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No but it’s making it worse because it’s giving false confidence in results and amplifying failures in experiment design. It’s also been held out as a fix for t

No but it’s making it worse because it’s giving false confidence in results and amplifying failures in experiment design. It’s also been held out as a fix for the reproducibility crisis but just as lots of statistical analysis has been done by people who don’t really understand the math but are just cargo culting other experiments, machine learning is taking that ignorance-of-your-toolset risk to the next level. 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.

Cacti 8y ago
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. This isn’t really true. For example, we can pass in an image to a convolutional net and see which filters are activated; this can give us a clear indication if it’s edge detectors that are activating or textures or specific shapes (eg a dog would activate edge detectors, textures that look like fur, and shapes that resemble a dogs face). We can also train models to disentangle its representations and make specific variables stand for specific things (eg for a net trained on handwriting,values in one variable can represent the slant of writing, another one the letter, another the thickness, etc.). There is also a ton of work being done in training causal models. We also have decent ways now of visualizing high dimensional loss surfaces. the field has come a long way since 2012, and the whole “it’s magic, we don’t understand why it works or what it learns” is no longer true.
sfossa 8y ago
Some of the points are true specifically the last part but you are wrong about the fact that people who write these software doesn't understand what patterns are being found. We can clearly see in ML and Deep models why the decision was made by the hypothesis using various libraries such as eli5, Tensorboard and others. Deep Learning models are in general harder to debug but still possible. Therefore we know why hypothesis produces wrong results but sometimes it not possible to mend the model due to outliers, rare events, lack of data and/or randomness that surrounds our world. Just as you point out that statistical analysis is done by people who don’t really understand the math, these false result can be due to scientists using ML without understanding its advantages and limitations.

Cacti 8y ago

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. This isn’t really true. For example, we can pass in an image to a convolutional net and see which filters are activated; this can give us a clear indication if it’s edge detectors that are activating or textures or specific shapes (eg a dog would activate edge detectors, textures that look like fur, and shapes that resemble a dogs face). We can also train models to disentangle its representations and make specific variables stand for specific things (eg for a net trained on handwriting,values in one variable can represent the slant of writing, another one the letter, another the thickness, etc.). There is also a ton of work being done in training causal models. We also have decent ways now of visualizing high dimensional loss surfaces. the field has come a long way since 2012, and the whole “it’s magic, we don’t understand why it works or what it learns” is no longer true.

sfossa 8y ago

Some of the points are true specifically the last part but you are wrong about the fact that people who write these software doesn't understand what patterns are being found. We can clearly see in ML and Deep models why the decision was made by the hypothesis using various libraries such as eli5, Tensorboard and others. Deep Learning models are in general harder to debug but still possible. Therefore we know why hypothesis produces wrong results but sometimes it not possible to mend the model due to outliers, rare events, lack of data and/or randomness that surrounds our world. Just as you point out that statistical analysis is done by people who don’t really understand the math, these false result can be due to scientists using ML without understanding its advantages and limitations.