –@rogerfederer

AI researchers seek to understand intelligence well enough to create beings of greater intelligence than current humans. Reaching this profound intellectual milestone will enrich our economies and challenge our societal institutions. It will be unprecedented and transformational, but also a continuation of trends that are thousands of years old. People have always created tools and been changed by them; this is what humans do. The next big step is to understand ourselves. This is a quest grand and glorious, and quintessentially human.

RIP Jim Simons

I can't escape the feeling that those leading the charge for AI music have a deep-seated hatred of music.

By assuming that technical talent is fungible and interchangeable, a previously-successful engineering company will be run into the ground... ...along with a few of its own products and everyone on board. It's astonishing how certain types of managers will prefer less-talented-but-more-controlable scientists and engineers over more brilliant and creative ones who may be more difficult to control, somewhat unpredictable, and sometimes annoying. Here is the thing: scientific and technical breakthroughs are unpredictable by management, and progress often comes from folks with unconventional personalities and backgrounds. As I was told by my management at Bell Labs: "You don't have to be crazy to work here, but it helps" 😄

The Nobel Prize @NobelPrize

2 years ago

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

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“Coding” was never the source of value, and people shouldn’t get overly attached to it. Problem solving is the core skill. The discipline and precision demanded by traditional programming will remain valuable transferable attributes, but they won’t be a barrier to entry. Many times over the years I have thought about a great programmer I knew that loved assembly language to the point of not wanting to move to C. I have to fight some similar feelings of my own around using existing massive codebases and inefficient languages, but I push through. I had somewhat resigned myself to the fact that I might be missing out on the “final abstraction”, where you realize that managing people is more powerful than any personal tool. I just don’t like it, and I can live with the limitations that puts on me. I suspect that I will enjoy managing AIs more, even if they wind up being better programmers than I am.

The Nobel Prize @NobelPrize

2 years ago

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

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One of my favourite letters, insightful & humane, from Richard Feynman to a former student who was having a rough time I was reflecting on it as an argument in favour of scope insensitivity, or even in favour of smaller problems:

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The future should be beautiful