Recent comments in /f/MachineLearning

sweatierorc t1_jcbg3ki wrote

Reply to comment by wywywywy in [D] What do people think about OpenAI not releasing its research but benefiting from others’ research? Should google meta enforce its patents against them? by [deleted]

But if they stop publishing, it will hurt adoption, SD1.5 has became the benchmark of txt2img models over dalle-2 or more recent SD models.

Another thing to consider is that not publishing will hurt recruitment. Character.ai founders left google to build their own company after working on Lamda.

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Eaklony t1_jcbfrk2 wrote

Reply to [D] What do people think about OpenAI not releasing its research but benefiting from others’ research? Should google meta enforce its patents against them? by [deleted]

There is a reason why the advanced countries in the world are capitalism countries. Money is mechanism to control long term advancement of pretty much anything in our society. I don’t think anybody should be surprised AI development can’t be forever fully open sourced. Maybe some day it will, but certainly not before some major (global) social reformation.

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underPanther t1_jcbf1l8 wrote

Reply to [D] What do people think about OpenAI not releasing its research but benefiting from others’ research? Should google meta enforce its patents against them? by [deleted]

Firstly, I don't see it as research if it's not published. It's a commercial product if they don't share it and profit from it. If you can reimplement it and publish it, it's yours for the taking.

Secondly, there's so much interesting work outside of large language models.

I don't care too much about what OpenAI get up to. They have a management team trying to become billionaires. That's fine. I'm happy doing science in my living room. Different priorities.

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EnjoyableGamer t1_jcbe3b3 wrote

Reply to [D] What do people think about OpenAI not releasing its research but benefiting from others’ research? Should google meta enforce its patents against them? by [deleted]

There is not much to release from OpenAI, just big model with big data on existing methods. Google Deepmind did go that route of secrecy with AlphaGo, if anything the easy access for anyone to try is cool and new.

In the long run it's their mistake, as research never stops. It won't build on GTP4 but other alternatives that I'm sure will come in the next months.

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gwern t1_jcb6nhe wrote

Reply to [D] What do people think about OpenAI not releasing its research but benefiting from others’ research? Should google meta enforce its patents against them? by [deleted]

> I feel it is fair for others to enforce their patents

There are millions upon millions of companies and orgs out there that release less research, and are more parasitic, than OA, many of whom are also making a lot more profits, if that's the problem. Why don't you go after them first, hypocrites? Why do you hate OA for being so much better than the rest?

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The_frozen_one t1_jcb4t0y wrote

Reply to comment by NoScallion2450 in [D] What do people think about OpenAI not releasing its research but benefiting from others’ research? Should google meta enforce its patents against them? by [deleted]

> Well that depends on whether OpenAI can prove Google is deriving commerical value from OpenAI's patented research.

That's not an issue, people make money due to patented technologies all the time. That's different from infringing on a patent. Either way, it would be an incredibly messy battle. Google invented the T in GPT, I can't imagine Google doesn't have a deep AI patent portfolio.

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haljm t1_jcazfgr wrote

Reply to [D] To those of you who quit machine learning, what do you do now? by nopainnogain5

Try more applied fields!

Not in industry (PhD student), but used to do deep learning type research with a computer vision background. I'm now working on applying ML for computer systems. At least 90% of my actual research work is building the system, collecting the data, and deciding how to frame the problem; for that last 10%, essentially anything reasonable will do. The catch is that if you don't have a solid ML background, you might completely miss that last 10% and never realize!

It's much more satisfying since if you're working on applications, you likely have potential downstream use cases lined up (that's what collaborators are for!). You're also probably the only person working on this specific problem formulation, so you don't need to worry about beating SOTA and squeezing out that last 5% -- your data-driven algorithm is 2x better than a traditional approach, so does it really matter?

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