Deep Learning: A Critical Appraisal (paper review)

Deep learning is becoming extremely popular. It is one of the fields of Machine Learning that is the most explored and exploited. AlphaGo, Natural Language Processing, image recognition, and many more topics are iconic examples of the success of deep learning. It is so successful that it seems to become the golden answer to all our problems.

Gary Marcus, a respected ML/AI researcher, published an excellent critical appraisal of this technique. For instance, he listed ten challenges that deep learning faces. He concludes that deep learning is only one of the tools needed and not necessarily a silver bullet for all problems.

From the security point of view, here are the challenges that seem relevant:

“Deep Learning thus far works well as an approximation, but its answers often cannot be fully trusted.”

Indeed, the approach is probabilistic rather than heuristic. Thus, we must be cautious. Currently, the systems are too easily fooled. This blog reported several such attacks. The Generative Adversarial Networks are promising attack tools.

“Deep learning presumes a largely stable world, in ways that may be problematic.”

Stability is not necessarily the prime characteristics of our environments.

“Deep learning thus far cannot inherently distinguish causation from correlation.”

This challenge is not related to security. Nevertheless, it is imperative to understand it. Deep learning detects a correlation. Too often, people assume that there is causation when seeing the correlation. This assertion is often false. Causation may be real if the parameters are independent. If they are linked/triggered by an undisclosed parameter, it is instead this undisclosed parameter that produces the causation.

In any case, this paper is fascinating to read to keep an open, sane view of this field.

Marcus, Gary. “Deep Learning: A Critical Appraisal.” ArXiv:1801.00631 [Cs, Stat], January 2, 2018.



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