Teaching machine learning within different fields

Everyone is talking about machine learning (ML) these days. They usually call it “machine learning and artificial intelligence” and I keep wondering what exactly they mean by each term.

It seems the term “artificial intelligence” has shaken off its negative connotations from back when it meant top-down systems (as opposed to the superior bottom-up “computational intelligence” that most of today’s so-called AI actually uses) and has come to mean what cybernetics once was: robotics, machine learning, embedded systems, decision-making, visualization, control, etc., all in one.

Now that ML is important to so many industries,application areas, and fields, it is taught in many types of academic departments. We approach machine learning differently in ECE, in CS, in business schools, in mechanical engineering, and in math and statistics programs. The granularity of focus varies, with math and CS taking the most detailed view, followed by EC and ME departments, followed by the highest-level applied version in business schools, and with Statistics covering both ends.

In management, students need to be able to understand the potential of machine learning and be able to use it toward management or business goals, but do not have to know how it works under the hood, how to implement it themselves, or how to prove the theorems behind it.

In computer science, students need to know the performance measures (and results) of different ways to implement end-to-end machine learning, and they need to be able to do so on their own with a thorough understanding of the technical infrastructure. (If what I have observed is generalizable, they also tend to be more interested in virtual and augmented reality, artificial life, and other visualization and user-experience aspects of AI.)

In math, students and graduates really need to understand what’s under the hood. They need to be able to prove the theorems and develop new ones. It is the theorems that lead to powerful new techniques.

In computer engineering, students also need to know how it all works under the hood, and have some experience implementing some of it, but don’t have to be able to develop the most efficient implementations unless they are targeting embedded systems. In either case, though, it is important to understand the concepts, the limitations, and the pros and cons as well as to be able to carry out applications. Engineers have to understand why there is a such a thing as PAC, what the curse of dimensionality is and what it implies for how one does and does not approach a problem, what the NFL is and how that should condition one’s responses to claims of a single greatest algorithm, and what the history and background of this family of techniques are really like. These things matter because engineers should not expect to be plugging-and-playing cookie-cutter algorithms from ready-made libraries. That’s being an operator of an app, not being an engineer. The engineer should be able to see the trade-offs, plan for them, and take them into account when designing the optimal approach to solving each problem. That requires understanding parameters and structures, and again the history.

Today, the field of ‘Neural Networks’ is popular and powerful. That was not always the case. It has been the case two other times in the past. Each time, perhaps like an overextended empire, the edifice of artificial neurons came down (though only to come up stronger some years later).

When I entered the field, with an almost religious belief in neural networks, they were quite uncool. The wisdom among graduate students seemed to be that neural nets were outdated, that we had SVMs now, and with the latter machine learning was solved forever. (This reminds me of the famous patent-office declaration in the late 1800s that everything that could be invented had been invented.) Fortunately, I have always benefited from doing whatever was unpopular, so I stuck to my neural nets, fuzzy systems, evolutionary algorithms, and an obsession with Bayes’ rule while others whizzed by on their SVM dissertations. (SVMs are still awesome, but the thing that has set the world on fire is neural nets again.)

One of the other debates raging, at least in my academic environment at the time, was about “ways of knowing.” I have since come to think that science is not a way of knowing. It never was, though societies thought so at first (and many still think so). Science is a way of incrementally increasing confidence in the face of uncertainty.

I bring this up because machine learning, likewise, never promised to have the right answer every time. Machine learning is all about uncertainty; it thrives on uncertainty. It’s built on the promise of PAC learning; i.e., it promises to be only slightly wrong and to be so only most of the time. The hype today is making ML seem like some magical panacea to all business, scientific, medical, and social problems. For better or worse, it’s only another technological breakthrough in our centuries-long adventure of making our lives safer and easier. (I’m not saying we haven’t done plenty of wrongs in that process—we have—but no one who owns a pair of glasses, a laptop, a ball-point pen, a digital piano, a smart phone, or a home-security system should be able to fail to see the good that technology has done for humankind.)

I left the place of the field of Statistics in machine learning until the end. They are the true owners of machine learning. We engineering, business, and CS people are leasing property on their philosophical (not real) estate.

 

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