12 ms·

> So if it's 2023 and you have 20 hours to learn something new, you can do much better than this book. Practically, which other book / ressource should someone

> So if it's 2023 and you have 20 hours to learn something new, you can do much better than this book. Practically, which other book / ressource should someone with little time check out first ?

bcrosby95 3y ago
People seem to say Norvig's "other" book is still relevant. I assume that's "Artificial Intelligence: a Modern Approach". My version is from when I went to school 20 years ago. I assume it's been greatly updated over the past couple of decades. I wonder if it's worth taking a spin through the new edition.
- Jtsummers 3y ago
  It's up to a 4th edition now (I had the 2nd when I was in school). Here are the full contents for the current edition: http://aima.cs.berkeley.edu/contents.html http://aima.cs.berkeley.edu/contents.html Chapters 19 on are going to be the biggest additions from the earlier editions.
  - phtrivier 3y ago
    The 4rth edition is from 2019. My understanding is that the world of AI has changed about a quadrillion times since those days, right ;) ? (Interesting that AI is finally catching up with javascript frameworks.) My personnal goal is to find some time to dig into https://course.fast.ai/ https://course.fast.ai/ , assuming it's not terribly outdated, either.
    - Jtsummers 3y ago
      > The 4rth edition is from 2019. My understanding is that the world of AI has changed about a quadrillion times since those days, right ;) ? The world isn't moving that fast. Transformers and LLMs are built on neural networks and lots of data and fast computers. You could jump straight to that point, but even the course you've pointed to starts off with more foundational ANN topics before getting to transformers. Much of which is at least in the TOC for the current edition of AIMA. Ought to be complementary texts. Also, only fools ignore history, "classical" AI and topics also covered in the book are still applicable. ANNs aren't going to solve all the world's problems. Other techniques that fall under the category of "AI" are still applicable and very effective for a large number of real-world problems (and much more efficient than LLMs).
BeetleB 3y ago
Introduction to Statistical Learning

> So if it's 2023 and you have 20 hours to learn something new, you can do much better than this book. Practically, which other book / ressource should someone with little time check out first ?

bcrosby95 3y ago

People seem to say Norvig's "other" book is still relevant. I assume that's "Artificial Intelligence: a Modern Approach". My version is from when I went to school 20 years ago. I assume it's been greatly updated over the past couple of decades. I wonder if it's worth taking a spin through the new edition.

Jtsummers 3y ago
It's up to a 4th edition now (I had the 2nd when I was in school). Here are the full contents for the current edition: http://aima.cs.berkeley.edu/contents.html http://aima.cs.berkeley.edu/contents.html Chapters 19 on are going to be the biggest additions from the earlier editions.
- phtrivier 3y ago
  The 4rth edition is from 2019. My understanding is that the world of AI has changed about a quadrillion times since those days, right ;) ? (Interesting that AI is finally catching up with javascript frameworks.) My personnal goal is to find some time to dig into https://course.fast.ai/ https://course.fast.ai/ , assuming it's not terribly outdated, either.
  - Jtsummers 3y ago
    > The 4rth edition is from 2019. My understanding is that the world of AI has changed about a quadrillion times since those days, right ;) ? The world isn't moving that fast. Transformers and LLMs are built on neural networks and lots of data and fast computers. You could jump straight to that point, but even the course you've pointed to starts off with more foundational ANN topics before getting to transformers. Much of which is at least in the TOC for the current edition of AIMA. Ought to be complementary texts. Also, only fools ignore history, "classical" AI and topics also covered in the book are still applicable. ANNs aren't going to solve all the world's problems. Other techniques that fall under the category of "AI" are still applicable and very effective for a large number of real-world problems (and much more efficient than LLMs).

BeetleB 3y ago

Introduction to Statistical Learning