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This is as good a place as any to ask this I suppose. Google results being as manipulated as they are these days, what is the best course/book to get to self t

This is as good a place as any to ask this I suppose. Google results being as manipulated as they are these days, what is the best course/book to get to self teach on artificial intelligence? I'd like to spend a few months familiarizing myself with concepts and writing programs with what I learn to see if anything useful comes of it. edit: Found this which answers this perfectly https://news.ycombinator.com/item?id=15689399 https://news.ycombinator.com/item?id=15689399

jacquesm 7y ago
fast.ai
- bgorman 7y ago
  Fast.ai is a great resource but it is really narrowly focused on deep learning and especially transfer learning
  - jacquesm 7y ago
    GP wants to do self study and only a few months, I'm not aware of a better resource for that purpose.
PrefixKitten 7y ago
I'd suggest a university statistics book and a book on data analysis or data mining
sillysaurusx 7y ago
For a non-traditional hands-on approach, I gathered some resources here: https://news.ycombinator.com/item?id=21241092 https://news.ycombinator.com/item?id=21241092
zitterbewegung 7y ago
I used this book in school: http://aima.cs.berkeley.edu http://aima.cs.berkeley.edu
dreamcompiler 7y ago
Russell and Norvig [0]. The best introduction IMO to "real" AI, as opposed to the ML stuff that currently passes for AI. [0] http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/
- etaioinshrdlu 7y ago
  I find this vaguely derogatory. What the linked course looks like to me is old-school, mostly symbolic AI, that most experts would agree hit something of a brick wall decades ago.
  - dreamcompiler 7y ago
    I'm an expert and I don't agree. The successes of symbolic AI have been many; they're just not called AI any more. The failures of ML are many; they're just ignored. How many layers do you need and why? How many training cases do you need and why? What has the network learned and how do you know that? What important things has the network not learned? When will it fail? Until you can answer these questions, you're not doing science. You're just throwing data at Tensorflow or Keras until you guess it's good enough. And then someone dies in a self-driving car accident because the network didn't learn that a transverse truck or a pedestrian was an obstacle. ML does impressive stuff but as several recent HN articles have pointed out it's basically just curve fitting. And voodoo curve fitting at that.
    - pk78 7y ago
      and in how many recent applications has this "old" symbolic "AI" surpassed ML? I agree with the parent commenter. >"hit something of a brick wall decades ago" Is true. Why do you disagree? What improvements did "old-school, mostly symbolic AI" bring to the current field of research? Sure, ML has failures - but those failures are in applications and fields where old school symbolic AI can't even reasonably be applied to. We have to start somewhere and just using symbolic AI is far behind in terms of the requirements we have currently. >"How many layers do you need and why? How many training cases do you need and why? What has the network learned and how do you know that? What important things has the network not learned? When will it fail?" A lot of these issues have been addressed in many recent papers. A lot of these papers have been solely focused on understandable/explainable machine learning which is an overarching topic that covers all your questions. >"Until you can answer these questions, you're not doing science." So, you are essentially saying a large part of CS academia is not doing "science". I'm not sure what kind of "science" you have done to make such comments. But I'm pretty sure there are plenty of researchers out there who are far more of an expert than you are in this field would wholly disagree with you.

jacquesm 7y ago

fast.ai

bgorman 7y ago
Fast.ai is a great resource but it is really narrowly focused on deep learning and especially transfer learning
- jacquesm 7y ago
  GP wants to do self study and only a few months, I'm not aware of a better resource for that purpose.

PrefixKitten 7y ago

I'd suggest a university statistics book and a book on data analysis or data mining

sillysaurusx 7y ago

For a non-traditional hands-on approach, I gathered some resources here: https://news.ycombinator.com/item?id=21241092 https://news.ycombinator.com/item?id=21241092

zitterbewegung 7y ago

I used this book in school: http://aima.cs.berkeley.edu http://aima.cs.berkeley.edu

dreamcompiler 7y ago

Russell and Norvig [0]. The best introduction IMO to "real" AI, as opposed to the ML stuff that currently passes for AI. [0] http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/

etaioinshrdlu 7y ago
I find this vaguely derogatory. What the linked course looks like to me is old-school, mostly symbolic AI, that most experts would agree hit something of a brick wall decades ago.
- dreamcompiler 7y ago
  I'm an expert and I don't agree. The successes of symbolic AI have been many; they're just not called AI any more. The failures of ML are many; they're just ignored. How many layers do you need and why? How many training cases do you need and why? What has the network learned and how do you know that? What important things has the network not learned? When will it fail? Until you can answer these questions, you're not doing science. You're just throwing data at Tensorflow or Keras until you guess it's good enough. And then someone dies in a self-driving car accident because the network didn't learn that a transverse truck or a pedestrian was an obstacle. ML does impressive stuff but as several recent HN articles have pointed out it's basically just curve fitting. And voodoo curve fitting at that.
  - pk78 7y ago
    and in how many recent applications has this "old" symbolic "AI" surpassed ML? I agree with the parent commenter. >"hit something of a brick wall decades ago" Is true. Why do you disagree? What improvements did "old-school, mostly symbolic AI" bring to the current field of research? Sure, ML has failures - but those failures are in applications and fields where old school symbolic AI can't even reasonably be applied to. We have to start somewhere and just using symbolic AI is far behind in terms of the requirements we have currently. >"How many layers do you need and why? How many training cases do you need and why? What has the network learned and how do you know that? What important things has the network not learned? When will it fail?" A lot of these issues have been addressed in many recent papers. A lot of these papers have been solely focused on understandable/explainable machine learning which is an overarching topic that covers all your questions. >"Until you can answer these questions, you're not doing science." So, you are essentially saying a large part of CS academia is not doing "science". I'm not sure what kind of "science" you have done to make such comments. But I'm pretty sure there are plenty of researchers out there who are far more of an expert than you are in this field would wholly disagree with you.