VB

[Sundar Pichai @sundarpichai]

An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells. 

With more preclinical and clinical tests, this discovery may reveal a promising new pathway for developing therapies to fight cancer.

[François Chollet @fchollet]

The idea that we will automate work by building artificial versions of ourselves to do exactly the things we were previously doing, rather than redesigning our old workflows to make the most out of existing automation technology, has a distinct “mechanical horse” flavor

[Patrick Collison @patrickc]

Over the past week, @arcinstitute published three new discoveries that I’m very proud of.

• The world's first functional AI-generated genomes. Using Evo 2 (the largest biology ML model ever trained, which Arc released in partnership with @nvidia in February), Arc scientists took advantage of the fact that Evo 2 is a generative model to produce completely new sequences for complete phage genomes. That is, they used AI to produce wholly new, never-before-seen-by-nature genomes. They experimentally synthesized these genomes and showed that these AI-generated phages actually work, killing E. coli bacteria with high efficacy.

• Germinal, an AI system for creating new antibodies. Antibody design is one of the great problems of medical biology given their obvious importance and usefulness for creating therapeutics. (Antibodies are tiny particles that help the immune system identify pathogens and other harmful intruders. See also the recent Works in Progress article on this topic: [1].) Today, designing effective antibodies is very expensive and slow. Germinal is a cheap and fast way to produce drug candidates, with success rates of up to 22%. This means that one can go from having to screen thousands of candidates in the lab to screening perhaps a few dozen. It's early, but I suspect that better methods for designing antibodies will be a very big deal for disease treatment in the coming years.

• Today, we published a paper showing that “bridge editing”, which Arc scientists first introduced last year, can make precise edits in human cells that are up to 1 million base pairs long, and without relying on intrinsically unpredictable cellular repair machinery (which CRISPR requires, often leading to editing mistakes). They showed that it’s possible to use this editing to cut out the DNA repeats that cause Friedreich’s ataxia (a neurological disease), an approach which should also be relevant to Huntington’s and other similar disorders. One particularly cool thing about it is that it’s possible to specify every nucleotide within the extended editing window, meaning that recursive bridge edits could potentially be a powerful way to reprogram even biological traits that are caused by many genetic mutations. (Genetic therapies today target single mutations.)

Arc is pretty new. Its doors opened in mid 2022, and it's now 300 people. I’m excited about these discoveries because they show that a number of our hopes in starting Arc are starting to pay off:

• AI/ML and computation are at the center of all three. That is obviously true for the first two, but the mobile genetic element behind bridge editing was also discovered as a result of a complex computational search. One of our premises in starting Arc was the belief that the intersection of software/AI and experimental wet lab biology should enable great things. (And besides requiring great computational work, all three of these also required strong wet lab work, tightly coordinated under a single physical roof.)

• We’ve been toying with the idea that a handful of technologies are enabling a new kind of “Turing loop” in biology: sequencing advances (including single-cell sequencing) give us new ways to read; transformers and AI gives us new ways to think; and functional genomics (such as bridge editing) give us new ways to ways to write. This trio of discoveries span each part of this loop, and we’re hopeful that there’ll be compounding returns in improving each part.

• Arc is a non-profit, which we hoped would make collaborating with others easier, since we can avoid worries about financial return. This is indeed proving important, and all three of these projects involved close partnership with others. Germinal was done in partnership with @SynBioGaoLab at Stanford; Evo 2 was trained in partnership with Nvidia. Bridge editing was jointly published with a structure from the @HNisimasu Lab at the University of Tokyo. Arc tries to make its discoveries useful (see the Evo 2 Designer[2]) for others, and the code behind the computational projects is open source, hopefully making it easy for others to spot new opportunities for collaboration and partnership in the future. Most of all, Arc itself is an ongoing collaboration with @UCSF, @UCBerkeley, and @Stanford.

• With Arc, we wanted to enable better bottom-up and top-down work. With the fully flexible, no-strings-attached funding that we provide to investigators, we want to enable completely unexpected discoveries and avenues of investigation. With our institute initiatives (around creating a virtual cell and curing Alzheimer’s), we want to bring to bear a scale and level of coordination that’s usually difficult in basic science. Germinal is a “surprise” discovery that didn’t involve top-down coordination, whereas Evo 2 is the result of ambitious high-level planning and funding.

• Humanity has never cured a complex disease (a category that includes most neurodegenerative diseases, most cancers, and most autoimmune diseases), and my hope is that Arc can help change this. It’s also clear that AI will revolutionize biology, and I hope that Arc can effectively aggregate the ingredients needed to fully capitalize on its promise. I’m biased, but I think some of the coolest biology in the world is currently being done at Arc. (They’re always hiring if you’re interested.)

While I’m a cofounder of Arc, I spend almost all my time on Stripe, where we spend our time building economic infrastructure for the internet. All credit for Arc’s progress should go to the remarkable scientists and staff who’ve made Arc their home or who’ve chosen to collaborate with us. (You can read more about these particular discoveries in these threads: [3], [4], [5].) I’m also very grateful to the amazing Stripe employees who’ve built the company that makes Arc’s ongoing work possible, and to the millions of customers who’ve chosen to partner with Stripe. John and I feel fortunate to be able to support Arc’s work to the extent that we do.

Maybe this is reading too much into it, but I sometimes feel that there’s a commonality between @arcinstitute and @stripe. Both biology and economic infrastructure involve reasoning about complex systems with many levels of emergent effects, and in both cases building the right tools can have almost unboundedly large benefits. Even though progress in both tends to take a long time, it also feels like the next five years in both will be some of the most interesting in living memory.

(If economic infrastructure is your jam, we have a whole slew of fantastic announcements coming up at Stripe Tour in New York next week. Tune in!)

[Jannik Sinner @janniksin]

Yesterday I didn’t feel great and I’m really sorry to disappoint you. Thank you so much to everyone supporting me on-site and from home, your support means the world ❤️ Congrats to @carlosalcaraz and his team, you’re having an incredible season and I wish you all the best for what’s ahead. Now it’s time to rest a couple of days before getting back to work 🙏🏻 @CincyTennis

[Elon Musk @elonmusk]

You can cut & paste your entire source code file into the query entry box on https://t.co/EqiIFyHFlo and @Grok 4 will fix it for you!

This is what everyone @xAI does. Works better than Cursor.

[Elon Musk @elonmusk]

Good one sigh

[François Chollet @fchollet]

The main benefit of having extensive programming experience isn't the ability to write software (which is a bit of a commodity -- you could hire someone to do it), it's how it changes the way you think.

[Nature is Amazing ☘️ @AMAZlNGNATURE]

Meanwhile in Japan...

[François Chollet @fchollet]

There's a big difference between solving a problem from first principles vs applying a solution template you previously memorized. It's like the difference between a senior software engineer and a script kiddie that can't code.

A script kiddie that has a gigantic bank of scripts might give you the illusion that they can program on their own -- until they encounter a problem for which they don't have the right script. And that's exactly what you see with LLMs. They're interpolative databases of millions of text-completion vector programs. They can do a lot, as long as they're in known territory. But give them something a bit unfamiliar, like an ARC task, and they fail.

[Vala Afshar @ValaAfshar]

Life lessons from @rogerfederer (must watch)

1 Effortless is a myth
2 Belief in yourself has to be earned
3 Grit > Gift
4 Discipline is talent
5 Trust and loving the process is talent
6 You can do your best and still lose
7 Life is bigger than the court

[Carlos Alcaraz @carlosalcaraz]

FINAL!!!!!! ❤️ @rolandgarros

📸 Getty

[Elon Musk @elonmusk]

@cb_doge @neuralink I should mention that the Blindsight implant is already working in monkeys.

Resolution will be low at first, like early Nintendo graphics, but ultimately may exceed normal human vision.

(Also, no monkey has died or been seriously injured by a Neuralink device!)

[Elon Musk @elonmusk]

@neuralink Long-term, it is possible to shunt the signals from the brain motor cortex past the damaged part of the spine to enable people to walk again and use their arms normally

[Garry Tan @garrytan]

Most v1 pitches only manage to describe what someone is trying to build, sometimes with a comparison to a well known well funded alternative, but misses the far more important part:

How are you better? How might it be better/faster/cheaper? Is there a quantified measure?

[Emmanuel Ameisen @mlpowered]

Claude 3 Opus is great at following multiple complex instructions.

To test it, @ErikSchluntz and I had it take on @karpathy's challenge to transform his 2h13m tokenizer video into a blog post, in ONE prompt, and it just... did it

Here are some details:

[Eren Bali @erenbali]

I’m with Peter Thiel. Failed startups don’t teach you much.

Best learning comes from succeeding after a long rocky road (Nvidia, Tesla, Apple, Amazon)

A distant second is lucky home runs (Google, Facebook, Microsoft)

If you almost succeeded and then spectacularly failed it might teach you about a few fatal mistakes.

But failure is the default outcome. Founders attribute it to all kinds of things and often come up with the worst “lessons”

[VB @vybs]

@MADdelaRosa yes

[#AusOpen @AustralianOpen]

Jannik is crowned the champion of the @AustralianOpen. Congratulations @janniksin on winning your first Grand Slam® title. #Rolex #AusOpen #Perpetual

[Tim Cook @tim_cook]

A huge thank you to the incomparable David Hockney for helping us get into the spirit of the season! Your new artwork, Bigger Christmas Trees, created on iPad looks incredible on London’s Battersea Power Station. Happy holidays everyone!

[Andrew Ng @AndrewYNg]

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by @jerryjliu0 and @datta_cs of @llama_index and @truera_ai , this teaches advanced techniques that help your LLM generate good answers.

Topics include:
- Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context.
- Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM.
- Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically.

Please check out the course!
https://t.co/O23Z2CDldk