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Earlier quoted context omitted.
Some days ago there was a post here on HN about a post doc who failed to get tenure. And every other comment was like: “What did he expect, he had much less than the usual two papers a year.” So it seems that even here on HN, the mindset of quantity over quality still persists.
Those were not normative statements.
Earlier quoted context omitted.
Some days ago there was a post here on HN about a post doc who failed to get tenure. And every other comment was like: “What did he expect, he had much less than the usual two papers a year.” So it seems that even here on HN, the mindset of quantity over quality still persists.
that author’s problem was that they had been a postdoc for 23 years. That duration alone raises fatal red flags.
I think there's actually a very simple explanation for this which lots and lots of people hate, so they're sort of in denial about it. Academia is entirely government funded and has little or no accountability to the outside world. Academic incentives are a closed loop in which the same sorts of people who are producing papers are also reviewing them, publishing them, allocating funding, assessing each other's merits etc. It's a giant exercise in marking your own homework.
Just looked at in purely economic terms, academia is a massive planned economy. The central planners (grant bodies) decide that what matters is volume and novelty of results, so that's what they get, even though the resulting stream of papers is useless to the people actually trying to apply science in the real world ... biotech firms here but the same problem crops up in many fields. It's exactly what we'd expect to see given historical precedent and the way the system works.
There's another huge elephant in the room here beyond the replication crisis ("to what extent are the outputs wrong") which is the question of to what extent are the outputs even relevant to begin with? Whenever I sift through academic output I'm constantly amazed at the vast quantity of obviously useless research directions and papers that appear to be written for their cleverness rather than utility. The papers don't have to be wrong to be useless, they can just solve non-problems or make absurd tradeoffs that would never fly in any kind of applied science.
I read a lot of CS papers and I've noticed over time that the best and most impactful papers are almost always the ones coming out of corporate research teams. I think this is because corporate funded research has some kind of ultimate accountability and connection to reality that comes from senior executives asking hard questions about applicability. For instance in the realm of PL research academia pumps out new programming languages all the time, but they rarely get any traction and the ideas they explore are frequently ignored by the industrial developers of mainstream languages because they're completely impractical. This problem is usually handwaved away by asserting that the ideas aren't bad ideas, they're just incredibly futuristic and 30 years from now we'll definitely be using them - but this kind of reasoning is unfalsifiable on any kind of sensible timescale so it's the same as saying, "I shouldn't be held accountable within the span of my own career for how I spend tax and student money".
As time goes by I am getting more and more sympathetic to the idea of just drastically cutting academic funding and balancing the books by drastically reducing corporation tax. The amount of total research would fall significantly because corporations wouldn't invest all the newly available money in research, or even most of it, but it's unclear to me that this would be a bad thing - if 75% of research studies coming out of academic biotech are wrong then it stands to reason that if standards were improved significantly, funding could be reduced by (say) 50% and still get a similar quantity of accurate papers out the other end. It's possible the science crisis is really just reflecting massive oversupply of scientists, massive undersupply of accountability and in general research should be a much smaller social effort than it presently is.
Curious (possibly naive) question: isn't there a fundamental difference between the goals behind creating models with ML vs the "old-fashioned" way? That is, in modern ML applications, you're creating a model with dozens/hundreds of potential variables, without a hypothesis of how they relate or contribute to the target (other than that they might, hence your including them in the modeling process). You're using the…
The tools are usually packaged for use by people who don't have the math background to understand the underpinnings. In the results of DOE's, I've seen everything that is now generalized as the "replication crisis."
Everything I've learned about ML so far (granted not a huge amount) invokes DOE -- fitting data sets to arbitrary functions whose form is more flexible than a Taylor polynomial but otherwise cut from the same cloth.
I've seen exactly the same problem as with ML, but 30 years ago: It can help you optimize a process that you don't understand, turning it into a better process that you also don't understand. But it can't tell you how something works. DOE seems to be a microcosm of ML, with all of the pitfalls such as overfitting and underfitting.
ML is not causing a reproducibility crisis, it just exposes one that is already there. > If we had an additional dataset would we see the same scientific discovery or principle on the same dataset? The same holds true for traditional science based on traditional statistics. It just seems that traditional datasets are under less scrutiny of reproducibility and are taken more easily at face value.
A specific issue with machine learning is overfitting and non-interpretability. The first means it is possible to get results that don't generalize (even if they survive cross validation). The second means it is a lot harder to detect use of correlations that cannot possibly be causal.
One article: https://slate.com/technology/2013/05/weird-psychology-social...
Could you not interpret this, in the widest sense, as overfitting interpretations to a 'weird' dataset, with the results that do not generalize, even though the stats (in this case, likely t-test) say everything is fine? In which case overfitting isn't a ML-only problem?
Fails to touch on the perverse incentives in academia, "publish or perish" etc. Torturing a dataset to find a p value that a journal will like (or equivalent stat measure) is better for your career than not publishing a paper that will be discredited in time. You have no incentive at all to decide "my results are unconvincing at this point, I'm not going to submit them" and every reason to write them up as a useful c…
Some days ago there was a post here on HN about a post doc who failed to get tenure. And every other comment was like: “What did he expect, he had much less than the usual two papers a year.” So it seems that even here on HN, the mindset of quantity over quality still persists.
Earlier quoted context omitted.
A specific issue with machine learning is overfitting and non-interpretability. The first means it is possible to get results that don't generalize (even if they survive cross validation). The second means it is a lot harder to detect use of correlations that cannot possibly be causal.
One of the issues with problems of reproducibility in the social sciences/psychology is that early studies usually choose WEIRD (white/educated from a rich/industrialised/democractic country) subjects, which are often uni students who are very different from the rest of the world. One article: https://slate.com/technology/2013/05/weird-psychology-social... Could you not interpret this, in the widest sense, as overfit…
The example you cite has an issue earlier in the chain. Here, we are dealing with a biased sample from some distrubution. The results from that won't generalize to the unbiased distrubution, no matter how great your model is.
Is machine learning really to blame for the reproducibility crisis? I'm not in academia, but it seemed to me that the problem was entirely present without machine learning being involed. For example, Amgen reporting that of landmark cancer papers they reviewed, 47 of the 53 could not be replicated [1]. I would have assumed that most of them didn't involve 'machine learning' [1] https://www.reuters.com/article/us-scie…
Not even the people who write the software understand what patterns are being found. All they can do is point to the results that seem good at a glance. But while we can train software to beat humans within a constrained dataset with careful checking, these techniques cannot find new insights. The software does not understand what the data it’s processing represents and thus it doesn’t recognize the abstractions and limits of the data. The patterns it finds are in the low resolution data, not in reality that data is a poor copy of. But science needs to be analyzing the real world and to do that you must comprehend the errors in your data and what they mean. We are nowhere close to making software that can do that.
I wonder: don't machine learning frameworks' results come with a level of confidence? Ps: I have no experience with anything regarding ML.
However, even if you're able to do that, the problem isn't solved. You have to answer level of confidence for what? The uncertainty / confidence that you get assumes your model is right. No model can tell you whether it is a true reflection of reality. I had written more about this on my twitter: https://twitter.com/paraschopra/status/1075033048767520768
Is machine learning really to blame for the reproducibility crisis? I'm not in academia, but it seemed to me that the problem was entirely present without machine learning being involed. For example, Amgen reporting that of landmark cancer papers they reviewed, 47 of the 53 could not be replicated [1]. I would have assumed that most of them didn't involve 'machine learning' [1] https://www.reuters.com/article/us-scie…
The problem was there before, but there are reasons why Machine Learning is amplifying bad practices. In the past people were manually fishing for results in available datasets. Now they have algorithms to do it for them. In medicine a popular way to use ML is to improve diagnosis. Now there's already a problem in medicine that the benefits of early diagnosis are overrated and the downsides (overtreatment etc.) usual…
Consider the simple task of peak fitting to determine the result for some data. You're probably using a commercial tool to identify the peak position, calculate the baseline, and come up with parameters for your model.
But if there's an error, and at least when I was in grad school the tools often would get stuck in weird local minima that take experience to recognize, it could easily just never be noticed. If your baseline is way off, good luck calculating your peak areas reproducibly...
Data analysis is hard, and it's easy to trust algorithms to be at least more reproducible than doing it more manually. Plus side if you provide your dataset and code others can at least redo the analysis! Really excited to see more Jupyter notebooks used for publications in the future.