451–460 of 630 posts
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.
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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?
Isn't that directionally what OpenAI's core message was for a while? A large amount of people building their own customized apps for themselves. Similarily, everyone becoming their own accountant / lawyer / other professional services.
These professional services will defend themselves with gatekeeping. Suddenly it doesn't depend anymore on the quality of your legal advice, but whether it has been stamped by a qualified Lawyer. It doesn't matter that your taxes are correct, but whether they are submitted by an approved accountant. Etc.
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.
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.
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.
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 NAE Grand Challenge problems. We think doing this well requires strong expertise…
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
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> 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, a…
> 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 .
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 NAE Grand Challenge problems. We think doing this well requires strong expertise…
Earlier quoted context omitted.
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.
A PBC is not a charity or a non-profit. PBCs are for-profit businesses with the goal of making money. In day-to-day business they're indistinguishable with other for-profit corporations, including fundraising and investment. The only real practical difference is that they give directors a little more leeway in their fiduciary duties to say "no" to doing evil things. The advantage to a PBC is protecting founders from…
A politician could trivially write a law to end this "problem", at any point. Or courts could start rejecting suits where investors sue. There is nothing inherent in nature that requires this outcome to exist.
This is an entirely self-made problem that society tolerates when it doesn't have to. Corporations used to need a blessing from the government to be formed, explicitly to avoid the risk of a massive corporation who can compete with the government and have investors that push anti-social goals.
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 NAE Grand Challenge problems. We think doing this well requires strong expertise…
> 9. Reverse Engineer the Brain For what purpose? To replace humans? To make social media more addictive? To master brain manipulation?