Name: L'algoritmo definitivo. La macchina che impara da sola e il futuro del nostro mondo
ISBN: 9788833927060

Domingos Pedro

L'algoritmo definitivo. La macchina che impara da sola e il futuro del nostro mondo

Pedro Domingos

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning aboutand solvingan extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars. These programs are already helping us fight the war on cancer and predict the movements of the stock market, and they are making great headway with instant language translation and discovering new laws of nature. But machine learning is incomplete, and its practitioners across the globe are seeking the most powerful algorithm of all. The Master Algorithm will not be limited to solving particular problems but will be able to learn anything and solve any problem, however difficult, and Pedro Domingos, a trailblazing computer scientist, is at the very forefront of the search for it. With the Master Algorithm in hand and data as its fuel, machine learningessentially the automation of discovery, a kind of scientific method on steroidswill become the most powerful technology humanity has ever devised. And The Master Algorithm will be its bible.

How the Quest for the Ultimate Learning Machine Will Remake Our World

The Master Algorithm

Timeo Williams

This book details what an algorithm is, how machine learning is changing the way we develop an algorithm( Big data), and the quest for the master algorithm. 

Domingos categorizes the best ML's of today in 5 categories:
The Symbolists, promoting logic, philosophy and symbolic learning and use inverse deduction
The Connectionists, studying networks of brain neurons and back-propagation theories
The Evolutionaries, genetic programming advocates
The Bayseans, using statistics and probabilistic inference
The Analogizers, known for psychology-based kernel machines

Each model has its pros and cons, and the master algorithm will need to incorporate all 5.

Gavin

Overambitious pop science from a lively and charming narrator. He tries to sketch all of machine learning in a couple hundred pages. The warmth of his teaching voice comes through the page:

  
As you read the book, feel free to skim or skip any parts you find troublesome; it’s the big picture that matters, and you’ll probably get more out of those parts if you revisit them after the puzzle is assembled.


but he needs a better editor, more even than Nassim Taleb does. This is often just a stream-of-consciousness analogy-dump, and with precise topics that just doesn't fly. (Both Penguin productions.) 

There's more wrong with it than prose, unfortunately: he gives equal time to unpromising approaches (genetic programming, analogical reasoning) and so has to skim over the single most important approach (deep learning), with no real sense of the giant differences in success. Couple this with his terrible argument against AI risk ("unlike humans, computers don’t have a will of their own. They’re products of engineering, not evolution... the evaluation function is determined by us. A more powerful computer will just optimize it better... The same reasoning applies to all AI systems because they all—explicitly or implicitly—have the same three components. They can vary what they do, even come up with surprising plans, but only in service of the goals we set them.") and it becomes actively unhelpful.

(Pedantic aside: he commits linguistic violence every time he uses "algorithm" instead of the unsexy true referent, "program". He obviously knows the distinction much better than I do, but skips this to talk down / excitingly to the audience.)

Read his great dense paper instead.

DNF 50%

A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.

tiff

This book presupposes that there is a master algorithm to be found in the field of Artificial Intelligence. That while computers will become anything but sentient, they will essentially make half the workforce obsolete. The author's laissez faire attitude about this bothers me. He's encouraging this.

I understand the excitement surrounding machine learning — we have Google Now, Siri, Amazon Echo, etc that understand what we want before we actually want it and as a developer, robotics fan, and data junkie, this excites me too.

But for years we've been excited by AI and machine learning, much like the resurgent VR. This is the new tech hotness. But where I take issue with this book is it looking so far into the future for a master algorithm that we can't even quantify right now and that, once we do, the loss of physical jobs to our Amazon Echo overlords is welcome by not just Domingos but most machine learning gurus.

Will we find a way to replace those lost jobs for the people it will affect? Who knows. The fact that it doesn't cross Domingos mind to give this dilemma more than a cursory mention gives this book an air of arrogance that I find distasteful. 2 stars.

Nelson Zagalo

Confesso que parti para "The Master Algorithm" (2015) com várias reservas: a primeira prendia-se com a dificuldade de trazer um assunto desta complexidade para uma discussão leiga; a segunda tinha que ver com a minha desconfiança sobre a possibilidade efetiva de se criar um algoritmo único, de tudo capaz. No final do livro tenho de dizer que Pedro Domingos, professor na Universidade de Washington, fez um belíssimo trabalho, não só o livro é acessível como nos abre o apetite para o tema. O que mais gostei, e acaba sendo o cerne do livro, foi da descrição das metodologias que estão a ser seguidas para que a máquina possa aprender, não por serem exóticas mas antes pelo contrário, por responderem por métodos que nós próprios, humanos, também temos vindo a utilizar para construir conhecimento.

Continua no VI em:
https://virtual-illusion.blogspot.com...

Victor Gomez Estevez

Una maravilla para entender cómo funcionan los principales algoritmos de IA y hasta dónde pueden llegar (Y por qué)

L'algoritmo definitivo. La macchina che impara da sola e il futuro del nostro mondo

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