Upvoted to encouraging people to get hands-on. Learning is good. Trying to go for a higehr level of understanding in whatever you do is a core rationality skill.
Sadly you stopped there though. For the sake of discussion, I’ve heard Artificial Intelligence: A Modern Approach is a good book on the subject. Hopefully a discussion could start here; perhaps there’s something flawed, or perhaps the book is outdated. If anyone here, and I’m looking at you, the AI, AGI, FAI, IDK and other acronym-users whom I can’t keep up with can provide some more directions for the potentially aspiring AI researchers lurking around, it would be very appreciated.
Those links are specific to MIRI’s rather idiosyncratic philosophy/math oriented research agenda. If you actually read all those books, you’re pretty much committing to knowing very little about practical AI and machine learning, simply by virtue of time opportunity cost.
There’s only two items on that list that are artificial intelligence related. One is an introductory survey textbook, and the other is really about probabilistic reasoning with some examples geared towards AI. The rest has about as much to do with AI as, say, the C++ programming manual.
Your definition what counts as “AI related” seems to be narrower than mine, but fine. I trust readers can judge whether the linked resources are of interest.
Assuming you have some exposure to linear algebra, calculus, and a little programming, I recommend Andrew Ng’s machine learning course on youtube. AI: A Modern Approach is still a good textbook, but I think machine learning specifically is where interesting stuff is happening right now.
Sure… but machine learning is very important for AGI, it’s not going to suddenly get replaced with hand-designed agents. This advice might apply better to subfields, like deep neural networks vs. hierarchical Bayesian models.
There are a lot of good online resources on deep learning specifically, including deeplearning.net, deeplearningbook.org, etc. As a more general ML textbook, Pattern Recognition & Machine Learning does a good job. I second the recommendation for Andrew Ng’s course as well.
Deep learning courses rush through logistic regression and usually just mention SVMs. Arguably it’s important for understanding deep learning to take the time to really, deeply understand how these linear models work, both theoretically and practically, both on synthetic data and on high dimensional real life data.
More generally, there are a lot of machine learning concepts that deep learning courses don’t have enough time to introduce properly, so they just mention them, and you might get a mistaken impression about their relative importance.
Another related thing: right now machine learning competitions are dominated by gradient boosting. Deep learning, not really. This says nothing about starting with deep learning or not, but a good argument against stopping at deep learning.
It depends on the competitions. All kaggle image-related competitions I have seen have been obliterated by deep neural networks.
I am a researcher, albeit a freshman one, and I completely disagree. Knowing about linear and logistic regressions is interesting because neural networks evolved from there, but it’s something you can watch a couple of videos on, maybe another one about maximum likelihood and you are done. Not sure why SVMs are that important.
Upvoted to encouraging people to get hands-on. Learning is good. Trying to go for a higehr level of understanding in whatever you do is a core rationality skill.
Sadly you stopped there though. For the sake of discussion, I’ve heard Artificial Intelligence: A Modern Approach is a good book on the subject. Hopefully a discussion could start here; perhaps there’s something flawed, or perhaps the book is outdated. If anyone here, and I’m looking at you, the AI, AGI, FAI, IDK and other acronym-users whom I can’t keep up with can provide some more directions for the potentially aspiring AI researchers lurking around, it would be very appreciated.
Well, there’s this …
[ETA: link is to MIRI’s research guide, some traditional AI but more mathy/philosophical. Proceed with caution.]
What does that have to do with artificial intelligence?
… quite a lot, no?
Those links are specific to MIRI’s rather idiosyncratic philosophy/math oriented research agenda. If you actually read all those books, you’re pretty much committing to knowing very little about practical AI and machine learning, simply by virtue of time opportunity cost.
There’s only two items on that list that are artificial intelligence related. One is an introductory survey textbook, and the other is really about probabilistic reasoning with some examples geared towards AI. The rest has about as much to do with AI as, say, the C++ programming manual.
Your definition what counts as “AI related” seems to be narrower than mine, but fine. I trust readers can judge whether the linked resources are of interest.
Assuming you have some exposure to linear algebra, calculus, and a little programming, I recommend Andrew Ng’s machine learning course on youtube. AI: A Modern Approach is still a good textbook, but I think machine learning specifically is where interesting stuff is happening right now.
There is also an argument for doing stuff that’s less in vogue right now.
Sure… but machine learning is very important for AGI, it’s not going to suddenly get replaced with hand-designed agents. This advice might apply better to subfields, like deep neural networks vs. hierarchical Bayesian models.
There are a lot of good online resources on deep learning specifically, including deeplearning.net, deeplearningbook.org, etc. As a more general ML textbook, Pattern Recognition & Machine Learning does a good job. I second the recommendation for Andrew Ng’s course as well.
I recommend against starting with deep learning.
reason? (I intuitively agree with you, just curious)
Here is one reason, but it’s up for debate:
Deep learning courses rush through logistic regression and usually just mention SVMs. Arguably it’s important for understanding deep learning to take the time to really, deeply understand how these linear models work, both theoretically and practically, both on synthetic data and on high dimensional real life data.
More generally, there are a lot of machine learning concepts that deep learning courses don’t have enough time to introduce properly, so they just mention them, and you might get a mistaken impression about their relative importance.
Another related thing: right now machine learning competitions are dominated by gradient boosting. Deep learning, not really. This says nothing about starting with deep learning or not, but a good argument against stopping at deep learning.
It depends on the competitions. All kaggle image-related competitions I have seen have been obliterated by deep neural networks.
I am a researcher, albeit a freshman one, and I completely disagree. Knowing about linear and logistic regressions is interesting because neural networks evolved from there, but it’s something you can watch a couple of videos on, maybe another one about maximum likelihood and you are done. Not sure why SVMs are that important.