The news of the existence of at least one collusion ring in the AI/ML conference peer-review system has made some waves recently (here and here are two recent reddit threads on this topic). What would be the most meaningful response to this kind of explicit fraud in the system? In this post, I’d like to express some possibly unpopular and uncomfortable opinions (which is something I like to do in general apparently :)) and toy with some radical ideas/suggestions for improving the overall AI/ML research ecosystem.
First of all, it’s important to realize that people respond to incentives. Although, of course, pointing this out doesn’t absolve individual culpability, issues like this point to systemic problems that need to be addressed systemically. It is hard to imagine something like this happening, for instance, if conferences weren’t such a high-stake game in AI/ML research. So, we have to ask ourselves why the stakes are so high. Michael Littman’s article partially answers this question:
… stakes are high because acceptance rates are low (15%–25%), opportunities for publishing at any given conference are limited to once a year, and publications play a central role in building a researcher’s reputation and ultimate professional success. Academic positions are highly competitive, so each paper rejection—especially for graduate students—has a real impact on future job prospects. Some countries correlate promotion and salary decisions to the number of papers accepted at a specific set of high-profile conferences (and journals).
Why are academic positions highly competitive? It’s because there are too many candidates for too few positions. These too many candidates produce too too many papers, too many of which are, to put it bluntly, worthless. Even when these papers are technically sound, they don’t address any interesting or important problems, they propose simplistic ideas in the context of toy problems that obviously won’t pan out for any sufficiently interesting and important large-scale realistic problem. The sad truth is that even if these papers are accepted by a conference, they won’t be read by anybody, won’t provide any benefit for any practical use, and won’t even have any tangible impact whatsoever on the field in the long run. There’s no reason for anybody to waste their time on papers like these, other than the Machiavellian reasons touched upon by Littman (basically to signal to their potential employers that they are “productive” and to chase after power, prestige, and money). There’s no good reason for the public to fund this kind of unproductive research with taxpayers’ money.
It could be argued that this situation is inevitable: most ideas will lead to dead ends, only a very small number of ideas will win out in the long run through a process of natural selection of ideas. But, this is not true: yes, some ideas will, of course, not pan out in the long run, but the current quality/quantity combination for research outputs in AI/ML is clearly not ideal. In my opinion, an alternative research landscape more or less exclusively dominated by a small number of large industry labs like OpenAI, Google Brain, FAIR, etc. as opposed to a large number of small academic labs would clearly land us at a much more favorable position in the space of quality/quantity of research outputs, so the current situation is not inevitable.
This problem, by the way, isn’t specific to AI/ML research, it afflicts most of academia, but probably becomes especially acute when a field becomes “hot.” I sometimes genuinely wonder: at what point do academics in general admit that their field is basically artificially driven by government money and by irrational incentives and rent-seeking behavior? That there are just too many people employed in their field going after too many unproductive, obviously flawed ideas, or uninteresting, insignificant questions? Perhaps the answer is never, because as Upton Sinclair once observed, “it is difficult to get a man to understand something when his salary depends on his not understanding it.” Can academics really justify that they should get this money instead of a public school, or a public hospital, or a homeless shelter, for instance?
What is my proposal then? What would a more rational system look like? First of all, I suggest that there should be a lot fewer people working professionally in AI/ML research. In recent years, most of the interesting and impactful work in this field has come from large industry labs that have the resources to run large scale experiments, so perhaps they should employ the overwhelming majority of the people working professionally in the field. This would mean basically winding down most of the low-impact academic research in AI/ML. Also, in a more rational research landscape, a lot more collective effort/resources than now would be spent on improving hardware and collecting/curating data.
For the rest, I propose a system similar to the marketplace for music production/consumption. The barriers to entry into the field aren’t very high in AI/ML research. Fortunately, large industry players generally share their tools/models publicly. Obviously, they can always do a better job in this respect, for example by making their internal large scale datasets public, by making large scale compute more affordable, more readily accessible to amateur researchers. Motivated amateurs would then produce “content” using these tools and share it publicly: if you think you built something cool, you should just put it out there: write up what you did in a report, put it on arxiv, put your models and code on github in an easily accessible format for others to use and most importantly, make demos to get people excited. If you really did something cool, people will notice it, including prospective professional employers. This would then be the motivated, talented amateur’s ticket to a professional career in AI/ML research.
As this system would eliminate most academic research in the field, there wouldn’t be any need for conferences/journals (of course, conferences could still be organized to meet with people and discuss ideas in person, but they would be a much more informal affair, perhaps more like workshops today). Peer review would be carried out publicly in the marketplace of ideas. There would probably be much less output overall, and whatever output is produced would be more likely to be interesting and impactful, because it would be produced by people genuinely driven to create something interesting and useful to others.
A good yardstick that I like to think about in this connection is OpenAI. Wikipedia says they employ over 120 people. Now, I don’t know how many of those are involved in research, but let’s say ~100. It’s probably safe to say that these are some of the smartest, most talented people in the field. Yet, if we consider their research output quantitatively, it’s not that much. Every year, they put out only a handful of extremely high-impact, high-quality papers/products, like GPT-3, DALL-E, CLIP etc. If the very same set of people were employed in academia instead, they’d probably produce at least one or two orders of magnitude more papers between them, but these papers would be much much less impactful and lower in quality, again attesting to the irrational, unproductive incentive structure of academia.
I should make it clear that I’m not advocating winding down AI/ML education in academia, just research. In fact, education could be the main legitimate purpose of academia under this system. I should also make it clear that I’m not suggesting this system as a model for research in all fields. Some fields with higher technical barriers for research (for example, molecular biology) clearly produce very useful, practical knowledge and/or make meaningful contributions to our understanding of nature (although as I mentioned above, I think the same bad incentives are at play in most places in academia to some degree, so shrinking the size of academic research in general would perhaps not be such a bad idea).
I know at least two other fields quite intimately: cogsci/psychology and neuroscience. Now, I’m going to make an extremely incendiary claim and suggest that research in neither of these fields has produced anything of much value in our understanding of how the mind/brain works and so both deserve a significant shrinkage in size in academia as well. It’s not an exaggeration to say that I have personally learned a lot more about the nature of intelligence, cognition, perception and about how our brains might be doing all these things (supposedly the main subject matter of psychology/neuroscience) from the deep learning research that came out in the last 5-10 years than from decades of simplistic, unfruitful, and sometimes frankly straight up silly psychology/neuroscience research (I’d be extremely willing to debate this issue with anybody who has a different opinion about it). I humbly but sincerely suggest that as a first small step toward improving itself, psychology/neuroscience research can start by putting an indefinite moratorium on the mind-numbingly and soul-crushingly dull and uninteresting left-right random dot motion discrimination task and all its equally uninteresting and insignificant variants. Please do it!