Since this post contains some criticism, a preliminary disclaimer is necessary. The conference was well-organised, and I congratulate the chairs for this great event. Social events, food, venue, etc., everything was great. My criticism will be more on what is considered good scientific work in the ML community. This criticism is not limited to ML. I attended an ML conference, so I limit my discussion to the ML community, even if I suppose one can observe similar observations in other communities. Hence, the concerns exposed in this post are not specific to ECML-PKDD or its organisation.

Moreover, I do not want to seem rude or smug. This post aims at sharing the first (positive and negative) impressions of a naive and young PhD student at an ML conference. See this post more as the first page of my research life diary rather than as a structured criticism of scientific practices.

Discovering a scientific conference with fresh eyes 🤓

ECML-PKDD 2022 was my first in-person conference. So I didn’t know what to expect. Some of my colleagues told me about conferences they went to, such as ICML, but I knew ECML-PKDD was smaller. So it was hard to picture exactly how it would feel to be at a scientific conference. At first, I was a bit amazed that such an enormous scientific event happened while the general public ignored their existence.

Specialized conference but already so many unknown topics

As a PhD student, I was overwhelmed by the program. So many topics, so many papers, and among them, very few that I can fully understand. It was a humbling experience to see how limited my knowledge is. I used this as an opportunity to get first intuitions about many unknown concepts. When I started my research work, I always wanted a perfect understanding of everything I read. I spent days on some articles to grasp all the details. With this kind of event, I learn to look for general insights and leave the details for later. Considering how rich scientific literature is, I cannot afford the price of exhaustiveness.

For example, I don’t work on concept drift in ML (concept drift defines a change in the distribution between the training data and real-world data). When I listen to a talk about concept drift, I don’t even want the details because I already ignore the state-of-the-art. I expect the speaker to explain why this paper changed the landscape of concept drift but not precisely how. I want a before/after photo, like for a haircut but for a scientific object. I want to understand why this new haircut (state of the art in the case of scientific objects) is different and avoid all the hair-cutting process. Maybe someday, I’ll work on concept drift and having concise conclusions I can remember is crucial so I have a starting point when this day happens. I should keep the discovery details for the day I need them because I’ll forget them anyway. Finally, these expectations are also in line with the time left to the presenters. A presenter should not even try to give details in a 10-minute presentation. Any attempt would result in a snorkel-like exercise where the speaker doesn’t breathe for 10 minutes to expose all elements.

Poster sessions are kinda cool

Before coming to ECML-PKDD, the poster format was not appealing to me. I felt like it was an old habit that researchers maintained as a tradition. I couldn’t imagine how wrong I was. Poster sessions are better than anything else to discuss with other researchers. It is way better than slides to encourage interactions.

Moreover, it forces the presenter to adapt the presentation to her audience. You can’t have an already written text that you read (as some speakers do with Powerpoint). As a result, the presentation seems more authentic, and I feel more comfortable asking stupid questions. Indeed, in front of a crowd, you always have an expert asking a super precise and intelligent question. I cannot take the mic after this person and ask a basic and possibly stupid question.

Brilliant researchers are way too normal

Finally, ECML-PKDD was an occasion to see some brilliant researchers such as Francis Bach and Yann Le Cun. I studied in a small French university, so I had fewer opportunities than other (Parisian) students to meet researchers of such world renown. I was a bit disappointed by how normal they seemed. I was expecting quirkier researchers. In the past, I read some stories about the mathematician Norbert Wiener, and I wanted to meet such unique personalities. There are two solutions to this enigma: either ML researchers act normal in public to avoid having funny stories published about them by their peers, or ML has become so mainstream that we lost all sense of quirkiness in the process :clown_face:

Disillusions 😢

Now, it is the challenging part for me and, possibly, the juicy part for you: it is time for some criticism. I encountered three main issues: over-specialization, (capitalistic) motivations of the papers, publish or perish.

Over-specialization 🔬

This first issue is related to my expectations for the presentations. Papers are increasingly specialised; non-expert researchers barely understand most of these papers. I had a similar remark on my first paper, but the problem became real when I attended ECML-PKDD. I saw presentations of ML papers from which I could not draw any conclusions because I could not understand anything. It can then be a beginner mistake.

I believe this is a more general issue. An ML conference is already a specialised conference, and any participant should be able to understand (at least partially) most presentations. Unfortunately, it is not the case, and it reminded me of a conversation I had during a summer school on sustainable AI in Saarbrucken (Germany). I met a PhD student in philosophy who asked the ML PhD students something like: “But you cannot even understand each other research, right?” (she said that, in Philosophy, any peer is usually able to give feedback). We somehow answered that we do understand each other to some extent. Unfortunately, she quickly identified a rather sad reality (according to me). Of course, I don’t expect any CS student to grasp what we do, but having any ML researcher would already be a satisfying first goal.

I am working at the edge of several CS fields: security, cryptography, and ML. Over my short career, I have seen researchers only look at the publications of their sub-community at the risk of reinventing the wheel. Over-specialisation is a problem because it makes conference participation less appealing than it could be, but it is also a problem for scientific production! I am probably judging researchers a bit too quickly there. Of course, having a fixed community is also convenient for collaboration and publication, but I still want to emphasise this impression I got.

Capitalistic motivations of the papers 💶

I want to start this paragraph with a simple statement: everything is political, including scientific work. I am one of those researchers irritated by the claim of “apolitical work”. Unfortunately, I don’t have time (and qualifications) to detail this topic, but I suggest reading [1] by L. Wiener, which is a great starting point for this kind of discussion.

Many papers had motivations that I find quite worrying. At the conference, you can hear all the buzzwords promoted by the industry. Usually, these papers improve an industrial use case. Industrial applications are not fundamentally bad, but they represent a large part of the conference. We have very few papers promoting applications with societal impact. The only effect I foresee for these papers is even more Big Tech companies, more monopolies, etc. For example, several talks on recommender systems explained how to improve Amazon-like recommendation systems. We live in a world where recommender systems create filter bubbles and may promote extremist ideas. Still, the motivation is to improve by 1% Amazon recommendation so they can sell us more unnecessary objects.

This point of view might seem a bit harsh, but I am part of a generation that will have to deal with many crises in the future: climate, the rise of far-right influence, etc. I see a lot of public-funded research to contribute to private interest while there are plenty of challenges to overcome nowadays. We should be more careful with our expectations in terms of motivation. These papers are usually applied papers where poor motivations induce poor quality, whatever are the improvements “shown” in the figures.

Recently, I read [2] by Rogaway, which detailed such criticism for the cryptography community. Rogaway highlights the role of cryptographers and questions the interests of the current research works. He even identified when a split happened in the crypto community: when the cryptographers started doing cryptography for cryptography instead of cryptography for the people. I think a similar discussion is necessary among AI researchers. We need to define the exact role of ML research in our societies. Yann Le Cun gave a first answer to this question during his keynote: he promoted autonomous intelligence (via self-supervised learning). This idea seems very similar to the dreams that the AI founders may have had. However, I can also criticize this vision and ask whether it truly fits the current challenges of our societies. Some researchers (such as Romain Couillet [3]) are developing a discourse about frugal AI and even question whether a sustainable AI can exist considering modern ML models’ carbon footprint. Finally, there is also the issue of developing AI for people. Currently, most AI models are only used by big companies to increase profit. Then, I want to add another question: can we use AI to truly improve the average human being daily life? If yes, is it what we are doing?

Publish or perish: motivations of the researchers 💀

Now that I have talked about the motivations of the papers, I should talk about the researchers’ motivations. I am not discovering the publish or perish concept (i.e., the pressure to publish more and more publish to succeed in academic careers). However, I found how researchers behave according to it. Usually, all researchers discussing this concept agree that it is a problem. But, at this conference, it became evident that many of them fully accepted it. I can give a concrete example: I discussed with a PhD student who presented his work vaguely without conviction during a poster session. At the end of our discussion, he even said that he published it because an implicit requirement is to have at least three papers by the end of your PhD. I was astonished by his sentence. I am sure many researchers share this attitude but don’t dare say it as explicitly as this student.

I had the same feeling during a Scientific Writing class where I was part of the minority defending that the first motivation when submitting a paper must always be “having results to share with the scientific community”. Professional success can only be a positive side effect. To justify that, I tried an analogy with surgery. A surgeon must undergo surgery if and only if it is necessary for the patient. The fact that the surgery may bring money or success to the surgeon should never be a motivation… at least, that’s what I expect from my surgeon.

I understand that many of us would appreciate a position in academia. However, I don’t think we should develop an unhealthy attitude to achieve our personal goals. These researches are funded with public money, and even if the media give little attention to Science, we should be careful in being exemplary. Our profession is still highly respected, and we must maintain this trust. Moreover, we are all researchers in ML, and we cannot argue that we absolutely need a job in academia to live. One can make good money in the industry with an ML degree. I acknowledge that academic research can be a dream job, but we should not support a toxic system to reach this goal.

Conclusion: what about me?

After all this criticism, you may want to confront me and ask whether I am a perfect scientist producing excellent scientific work with pure motivation. First, I am nobody in the scientific community: I have two published papers and am early enough in my PhD not to worry about actions I should take to preserve my academic career. Hence, I have no authority, and I accept that one may consider all my thoughts rubbish from a young student who doesn’t understand the scientific world. However, my ideas might be interesting because they confront the current scientific practices with a PhD student’s naive and ideal expectations.

Undoubtedly, I will publish over-specialized papers (and already did!), I’ll have publications with purely capitalistic motivations, etc. However, I’ll also try to have scientific practices that converge as much as possible towards the ideals I had in me on September 19, 2022, when I arrived in ECML-PKDD.

References 📚

  1. Winner, L. (2017). Do artifacts have politics?. In Computer Ethics (pp. 177-192). Routledge.
  2. Rogaway, P. (2015). The moral character of cryptographic work. Cryptology ePrint Archive.
  3. Couillet, R. et al. (2022). L’intelligence artificielle peut-elle devenir un outil convivial? Preprint. In French only.