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Lessons from a year of AI research

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Re: Lessons from a year of AI research

#41
post #40

Do AI researchers have a commonly accepted definition for intelligence ?

Not really, AI is more of a large collection of activities. I would distill it down to "problem-solving". The irony is that once you solve a problem, it's not a problem, so people's natural reaction is to only call things AI when they're unsolved. Once it's solved, "that's not AI".

Ability to solve novel problems with little experience. Skill acquisition efficiency.

On the Measure of Intelligence, François Chollet https://arxiv.org/abs/1911.01547

Re: Lessons from a year of AI research

#42
post #41
post #40

Earlier quoted context omitted.

Not really, AI is more of a large collection of activities. I would distill it down to "problem-solving". The irony is that once you solve a problem, it's not a problem, so people's natural reaction is to only call things AI when they're unsolved. Once it's solved, "that's not AI".

Ability to solve novel problems with little experience. Skill acquisition efficiency. On the Measure of Intelligence, François Chollet https://arxiv.org/abs/1911.01547

That's not really compatible with the history of AI, where designing systems to solve a problem has traditionally been considered in scope. Seems that, like many people, Chollet wants to think of AI in terms of AGI (artificial general intelligence).

Re: Lessons from a year of AI research

#43
post #39
post #19

Good list! As a PhD student and therefore AI researcher for a few years now, a lot of this rings true. Though 100 lessons is too much and some of these are obvious/minor, i'd distill it down to the main ones. Here's my 2 cents on the topic from a thing I wrote last year ('Lessons Learned the Hard Way in Grad School (so far)'): https://www.andreykurenkov.com/writing/life/lessons-learned-...

Love the timeline of failure and success (the latter of which is what one usually sees). I think there may be some who breeze through grad school, likely by being in a strong research environment beforehand, or by having lots of support. I mean, they have to exist? Have I genuinely met anyone like that? Nah. And, oh boy, can it be a struggle! During a PhD, it's very easy to put yourself into an increasingly hopeless…

Your post rings true. On the contrary the linked article was written by an undergrad student having some introduction to research, for a short time (a year) with a supporting supervisor and network so of course his list is naive and idealistic. In reality research and methodology etc. doesn't matter much, it's baseline skill. 70-80% of a PhD come done to social things like relationship with advisor, ability to write papers that will be accepted for publication even if they're bullshit.

Re: Lessons from a year of AI research

#44

Earlier quoted context omitted.

PG considered them a special case of linkbait. Which is the only other time you are supposed to modify the title.

I think there are many click bait titles other than "10 things ..." lists. Still don't understand why there's a special rule just for these. "You Can Now Save Money with Y New Strategy” "You Can Now Travel Abroad Without Having to…" "The Last … You’ll Ever Need" "You Won’t Believe… What Y has Found" "Why You Should…" "Why You’ve Never Heard of This Top Travel Destination" "This is why you’re losing money" Just a few…

Most of those would be rewritten to a more descriptive title if they hit the front page.

Re: Lessons from a year of AI research

#45
post #42
post #41

Earlier quoted context omitted.

Ability to solve novel problems with little experience. Skill acquisition efficiency. On the Measure of Intelligence, François Chollet https://arxiv.org/abs/1911.01547

That's not really compatible with the history of AI, where designing systems to solve a problem has traditionally been considered in scope. Seems that, like many people, Chollet wants to think of AI in terms of AGI (artificial general intelligence).

Yes, that's what he focuses on. General intelligence, not narrow task-related intelligence. If a system uses too much experience (training samples) to learn a task then it's not intelligent. It just brute forces the problem. Intelligence requires quick learning from very little experience.

Re: Lessons from a year of AI research

#46

Earlier quoted context omitted.

PG considered them a special case of linkbait. Which is the only other time you are supposed to modify the title.

I think there are many click bait titles other than "10 things ..." lists. Still don't understand why there's a special rule just for these. "You Can Now Save Money with Y New Strategy” "You Can Now Travel Abroad Without Having to…" "The Last … You’ll Ever Need" "You Won’t Believe… What Y has Found" "Why You Should…" "Why You’ve Never Heard of This Top Travel Destination" "This is why you’re losing money" Just a few…

Agreed.

Re: Lessons from a year of AI research

#47

Lol this is so aspirational it could only come from an undergrad. Let me tell you that I've finally made it to the stressful part of the being a serious "AI" researcher, where I have a real project (as in difficult to achieve goals, not just "turn the crank" stuff) and real deadlines (deliverables on collaborators projects and my own conferences submissions) and the only thing I prioritize above doing the work itself…

"but", Zotero is really neat if you use the plugins which make it just one-click-to-save it in your Zotero from browser. Hardly any overhead with that.

Re: Lessons from a year of AI research

#48

-> 87. Don't let yourself be too affected by the opportunity costs of doing research. Why not? Is there a strong payoff later? How did you learn this lesson? :) Good listicle!

I reflected upon how I approached many past opportunities, and realised that I only truly enjoy the process if the nature of work is interesting to me because I'm mostly interest-driven. I also found that enjoying the process is already rewarding regardless of the outcome, and at the same time have much more control over the process than the outcome. :)
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