Reading this paper reminds me how important it is for AI to continue to evolve it’s core algorithms.

Deep learning models are effectively pattern matching machines that cannot separate causality from correlation. As we throw more compute and $$$ at deep learning models we will experience diminishing returns in performance because of this.

For us to achieve AGI[0], the holy grail of AI, we will need to develop algorithms that can recognize causality somehow. Judea Pearl’s “The Book of Why”[1] does a great job articulating why this is important. Deep learning is a big leap forward and we’re only beginning to see its impacts, but it’s not sufficient to achieve AI’s most ambitious goals.

[0] https://en.m.wikipedia.org/wiki/Artificial_general_intellige...

[1] https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

There's also "The Book of Why", which is a pop-sci-ish book by Pearl and Dana Mackenzie. It contained just enough math and examples to get me really excited for causal inference, so I just bought "Causal inference in statistics" to see the theory in detail. If you want to learn what causal inference is about, but don't necessarily want to wade through a textbook immediately, I highly recommend "The Book of Why".

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

A few recommedations:

1. Black Like Me - John Howard Griffin - https://en.wikipedia.org/wiki/Black_Like_Me

2. More Matrix and Philosophy - William Irwin (ed) - https://www.amazon.com/More-Matrix-Philosophy-Revolutions-Re...

3. Godel, Escher, Bach: An Eternal Golden Braid - Douglas Hofstadter - https://en.wikipedia.org/wiki/G%C3%B6del,_Escher,_Bach

4. The New Jim Crow - Michelle Alexander - https://en.wikipedia.org/wiki/The_New_Jim_Crow

5. Capitalism: The Unknown Ideal - Ayn Rand - https://www.amazon.com/dp/0451147952/ref=sspa_dk_detail_4?ps...

6. The Fountainhead - Ayn Rand - https://en.wikipedia.org/wiki/The_Fountainhead

7. The Book of Why: The New Science of Cause and Effect - Judea Pearl - https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

8. The Education of Millionaires - Michael Ellsberg - https://www.amazon.com/Education-Millionaires-Everything-Col...

9. The Silent Corner, The Whispering Room, and The Crooked Staircase - Dean Koontz - http://www.deankoontz.com/book-series/jane-hawk

10. Godel's Proof - Ernest Nagel & James Newman - https://www.amazon.com/G%C3%B6dels-Proof-Ernest-Nagel/dp/081...

11. After Dark - Haruki Murakami - https://www.goodreads.com/book/show/17803.After_Dark

I'm not well rounded enough to draw a clear path from where you are. For me Gelmans Data Analysis Using Regression and Multilevel/Hierarchical Models [0] drove home many, many points. More recently, I have a sense/hope that Pearl's The Book of Why [1] might take this to yet another level.

[0] http://www.stat.columbia.edu/~gelman/arm/ [1] https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...