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Comment by nothis | Hacker News Reader

I feel like there's about 1000 hours of high quality AI lectures available for free on the internet and while I do believe in a certain amount of selflessness in education, I am skeptical that any of that is providing more than a glimpse of what you need to know to be productive at it. In other words, there's a thousand hours of material out there, which probably takes 10000 hours to actually get into so you might as well put all that effort into properly studying it at a university, getting the knowledge not covered in the lectures alone and a degree to prove it in the end.

If you're just "curious" about AI, a really good half hour lecture should get you up to speed.

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> I feel like there's about 1000 hours of high quality AI lectures available for free on the internet and while I do believe in a certain amount of selflessness in education, I am skeptical that any of that is providing more than a glimpse of what you need to know to be productive at it.

Why? Whether you go to the lectures in a university or watch them online, it makes no difference. I prefer and recommend textbooks over videos though.

> In other words, there's a thousand hours of material out there, which probably takes 10000 hours to actually get into so you might as well put all that effort into properly studying it at a university, getting the knowledge not covered in the lectures alone and a degree to prove it in the end.

What knowledge "not covered in lectures alone" or books do you need? Why do you think having a signal of pedigree somehow confers this knowledge onto people.

> If you're just "curious" about AI, a really good half hour lecture should get you up to speed.

You don't have to either be an expert or a complete layman. This gatekeeping is ridiculous. I've worked with many phds and most of them are not even close to as competent as folks who are naturally gifted and put in the work to pick up the topics.

Why do you have to go to university to properly study machine learning?

Studying it at a university means moving and having a certain background. While i agree that these resources are hard to break down into a curriculum, theres nothing stopping you from copying a university curriculum at home and doing work on your own...

> theres nothing stopping you

Yet most people don't. Same for learning an instrument, carpentering etc.. While you could in principle self-study lots of things, study groups, structure, people to talk to and discuss with and even just "we meet every Thursday at noon to ..." are not negligible.

> study groups, structure, people to talk to and discuss with and even just "we meet every Thursday at noon to ..." are not negligible.

Indeed. These resources are stupendously valuable, and probably somewhat easier to find/generate in some areas than most others.

For example, it would be refreshing to see a deep-learning course with math at a higher level than high-school level.

ML is a very applied subject. There is entirely too little theory people need to know. In fact the most impressive vision/nlp architectures are indeed uninterpretable alchemy. It would make very little difference to study it at a university. Unless of course you 're going for probability theory.

Are you defining "need to know" as in the state of the art methods of today? Problem is the next stage after mature technology is commodity. We'll be getting daily spam from India offering to fulfill all our "A.I. design" needs just as with web design. If all you have to do is follow a blog post or video on how to use a prepackaged framework to get a job done, than everyone else can do it too.

And honestly those commodity AI networks from India will probably solve 90% of corporate needs, when properly pipelined.

i thought we are already at this stage? You can find plenty of tutorials on how to e.g. use BERT and there's plenty of people who will do that for you

These lectures are for university students as well. Anecdotally, MIT lectures were a vital part of my education while at university; they perfectly complemented our own. Some professors even endorsed them.

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