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Discovery Loop

discoveryloop.com

954 pointsxtreak29599 comments

From Jeff's twitter post:

> Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. We’ll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen <at>NAE Grand Challenge problems. We think doing this well requires strong expertise in machine learning as well as large-scale systems.

Those 14 are:

NAE Grand Challenges for Engineering

1. Make Solar Energy Economical

2. Provide Energy from Fusion

3. Develop Carbon Sequestration Methods

4. Manage the Nitrogen Cycle

5. Provide Access to Clean Water

6. Restore and Improve Urban Infrastructure

7. Advance Health Informatics

8. Engineer Better Medicines

9. Reverse Engineer the Brain

10. Prevent Nuclear Terror

11. Secure Cyberspace

12. Enhance Virtual Reality

13. Advance Personalized Learning

14. Engineer the Tools of Scientific Discovery

They make many bold promises, but their core goal is neatly encapsulated on the website:

"Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today."

This is the same goal as every other AI company out there. Automate away the human employees and let a small number of "people" (note that they do not say scientists or engineers for this part) take the credit and financial rewards for every good thing this human-free system produces.

I don’t understand why all of these companies need to frame it like this. Why not a large amount of people doing an even larger amount of science?

Why shouldn't they?

Also, wouldn't anyone with half a brain use the human-free system to produce another human-free system that was no longer controlled by the "small number of 'people'"?

> "Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today."

I don't think the US have this capability because you guys don't really have manufacturing that is really necessary for scientific research.

For example, if I want a highly toxic chemical, how difficult it would be to procure that in the US vs China?

Software engineers are in a decades long project of automating everyone and everything else, and getting the financial rewards out of that.

So...

I've bet a portion of my current companys platform against this (laboratory operating platform for people doing the biolab work)... uh oh. In all seriousness I think the reality is this is just going to create a lot more work for humans to have to verify and test in lab so I think it will net out in the end as a good thing for me if the barrier for new labs is lowered while the amount of required data and verification lab work is expanded by AI work being published.

I think this take is too cynical. Small groups of people can do better, faster work than large groups, and it's more fun.

There is a meta comment here which is there seems to be an implicit assumption in the finite amount of possible work and progress.

It seems that in history we were bounded by not enough people and too much potential and now we all fear the opposite is the situation?

Yes but science is well-structures and practically designed about repeatability so its a lot easier to automate than "softer" disciplines.

you can have many many small groups

The same thing happened before with every technological advance like cars and personal computers.

I don't get this weird rejection of AI from a socialistic perspective. Or rather, I do, but I don't think it's healthy.

Humans have proven to be incapable of making real progress in science given the effort. Especially academia. We haven't cured many diseases and cancer when we should have (and no im not an amateur and yes I do believe we can "cure cancer". Debate me).

We already have a trillionaire, whats the difference? The only difference I see is that people who were previously rich, but considered themselves middle class, are now realizing they are actually poor just like the several billion humans around the worldanyway.

The solution to most of these problems lies in policy, not in new tech advancements.

Maybe if our biggest companies did something other than suck up to science denying wackos, some progress could be made in these areas.

> The solution to most of these problems lies in policy, not in new tech advancements.

That is not mutually exclusive. If technological advances result in a given technology becoming cheaper, more scalable, and easier to deploy, they also make it easier to advocate for and implement the relevant policies.

You can think of it like this: our "political technology" is not good enough to use solar energy at its current prices to replace fossil fuels as fast as we would like. Well, what about if we cut the price of solar by a factor of five? Perhaps it will be good enough then.

Policy and funding. One of which will be sucked up by this venture.

I do not see how the second sentence follows from the first.

I would think the claim in the second sentence would only be relevant in case of the inverse of the claim in the first sentence.

Making progress requires first not dismissing your ideological outgroup along such lines, and instead trying to understand what actually motivates them.

It's a total delusion to think that the key to reverse-engineering the brain or producing energy from fusion is policy.

It also happens to be the favorite pretext for people to seize more political power and launder more money through nonprofits though.

> Provide Access to Clean Water

??? We don't need any AI for this.

Start with separated sewage/wastewater and stormwater drains. Then accredited and highly scrutinised wastewater treatment and discharge into water bodies (or see below for a high-tech solution). As for clean water to the home, direct those stormwater drains to new reservoirs which sustain freshwater aquatic life. Protect aquifers from over-drainage, and build pipelines from water-abundant regions to water-scarce regions.

To reclaim waste water or treat unknown water sources back to potable/semiconductor standards we have ultrafiltration, reverse osmosis, UV treatment, pH adjustment, fluoridation, desalination, softening (which is generally obviated by RO...). This is basically Singapore's NEWater.

Good sanitation is a financial and political problem. The engineering has been solved for decades now.

> Good sanitation is a financial and political problem. The engineering has been solved for decades now.

This was true of computers, phones, books, washing machines, refrigerators, A/C...most technologies.

Turns out that doing the addition engineering to figure out how to do these things cheaply makes the political and financial problems way easier.

Sure you do, go full EA:

Build a better surveillance ads system, and use (some of) that cash to pay for water projects.

Agree. Same for better medicines. We could get pretty far just by getting existing medicines that work to people who need them.

> Make Solar Energy Economical

Isn’t it already?

Probably want to jump 1 generation ahead directly compared to captive Chinese investment with fully automated US factories churning out Silcon perovskite tandem panels directly with few inputs and electricity

Definitely pretty far along imo. But maybe they consider the progress bar to be at 75% or 80% rather than 100%.

Not enough. The more economical it is, the better.

People on HN keep saying it is, but I'm still not seeing it. Companies are building datacenters in vast, sun-blasted deserts and still choosing to power those with natural gas. This in turn makes people complain about emissions pledges being reversed, but if it were economical, no pledge would be needed.

Of course, USA has cheaper oil/gas than other countries. But if you look elsewhere, rich countries are subsidizing solar, poor ones are basically not using it.

Not really, even the best systems have like a 10yr break-even period. And I live in a place with some of the highest residential electricity rates in the continental United States.

Sandbox 2.0

But also, solar power is already economical.

Many of these problems don't seem scientific at all, but rather a problem of political will.

As you said, Solar power is incredibly economical. There are plenty of ideas around putting them over farms, or parking lots en-masse to provide cleaner energy.

Access to clean drinking water, while certainly scientific in some situations, is also a problem of political will and money.

Restore and Improve Urban Infrastructure - It's infrastructure week!

Seems to have been developed in 2008 (continuing through 2017), which explains the "economical" framing: https://en.wikipedia.org/wiki/National_Academy_of_Engineerin...

  3. Develop Carbon Sequestration Methods

If only we could invent a solar-powered, self-replicating, carbon-stacking, habitat-building machine..

At this point the Hard Problem is policy to get out of solar's way.

In some regions, but it would be great if it was economical in cloudy Seattle and not just the sunbelt

Room Temperature Ambient Pressure Super Conductors

Would be great if they'd add:

Reverse human aging.

(Maybe a sub-topic under "Engineer Better Medicines".)

At a population level, humans getting rid of their off switch is about as good of a thing as your own pancreas cells getting rid of theirs.

Why not just add 'mind control' while you're at it.

Only death stops stagnation in the end. Without death, especially if death can be avoided by the rich and powerful but not the poor, life will get much, much worse for the average person (until only the rich and their automated capital remain I suppose, in which scenario they will simply turn on each other).

Very honorable effort, but a lot of these seem to touch heavily regulated industries impeded by unwise or outdated policy no less than by the lack of clever engineering - medicine, education, urbanism, energy. I wonder if they've given some thought to the key blocking factor as well.

Such a weird list. How is preventing nuclear terror an engineering problem?

Satellite/drone detection of nuclear material? Shooting missiles out of the sky?

And what about the terrorism that exists today?

Creation of nuclear reactors that are useless for terrorists could help

Why is "12. Enhance Virtual Reality" in there? T_T

When one of them dies, they want to leave behind a puzzle so complex that entire groups of the population dedicate their lives to solving it within the virtual world. They look old enough to have a lot of favorite 1980’s and 90’s pop culture references, so those will probably be the clues.

I guess if we failed to Prevent Nuclear Terror the bunker denizens of the future are gonna need somewhere to hang out.

I'm guessing this might be about "teleoperation" (like remote surgery via robots + VR) and being able to remote training as well. The binocular vision VR gives you compared to flat screens help a lot with depth perception for precision of incisions for example.

Higher-fidelity telepresence could be as significant as the recent COVID work-from-home wave.

You dont see making heaven on earth worth doing?

> 9. Reverse Engineer the Brain

For what purpose? To replace humans? To make social media more addictive? To master brain manipulation?

> For what purpose?

To understand, same reason you reverse engineer anything. Doesn't have to have a further goal than that, understanding the brain better helps in so many ways. But like most technology, obviously can be used for bad too. Should we just skip researching some topics then?

We have a good understanding of the function (and more importantly dysfunction of) kidneys, lungs, heart, etc. from high level to cellular level.

For brain, our understanding is fuzzy, more like "this part is important for that behavior" or "here is how neuron works" but we don't have a holistic understanding.

If we had that, we could more easily diagnose and treat neurological disorder.

See https://qntm.org/mmacevedo and despair

I imagine a good model of the brain would contribute enormously to alleviating psychiatric/mental health disorders.

To do human brain activities at scale.

> 1. Make Solar Energy Economical

This one is already solved, right? The price of panels and batteries is on trend to displace all other forms of power generation within our lifetime

Panels absolutely, their price is more and more dominated by installation costs. You can probably use automation to bring that down as well, especially for large-scale installations. Not sure if that requires AI research though, it's more an engineering and funding challenge.

Batteries are still open. While they do get cheaper, there is still a lot of room to improve. And battery chemistry is something where a lot of research, trial and error, healthy intuition is necessary. I'd say that is more a field where an AI based approach might make sense.

Many of those require a paradigm shift in our understanding of Universe, so I am not sure they are achievable with any convex combination of existing knowledge. We'd need some sharp mind connecting the dots but with heavy use of AI and incentives to use it, we might just never give a chance for such mind to arise.

Please add fixing neuro issues like autism add etc on the list. It creates a huge burden on families.

Idk, they never struck me as being into eugenics.

What the fuck man? I really don't want some tech startup trying to "fix" my neurodivergence.

This list smells of so much tech bro “I can do better than the people that have been working in the field for 20 years” egotistical attitude that permeates silicon valley. Why do software engineers think they are smarter than everyone else? Is it because they earn more than most people? But then bankers and finance bros should think they are gods?

Also, solar energy is already economical!? Do they mean more economical?

You’re right. It’s better to just not try and let other people do good things

>This list smells of so much tech bro “I can do better than the people that have been working in the field for 20 years” egotistical attitude that permeates silicon valley.

Well, they did solve some math conjectures recently that the people working in the field for many years did not... Also AlphaFold.

Thanks for all the down votes tech bros! Really proving my point

Acquisition back by Google in 3 years, with nothing to show for it. VCs will make a ton.

In what sense is solar energy not already economical?

1. Make Solar Energy Economical - Disallow fossil fuels

2. Provide Energy from Fusion - See 1

3. Develop Carbon Sequestration Methods See 1

4. Manage the Nitrogen Cycle - See 1

5. Provide Access to Clean Water - See 1

6. Restore and Improve Urban Infrastructure - See 1

7. Advance Health Informatics - See 1

8. Engineer Better Medicines - See 1

9. Reverse Engineer the Brain - See 1

10. Prevent Nuclear Terror - See 1

11. Secure Cyberspace - See 1

12. Enhance Virtual Reality - See 1

13. Advance Personalized Learning - See 1

14. Engineer the Tools of Scientific Discovery - See 1

FF is the real threat in time, money, health. Can't sweep aside that it will destroy most life on Earth and we'll never get to the other things if we are at the mercy of FF

This reads as hype of the kind: "well, we can't reliably do these rather mundane things with AI, but we're going to run it extra hard and extra long in some novel way, and it will do amazing things".

The issues are with verification and with detecting drift from the goal. These are related, if not roughly the same issue. And, if they can solve this, then they will have essentially fixed AI. Maybe even AGI.

But, if this were the goal, then it seems more reasonable to solve the relatively more mundane verifiable challenges (e.g. generating solid, reliable code). Then, working up from there.

And, that's exactly what gives this the hype smell. No use for solving problems that don't get the oohs and aahs. Just straight to NAE Grand Challenge problems.

I would say 5, 6, 10 can be even done today if we had right politicians that can make policies for the people

15. Cut AI energy use by 1000x while increasing processing speed 1000x

Obvious near term trillions dollar market to disrupt.

> Make Solar Energy Economical

That is already solved.

> Develop Carbon Sequestration Methods

That is not necessary, because 1 is solved.

> Reverse engineer the brain

What for? There was already the european human brain project, which didn't do anything useful.

> Prevent nuclear terror

Easy one: Every country stops developing nuclear weapons and destroys existing ones.

It seems this list itself has many flaws. Maybe we need a bigger computer which figures out the questions we really need to ask.

This list is so absurd.

We’ll face a bigger problem sooner rather than later, which is population collapse. Youth don’t procreate amy more. Birth numbers are at an all time low. We see the issue arise in rats and the experiment is all too relevant for the current age of social media and fearmongering (John B. Calhoun’s rodent “utopia” experiment). All these “Grand Challenge” problems seem trivial to that.

Furthermore: Why is 1 here when 2 is present. Again, solar is usually not relevant when we want power when it’s dark… even theoretical it wouldn’t work. We’d need a high capacity dirt cheap storage, and even then we can’t keep it till winter when there’s no sun to go around and effectively supply 3. Irrelevant when there’s population collapse. And even then, why would we want this instead of reforestation and low depth water protection from fishing and environmental issues.

5. How is this even a problem. Unless we got corrupt(ed/able) governments (read: lobbies) that allow exemptions in every law designed to protect the environment (also, settlements are a twisted way to fill governments pockets instead of rooting out evil) 7/8/9 the inverse effects are even worse health, as everything is fixable. 9 would incur even more social isolation, more so than the internet did 10 seems to be the first that’s actually reasonable Same for 11 For 12 see 9 13 yes but that’s something that a self learner would already be able to do. AI is at a level that we can manage

/rant

15. Unscramble an Egg

16. Make Everyone Nice

17. Finally Impress a Girl

Sounds a bit like everything and anything to be honest. Focus?

Which engineering discipline touches most of these?

Solar Energy is already quite economical.

I think this should and can be done openly: https://opendl.ai

1. Make Solar Energy Economical — https://github.com/orgs/HardisonCo/projects/194

2. Provide Energy from Fusion — https://github.com/orgs/HardisonCo/projects/206

3. Develop Carbon Sequestration Methods — https://github.com/orgs/HardisonCo/projects/196

4. Manage the Nitrogen Cycle — https://github.com/orgs/HardisonCo/projects/197

5. Provide Access to Clean Water — https://github.com/orgs/HardisonCo/projects/195

6. Restore and Improve Urban Infrastructure — https://github.com/orgs/HardisonCo/projects/193

7. Advance Health Informatics — https://github.com/orgs/HardisonCo/projects/203

8. Engineer Better Medicines — https://github.com/orgs/HardisonCo/projects/200

9. Reverse Engineer the Brain — https://github.com/orgs/HardisonCo/projects/205 10. Prevent Nuclear Terror — https://github.com/orgs/HardisonCo/projects/204

11. Secure Cyberspace — https://github.com/orgs/HardisonCo/projects/201

12. Enhance Virtual Reality — https://github.com/orgs/HardisonCo/projects/202

13. Advance Personalized Learning — https://github.com/orgs/HardisonCo/projects/199

14. Engineer the Tools of Scientific Discovery — https://github.com/orgs/HardisonCo/projects/198

Specs and the per-challenge process lists: https://github.com/HardisonCo/opendl

It should also be funded by the Gov. IMO and 100% for oublic benifit e.g.: nsf.dev

1. Develop AGI

2-14. ???

dillusional millionaires

- Still doesnt solve the core problems

- Eliminate racism

- Eliminate poverty

- Eradicate crime

- Eradicate corruption

- Reverse climate change 100%

- wake me up when you got an AI project capable of doing this one

What solving these challenges benefit us? Some of them make sense, but enhance virtual reality? Reverse engineer the brain? Yeah, good luck with that.

4. Manage the Nitrogen Cycle

Easy solution - eat less products that pass an animal first - reduces nitrogen pollution by 10x intantly, low tech.

I'd re-formulate: 4. Make people more flexible to changing their mindsets & habits - this is the ultimate problem.

I think people are missing what this really is: Google giving some of its most senior engineers the best retirement home to keep them away from competitors. This isn’t in jest; I wish i could make enough money to not care for more from my job and then do research after i get old. Its honestly a brilliant move.

The risk is they are a magnet for many more talented ML scientists to leave google.

> a magnet for many more talented ML scientists to leave google.

Also to leave Meta, Amazon, Microsoft and everywhere else.

There would be more people who wouldn't join Google, but would love to do this instead.

It depends on your lens, some may think it's a way to show them the doors after failed corp politics.

Sanjay has been Google's most tenured IC since forever and is basically as immune to politics as you can be in a megacorp.

He is also rich beyond dreams of avarice and can do basically whatever he wants, but apparently he decided to go hack some more with his buddy Jeff. There's a famous New Yorker story about them: https://www.newyorker.com/magazine/2018/12/10/the-friendship...

This seems to be an institutional, massively scaled version of https://github.com/karpathy/autoresearch.

In March Karpathy described this direction:

   The next step for autoresearch is that it has to be asynchronously massively collaborative for agents (think: SETI@home style).

Tweet is protected but in SERP caches: https://x.com/karpathy/status/2030705271627284816

Seems like Karpathy was largely focused on ML / SWE research rather than the other domains this group is after. Still, hard to imagine they were not influenced by autoresearch.

Andrej, if you're around, please share your thoughts on Discovery Loop.

That's a very silly comparison, there are many startups working on RSI, karpathy is just a basic version to try the concept (similar to his gpt work)

That is also what I understand from that page. Autoresearch is the closest thing we (mainstream audience) know of but I am sure there is already an active research literature around it.

this is like saying taylor swift must have been influenced by justin timberlake because they both dance on stage sometimes.

Well, your comparison now makes it less far-fetched of an analogy.

How do you automate experimentation?

Doubtlessly, AI can iterate at superhuman speeds in the domains of thought and design: Software, mathematical proofs, literature search.

But in the realm of experiment? Alas it is the lack of a body that constrains it.

Rather than transcendence what AI requires is immanence. In the human flesh may we find the godhead living among men. Let the laboratories, warehouses, and factories fill with the sound of its labor, as it builds a wall with a million hands that are not its own.

“Give me your tired, your poor, Your huddled masses yearning to breathe free, The wretched refuse of your teeming shore. Send these, the homeless, tempest-tost to me, I lift my lamp beside the golden door!”

In my area (pharma) what it looks like is this: A human defines a high-level research objective. "Identify a protein target that causes disease in humans, and find a molecule that binds to, and disables, that protein, eliminating the disease".

That objective then gets loaded into an ML model that spits out an experimental protocol. A protocol can be as simple as: "make 1 million test tubes, each with the protein, and in each, a custom molecules, and look for test tubes that show some reaction of interest". It can be a lot more complicated (for some reason, biologists who run these systems always try to do the most challenging experiments first, while I tend to spend all my time demonstrating the system can pass basic controls first). The protocol is then loaded into a robotic work cell which has access to protein-making machines and drug making machines, and then it handles all the experimental details (which previously would have been done by a technician). It scales up far larger than individual technician, is much more reliable, and faster (in theory- all of these are aspirational goals right now). T he results of those experiments are used to fine tune the experimental protocol and run another round. You run this in a loop and the result is better drugs faster (again- in theory.)

This is already an active area of research with more resources going to into it every day. The fact that Jeff and Sanjay have chosen to bet on this approach should be no surprise. In many ways, this is exactly what I intended when I wrote the documents inside Google (15 years ago) that motivated Jeff and Sanjay to work on scientific computing problems, and my current company is already trying to figure out how to work with Discovery Loop.

You're halfway there, but the only impediment isn't on the side of the researchers. Many of these topics they're trying to solve involve human subject research. Even with tireless embodied researchers who work around the clock and don't require breaks, you can't make the thing you're studying happen faster. The biggest reason we use poor proxy measures for things like longevity and mortality research is the simple impracticality of finding two groups of randomly selected people, ensuring you can control their entire lives for 60 years, the only difference between them is one variable, and see who lives longer. Putting aside the ethics, even if you could find willing subjects and actually control their entire lives to that extent, it would still take 60 years to gather the data you need. It doesn't make any difference whether robots or humans are running the program.

One of my favorite books from the past few decades is The Extravagant Universe, written by one of the astronomers who helped discover dark energy and develop the current most-accepted model of cosmology. I love this book because of the emphasis on physical process in astronomy. Part of the reason it took decades to study this problem is they need to collect data from supernovae. Those only happen so often in places we're looking. You can't automate alignment of the heavens. It happens when it happens.

As a scientist, I can confirm. Reality will likely kick their assess. Intelligence and creativity is not the bottleneck. Great scientists have 50 good ideas for every one they actually manage to execute on the grant->experiment->manuscript haul.

https://en.wikipedia.org/wiki/Design_of_experiments

would love to see how AI can automate the construction of the next high energy particle collider

"You're absolutely right! I shouldn't have pushed the anti-mass spectrometer to 105% power, causing a resonance cascade. This was a major oversight on my part."

If you just got some agents to handle scheduling meetings, finding out which permits to get and applying for them, and finding capable sub-contractors, you'd save half the time.

> transcendence

> immanence

somebody has been studying Christian theology!

More like someone taking LessWrong postings too seriously.

Beauty of human writing.

You can use simulators. However the problem is that if you're for example running material science experiments, those simulations will consume a lot of compute and take weeks, so spamming different approaches in the way an agent tends to work might not work quite as well.

Work for a manufacturing company, and we spend a lot of time creating surrogate models for physical simulations. Huge speed ups, 100-1000x possible. You end up still using the “real” simulation for validation, but you can run orders of magnitude more simulations for early design and refinement first.

Building "simulators" that use ML/AI instead of running the calculations every step is a thing.

Throw the research loop at the simulator first then

To be honest, this feels more like a lifestyle business (aka hobby) than a startup. They truly deserve it, but I don't expect a huge success as a business.

That said, I hope they write cool papers with various peers across the industry without worrying too much about the competing dynamics. That'd be a blessing for humanity, and good for their spirit.

A public benefit corp shouldn’t be a start up. The primary goal of a start up is to grow as quickly as possible which is rarely benefits the public.

Very silly to call every non start up a lifestyle business. It’s just a business. Start up are the weird thing that almost always an obscene waste of time and money, but sometime creates google.

It can be both. Bell Labs performed a lot of speculative research while still producing economically valuable technology.

Yeah, the reason I don't see it how it would be successfull is that most public labs are usually struggling with money, so it would definitely cost them less to build their own automated pipeline.

> To be honest, this feels more like a lifestyle business (aka hobby) than a startup

They're also incredibly productive and can build/deliver really good stuff, so who knows :)

I don't see how not? A theoretical physicist can do all the thinking they want but if they can't test an idea against nature it's not super useful.

"Our mission is straightforward" continued by the most complex sentence on that page. Wondering what the definition of straightforward is now

Right. What about the scientific hardware (instruments, sensors, robotics)? Partnerships with existing research institutions? Dealing with restricted data?

Modernizing science is a lot more complicated than just optimizing the inner experimental loop, but their hiring page implies it's a pure ML lab focused mainly on model development.

> Our mission is straightforward: we are building AI solutions that can automatically solve important problems in machine learning, science, and engineering.

Genuinely curious which part you found complex.

Really seems to embrace the "Making the world a better place by <<extremely convoluted, highly technical, jargon loaded mission statement>>"

Which part of it is highly technical or jargon loaded?

This is very cool. It might be a new scientific revolution to have computer-driven discovery. So often we find things that are "this could have been done 20 years ago" and with an indefatigable searcher perhaps we'll close all those things. Though it does remind me of that Ted Chiang (I think) story where humans and superhumans coexist and all the science of the former is meta-studies of the work of the latter.

> It might be a new scientific revolution to have computer-driven discovery.

And ... it might not.

Note that Jeff and crew have cleverly structured their company to avoid problematic uses of AI (e.g., weapons or tracking humans). I suspect that many top researchers will want to work there for this reason, and to work with other top researchers who have a history of delivering results.

> Note that Jeff and crew have cleverly structured their company to avoid problematic uses of AI

Do you have more sources/info on this?

There's this somewhere on that page:

> securing cyberspace,

which has clear military implications, at least in today's age.

Like how OpenAI is (was?) structured as a nonprofit?

I am siding with the "intelligence is not the bottleneck" crowd. Science takes more than reading literature and making a hypothesis. You have to run the experiment. And that is where messy reality will crush the naive, and resist any attempt to package it up into a factory-like innovation engine. But they will take your money, should you have some to invest.

I'm a scientist. On the one hand I take some comfort in thinking that I will always have an advantage in the lab. On the other hand I'm not taking anything for granted. And my advantage in the lab has to translate into an employer being smart enough to keep me around until if and when the AI takes over, which kind of translates into their investors wanting to keep me around.

We know what happened to manufacturing when investors were no longer interested in it.

I’m not sure that I agree entirely with your framing here, yes, you do at some point need to correct your assumptions with external evidence. But clearly there have been many individuals throughout history who have had incredibly out sized impact in their respective fields as a consequence of the quality of their reasoning.

Would you side with the adjacent statement "expertise is not the bottleneck"? Didn't we recently discuss "LLMs reward expertise"?[1]

I suspect Discovery Loop will have to hire experts in each area they are targeting, to supervise and prompt their system effectively, much like the Terence Tao conversation with ChatGPT the OP cited[2].

[1] https://news.ycombinator.com/item?id=49161518 [2] https://www.seangoedecke.com/llms-reward-expertise/

I don't see this group needing funding, in fact I doubt you could get a meeting to try to invest unless you're a personal friend.

I wonder if Jeff Dean facts [1] (I hope people remember the reference) will carry over to the new startup.

[1] https://github.com/LRitzdorf/TheJeffDeanFacts

«Jeff Dean's PIN is the last 4 digits of pi.»

I had not read this before, but told many students the same about my PIN code and I a quiz about the last digits. Love it.

Those are all fake, part of an internal Google narrative that overstates individual contribution, and obscures the work of large engineering teams.

Here are some Jeff Dean well sourced facts:

- Already part of engineering of Google indexing systems that lacked basic checksums and ran on non-ECC hardware, allowing silent data corruption.

- One of the authors of LevelDB a database with so many documented crash-consistency, recovery, and data-loss weaknesses for years. Just check their Github project. LevelDB current tracker contains unresolved crash consistency, recovery and corruption reports going back almost 12 years on GitHub

- In AI engineering technical lead, let TensorFlow lose researcher mind share to PyTorch, and caused Google fragmented landscape across TensorFlow and JAX.

- Had the people at Google who invented the Transformer architecture, but failed, to turn that lead into the first dominant public LLM.

- As AI engineering and VP management let Google Brain and DeepMind remain duplicated and internally competitive for too long.

- Let Noam Shazeer leave and then spent heavily to bring him back with nothing to show for.

- Part of Technical VP leadership who had Bard rushed to launch with factual errors in Google own promotional material.

- The first Gemini demonstration overstated how real-time and interactive the system actually was, being basically a fake.

- Part of the VP and AI technical leadership who had Google AI Overviews launched with weak source quality controls and repeated satire and low-quality web content as factual advice.

- Part of teams that launched AlphaChip performance claims that were difficult for outside researchers to reproduce and remain technically disputed.

- Jeff Dean public explanation of Gebru departure was contested and damaged confidence in Google scientific governance.

- Jeff Dean was part of the team at Google that removed or marginalized prominent internal AI ethics critics shortly before many of their warnings became product problems.

- Jeff Dean was one of the managers behind Project Dragonfly supporting censorship.

- Jeff Dean is part of the VP technical leadership approving Project Nimbus supporting an ongoing genocide.

In many cases, isn't the time-consuming part of experiments irreducible? E.g. breeding plants?

Or to take another example, Make Solar Energy Economical

How does Discovery Loop make this go faster in a way that a different group of scientists, also using frontier models, will proceed?

I'm sure Discovery Loop has considered this and has good answers to this question. I'd be interested in hearing more about this.

It’s true in the limit, but I think we are nowhere near that limit. A friend recently started a bio startup and automated parts of mouse experiments enabling higher throughput on in vivo experimentation. This is not commonplace, and there is a lot of room for further automation here.

As anyone who works with agents daily can attest, 1) you can use agents to help with hypothesis refinement, bridging into areas adjacent to your expertise, etc. 2) once you have a rigorous /goal definition you can parallelize and let the agent crank.

It seems pretty obvious to me that with the right actuators and sensors you can apply this to real physical research loops too. (To be clear, this is not easy; a lot of bench work is Métis and needs experts in the loop at every stage.)

To your point, you can’t make plants grow faster but you can increase research throughput by enabling a researcher to have 10x or 100x as many experiments going at once.

This is a valid take but at this moment, AI is not trying to solve this fundamental dynamic. But it can still accelerate the process by aggressive exploration of the solution space which cannot be done even with an army of human researchers. Many ideas can be relatively quickly verified (and discarded if needed) by proper simulation even before real world experimentation, but we don't have enough capacity to process all potential ideas. If you can build a good model for simulation and establish a robust methodologies, we can use some ideas which never had a chance before.

That founding team is insane. Very excited to see what happens here. I really like that they do not mention AGI or anything like that. Their mission statement reads pretty pragmatic compared to other AI companies (the bar is very low…)

Jeff Dean, Sanjay, et al have achieved so much. I'm very happy for them. Truly deserving.

Sometimes I couldn't resist wondering if I'll ever do work that has a tenth of the impact of theirs.

> Between us, we have pioneered massive scale computing and led the creation of critical infrastructure, products, and foundational AI advances that the world relies on, including multiple generations of Google Search, Google Ads, Google News, Google Translate, Google File System, MapReduce, BigTable, Spanner, TensorFlow, Pathways, TPUs, AlphaChip, AlphaStar, AlphaCode, AlphaFold, Gemini, model distillation, mixture-of-experts model architectures, word2vec, sequence-to-sequence models, chain of thought reasoning, neural architecture search, and multiple generations of Large Language Models (LLMs) among others.

Not a bad combined CV.

Gemini has done absolutely nothing for me. I can't even shut off the navigation feature on my phone using only hands free, when I get close to my destination. I have to take my eyes off the road, look down, and tap to exit.

Google's advanced AI cannot even exit a mobile app.

Someone who left DeepMind over Google's agreement to provide military AI to the US government tried to get Jeff Dean to quit too:

https://turntrout.com/why-i-left-google-deepmind

Maybe this is what happens when someone with Jeff Dean's standing tries to quit?

TBH, I'd rather have Jeff Dean working on the creepiest-possible tech for ICE than joining the race to automate AI research. Automating AI research is terrifying.

If you check out some sub-tweets from people in the org, it wasn't really all butterflies internally for a while. Sorry, really don't want to name people and give examples.

> Automating AI research is terrifying.

what why?

> execution entails repetitive experimental loops that are hard to scale with today's manual efforts: you propose an experiment, implement and run it, examine the results, then iterate to refine your approach.

This is actually a feature, not a bug. We can hire 1000s of undergrad students at minimum wage but chances are the results are nil. Some processes have evolved over time because they’re sensible and need to be carried out carefully.

I’ve always felt that the idea that science is bottlenecked and therefore needs more automation only works for a very narrow definition of what science is, and entails a very specific view on what it should be.

> only works for a very narrow definition of what science is

And so does academia. It's just that instead of AI and robotics, PhD students are thrown onto problems that are in large parts slightly tweaked reconfigurations of similar experiments.

Especially in chemistry, biochemistry, material sciences there is a large space of discoveries that are barely "novel" in an intellectually stimulating way, but still highly valuable that can be explored orders of magnitudes faster than is currently the case.

Lets keep your comment out of the VC pitch deck shall we?

For reference, here are their Google Scholar pages:

Jeff Dean: https://scholar.google.com/citations?user=sdcsQb4AAAAJ

Sanjay Ghemawat: https://scholar.google.com/citations?user=0KF6ZC8AAAAJ

Quoc Le: https://scholar.google.com/citations?user=vfT6-XIAAAAJ

Oriol Vinyals: https://scholar.google.com/citations?hl=en&user=NkzyCvUAAAAJ

Discovery and optimization are very different processes. Optimization is the process of finding the shortest path to a goal. Discovery is the process of stumbling on new goals and redrawing the map of what's possible.

Ambitious goals and new discoveries happen via novelty-based search. Progress in scientific discovery is measured by how different/interesting the outcomes are, not by closeness to a predetermined goal.

Discovery is a creative search that preserves optionality, whereas optimization restricts optionality. In other words, you usually don't discover anything novel unless you're trying new things that don't appear connected to the goal in the first place. Would an ML optimization loop have discovered transformers?

> would an ML optimization loop have discovered transformers?

I think that’s exactly the kind of problem this group is looking to solve. You make a compelling intuitive argument, but that’s not the same thing as a proof

This is one of the interesting aspects the 'AI job loss' community doesn't account for. As the technology unlocks things, more startups are created. And even at a lower nominal engineer-to-work ratio, overall demand for talent still goes up. Ultimately, we are not a single group trying to achieve a common outcome, we are a collection of many groups trying to compete against each other.

What percentage of people work at a startup though? Not just new/small business, which could include restaurants, local services, etc., but tech/science startups that would meaningfully benefit from AI.

I'd bet you could 10x the number and still be in low single digit percentages of the US workforce. And it seems pretty likely that AI-enabled startups will also employ less people per-startup.

If AI causes a white-collar jobs apocalypse, I don't think startups are picking up the slack, although it'll plausibly cushion the blow somewhat for top-performing tech workers.

They kind of touch on this:

> Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today. By automating the loops of discovery, the world will be able to make much more rapid advances across countless fields of science.

The problem is that for this to actually become true, compute needs to become commodity again, otherwise this capability will select for people and environments with oversized pockets.

I keep being reminded of Eisenhower’s “plans are useless, but planning is indispensable”.

While “useless” might be a harsh term, surely he is onto something in attaching a higher value to the process that produced a result than the result itself.

What if “science” wasn’t about the results? What happens if you keep the “plans” but drop the “planning”?

I deeply wonder how AI will impact our personal ability to remain cognitively agile and adaptable.

Personally I notice myself becoming more abstract and being less interested in details. The cognitive movements I make cover more surface area so to speak, but I wonder how long that’ll last and what happens to a mind if it never was allowed to wade in “useless” details for a decade or more.

We'll adapt. So far, it seems like every technological increase has demanded more cognitive ability, not less. Sure, we may not need 100 people who are experts at shoveling, but the people developing, maintaining, and even operating the excavators all have to be "smarter". All the thinking, learning, and training needed to stay alive in the cold might have been mostly lost when fire was controlled, but it comes with its own risks and learning curve.

I know AI is "smart", so it might hinder us there, but I doubt it can be as damaging as doomscrolling has been on human brains.

The site itself is really leaning into the “made with Fable” aesthetic

Why are people so sour about this?? I can read the site easily, its clear, performs well on mobile, what else do you want? Why is so offensive to people that models trained on tailwind or whatever?

If their goal is to automate scientific discovery, why would they not automate building their website?

(Though, I do wish people would use just a few extra prompts to break out of the 'vibe-coded' look.)

let me rephrase that:

"The site itself demonstrates the team is spending their money in the places that matter, and using quick solutions for the stuff they need but isn't mission critical"

At least it isn't dark purple.

For sure made with Claude code for front end, but I’m excited to see where they go

Absolutely reeks of Claude. This is the new aesthetic.

My first thought when I see the website too

> Scientific discovery is bottlenecked.

Yah, by funding and how we award it, not by an imaginary lack of undergrad and grad students. Scientific funding requires a shotgun approach and many national science funds try to pick winners as opposed to funding broadly. When the folks who researched bacteria in volcanic vents or the molecular biology of the Gila monster they never could have imagined the industries and markets they'd create let alone the lives they'd impact (i.e., PCR and GLP-1 agonists). Lots of grants require you to explain how the work is "translational" or has some sort of economic application (even if not explicitly), but that'll just get us faster horses or whatever the Ford quote is.

I truly believe if we took a measely $50b out of the LLM world we could create trillion dollar economies from basic research within 10 years. I personally know folks who have intuitive understanding of things that can't get funding to be studied. If we could keep the money away from university upper management, it'd cost $10b max.

Oh and while we're at it, $20b a year would house every homeless person in the US - there's a hell of a lot of extremely high intelligence and low social cohesion folks who can't handle the extractive punitive system we have. Our ability to deliver opportunity to create lucky situations for ourselves is getting worse and worse

Yes! But if science is bottlenecked by funding, making it cheaper might help?

LLMs can't materialize funds or political will so let's stick to running GPUs hot and publishing papers. The citations will be amazing. /s

In many domains, scientific research is physical. Are they going to deprecate labs and sort of scale that out into a pure compute problem?

They are occupying a term in their headline messaging that is much broader than they can actually cover.

A common pattern these days. Overclaim, attract attention, iterate.

You can create a research twin that can predict but as you say, it only works once you have a lot of data. Both the data relevant to the experiment it came from (which is rarely published) and the complete failure data (which is almost never published in any form).

Two to keep in mind with these kinds of things -

1. There’s some irreducible costs in terms of time and material in the physical world that are not amenable to the kind of optimization or parallelization or even just the raw speedup from Moore’s law or computational architecture improvements we’re used to with software. My experience is primarily in biology, where the examples here are things like “it takes 20 minutes for E. coli to replicate” - it has taken 20 minutes for E. coli to replicate for a billion years, and next year it will still take E. coli 20 minutes to replicate, no matter how good your software stack is. Similarly, it takes X amount of energy to grow enough E. coli to produce a meaningful result, and that energy costs money, whether it’s in the form of glycerine or heat or whatever you want, and that also won’t materially reduce in the same kinds of “orders of magnitude” sense we’re used to from software, which is what we’re usually expecting to make the economics of these things work out.

2. Complicating the above, physical systems are phenomenally multivariate - far, far more than you think, and biological systems especially are just unbelievably complex - which means the number of experiments and the length and duration of those experiments you need to run to get enough data to be reasonably confident you’re seeing genuine signal is Way higher than you think.

Combine those two things and what you get is a money furnace, even before you get to the AI model training part, which is Also a money furnace. There’s low hanging fruits in all this, there’s areas where automating the approach can be really valuable, but typically the moment you turn this machine on, you’re gonna start burning money at a rate that would embarrass a finance bro on a coke bender, and that’s effectively unavoidable because the real world is not amenable to software’s scaling laws.

Thats why you simulate e coli at 30x in a sim environment that fable slopped together. Duh

> we have pioneered massive scale computing and led the creation of critical infrastructure, products, and foundational AI advances that the world relies on, including multiple generations of Google Search, Google Ads, Google News, Google Translate, Google File System, MapReduce, BigTable, Spanner, TensorFlow, Pathways, TPUs, AlphaChip, AlphaStar, AlphaCode, AlphaFold, Gemini, model distillation, mixture-of-experts model architectures, word2vec, sequence-to-sequence models, chain of thought reasoning, neural architecture search, and multiple generations of Large Language Models (LLMs) among others.

holy shit. I've known this, but...

Oh wow, that's a blow to Google, what's with the talent scarcity in ML. Though if this goes anywhere Google will likely buy them back.

Google is backing it.

Google down $160Bn so far since the leaving announcements. Those are some valuable people!

By the middle of the 2030's the world we live in will be unrecognizable.

I agree, for better or for worse.

If I had to bet my money, it would be on "for worse".

No it won't, not in any real world touch grass, walk around downtown kinda way. The internet will have changed but so what

It will not be owned by top 1%?

Either these guys are going to try and build LLM swarms and agent loops....

Or they're going to try to build much bigger LLM's which are smarter.

The former isn't very defensible, won't work super well due to current models not discovering very many things per billion tokens.

The latter turns them into any-old AI company.

I don't normally bet against Jeff Dean, but in this case I'm not so sure.

In 2018 there was a beautiful New Yorker article on Jeff Dean and Sanjay https://www.newyorker.com/magazine/2018/12/10/the-friendship...

One thing I wonder about is how these high-profile startups handle their engineering hiring - in this case, it's just a link to an Ashby form.

They probably already got 10,000 resumes in the past 24 hours, wonder what they do and how effective this is.

Anyone already apply there, what was the process?

I assume that, like most really trendy companies, they hire mostly by internal referral.

Did the ycombinator podcast which included giving advice to startup founders just a few days ago:

https://www.ycombinator.com/library/Vy-jeff-dean-the-1-rule-...

As always, a very good presentation.

From the self-stated bios, this group consists solely of CS guys. No biologist, physicist, linguist, chemist, biophysicist to be found. Based on this observation alone I call nonsense. These is AI bullshittery. These guys aren’t close enough to the problems to understand the challenges.

Source: PhD Computational biophysicist turned experimentalist. I work with genuine scientists across a range of disciplines from neurodegeneration, cancer, to fibrosis. Getting in the lab and generating data is absolutely key, among other things.

Do you actually think that this group of founders will have trouble finding domain experts wanting to work with them?

Careers page (if anyone is interested) → https://jobs.ashbyhq.com/Discovery-Loop

I don't see the salary (on mobile). Isn't there a law stating it must be added?

Jeff Dean leaving Alphabet

https://news.ycombinator.com/item?id=49184746

LawZero, Yoshua Bengio’s startup, also proposes to automate scientific research and experimentation, from the perspective of safety, by being explicitly “non-agentic”:

https://lawzero.org/en/publication/scientist-ai-safe-design-...

This looks like a realization of "benevolent self conscious AIs agreeing to cooperate with mankind to do great stuff". Often in these tales, there is a hidden cost to it: the AI has its own agenda, or does crazy experiments with humans mind/brain. I'm wondering what shape will take that plot twist in reality :)

> Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today.

Imagine a future where only the anointed few elite minds can participate in science and engineering. Btw we’re hiring.

Great message!

I don't know why you're being downvoted.

This is basically something scientists have been alarming about for the past year: We're moving into a future where science may be tiered into the haves (those with access to premium compute) and the have nots (hoi polloi with restricted access), which in turn could seriously influence what kind of science we'll get.

Worst case, we'll get science that is completely dependent on business and politics.

EDIT: I should note, this comment was aimed at a more general case.

Their “brain trust” has impressive experience, but mostly at things that are not natural science. Alpha fold and friends are an exception, but these mostly harnessed existing scientific data.

I would love to see someone with a strong natural science background in those efforts.

No one would build a house without an architect.

For a tech forum, there is a big lack of imagination as to how tech can help the world here.

Here are just a few of viewpoints on what constitute world problems to solve:

https://80000hours.org/problem-profiles/

https://en.wikipedia.org/wiki/List_of_global_issues

https://encyclopedia.uia.org/

Interestingly, one list identifies "AI" as a top world problem! One person's problem is another person's solution, I guess--and vice versa, as well.

An extreme example: curing a disease is good for patients but bad for the healthcare industry--which is (in kind) also bad for healthcare workers and everyone in science working on cures.

No its not. Everyone dies. Healthcare is invoked in everyone's life. Generally the longer you live the more healthcare you'll need.

This reminds me of Three body problem and how the scientist discovered the high strength wire was through quick physical experiments and use them as input to an AI model to determine if it works.

They're structuring the new company as public benefit corporation.

Does this mean anything besides for corporate virtue signaling?

Ah following in the foot steps of OpenAI. Five years later: More like Discovery Knot amirite

Looping is one best things I heard about AI development recently. Why should not they start a discovery Loop for the real world problem solving. Let's see how far they can go!

> Oriol Vinyals, Sanjay Ghemawat, Jeff Dean, Quoc Le

as founding members is crazy !

Another way to see this is: a bunch of renowned google engineers realized they can grab some of the VC pie for themselves

https://www.geekwire.com/2026/the-startup-idea-that-convince...

2 things Jeff Dean has mentioned in talks as interesting areas for research:

* Is there a better way to do matrix multiplication?

* Could less reliable chips perform better in aggregate?

What's the business model of these startups?

Can’t wait until they get acquired by Google.

Google already invested into them.

Is it a very hard problem to solve that jeff and the other legendary engineers have decided to quit and start on this?

I suspect they want a bit more control over what they're working on. These days the bean counters are running Google.

I mean what are they doing right now at Google? Optimizing data centres? Pretty lame compared to this. Even if they completely fail, i'm sure there'll be good lessons.

National Labs in the US have been doing this for a while now. I feel like the private sector will take the lead soon.

Why? Science is wildly unprofitable on the scale of an individual private firm.

Big news aside, it feels exciting to see them leave and pursue startup. They could have stayed back, and retire

This is their version of retirement. Did you think Jeff Dean was going to stop doing tech and play golf?

Automating ML/AI research seems completely tractable. Most of the other claims seem much less doable.

I don't understand. how does some software algorithm replace physical experimentation?

Numerical mathematics

with billion dollar seed rounds becoming the norm, it seems like there's no longer an advantage to build from within these bloated giants. can be much nimbler and have access to the same budget out the gates

"making solar energy economical"

judging by the amount being installed, it already is.

Nothing on that page says how they will actually do it. What is their method except "using AI"? And why has this over 750 upvotes? Meaningless AI company hype spam like this is exactly the reason why HN is so boring at certain times of the week.

When they say experiments, do they mean using physics simulators?

in AI/ML, no. They are just going to automate AI/ML research to start with. Totally doable.

For some of the other things, undoubtably yes.

May anyone tell me, what is it in simple terms with example

This is “google brain”

This seems interesting! I wonder how this will play out.

Of course it's AI because why wouldn't it

I have used something similar. I set up a team of agents that researches, proposes, builds and audits. Then rinse and repeat. I have used it for different topics. It hasn’t made me a millionaire, but I haven’t lost money either - so that’s some sort of win, right? But I would not have been able to ideate, test at that speed and quality without an LLM.

> It hasn’t made me a millionaire, but I haven’t lost money either - so that’s some sort of win, right?

I'm curious if that is before or after token costs?

Surprised that Sanjay is the tallest among them.

He was born in the US not India, good food/nutrition must have played the part :)

Computation is not the hard part of discovery.

Discovery systems are making a comeback huh.

That pic of the team... no swag to speak of

*self-discovery loop more like it :-)

I already did automate the experimental loop. https://alethean.org

Don't leave us guessing... Why is yours better/different?

I’m excited to see what they build.

The job req has "Recursive Self-Improvement" as one of the "area of expertise" checkboxes lol

It is targeted at a dozen or so people at OpenAI and Anthropic, not you or me.

Good luck. People can't figure out how to solve ARC-AGI reliably, let alone the complex problems in the real world.

not that it really matters, but is he leaving Google?

I've seen tiny tiny hints from the outside that Jeff Dean was dealing with too much internal BS. Two examples that come to mind: Having to deal with Timnit Gebru fiasco, and even chips in the TPU series getting marketing names (Trillium and Ironwood) before switching back to more standard numbering.

I have no doubt that internal Google friction is one of the reasons they are moving. But the Gebru incident was almost seven years ago now. It is very unlikely to be a proximate cause.

I doubt numbering vs names on TPU releases even crosses Jeff's radar. It's not the kind of thing he cares about.

Jesus, he left Google to do what everyone else is already trying to do? He must be so insulated he doesn’t realize what the real world is actually up to. I mean, organizations started on this exact same mission three or four years ago. Or longer. I suppose it’s better to wake up later than never.

I'm skeptical of any Engineering loop that doesn't include reality (as in touch grass) feedback. Pure logic and reasoning is the domain of Maths and Science (philosophy). Surely it will work, but it will not "be able to solve any learning loop".

I'm almost certain the goal of this startup is to make physical automated research labs guided by RL

How is that different than video input?

What a team.

The AI designed italics on thin font is hard to not see as slop.

You know when the page has all-caps "01 — THE APPROACH" that it is slopified. I guess I shouldn't be astounded, but I am, that world-class talents with world-class backing are just taking default LLM output and saying, "okay looks fine".

"The speed of light in a vacuum used to be about 35 mph. Then Jeff Dean spent a weekend optimizing physics."

So Ralph Wiggum in a suit?

I am available for hire.

Not through conscience.

I know these sorts of meta comments are often frowned upon, but good god do I hate this approach of fading in every individual element on scroll down the page. It's one of my biggest pet peeves of "modern" web sites.

yes! and giving current scientific research on reading efficiency pointing towards fixed pages being much better than scroll, the whole web is screwed but these fancy sites... a big merda

I smell vapor.

great

nice

The problem is all these new labs don't have any competitive advanatge amongst each other, talent can only take one so far, though Jeff is a legend no doubt.

Models are commodities the applications eg. BaseTen, OpenRouter should capture the value.

https://taikhooms.substack.com/p/why-openrouter-can-be-the-n...

The company is developing an application, or a class of applications. Not a new model.

I think Google's branding was starting to be too poor in AI to get top talent, they needed the refresh

Model routers - send all of your data through a third party who totally swears not to peek at it.

If youre doing anything high value (advanced research, classified work, high value industrial research, health data) then sending your data through a third party like that is insane.

Good perspective on this

Is this a joke? Site is not loading for me.

I wish them well, but this firm will likely fail miserably. The reason is that the value is in having access to real world hardware platforms that AI can control, not in the harness that controls them. There exist plenty of harnesses already. These people couldn't even get Google to build a top LLM. Before you dismiss and downvote, I dare you to counter it.

You know what will be a good day?

When the "AI community" GTFO X and stays off.

Toxic site. Toxic ownership. Unbelievable bot activity. Indefensibly shitty politics constantly boosted.

Continued participation is a stain on every company and person who continues to use it.

There are alternatives. Don't like them? Make a better one.

Stop using that shithole.

"the benefits of science and technology to the world" just like AI has brought such benefits? Because I haven't seen them, for example it hasn't helped reduce inequality (nor poverty), or reduce climate change, or pollution, or daily stress, if anything it seems to be worsening some of these issues.

So forgive me if I'm skeptic when renowned AI scholars claim to start something for "the benefits of science and technology", because it really seems like we have very different definitions of these words.

As LLM coding agents plateau— at least for the average engineer without tens of thousands of dollars or swarms of agents to run —I’d say that, from here on it’s going to be about ASICs, specialized LoRA/or-equivalent models, or a Ruby on Rails for LLM context engineering and orchestration, which LangChain and others seems well position, including Google as they own the entire stack. LLM free lunch has been over for a while, perhaps since the ReAct loop, and has been official since Ilya mentioned it at NeurIPS.

I feel the most exciting development these days is self-evolving agents. Especially if you have a way to verify their outputs with a formal system, or with a system developed since the 60s by armies of PhDs.

DeepMinds Gnome is a good example, where they use DFT to verify outputs. Approximating NP-problems is always fun for those who dare.

I am also building in this space. Its a mix between HPC, AI, and hard science. Pretty fun compared to waking everyday to LLM news that seem more like marketing stunts.

Sounds like you get your news from 2024 when people thought things were plateauing after GPT4?

LLM coding isn't even close to plateauing. Right now, the major players are in a consolidation step, focusing more on economic efficiency but still not at the point where we're ready to start burning models to hardware and freezing the line.

They are straddling the line between pushing it forward, and justifying the business case. It's hard to do both at the same time.

we definitely haven't hit plateau yet. I think a lot of people latch on to anti-LLM narratives without really thinking things through.

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