Earlier quoted context omitted.
I estimated that any paper that has mouse work and produced in a first world country (I.e. they have to do good by the animals), the minimum cost of that paper in expenses and salary would be $200,000. Average likely higher. Tens of thousands of papers a year published like this!
These are mostly fixed costs. If you produce a hundred papers from the same team and same research, the costs aren't 100x.
Calculating the cost of a Google DeepMind paper
141–150 of 160 posts
Re: Calculating the cost of a Google DeepMind paper
#142Earlier quoted context omitted.
I hope someone could share their insight on this comment. I think the other comments are fragile and don't hold too strongly.
Marketing of some sort. Either “come to Google and you’ll have access to H100s and freedom to publish and get to work with other people who publish good papers”, which appeals to the best researchers, or for smaller companies, benchmark pushing to help with brand awareness and securing VC funding.
Re: Calculating the cost of a Google DeepMind paper
#143Earlier quoted context omitted.
But starting from the 10th paper, the value is also pretty low I imagine. How many new things can you discover from the same team and same research? That's 3 papers per year for a 30-year career. Every single year, no breaks.
Well, to be sure, mouse research consistently produces amazing cures for cancer, insomnia, lost limbs, and even gravity itself. Sure, none of it translates to humans, but it's an important source of headlines for high impact journals and science columnists.
Re: Calculating the cost of a Google DeepMind paper
#144Earlier quoted context omitted.
These are mostly fixed costs. If you produce a hundred papers from the same team and same research, the costs aren't 100x.
I agree with you but it also makes me think: Google's TPUs are also fixed costs and these research experiments could have been run at times when production serving need isn't as high.
Re: Calculating the cost of a Google DeepMind paper
#145Earlier quoted context omitted.
Well, to be sure, mouse research consistently produces amazing cures for cancer, insomnia, lost limbs, and even gravity itself. Sure, none of it translates to humans, but it's an important source of headlines for high impact journals and science columnists.
Did you chuck gravity in there to be hyperbolic or has someone really published a paper where they have data implying they got gravity not to apply to mice?
Re: Calculating the cost of a Google DeepMind paper
#146Earlier quoted context omitted.
> They chose to publish. So they are interested in seeing it reproduced or improved upon. Not necessarily, publishing also ensure that the stuff is no longer patentable.
Forgive me if I am wrong, but all of the techniques explored are already well known. So, what is going to be patented?
Re: Calculating the cost of a Google DeepMind paper
#147A lot of misunderstandings among the commenters here. From the link: "the total compute cost it would take to replicate the paper" It's not Google's cost. Google's cost is of course entirely different. It's the cost for the author if he were to rent the resources to replicate the paper. For Google, all of it is running at a "best effort" resource tier, grabbing available resources when not requested by higher priorit…
it is a hustle only for the near future while this bubble lasts, but can help reduce costs.
Re: Calculating the cost of a Google DeepMind paper
#148Earlier quoted context omitted.
This is also true for machine learning papers. They cure cancer, discover physics, and all sorts of things. Sure, they don't actually translate to useful science, but they are highly valuable pieces of advertisements. And hey, maybe someday they might!
AlphaFold has basically paid the bills for a decade's worth of machine learning research. It's been that transformative.
Re: Calculating the cost of a Google DeepMind paper
#149Earlier quoted context omitted.
AlphaFold has basically paid the bills for a decade's worth of machine learning research. It's been that transformative.
Which bills and how? Not disputing the claim, would just like to understand it!
I just mean it demonstrated solving something challenging in a convincing way, justifying a great deal of additional resources being dedicated to applying ML to a wide range of biological research.
Not that it actually generates revenue or solves any really important health problems.
Re: Calculating the cost of a Google DeepMind paper
#150Earlier quoted context omitted.
Which bills and how? Not disputing the claim, would just like to understand it!
I shouldn't have been so literal. I just mean it demonstrated solving something challenging in a convincing way, justifying a great deal of additional resources being dedicated to applying ML to a wide range of biological research. Not that it actually generates revenue or solves any really important health problems.
The big problem with the latter statement isn't so much exaggeration, but something a bit more subtle. It is that people start to believe you. But then they sit waiting, and in that waiting eventually get disappointed. When that happens the usual response often feeds into conspiracies (perpetuating the overall distrust in science) or generates an overall bad sentiment against the whole domain.
The problem is that companies are bootstrapping with hype. The problem is that this leads to bubbles and makes it a ripe space for conmen, who just accelerate the bubble. There's no problem with Google/Microsoft/OpenAI/Etc talking to researchers/developers in the language of researchers/developers, but there is a problem of them talking to the average person in the language of the future. It's what enables the space for snakeoil like Rabbit or Devin. Those steal money from normal people and takes money from investors that could be better spent on actually pushing the research of the tech forward so that we can eventually have those products.
I understand some bootstrapping may be necessary due to needing money to even develop things, but certainly the big companies are not lacking in funding and we can still achieve the same goals while being more honest. The excitement and hope isn't the problem, it is the lying. "Is/Can" vs "will/we hope to"