Hacker News

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.

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.

> 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.

> Make Solar Energy Economical

Isn’t it already?

Sandbox 2.0

But also, solar power is already economical.

Room Temperature Ambient Pressure Super Conductors

Very honorable effort, but a lot of these seem to touch heavily regulated industries impeded by unwise or outdated policy no less than by the lack of clever engineering - medicine, education, urbanism, energy.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?

Would be great if they'd add:

Reverse human aging.

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

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

In what sense is solar energy not already economical?

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.

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?

> 9. Reverse Engineer the Brain

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

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

Which engineering discipline touches most of these?

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

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

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)

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

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.

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

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.

> transcendence

> immanence

somebody has been studying Christian theology!

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

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.

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.

Which part of it is highly technical or jargon loaded?

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.

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.

> 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 :)

> 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

Do you have more sources/info on this?

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

There's this somewhere on that page:

> securing cyberspace,

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

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?

«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.

> 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.

[dead]

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.

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

> 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?

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.

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.

I agree, for better or for worse.

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

It will not be owned by top 1%?

Google is backing it.

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

As always, a very good presentation.

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

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

Does this mean anything besides for corporate virtue signaling?

Google already invested into them.

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.

> 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?

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

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.

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.

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

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.

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?

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.

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".

Yeah what these guys are mainly known for is vaporware

> 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.

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

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

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.