Periodic reminder that the lessons (of classical AI - the base) of the late Prof. Patrick Winston, MIT, are freely available (at MIT OpenCourseware, also on YT).
I have actually read the majority the AI book, it was the course textbook for the AI paper I did at university. The reason it is there is because I want to re-read parts for a project that I have never started.
I don't know anything about what you're talking about. Where do I start to learn some of the AI terminology, models, benefits and drawbacks of each, etc?
My sincere apologies but I don't think this conversation is going anywere. I would prefer to end it here.
For an introduction to AI, I recommend the classic AI textbook by Russel & Norvig, "AI, A Modern Approach". Go for the most recent edition you can find.
From my little experience with the AI community, I think people in it love to obfuscate things. Any attempt to make a topic approachable, even if some of the details are lost, get smacked around. I face this every day in my Masters. If you don't already come with a knowledge of AI + Stats, you're on your own. The community, including the teachers, don't want to teach the mundane.
I've read the book from Peter Norvig and Stuart Russell (Artificial Intelligence: A Modern Approach [1]) which was great but now I'd love to work with more practical material.
Things like code samples in python, showcasing the use of GPUs with CUDA or OpenCL for practical purposes etc.
I can keep searching the web and work with that but reading good books adds another dimension.
I wouldn’t worry too much on a lot of these points.
I won’t say the math behind AI is simple, but it’s mostly undergrad level. You can get up to speed on it if you really want to. The hard part is writing fast implementations, but many others are already doing this for us.
We do not have a grand theory of AI or a deep understanding, but every year we make improvements in machine understandability, and you can “debug” models if need be.
Lastly, the author is right, the best models are closed source, but open source is hot on its tail. There are plenty of good local LLMs and they get better every month. Unfortunately it still is out of reach for a hobbyist to train a good LLM from scratch, but open source pretrained models can mitigate this for now.
"7, knowledge bases, is particularly concerning on that list. A truly intelligent AI shouldn't require humans to input knowledge about the world into it, it should be able to learn it itself."
I agree it's a great book but IMO it's important to understand that modern AI is about:
1. Classification and Learning (aka is that apple ripe? or what's the best website for a given search?)
2. Modeling / Machine vision (Where are the rocks around this rover.)
3. Goal Seeking (AKA what's the best path from here to DC.)
With enough resources we can do any of the above fairly well. So using AI is more about understanding how to link the above activities to some useful problem. AKA control a rover when your ping is 15 min or solve a CAPTCHA.
Books should not be burned, nobody should be shielded from knowledge that they are old enough to seek and information should be free.
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