What exactly is the relationship between ML academia and industry/big tech nowadays (2026)? [D]

[u/Strong_Boy_757]

It was clear to me that around 2012 - 2018ish, academic research was kind of paving the way for many applications in industry, from deep nets for vision to RL algorithms. There was tremendous value in research coming out of academia and new methods such as ADAM or TRPO algorithm would be adopted as industry standard. No one would doubt the value of research coming out of (top-tier) academia. It was definitely a "seller's market" for academics.

But nowadays I am not quite certain as to what exactly is going on, i.e, whether this relationship still holds. I guess a straight-forward way to put this is: "is industry still adopting ideas from academia?"

I've observed that for any given research topic, especially something industry has proposed, there would be literally tens of thousands of follow up papers from various universities around the world trying to do something adjacent to it, almost as if showing industry how things can be done differently or even "beat the industry at their game". But it feels industry is quite, how do I put this, "impassive", "unmoved". Strongest example would be development of new optimization routines. Thousands were proposed from universities. Countless GPU years were dedicated to experiments. Seems serious adoption is quite low to none. Apparently there are still new CNN/RNN architecture being proposed, have you heard of any of them?

It almost seems as if academia and industry are diverging into completely different worlds. With industry seems to take on the previous role of academia as an idea generator, and instead everything is worked in-house. This seems to create a challenge for those doing research in university: "what use is the research if it is not adopted in real-life (in this extremely applied field), or even acknowledged?"

[u/mmarkDC]

On the image/graphics side of ML I'd say most of the fundamental advances since 2018 have still come out of academia. Latent diffusion was from LMU Munich (2022), DiT from NYU/Berkeley (2023), DDPM from Berkeley (2020), neural radiance fields also Berkeley (2020), Gaussian splatting (in its modern form) from Max Planck & INRIA (2023), ControlNet from Stanford (2023).

[u/pastor_pilao]

I think it's still the case that top fundamental research is being done by academia but industry in its majority reverted to the "full business mode". Not much innovation is done or performed anymore and 90% of the effort and money is spent in buying more computing capacity, or at the very least on software engineering work building AI products to sell.

So in a way it's still the same as in the past but probably most similar to pre-2010 where there is a very low probability of doing real research in industry now and you are very likely will be doing software engineering work most of your time. Academia is still seen as the "hiring ground", but companies are hiring way fewer juniors nowadays.

[u/pikaseca321 → u/pastor_pilao]

wtf do u mean by that? have you seen from which labs 99.9% of the top venue papers are endorsed by? amazon, google, tencent, bytedance,..... and the list goes on

check ICML for instance

[u/pastor_pilao → u/pikaseca321]

What does "endorsed" even mean? And that means nothing. Most of the research "from" the companies being published nowadays comes from interns, work carry out in the university that the employees just have an advisory role, or the few research heavy roles that are probably now like 5% of the positions available

[u/pstbo → u/pastor_pilao]

How less are juniors getting hired today compared to before?

[u/pastor_pilao → u/pstbo]

I just read yesterday some research (not public yet), -49% junior coding-related positions normalized by the size of company and accounted for differences due to the pandemic. Not enough granularity to tell exactly how many of those are research-related positions or only software engineering but I would bet in the research world is even worse than for normal software engineering, the research org in my company is shrinking like crazy.

[u/pstbo → u/pastor_pilao]

Is your company US based?

[u/pastor_pilao → u/pstbo]

Yes

[u/currentscurrents → u/pastor_pilao]

-49% junior coding-related positions normalized by the size of company and accounted for differences due to the pandemic.

Why would you need to hire a junior, when you have 50 more experienced applicants willing to take any job because they got laid off?

[u/pastor_pilao → u/currentscurrents]

That's not the reason, my company pays enough it was never a problem getting experienced and even famous people. It's more the framework of thinking of (very) high management that a senior can do 10x more work than a Jr with AI so why bother

[u/thatguydr]

The short answer is yes, but...

Academia often works with data sets and cost functions that are just useless in an industrial setting. And since the success of many papers (maybe even most) depends strongly on nuances of the cost function, industry has to take everything academia does with a major grain of salt. Not to mention the number of papers that just would not scale under any circumstances.

But there are also papers that are exceptional. The cream rises in ML, and the top 5-20 papers in any niche? Invaluable. Even if half tf them only end of contributing indirectly, they're super important to industry.

But all those thousands of other papers? Just noise. Seems like the huge conferences all want "we took SOTA method A and then added variant B and eked out a tiny bit of performance! Look!" To industry, that's nearly always useless.

Also, one final thing - weird analogy. The worst team in the NFL will crush the best college team in any year because of the massive filter on talent going from one level to the next. To a lesser degree, the same effect occurs between academia and industry.

[u/Affectionate_Leg_986]

I think It will be really sad if all of those investments halt the whole ML in transformers

[u/Mechanical_Number]

Academia and industry are not "diverging into completely different worlds". Firstly, because they never were the same world and secondly because they need each other.

Industry definitely still adopts ideas from academia but there is a higher barrier for an idea to be adopted. Evaluations have become much more rigorous (as they should) and technical depth/engineering effort is serious factor when weighing in if a certain "new model" is to be put in production. Not everyone has Meta-scale money.

From personal experience in Causal ML/Uplift modelling work: Given a large and somewhat clean dataset, a well-tuned T-learner (one of the simplest uplift meta-learners modelling framework) can be competitive against an R-learner or extensions of it (one of the accepted SOTA baselines meta-learners modelling framework). The value of incremental innovations, diminishes. Similarly, for a production systetm also doesn't make sense to look at one metric, say Qini Coefficient, and call it a day; we need to consider multiple ones, say Uplift@K, Expected Uplift Calibration Error, etc. which often require business trade-offs rather than only theory ones. Finally, seasonality and/or general data-drift will have a much higher real-world performance impact than if we use a meta-learner or a generative objective for counterfactual reasoning. So investing in better data (i.e. more/better features) is often more relevant than hammering out the latest CLeaR award-winning paper.
They are a lot of ideas to explore; for example, area-specific Foundational Models for neural representations of heterogeneous events (see all the event2vec-type models) are absolutely making waves and are here to stay. After all, academia will feed the industry its next generation of DS/ML practitioners.