I like presenting the correlation of results between different benchmarks - I'd be interested in hearing to what extent this problem exists in more traditional benchmarking. One difference is that ML has this accuracy/quality component where in the past we've been more concerned with performance. Unfortunately this paper doesn't really address the long history of non-ML benchmarking, and I find it hard to believe no one has previously addressed the fragility of benchmark results.
What are some non-ML benchmarks? Or put another way, what domain are you referring to? Some examples I can think of are bit error rate for communication and full width at half maximum of the point spread function for image resolution.
BER is a much more objective benchmark (even if alone it might miss power or some other factor). FWHM has it's own set of problems, because unless your an astronomer, the thing you're imaging probably isnt a point source. So you get into more subjective resolution phantoms, e.g. in medical imaging, or comparisons in how some part of Lena looks under different treatments.
But mostly,these benchmarks are all much simpler and objective than something like imagenet that has so much randomness in it, and no real underlying principle other than it being diverse enough to be general. I'm curious if there are other similarly random benchmarks used in other domains?
Yes. The lottery is (supposed to be) uniformly random. You cannot do better than choosing a random number. If you could do so provably, that would mean the lottery wasn't fair.
We highlighted something similar in the multi-objective optimisation literature [1]. Unfortunately, it looks like comparing benchmark scores between papers can be unreliable.
- _Algorithm A_ implemented by _Researcher A_ performs different to _Algorithm A_ implemented by _Researcher B_.
- _Algorithm A_ outperforms _Algorithm B_ in _Researcher A's_ study.
- _Algorithm B_ outperforms _Algorithm A_ in _Researcher B's_ study.
That's a simple case... and it can come down to many different factors which are often omitted in the publication. It can drive PhD students mad as they try to reproduce results and understand why theirs don't match!
This really sucks when some papers don't come with code that can reproduce the benchmark results. I wish there was a filter for "reproducable" in search results
>> Thus when using a benchmark, we should also think about and clarify answers
to several related questions: Do improvements on the benchmark correspond to
progress on the original problem? (...) How far will we get by gaming the
benchmark rather than making progress towards solving the original problem?
But what is the "original problem" and how do we measure progress towards
solving it? Obviously there's not just one such problem - each community has a
few of its own.
But in general, the reason that we waste so much time and effort on benchmarks
in AI research (and that's AI in general, not just machine learning this time)
is because nobody can really answer this fundamental question: how do we
measure the progress of AI research?
And that in turn is because AI research is not guided by a scientific theory:
an epistemic object that can explain current and past observations according
to current and past knowledge, and make predictions of future observations. We
do not have such a theory of artificial intelligence. Therefore, we do not
know what we are doing, we do not know where we are going and we do not even
know where we are.
This is the sad, sad state of AI research. If AI research has been reduced,
time and again, to a spectacle, a race to the bottom of pointless benchmarks,
that's because AI research has never stopped to take its bearings, figure out
its goals (there are no commonly accepted goals of AI research) and
establish itself as a science, with a theory - rather than a constantly
shifting trip from demonstration to demonstration. 70 years of demonstrations!
I think the paper above manages to go on about benchmarks for 34 pages and
still miss the real limitation of empirical-only evaluations in a field
without a theoretical basis. That no matter what benchmarks you choose and
how, without a theoretical basis, you'll never know what you're doing.
>> I don't think there's not a universal theory of AI for lack of trying - it's just really hard and non obvious.
I agree, but in that case most of the work done until now is inconsequential and a waste of everybody's time, and we should probably drop all of it and focus on setting down a solid theoretical foundation for future research. But there is lack of any incentives to do that and there are strong incentives to go on as we are now, from demonstration to demonstration of results and techniques that never get anywhere.
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[ 2.8 ms ] story [ 34.3 ms ] threadBER is a much more objective benchmark (even if alone it might miss power or some other factor). FWHM has it's own set of problems, because unless your an astronomer, the thing you're imaging probably isnt a point source. So you get into more subjective resolution phantoms, e.g. in medical imaging, or comparisons in how some part of Lena looks under different treatments.
But mostly,these benchmarks are all much simpler and objective than something like imagenet that has so much randomness in it, and no real underlying principle other than it being diverse enough to be general. I'm curious if there are other similarly random benchmarks used in other domains?
>Sir, this is a Wendy’s.
I very much approve.
Congrats on beating the baseline! At this rate, surely you're only a few improvements away from winning the jackpot!
- _Algorithm A_ implemented by _Researcher A_ performs different to _Algorithm A_ implemented by _Researcher B_.
- _Algorithm A_ outperforms _Algorithm B_ in _Researcher A's_ study.
- _Algorithm B_ outperforms _Algorithm A_ in _Researcher B's_ study.
That's a simple case... and it can come down to many different factors which are often omitted in the publication. It can drive PhD students mad as they try to reproduce results and understand why theirs don't match!
[1] https://link.springer.com/article/10.1007/s42979-020-00265-1
But what is the "original problem" and how do we measure progress towards solving it? Obviously there's not just one such problem - each community has a few of its own.
But in general, the reason that we waste so much time and effort on benchmarks in AI research (and that's AI in general, not just machine learning this time) is because nobody can really answer this fundamental question: how do we measure the progress of AI research?
And that in turn is because AI research is not guided by a scientific theory: an epistemic object that can explain current and past observations according to current and past knowledge, and make predictions of future observations. We do not have such a theory of artificial intelligence. Therefore, we do not know what we are doing, we do not know where we are going and we do not even know where we are.
This is the sad, sad state of AI research. If AI research has been reduced, time and again, to a spectacle, a race to the bottom of pointless benchmarks, that's because AI research has never stopped to take its bearings, figure out its goals (there are no commonly accepted goals of AI research) and establish itself as a science, with a theory - rather than a constantly shifting trip from demonstration to demonstration. 70 years of demonstrations!
I think the paper above manages to go on about benchmarks for 34 pages and still miss the real limitation of empirical-only evaluations in a field without a theoretical basis. That no matter what benchmarks you choose and how, without a theoretical basis, you'll never know what you're doing.
e.g. physics and chemistry took centuries of empirical fooling around to develop the highly predictive theories we have now.
I think one of the points of this article is that we need to think deeply about what our empirical foolings around are actually telling us.
I agree, but in that case most of the work done until now is inconsequential and a waste of everybody's time, and we should probably drop all of it and focus on setting down a solid theoretical foundation for future research. But there is lack of any incentives to do that and there are strong incentives to go on as we are now, from demonstration to demonstration of results and techniques that never get anywhere.