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A key problem is Google alone has displaced academics e.g. at NeurIPS https://www.reddit.com/r/MachineLearning/comments/185pdax/d_neurips_2023_institutions_rank

A key problem is Google alone has displaced academics e.g. at NeurIPS https://www.reddit.com/r/MachineLearning/comments/185pdax/d_neurips_2023_institutions_ranking/ https://www.reddit.com/r/MachineLearning/comments/185pdax/d_... This trend heavily biases AI research towards Google problems. Perhaps things will swing away as LLMs (and especially smaller LLMs) take over the field.

vinni2 3y ago
I would say this has a cascade affect and big well funded universities are displacing smaller institutions.
- mistrial9 3y ago
  and add that a lot of mid-tier nations around the world, at their best universities, are getting very worried
light_hue_1 3y ago
I would not use publication numbers to argue anything. I'm an ML researcher at a university and publish at places like NeurIPS regularly. There are so many edge cases that render this data meaningless. There are people who have their name on a dozen papers. Reviewers are terrible and papers with a lot of graphs run at large scale and easy messages tend to get in (I'm not saying that this is the only thing Google publishes, but people with access to a lot of compute tend to publish papers like this). The funding gap between Google and academia is also massive. The fact that we're still competitive with Google is simply a testament to how incredibly efficient universities are and how inefficient Google is. They pay their people several times what we get paid. They have engineers which we don't. They have orders of magnitude more compute. They don't waste time on grants. Their total AI research budget is larger than the total combined AI research budget of all US universities. But.. somehow, they still account for only a small fraction of papers.
- DrFalkyn 3y ago
  > But.. somehow, they still account for only a small fraction of papers. They are only going to publish results which have no effect on their competitive advantage as a business ... which could be a small fraction of their results.
  - light_hue_1 3y ago
    Eh. I don't buy that. Business is built on engineering. That's what they spend the vast majority of their resources on. No one has any secret sauce science at the moment. And if they did their engineers would be bragging about it in private, bringing it to new companies when they move, and we would be seeing it in products or features. Heck they would be telling us when they apply to grad school.
    - Jensson 3y ago
      Imagine if Google didn't publish the transformers paper, Google does not just do engineering they also do research. If they find something similar today it wont go into the public since OpenAI set a precedent of not saying what they do.
      - light_hue_1 3y ago
        If Google didn't publish the Transformers paper their engineers would have given talks about it, would have used those ideas to apply to grad school, would have switched companies and built a similar model that's just different enough not to technically be the same, and we would have reverse engineered it immediately. Simply knowing a few keywords would be enough for anyone competent to reproduce all of the work. The space of possible models is huge. And we don't know in which direction to go to build better models. But knowing the vague general direction is all it takes. Any researcher can follow along given that knowledge. That's true of a lot of science not just ML. No one has any secret sauce. Not even OpenAI.

vinni2 3y ago

I would say this has a cascade affect and big well funded universities are displacing smaller institutions.

mistrial9 3y ago
and add that a lot of mid-tier nations around the world, at their best universities, are getting very worried

light_hue_1 3y ago

I would not use publication numbers to argue anything. I'm an ML researcher at a university and publish at places like NeurIPS regularly. There are so many edge cases that render this data meaningless. There are people who have their name on a dozen papers. Reviewers are terrible and papers with a lot of graphs run at large scale and easy messages tend to get in (I'm not saying that this is the only thing Google publishes, but people with access to a lot of compute tend to publish papers like this). The funding gap between Google and academia is also massive. The fact that we're still competitive with Google is simply a testament to how incredibly efficient universities are and how inefficient Google is. They pay their people several times what we get paid. They have engineers which we don't. They have orders of magnitude more compute. They don't waste time on grants. Their total AI research budget is larger than the total combined AI research budget of all US universities. But.. somehow, they still account for only a small fraction of papers.

DrFalkyn 3y ago
> But.. somehow, they still account for only a small fraction of papers. They are only going to publish results which have no effect on their competitive advantage as a business ... which could be a small fraction of their results.
- light_hue_1 3y ago
  Eh. I don't buy that. Business is built on engineering. That's what they spend the vast majority of their resources on. No one has any secret sauce science at the moment. And if they did their engineers would be bragging about it in private, bringing it to new companies when they move, and we would be seeing it in products or features. Heck they would be telling us when they apply to grad school.
  - Jensson 3y ago
    Imagine if Google didn't publish the transformers paper, Google does not just do engineering they also do research. If they find something similar today it wont go into the public since OpenAI set a precedent of not saying what they do.
    - light_hue_1 3y ago
      If Google didn't publish the Transformers paper their engineers would have given talks about it, would have used those ideas to apply to grad school, would have switched companies and built a similar model that's just different enough not to technically be the same, and we would have reverse engineered it immediately. Simply knowing a few keywords would be enough for anyone competent to reproduce all of the work. The space of possible models is huge. And we don't know in which direction to go to build better models. But knowing the vague general direction is all it takes. Any researcher can follow along given that knowledge. That's true of a lot of science not just ML. No one has any secret sauce. Not even OpenAI.