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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.
If you give ten people the exact same stimuli you will get ten different reactions to that stimuli. There will be a dominant leaning reaction but each person will asses the stimuli based on their personal history and beliefs. AI is an attempt to mimic the human thought process so if successful the same stimulus will start to generate different results as new data is processed. In fact the same stimulus can be perceived differently by the same person given different context. If you come to my door in the afternoon I might be glad to see you but if it is 3 AM I probably won't be.
"A person is smart. People are dumb, panicky dangerous animals and you know it." - K
Science has a Replication problem
This is not really the same issue. Replication failures in the physical and social sciences are difficult to fix, since they are can be caused by small differences in data collection, experimental procedures, and statistical analysis. It is a hard problem.
Fixing the replication problem described in TFA is drop dead easy, since it has exactly two causes: closed data, and closed source. The fix? Reject any paper for publication if full source and data is not available. Science is based on openness, not secrets.
Very true. Also, calling an utterly dumb statistical classificator "AI" does not make it intelligent. I like the old terminology better where pattern recognition, planning algorithms, fuzzy database searches, etc. were just called "automation" an it was amply clear that they are not intelligent in any way. As to what is today called "strong AI", I fully agree that at this time we do not even know that it can be done and all available evidence pretty clearly indicates that it probably cannot be done.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
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.