Adrien Foucart, PhD in biomedical engineering.
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In competitions or in original research papers which compare the results of some algorithms on a given task, the centerpiece is generally the Big Table of Results. The Big Table of Results is where you put a list of algorithms on one axis, a list of metrics on the other axis, and you put in bold the algorithm that performs best. In original research papers, it’s where you justify that your method is better than the others, with tables such as the one below. See? It’s in bold!
| Method | Result |
|---|---|
| Old Classic Baseline [1] | 0.71 |
| State-of-the-art from a few years back [2] | 0.82 |
| Previous work from the authors [3] | 0.82 |
| This work [4] | 0.84 |
In a competitions, this gives us the leaderboard, which will look something like this:
| Rank | Team | Result |
|---|---|---|
| 1 | Big AI Research group | 0.91 |
| 2 | Big AI Company | 0.90 |
| 3 | Someone with 2 GPUs | 0.87 |
| 4 | Someone with 1 GPU | 0.84 |
| … | … | … |
| 157 | I don’t know what I’m doing | 0.42 |
Needless to say, things are a bit more complicated than that. In our new preprint, accepted at the ESANN 2025 conference and available online (PDF), we argue for a more nuanced approach to ranking where, instead of saying “this is the best method”, we compute confidence intervals on the rankings based on the assumption that the test set is a random sample from the larger population of “all cases where we may want to apply our algorithm”. We take the general procedure proposed by S. Holm in 20131, which uses the result of pairwise statistics tests to infer the confidence interval.
Several options for these statistical tests are evaluated using a Monte Carlo simulation on synthetic data. The procedure that appears to be the most robust based on our experiments is:
We release with this paper the cirank
Python library, which you can use to compute the confidence intervals
with:
from cirank import ci_ranking
import numpy # for the example
# example results:
results = [np.random.random((10,)) for _ in range(5)]
# default method
rankings = ci_ranking(results)
print(rankings)This paper (and the library) have large limitations in scope, and are likely to be expanded in the future – as discussed in the paper.
Reference:
A. Foucart, A. Elskens, C. Decaestecker
Ranking the scores of algorithms with confidence
ESANN 2025 (Accepted).
S.Holm. Confidence intervals for ranks. https://www.diva-portal.org/smash/get/diva2:634016/fulltext01.pdf, 2013.↩︎
Advent of Code is a yearly event, running since 2015. It is made by Eric Wastl, and it is “an Advent calendar of small programming puzzles”.
I had never heard of it until this year, when I started following the blog of Juha-Matti Santala, and saw him document his progress. I thought it would be fun – and I was right. I also thought that the practice of writing up how I came to the solutions was nice, and decided to do it as well. All of my notes on the puzzles can be found here: https://notes.adfoucart.be/aocode24. And all of my code, the good and the bad, is on my Gitlab.
One thing I really like about it is that there are basically no rules except the ones we bring with us. Eric posts a two-part puzzles every day, where you have to solve the first part in order to get the second part. You solve the puzzle by writing (or rather copy-pasting) a number into a box, and the only check that is made is if you have the right number. It doesn’t matter which language you used, or if your solution is clever, or efficient, or even would generalize to any other inputs than those he gave.
There is a leaderboard, but it is obviously just there for people who really want a leaderboard. The only metric used for it is how fast you solved the puzzle. I didn’t notice it was there until several days into the challenge.
So why do I do it? I decided to use it to get better at using the Python standard library efficiently, and to practice finding a good way to frame the problem so that the solution is generic and modular enough that, when part 2 is revealed, I can easily add to the existing code without modifying too many functions (ideally, none).
Sometimes it works, sometimes not. We’re halfway through, and I’m reasonably satisfied with most of my solutions so far. If you’re into that kind of things, I highly encourage doing it. All of the puzzles from the previous years are available as well.
Over on Aeon, psychologist Robert Epstein wrote a very interesting piece about how we often explain the functioning of the brain as if its a computer, retrieving memories and processing information, despite the lack of scientific grounding for that metaphor. Our brains don’t “recall” information, they “re-live” it. Information is not “stored” in neurons: our brain constantly evolved based on our experiences, and events can trigger our brains into re-activating the areas that were activated during a previous event, thus creating the impression of a “memory” for us.
This, I think, is the flip side of a discussion that I have had very often this past year about whether AI as it exists today can qualify as “intelligence” in any way. Just like “the computer” is not a very good metaphor for the human brain, the human brain is not a good metaphor for how AI algorithms work, yet this metaphor has completely taken over the way we think about AI. In fact, the whole vocabulary around AI is filled with human-intelligence metaphors: neural networks, learning, AI that explain their reasoning… and obviously “intelligence” itself. These metaphors may be useful to get a surface-level intuition about how AI algorithms work, but they are very much untrue and should not be taken as more than that: metaphors.
There’s this idea that, if we can make an artificial neural network that is as complex as the human neural network, then we should be capable of creating an actual intelligence. But that makes two hypothesis which are unsupported by evidence: that artificial neural networks are good models of human neural networks (they aren’t), and that human neural networks are solely responsible for human intelligence. That doesn’t seem to be the case either. Our brains work as part of an intricate, interwoven network of systems which all interact with each others in ways that are very hard to capture. Our neurons without our endocrine system, our immune system, etc., are a very incomplete snapshot of who we are. We can’t – and maybe won’t ever be able to – upload our brains to the cloud.
Artificial Intelligence is not Human Intelligence, and it doesn’t really make any sense to compare them in any way. Any AI algorithm has capabilities and limitations, and we can study those without falling into the trap of anthropomorphism. That’s why I don’t like it when LLMs are compared on benchmarks such as law exams, or common coding exercises. Those tests were designed for humans, to test capabilities that are often difficult to acquire for human intelligence. For an AI, it doesn’t tell us much. Importantly, it doesn’t tell us that this AI is a good lawyer, or a good software developer. Yet these are still the kind of tests used to compare LLMs today. Perhaps the most used test right now is the “Massive Multitask Language Understanding (MMLU)” test. The MMLU is composed “of multiple-choice questions from various branches of knowledge”, which were “manually collected by graduate and undergraduate students from freely available sources online”. But we aren’t using LLMs to solve multiple-choice questions from various exams and tests collected around the web. For any actual task that we have in mind for such a model, we would need to prove that the MMLU is a useful proxy to evaluate the capability of the model to perform the task.
The fact that the MMLU is collected from online sources also makes it extremely difficult to use it as a benchmark for modern LLMs, which are often trained on very opaque datasets, also collected from all around the web. The risks of contamination are huge, and the measures taken by LLM developers to mitigate this issue are often doubtful, if they are even described at all. The Gemini paper, for instance, states that they “search for and remove any evaluation data that may have been in our training corpus before using data for training”, but don’t provide any detail as to how this search was done. If they used the same method as GPT-4 (i.e. looking for exact matches on substrings of the questions), then the risk of contamination is high.
There is often a very large gap between the performances of the LLMs in benchmarks and their performances in real-world applications, and this is probably part of the reason: the benchmarks are not made for the evaluation of a computer program, but of a human brain… and those are way too different for it to work that way.
I must confess that, sometimes, I Google myself. I typically know what I’ll find: I have enough of an online presence that most of the results are related to me (and one other Adrien Foucart who competed in Judo fifteen years ago), and it’s typically a mix of my blogs, scientific papers, social media, and from time to time a post about things I’ve written or done. Finding those is the main reason that I do this exercise every once in a while. Today, however, I got a surprising result, from a website that I had never heard about: SciSpace.
SciSpace is yet another GPT-powered chatbot, aimed at scientists who want to outsource their research to a machine. You ask a question, it answers with a summary built from scientific papers, with citations to those papers so that you can read them if you want to do some work somewhere in the process. I don’t think it’s a good idea: doing those kind of summaries is how you actually gain the understanding of your field, and you’ll necessarily miss a lot of the nuance of what’s happening in the field if you just get the AI-generated “summary”. So even if it worked perfectly as advertised, I wouldn’t recommend using it. But the reason I’m writing this is that it fails pretty spectacularly at its job.
It seems that SciSpace allows you to browse questions, presumably asked by other users. Google indexed a question where, surprisingly, I appeared in the answers. I say surprisingly because the question is not quite in my field: “What are the specific cultural criticisms associated with the implementation of Panopticon in various societies?”
The beginning of the answer seems to be on-topic, although since it’s a topic I know nothing about, it could all be bullshit for all I know. But it’s around the end that I suddenly appear, with this tangent:
Lastly, Adrien Foucart and colleagues critique the Panoptic Quality metric in digital pathology, illustrating the challenges of applying panoptic principles to complex, nuanced fields [10].
This is a reference to my Scientific Reports paper “Panoptic quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology”, which is probably the paper I like the most out of my publications, but which has absolutely nothing to do with the Panopticon, outside of the fact that there is “Panoptic” in the title.
And this, I think, is where GPT went wrong. Because GPT predicts tokens, which encode parts of words, “Panoptic” and “Panopticon” likely share one or several tokens in common. This alone shouldn’t be enough to trip the model, but it also happens that one of the names most associated with discussion of the Panopticon is Michel Foucault, and “Foucault” also shares at least one token with “Foucart”. This taken together probably sent SciSpace’s GPT into the wrong direction. Because, as we should all know by now, GPT has no fucking idea what it’s talking about. It’s all just a statistically likely string of tokens, with no understanding whatsoever of what’s going on.
So Foucault and the Panopticon end up mixed with Foucart and Panoptic Quality, even though these come from completely different domains. I should also note that for some reason my paper is mistakenly cited as coming from “Dental science reports” instead of “Scientific Reports”. No idea what happened there, but another clear reason that this tool is absolutely useless.
From the CEO’s LinkedIn profile, the platform is “used by more than a million researchers worldwide, including Nobel Laureates”. I highly doubt, however, that Nobel prize worthy science will be done with it anytime soon.