Sara Hooker
@sarahookr
Building intelligence that evolves @adaption_ai. Built @Cohere_Labs, @GoogleBrain, @GoogleDeepmind. ML Efficiency, Multimodal\lingual.
11.5K Following 64.4K Followers
Now’s prob a good time to mention that I’ll start as an Assistant Professor with appointments at @Kennedy_School and @HarvardEngineer in 2027, working on… AI evals! My lab will also work on monitorability, incident analysis, and verification to advance technical AI governance 1/
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Guess where I am in the world.
Very nice. Jumping to citations is personally what I find most disruptive about paper reading.
Research papers are filled with citations, but clicking on any of them makes you lose your place in the paper
We made citations much easier to navigate, with extra information on hover including authors and institutions
Now, you can hover over any in-text citation to instantly preview the referenced paper, jump directly to the full citation, and return to exactly where you left off with a single click
This includes quick access to the authors and their organizations, so you can understand the people behind the research without breaking your reading flow
Try it now on any paper at
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One hiring signal I’ve found useful: leave enough time for candidates to keep asking questions. Don't limit to just to last 5 minutes.
The first couple are usually prepared. The questions that follow often reveal the difference between someone casually exploring and someone genuinely curious and invested.
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Create your own data with a single line of code.
The journey of owning your intelligence starts with data, and it's never been easier.
How we judge and improve AI shapes what we build and how people experience it. These questions are central to how we think about adaptive intelligence at @adaption_ai, and I can't think of a better person to discuss them with than @sh_reya.
Join us and bring your questions!
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Hugo is absolutely the best, it was a joy to work with him at Google Brain and at @GoogleDeepMind. He's a tireless champion for all of the junior researchers in the machine learning world, and for their work.
Can't wait to read this profile, just know that it'll only be able to tell a small fraction of how much he means to all of us. ♥️
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@hugo_larochelle was an incredible and supportive PhD co-advisor who was there during my “explore-phase” of machine learning which spanned forgetful Atari agents and (the weirdest) training GANs on images of me training GANs. Finally I found my interest in large scale language models and have been following that gradient for the past 7 years.
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Such a pleasure talking with @ShivAroor and @ndtv.
We should have more accountable conversations about risk. If someone said a tsunami was coming tomorrow, we would ask for evidence.
10% extinction being thrown around without justification feels highly irresponsible to me.
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⚠️ Finally, someone puncturing the AI slowdown panic from Sam Altman, Dario Amodei and Elon Musk, calling out an alarm that looks increasingly cosmetic, conflicted & questionable.
Must hear @sarahookr’s views:
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When I started as a grad student in Yoshua's lab, I set myself a goal to understand @hugo_larochelle when he speaks French. He was genuinely helpful, I credit him for jump-starting my quebecois progression ;)
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Omg Sherbrooke not Sheffield.
But the lectures were still 🔥 independent of university origin.
Very happy to see this profile of @hugo_larochelle
Before I join Google Brain, I watched all his lectures from Sheffield. You have to remember at the time, all of the knowledge for deep learning belong to less then 100 people. He made it available to many more.
He became my PhD advisor many years later. Hugo is an exceptional researcher, has shaped so much of our ecosystem.
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It’s really unfortunate the current discussion about ai risk is also becoming politicized.
It’s the ultimate proof this is a value driven debate, and not an evidence based one.
Introducing the invent api.
Describe the dataset you want.
A few lines of code.
Returns a diverse and high quality training dataset. No terms that prevent you training on it. Your data. Your AI.
Plug it into all your auto research agents today 🔥🎉
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Last week we introduced Invent a Dataset. Describe what you need. Hit go.
Today, we’re making it even easier with the Invent API.
A few lines of code. AI ready training datasets in minutes.
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Very happy to see this profile of @hugo_larochelle
Before I join Google Brain, I watched all his lectures from Sheffield. You have to remember at the time, all of the knowledge for deep learning belong to less then 100 people. He made it available to many more.
He became my PhD advisor many years later. Hugo is an exceptional researcher, has shaped so much of our ecosystem.
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A more serious take on what is happening here. I am in an airport lounge so have some time.
Enterprises have made an uneasy truce with frontier labs over last few years: strict contracts that ban training on corporate data in exchange for letting employees use APIs.
The problem? Labs don't need to train on your raw data to copy IP.
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This is why most companies realize they have a small window of time to build their own intelligence.
Or just accept the arbitrary terms set by 3 private providers.
Most model trainings have failed outside of frontier labs.
Even inside frontier labs, knowing how to train for very different capabilities is often a matter of taste.
Today, we introduce AutoScientist by @adaption_ai which sets out to change that.
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