Comment by KSS42 | Hacker News Reader

Comment by KSS42 | Hacker News Reader

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University of Toronto Engineering also is starting a Machine Intelligence option:

Engineering Science - Machine Intelligence Option

http://engsci.utoronto.ca/explore_our_program/majors/machine...

What is the difference between the Machine Intelligence major in Engineering Science, and an undergraduate degree in Computer Science?

While there are some commonalities between the Machine Intelligence major and what is offered through Computer Science, engineering offers a unique perspective.

First, graduates will have a systems perspective on machine intelligence, which integrates computer hardware and software with mathematics and reasoning. This enables a focus on algorithm development and the relationship between machine intelligence with computer architecture and digital signal processing.

Secondly, graduates will benefit from an approach that encourages problem framing and design thinking. Design thinking is a method for the practical and creative resolution of problems, which encourages divergent thinking to ideate many solutions, and convergent thinking to realize the best one. Students will be able to frame and solve problems in the MI field, and apply MI tools to problems in many application areas. These include finance, education, advanced manufacturing, healthcare and transportation. This field is in a phase of rapid development, and engineers are well equipped to contribute as a shaping force.

I got my BSc at U of T, in Artificial Intelligence (and Cognitive Science)... in 2006. So ahead of the curve! This was right before the big deep learning explosion, at the very end of the last AI winter. Our lecturers spent a whole lot of time lamenting at the endless disappointments of AI research. I walked away deeply skeptical, and can't help but see the current ML hype as a glass half empty.

I'm currently about to enter my freshman year of College, and have been looking at majoring in Cognitive Science. What were your thoughts on it/it's applicability to the rest of your life?

I think it very much depends on where you're doing it. There are many approaches to cog sci, so your experience will likely be different depending on your profs, their schools of thought, and the kind of research being done at your institution. U of T at the time was dominated by people doing work in embodied cognition (e.g. Evan Thompson), neo-continentalism / phenomenology, philosophy of mind, and a few dynamical systems people. I very much enjoyed it, but I later learned that this was a rather unorthodox take on cognitive science, not at all representative of how things are done elsewhere. I'd stay away from programs too rooted in more traditional experimental cognitive psychology, or developmental psychology. To me this seems incredibly dry, but I guess it depends on your own personal proclivities.

What were they disappointed in?

Also, what do you mean by "see the current ML hype as a glass half empty"? I take it that you are also disappointed with the recent research

I'm just getting into the field, but it seems to me at least in computer vision, voice recognition, and text to speech there have been great strides in the recent years

Have a look at https://en.wikipedia.org/wiki/AI_winter

I personally wasn't disappointed — I'm really glad I did this as my undergrad. AI research however tends to go through periods of hype followed by disillusionment. There's a history of promising developments that hit a wall or fizzle out in the long run. That's not to say there hasn't been progress (there's been tons!), but based on track record alone, it's prudent to be skeptical of overly optimistic pronouncements — we're probably much further from the "singularity" than one would think, based on current wave of ML hype anyway.

You’re over emphasizing a particular historical fable. Yes, some AI hype has happened. We’ve all read the “summer project” of McCarthy, Shannon, et al.

But all these recent advances are not just hype. It’s real. Anyone who has been following this area for a long time knows that some big problems (like large-scale image classification) have been solved, and in an orderly way that builds on prior work going back to the 1990s and before. (My ML PhD was in 1995.)

Nobody here is referring to the “singularity” - that is obviously speculation that has nothing to do with the CMU program.

> we're probably much further from the "singularity" than one would think

For your - and my - sake I hope that that is true. If not all bets are off and you might not like the end results.

Singularity of stupidity is already here. I mean, you have to be pretty stupid to take something as absurd and sci-fi nerd bs like "singularity" seriously.

Ah, EngSci - the most freakishly difficult undergraduate curriculum on the planet.

Care to elaborate?

The University of Toronto Engineering Science program has a reputation of being very rigorous, difficult.

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