[jlgray]

If you have mastered the basics (e.g. Norvig's AIMA, Hastie and Tibshirani's Elements of Statistical Learning, Koller's PGM), then I would suggest that the only place to really get a view of the state of the art is by reading papers.

In general, scientific books are an overview of a field, which can only occur with sufficient time for hindsight and synthesis. Even a thousand page book such as Koller's PGM will be littered with references and suggestions of papers to read for a deeper understanding.

One partial exception might be the Deep Learning book by Goodfellow and Bengio, which was made public only a month or so ago. Even this, however, is just an overview. http://www.deeplearningbook.org/

[essofluffy]

Is there anyway to find papers without the references and suggestions in books?

[jlgray]

As T-A suggested, arXiv is a great source, especially with the frustration over the profiteering of the publishing industry, although you always have to be aware that arXiv papers are not peer-reviewed.

There is also http://gitxiv.com/, which includes source code.

If you are at a university or a company with deep pockets, you can also use http://dl.acm.org/ or http://ieeexplore.ieee.org/Xplore/home.jsp.

I've also heard of certain hubs of science on the internet, but I wouldn't know anything about that.

[T-A]

Obsessively watch https://arxiv.org/list/cs.AI/recent ?