Don't get me wrong, Sutskyver (et al) has done incredibly good work previously, but when it comes to the products, they're much more engineering and marketing polish than scientific endeavours.
Botvinick's work on metaRL for example is an interesting direction that Deepmind has shown that few other companies that are only interested in engineering would venture towards.
So yeah, it's essentially a PoC PR stunt factory. Just look at AlphaZero. They make a huge deal about a suspiciously set up match against Stockfish. Supposedly revolutionising computer chess. But the problem is the computer chess community had to redo all of the work, including all the training to build Leela Chess Zero. Due to lack of Google-sized datacentres the training took years to catch up to the weights in AlphaZero. Same thing with AlphaGo, same thing with transformers.
Now, in AI, usually getting a proof of concept is the easy part. Developing that into an idea that actually works in real world situations is usually the hardest part. I completely reject your idea that somehow the work by OpenAI is less worthy of recognition. I think that's just nonsense.
And surely, Google created Deepmind to actually make them product ideas, not create new competitors, which is what has happened.
I disagree. Of course there is a lot of engineering involved and it's also very important but it's much easier to rebuild things based on published research than develop novel ideas.
There must be a "demonstrate (1) DeepMind #Win per <interval>" requirement somewhere that gets the once-over from the marketing dept. to meet some MBOs.
1) you could see increased activity in go clubs and online go servers
2) the analysis of the games published by Deepmind has resulted in interesting "discoveries" (or rediscoveries) and changes to what is considered joseki.
3) many people started analyzing their kifus using AI, to find fluctuations in estimated win rate across moves.
So I disagree entirely
Can you explain this for someone unfamiliar with the game?
https://www.youtube.com/watch?v=H4DvCj4ySKM
EDIT:
Also, if you want to get into it, Micheal Redmond's Go TV on youtube has an amazing beginner playlist, watch some of that then maybe blacktoplay.com and if you likey play :)
https://forums.online-go.com/t/how-does-the-rating-system-wo...
We (hacker news) discussed Lee Sedol's retirement here: [1]
To active go players at the time, Alpha Go and Alpha Zero really were as shocking as the debut of Chat GTP was recently.
I think their assertion is that the release of AlphaGo has actually made human Go players worse at the game, contrasted with chess where most agree that the introduction of Superhuman chess engines has elevated the (human) state of play.
But I don't think there is actually much evidence for that. I'm sure the introduction of AlphaGo did take the wind out of some players sails, who thought of themselves as superior to our best computers, but for everyone else it seems to have elevated the overall level of play just the same as the chess engines have done.
[0]: "The sudden overall increase in agreement in 2016 also reinforces the belief that the introduction of powerful AI opponents has boosted the skills of professional players." https://ai.facebook.com/blog/open-sourcing-new-elf-opengo-bo...
Geez. We are talking about pebbles on a wooden plank. They are not even colourful!
Go is super cool game, but it is that. Just a game. We are not talking about curing cancer, or solving world hunger, or reversing climate change here. So by the very formulation a Go playing AI can be cool, or interesting, or promising. But could it really be useful/important with all-caps? It sounds like you have too high expectations here.
AlphaFold catapulted protein structure prediction forward, and it's hard to overstate how important understanding protein structure is in modern drug development
As an example of how this will be used to help actual people, here's a paper that uses AlphaFold to identify the parts of cancer-associated proteins that interact with each other.
https://onlinelibrary.wiley.com/doi/full/10.1002/pro.4479
The obvious next step is to develop drugs that disrupt these interactions and thereby disrupt cancer. But, it's going to take years, maybe decades before any drug resulting from this research is in actual patients.
There are dozens of other papers like this.
1) The drug couldn't have been discovered without AlphaFold 2) It has been proven to reduce all cause mortality (the thing real patients actually care about) in a randomized controlled clinical trial BETTER than the prior standard of care (or significantly more cheaply, or with significantly reduced side effects)
It's like pointing to special relativity in 1905 and saying it'll never be useful for anything.
It might, and in fact I think it probably will. But it hasn't yet.
It’s not hard to see why, with the emergence (ha) of OpenAI, Midjourney and all of this generative modelling, what has DeepMind done? I imagine the execs at Google are asking them some very probing questions on their mediocre performance over the last 5 years.
Deep minds work on Density functional theory was complete rubbish, and everyone in computational chemistry knows it. They simply modelled static geometry and overfit their data, we wanted this methodology to work, computing DFT is expensive, and we did multiple months of rigorous work and the reality of the situation is that a bunch of machine learning engineers with a glancing amount of chemistry knowledge, made approximations that were way too naive, and announced it as a huge success in their typical fashion.
What they then count on is people not having enough knowledge of DFT / Quantum property prediction to query their work and make claims like “it certainly does move academia way further ahead” - which is total rubbish. In what way? Why aren’t these models being used in ab initio simulators now? The answer to that is simple: they are not revolutionary, in fact they are not even useful.
Go players are using AI to get better at Go.
Read this for example. The author is Korean pro.
"The upside is that we sometimes see a player who was somewhat past his prime suddenly climb back to the top, having trained with AI more intensely. There are a growing number of young and new pros who demonstrate surprising strength. This change gives hope to all pros who dream to become number one, and also makes competitions more interesting to fans as well." [0]
There are serious downsides too.
Also [1]
[0] https://hajinlee.medium.com/impact-of-go-ai-on-the-professio...
[1] https://www.newscientist.com/article/2364137-humans-have-imp...
Hear me out - what if we learned something about creating AI by creating a new AI?
A rising tide floats all ships – if the best in the world becomes better, others can look at the best and learn from it. What difference does it make if the best player is an AI or a human? The better moves and strategies are still better moves and strategies.