College of Statistics and Nonlinear Function Approximation
People want to be become machine learning engineers because it's the sexy thing right now, but they don't want to learn the necessary statistics/linear algebra/optimization necessary for the roles. In my experience, these "AI" and "datascience" programs are largely just cash-grabs at most universities. I don't doubt that M.I.T.'s will be rigorous, but I'm largely skeptical of how useful these programs actually are.
MIT alum here. I expect this new department to be a gross embarassment compared to the rest of campus initially. Then they'll enforce standards to bring it into line, which will suppress enrollment, and then the department will be merged into the CS department. Won't be the first time.
Story time?
Generally, the traditional university model has been adding "<insert job title> program" for a long time, whether or not they have a useful curriculum to put students through.
There are office management and msft liscencing degrees. The most popular degree (at least where I live) for both undergraduate and graduates are various generic "business degrees."
In practice, universities have curriculums for accounting, finance and ecocomics.
There is no curriculum for social media or digital growth hacking. There are job titles. There are students who want to enroll. Employers are asking for these graduates (in theory, graf salaries for these are low). Politicians are willing to fund them...
Does a bachelors of social media businessing serve a student 10, 20, 30 years later?
So, will these ml/ai these programs really produce better graduates than maths, statistics or CS programs? Dunno. I'll wager that they're a whole lot better than business stuff degrees.
I'd actually wager that MIT will put students through decent statistics classes. Hopefully they'll also have them write a decent amount of code too.
It has always seemed to me that it is correct distinction. Although there exists a branch of mathematics called mathematical statistics, by its very nature statistics is more like physics, in that it is trying to develop efficient methods of getting information about the objective world based on a certain kind of observations and measurements; and, of course, just as theoretical physics, statistics is highly mathematical, which often creates confusion regarding the actual subject of investigation.
Is there a classic textbook on the subject? Are there any free online courses that are considered good?
Depending on your problem, you might be able to exploit special structures to solve problems faster than just doing gradient descent. If you know linear algebra and stats, you'll be fine getting through an optimization book.
Boyd's book is canonical at this point, but might be hard to get through. Before you get to actually optimizing anything, you need to make your way through some chapters on convex analysis with little application.
Most people who will do these jobs, will stitch libraries into producing an application, like every other programming job. They will need passing knowledge of things, but only make things work.
This is for the same reasons why anyone build an web app is not writing their own TCP/IP stack and their own operating system.
As a matter of fact I wouldn't be surprised, If most people who are claiming to do AI are just writing SQL queries to get Averages and means.
In the hey days of Big data craze, people were using Hadoop and Pig to deal with files a few kilobytes in size, and calling it 'Big Data'.
I don't believe that you need an advanced degree to become a component ML engineer, but the math/stats is necessary pre-requisite and these pre-reqs are often poorly defined. At my college, the only pre-req to the graduate-level ML course was the freshman level intro to stats class and multivariable calculus. About 50% of the class dropped when they realized they didn't know how to construct Gaussian models or perform convex optimization.
The underappreciated parts of AI, in my experience, are more philosophical; about the nature of reasoning and approximating or beating human thought. About autonomous agents, non zero-sum games and ethical, non-maximizing functions. There's a huge overlap with logic (philosophical and mathematical) here, and I haven't seen that really broached at any of these big programs.
I am interested in the points you raise, but also realized that I would not find a good environment for it at MIT in EECS, for reasons that are rather obvious from the article's subtext. As such, the last year or so has been spent in a search for good alternatives in terms of research, and I am slowly finding answers. I am happy to discuss more over email.
Long story short: you are certainly not the only one who thinks that way.
EDIT: added a video link to Mikhail Gromov's actual views for better accuracy.
It must be noted, though, that "approximating human thought" is just one direction of investigation - and not the most important one at that; as interesting as it may be, it makes almost as much sense as trying to have computers resemble human brains. In other words, the true AI, when it arrives, will not think like us humans (even if at some level it might pretend that it does).
> ethical
The AI will be just as "ethical" as a computer or an assault rifle.
Sort of my point. Current (by that I mean post-early 20th century) approaches were to mimic what we believe to be human reasoning. That's clearly limited.
> The AI will be just as "ethical" as a computer or an assault rifle.
I think that's reductive. Reasoning is not entirely analytical. There are other implications and concerns to artificial sentience.
Did you mean 'competent ML engineer'
- Math, as you say
- Software engineering, especially data engineering
- Design - since the the math and engineering enable new kinds of problems to be solved by computers, there's a lot of unexplored design territory
- The domains of all the input and output modalities it touches, like linguistics, computer vision, etc.
- Increasingly, ethics
Sure, each of these topics are already covered by existing university departments, but the boundaries between departments are arbitrary and often limiting anyway. Why not establish a new locus that brings much of the above under one physical and administrative roof?
I wonder how this is going to play with the new MIT Schwarzman College of Computing, and what each entity will choose to focus on.
http://news.mit.edu/2018/faq-mit-stephen-schwarzman-college-...
In general, one has to remember that mathematical notation was invented to make calculations (on paper) more efficient and not with the goal of making it easier to understand things.
Historically, it was as likely to be part of the math department as it was to be part of electrical engineering. In part, this was because at the time electrical engineering was much more about analog circuits, power systems, etc.
I have always felt course offerings from CS are more approachable/amenable to beginners in this area. Maybe anecdotal, but statistics depts. have a gate-keeping attitude , a sense of 'oh you don't know the math already? too bad, we are not for you'.
P.S: Approachable/amenable does not mean it cannot be rigorous or you have to cut short the math. You just built up gradually rather than throwing math books in people's face from the very beginning.
Not to be despicable. On the contrary, function approximation is a very rich and deep topic.
College of General Purpose Computational Heuristics.
Medical College of Frontier Brain Prosthetics.