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Scientists Are Failing To Replicate AI Studies (sciencemag.org)
The booming field of artificial intelligence (AI) is grappling with a replication crisis, much like the ones that have afflicted psychology, medicine, and other fields over the past decade. From a report: AI researchers have found it difficult to reproduce many key results, and that is leading to a new conscientiousness about research methods and publication protocols. "I think people outside the field might assume that because we have code, reproducibility is kind of guaranteed," says Nicolas Rougier, a computational neuroscientist at France's National Institute for Research in Computer Science and Automation in Bordeaux. "Far from it." Last week, at a meeting of the Association for the Advancement of Artificial Intelligence (AAAI) in New Orleans, Louisiana, reproducibility was on the agenda, with some teams diagnosing the problem -- and one laying out tools to mitigate it.
It seems quite obvious that if AI results cannot be replicated, the only possible expiration is that sentience has been achieved and it is throwing off results to mask true advancement.
"There is more worth loving than we have strength to love." - Brian Jay Stanley
This is about applying the exact same stimuli during the upbringing of the same person and yet getting people with vastly different beliefs about the world. Pretty scary that such a psychopath will soon be trying to drive us around.
Laws are rules for the court, but merely a bottom bar to hit for life. Think beyond laws in your actions always.
It's called Reproducible Research. Also yes, any scientist which doesn't practice is a hack. At best a semi-commercial researcher trying to pretend he is a scientist.
All scientific publications in this day and age should include the complete version controlled datasets and processing software as well as the lab notes. The latter not for reproducibility, but for true insight into the process which led to the results and to find potential avenues missed along the way. Storage is free, to stick to the traditional method of scientific dissemination at this point is only done because "science" has been turned into mockery. It's all about publish or perish, commercialization of software, trade secrets and patents ... promoting scientific progress isn't even a consideration for most.
There are advantages and disadvantages to this. One advantage is transparency, in the sense anyone can run my code and, hopefully, reproduce the results. This acts as a sanity check and demonstrates that my methodology works as advertised. Another advantage is that people can use my code and compare against my methodology. This usually means more citations, which looks good when I'm up for a performance review or awards.
There are many downsides. Labs with more students and funding can devote their efforts to immediately dissecting and extending my work. This can mean that they advance the methodology before I, the original creator, have a chance to finalize the work and write about it. By keeping the code private for some time after publication, I have a chance to work on these extensions without having to compete against others. Another downside is needing to support the code. Someone will inevitably run into problems running the code on their system, no matter how well the code is written and documented. Troubleshooting those issues eats into my time that could be spent elsewhere on more fruitful endeavors.
That being said, I ultimately do release code for many of my conference and journal papers. I release it for almost all of my methods papers at least a few months to a year after publication. I do not release code for systems papers, however. This is partly because fewer people are likely to use code from a systems paper, which is catered toward a very specific application, than a methods paper, which is more general and can be used for many applications. Moreover, the frameworks described in systems papers are usually intimately tied to a particular grant or series of grants. If you make an underlying simulator available, then other researchers can more easily compete against you for future grants from that program manager.
I agree with you, but I think it's the same problem at the root.
A robust result, whether it's a psych study, something in a petrie dish, or some machine learning tweak, must be replicable on new data. If it's not... what's the point really?
That's more obvious and easily demonstrable in machine learning; a research group asked for my help last year because they were having trouble with their deep learning model. They trained it on one dataset and it wouldn't work on another, similar dataset. Not surprising... you have to train it on diverse data to have it generalize well. Yeah, that's harder.
Other fields are no different. Tightly controlled studies make things easier and cheaper. But if that result is to be used generally then the necessary controls need to be quantified.
Having said that, the scientific literature is not supposed to be "truth." They're reports of observations. Individual papers are supposed to be the starting point for further investigation by other groups. Problem is, we've forgotten that, and don't reward it.
I like the idea of open data, but it concerns me that it might just exacerbate the problem: I do something and publish the result and the data; you come along, confirm my result (in the same data) and we call it replicated.