OR

Pedro Domingos, The Master Algorithm
(more difficult to read)

You should. It directly addresses the idea of blending different fields of AI.

[1] http://homes.cs.washington.edu/~pedrod/

“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”

-- Pedro Domingos, in The Master Algorithm (2015)

Type 2: The Master Algorithm by Pedro Domingos

For something lighter but insightful, Pedro Domingo's The Master Algorithm is quite fun.

A great number of classes are now available online. I prefer the Stanford classes. http://cs229.stanford.edu/ and http://cs224d.stanford.edu/ are good places to start. There are more.

[1]: https://en.wikipedia.org/wiki/The_Master_Algorithm

[2]: http://homes.cs.washington.edu/~pedrod/

I'd invite you to read "The Master Algorithm" to understand exactly how they failed the first time and how they aren't the route forward: https://en.m.wikipedia.org/wiki/The_Master_Algorithm

Anyway, just read "The Master Algorithm" by Pedro Domingoes. Fantastic read. The most interesting part to me was his survey of the five tribes of machine learning: symbolists, connectionists, geneticists, Bayesians and analogists.

After the survey he goes on to talk about some other aspects. And then discusses Alchemy and the possibility of uniting the techniques of all five tribes into one algorithm (hence the title).

I found his writing on a dense subject easy to read and great at conveying the concepts. Well worth checking out.

Edited for typos

In addition, I started to apply my learnings in JavaScript [3]. Even though it's not the best language for ML, it makes it simpler to learn only one new thing and stick to known technologies for the rest. I have lined up ~7 articles about ML in JavaScript, so if you are interested, you can keep an eye on it :)

- [0] https://www.coursera.org/learn/machine-learning/

- [1] http://ocdevel.com/podcasts/machine-learning

- [2] https://www.goodreads.com/book/show/24612233-the-master-algo...

- [3] https://www.robinwieruch.de/linear-regression-gradient-desce...

The Revenge of Geography - Robert D. Kaplan

Prisoner's Dilemma - William Poundstone

The Master Algorithm - Pedro Domingos

Zero-Sum Future - Gideon Rachman

The End of History and the Last Man - Francis Fukuyama

Entanglement - Amir Aczel

- The Master Algorithm: made for a general audience, gives you a lay of the land

- Python Machine Learning by Sebastian Raschka: gives you practical skills using python, scikit-learn, numpy, jupyter notebooks, pandas etc. From zero to kaggle in 4 chapters, goes deeper after that. Also goes into enough theory you aren't flying completely blind.

After that, I'm afraid I think you do need to go "academic", if by that you mean learning some of the underlying math to approach AI / ML from a more rigorous probabilistic perspective. I'd recommend studying probability theory and then working your way through Bishop's Pattern Recognition and Machine Learning. After that a lot more doors open up too more specialized topics like computer vision, reinforcement learning etc.

I've written up a lot more about this here:

http://karlrosaen.com/ml/

- Lost and Founder - the founder of Moz shares his advice and experience from building a 40M/year company. I found the things he says about building a startup extremely insightful and practically useful. Reading it feels like having a dinner with a friend who shares with you the things he has learned in a very honest, down to earth way. Highly recommend it.

- Rationality from AI to Zombies - probably the most influential book I've read in my life, profoundly changed the way I think. It's a collection of LessWrong essays on science and rationality. (recently they've released an an audio version by the way).

- "A Short History of Nearly Everything" and "Our Mathematical Universe" - two general popular science books I'm enjoying a lot. Haven't finished reading them yet, but so far they're brilliant(and very easy to understand, authors do an amazing job explaining complicated things in a simple, accessible way).

- Hacking Growth - an AMAZING book on "growth hacking". It provides a framework for marketing a startup, gives a ton of practical advice and specific tactics. It breaks down step by step how startups and big tech companies grow their products. Most of the books I've read on the subject were bullshit, but this one is absolutely fantastic, can't recommend it enough.

Other great books I should mention: This Idea is Brilliant, Actionable Gamification, The Design of Everyday Things, The Master Algorithm (great overview of machine learning techniqes), Springfield Confidential (fun behind the scenes from one of the writers on Simpsons), Homo Deus(from the author of Sapiens).

I don't know that anybody seriously proposes that deep learning is the be-all end-all of AI techniques. It's VERY powerful for a lot of things, but I think DL researchers are aware of things DL doesn't do / isn't good at. Look at the recent book The Master Algorithm which breaks down a lot of what it would take to create a truly general purpose learning algorithm: If you believe the author's thesis, Deep Learning (or something like that) is just one piece of a much larger picture.

And without trying to start a debate over the merits of ML versus "GOFAI" or symbolic computation, etc., I think it's fair to say that DL doesn't really add anything in terms of reasoning. It's great at saying "this picture has a cat in it" or "this wav file says 'Hello, my name is mindcrime'", but that's a pretty small part of what human intelligence can do.

Of course, it might just be that this is his pet "thing" and for all I know he could be totally wrong, but it struck me as interesting enough to start doing some reading on. And hence this post.

[1]: https://homes.cs.washington.edu/~pedrod/

[2]: http://www.amazon.com/The-Master-Algorithm-Ultimate-Learning...

> But everyone has only a sliver of it [information about you]. Google sees your searches, Amazon your online purchases, AT&T your phone calls, Apple your music downloads, Safeway your groceries, Capital One your credit-card transactions. Companies like Acxiom collate and sell infor- mation about you, but if you inspect it (which in Acxiom’s case you can, at aboutthedata.com), it’s not much, and some of it is wrong. No one has anything even approaching a complete picture of you. That’s both good and bad. Good because if someone did, they’d have far too much power. Bad because as long as that’s the case there can be no 360-degree model of you. What you really want is a digital you that you’re the sole owner of and that others can access only on your terms.

Does this mean that effectively all of Facebook, Amazon, Google, IBM and Microsoft will have the whole picture? That makes me worried.

[1]: https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Ma...

Mind me asking where in South Texas? Austin here. I have quite a few technical books lying around and plenty of colleagues who likely do as well.

Code by Charles Petzold.

Of the ones that I haven't finished, but have at least looked at, I think I'd say:

Machine Learning for Hackers by Drew Conway and John Myles White

and

The Master Algorithm by Pedro Domingos

The Idea Factory: Bell Labs and the Great Age of American Innovation - Jon Gertner

Bell Labs, the RnD wing of AT&T-was the best laboratory for new ideas in the world. The book tells a story about the life and work of a small group of brilliant people - Mervin Kelly, Bill Shockley, Claude Shannon, John Pierce, and Bill Baker.

Peak - Anders Ericsson and Robert Pool
You must have heard this a 1000 times - "you can do this", the book is about - "yes, and this is how"

The master algorithm - Pedro Domingos
Book details out the philosophies of various schools of thought in AI - deep learning, bayesian, genetic, reasoning - in a very simple language.

What technology wants - Kevin Kelly
Technology is a living organism and there are patterns to the technology evolution, not unlike the organic evolution.

* ... tuning again requires high level expertise and tutelage.*

Appears to be more luck (or "serendipity", if one is generous) that is required!

As new algorithms crop up, everyone moves to them to redo older problems. That continues until the next new thing pops up. Pedro Domingo's book, The Master Algorithm", describes this somewhat:

http://www.amazon.com/Master-Algorithm-Ultimate-Learning-Mac...

If by symbolic AI we mean GOFAI, expert systems etc, I don't think that there will be ever a resurgence. But if by symbolic AI we mean machine learning algorithms that are somehow based on symbolic reasoning, I do think that there will be a resurgence. In particular, this resurgence will start when:
a) Deep learning arrives to its limit (ie. research gets stuck)
and/or
b) Someone finds a scalable and SOTA-ish way to integrate symbols into gradient based algorithms.

> What is it about information machines (as opposed to humans) that make them unable to handle ambiguity?

Of course, if designed for ambiguity, information machines can handle it.

But in my experience, solving a problem with software first involves defining the problem. Most problems are "squishy", in that they are ill defined. The end user has needs and knows those needs, but hasn't thought through all the ramifications of automation.

I've started a requirements process many a time with the question: "what do you want this to do", and then diving down to specify each behavior, including critical path functionality, error conditions, alternate paths, roles in the system, performance, timeframes, and other attributes. All of these are fundamental pieces of automating information flow, but aren't typically considered by a non technical person. Hence my use of the term "squishy". (I wrote a blog post in 2003 about how software crystallizes business processes: http://www.mooreds.com/wordpress/archives/46 )

And I don't know of any software process that can handle that. Even tools designed for non developers like Excel and Zapier force users to go through edge cases.

Finally, I'm certainly no expert on some of the new AI technologies that might be game changers. (I did enjoy reading The Master Algorithm, which talks about the schools of AI and some of the achievements.)

It's hard to say. It's not a deeply technical book, I will say that. The main value I found in it, is that he covers a broad base of different techniques and then lays out some ideas on how they could all be combined to make a "general purpose learner". I found it worth reading, but YMMV.

One of her targets is Pedro Domingos, who is a professor at the University of Washington and a well-known expert in the space (he authored "The Master Algorithm"). Domingos is also known for being against the infiltration of "woke" culture into academic institutions, because it corrupts the purity of research and introduces political biases. This came up recently because NeurIPS (an AI conference) is going to require an "impact statement" for all submitted research papers, which has raised concerns about political bias at the conference, since researchers will likely include pandering progressive impact statements just to get through the process without controversy. Anandkumar has been one of the prominent activists pushing for NeurIPS to include impact statements, and was formerly involved in a campaign to push NeurIPS to be renamed from NIPS to NeurIPS (https://www.wired.com/story/ai-researchers-fight-over-four-l...).

This new letter to ACM was already discussed on Hacker News (see https://news.ycombinator.com/item?id=25575321). The letter includes Domingos as an initial signatory, and the group the letter is being sent to (Communications of the ACM) lists Anima Anandkumar (who tried to cancel him) on their Editorial Board (https://cacm.acm.org/about-communications/editorial-board/).

I was reading Domingos' "The Master Algorithm" several days ago and a mathematician inquired about the book. He knew a group of ML developers. His opinion was that "ML doesn't look very interesting: all you do is play with the parameters, turn the knobs, and/or change the model until something works. There's no real progress there; nothing substantial."

Rather than sending a batallion of bright developers into the ML swamp where they will largely be frustrated, learn little and contribute less, I'd be tempted to guide them into other fields.

02. The Firm: The secret history of McKinsey and it's influence on American business

03. The Simpsons and their mathematical secrets

04. League of denial

05. The Martian chronicles

06. The Sixth extinction

07. Lost stars

08. The Devil in the white city

09. China in ten words

10. The Fourth revolution

11. Red Mars

12. Iron Curtain: The Crushing of Eastern Europe

13. Grit: Passion, perseverance and the science of success

14. The Signal and the noise

15. The Third chimpanzee

16. The Willpower instinct

17. The Master algorithm

18. The Emperor of all maladies

19. 1491

And I'm reading Bury My Heart at Wounded Knee.

Honestly, I really enjoyed League of Denial about all the shady stuff the NFL did around CTE, Lost Stars which is an incredible Star Wars book, The Willpower Instinct, and 1491. Everything else was kind of take it or leave it. I doubt I'll read as many books next year