It’s worth it to take a step back and try to understand which judgments are important and how to make them properly.
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In my previous posts in this series, I’ve essentially argued both sides of the same issue. In the first, I explained why deep learning is not a panacea, when machine learning systems (now and likely always) will fail, and why deep learning in its current state is not immune to these failures.
In the second post, I explained why deep learning, from the perspective of machine learning scientists and engineers, is an important advance: Rather than a learning algorithm, deep learning gives us a flexible, extensible framework for specifying machine learning algorithms. Many of the algorithms so far expressed in that framework give orders of magnitude-level improvement on the performance of previous solutions. In addition, it’s a tool that allows us to tackle some problems heretofore unsolvable directly by machine learning methods.
For those of you wanting a clean sound-byte about deep learning, I’m afraid you won’t get it from me. The reason I’ve written so much here is that I think nature of the advance that deep learning has brought to machine learning is complex and defies broad judgments, especially at this fairly early stage in its development. But I think it is worth it to take a step backward and try to understand which judgments are important and how to make them properly.
This series of posts was motivated in part by my encounters with Gary Marcus‘ perspectives on deep learning. At the root of his positions is the notion that deep learning (and here he means „statistical machine learning“) is, in various ways, „not enough“. In his medium post, it’s „not enough“ for general intelligence, and in the synced interview it’s „not enough“ to be „reliable.“
This notion of whether current machine learning systems are „good enough“ gets to the heart of the back and forth on deep learning. Marcus cites driverless cars as an example of how AI isn’t mature enough yet to rely on 100 percent, and that AI needs a „foundational change“ to ensure a safe level of reliability. There’s a bit of ambiguity in the interview about what he means by AI, but my own impression is that this is less of a critique of machine learning, and more of a critique of the software around it.
For example, we have vision systems able to track and identify pedestrians on the road. These systems, as Marcus says, are mostly reliable but certainly make occasional mistakes. The job of academic and corporate researchers is to create these systems and make them as error-free as possible, but in the long run, they will always have some degree of unreliability.
Something consumes the predictions of these vision systems and acts accordingly; it is and always will be the job of that thing to avoid treating these predictions as the unvarnished truth. If the predictions were guaranteed to be correct, the consumer’s job would be much easier.
