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Ask HN: AI is going to be big. How should we learn?

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Re: Ask HN: AI is going to be big. How should we learn?

#22
post #16

Knowing AI will be useless, it'll be like knowing and programming assembly language, for high end specialists only. You'll get a framework/app/api that will deliver you the info programmed by someone really into AI. It'll be business as usual. Know API's, frameworks, how to analyse data and tools around this, know people. AKA Just be a good programmer.

Thank you for this very anti-intellectual answer, now I don't have to wonder anymore why programmers and engineers are getting commoditized. Seriously, does for you being a good programmer only consist in calling some API or knowing some framework?

You react very negatively to an alternative opinion. I think it's a good and valid point considering the OP proposed the idea that knowing/learning/understanding AI will be critical.

Re: Ask HN: AI is going to be big. How should we learn?

#23
post #16

Knowing AI will be useless, it'll be like knowing and programming assembly language, for high end specialists only. You'll get a framework/app/api that will deliver you the info programmed by someone really into AI. It'll be business as usual. Know API's, frameworks, how to analyse data and tools around this, know people. AKA Just be a good programmer.

Thank you for this very anti-intellectual answer, now I don't have to wonder anymore why programmers and engineers are getting commoditized. Seriously, does for you being a good programmer only consist in calling some API or knowing some framework?

Seems pretty intellectual to me. He just has a different priority on skill acquisition. Both types of people are required.

Re: Ask HN: AI is going to be big. How should we learn?

#24
I've been thinking about this as well. Where my head's at right now:

- Conscious AGI is not even close. Many fundamental breakthroughs in philosophy/science required (binding problem, how sensations arise from physical processes, etc)

- Smart non-conscious AI approaching AGI requires getting the balance between many sub-modules correct (see openCog), and probably requires a lot of time and research to get right

- Non-conscious domain-specific supervised-learning has strong potential in the next 5-10 years (any problem a human can solve in a few milliseconds is a good candidate, but usually requires a lot of data to supervise the learning algorithm)

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