I recently finished reading The Master Algorithm (Amazon) by professor Pedro Domingos, which is an overview of the current state and near future of machine learning research, targeted for general audience. It can be warmly recommended for everyone interested in machine learning and artificial intelligence. It requires no special background knowledge, although a basic understanding of programming, optimization and principles of artificial intelligence is helpful.
The author Domingos works as a professor in the University of Washingon in Seattle, and is a very respected authority in the field of machine learning. In my own upcoming doctoral thesis I cite many of his scientific articles. Comparing to many other experts on this field, his writing is very clear and easy to understand. Also The Master Algorithm, published a bit over a year ago, is very clearly written, and it is targeted to those not familiar with the field and its jargon. A lot of insight can be gained from the book, provided that the reader has the motivation for thinking through the ideas and thought experiments. The book has received some well-earned praise from such important experts as Judea Pearl and Sebastian Thrun. The focus on general audience means, that many complicated ideas on a difficult field have been simplified greatly, but there is an excellent collection of further reading for those who wish to study the details.
Domingos organizes the scholars of machine learning into five ‘tribes’ or schools of thought, and they are researchers and developers relying on either logical deduction, neural networks, Bayesian statistics, genetic algorithms or analogies for creating learning systems. The advantages and disadvantages of the different tribes and their algorithms are described intuitively, and finally the idea of developing a ‘master algorithm’, which combines the best ideas of the five tribes, is proposed and developed further. This kind of ‘general learner’ could accept any kind of data, learn the learnable from it, and apply the learning to anticipate similar situations in future. The proposed ideas and goals are somewhat realistic, although a bit simplified and overly optimistic.
Today, this topic is naturally very timely and important, and it’s difficult to avoid exposure to it, since the first truly intelligent systems are entering the public stage. Some examples include IBM’s Watson, Google’s AlphaGo, self-driving cars such as those sold by Tesla, and digital assistants such as Apple’s Siri, Google Now, Amazon’s Alexa and Microsoft’s Cortana. Some people probably wonder, what kinds of benefits and business opportunities could be enabled by these technologies, while others observe the progress more cautiously and worry about the possible threats and harmful effects.
In the conclusion of his book, also Domingos discusses in my opinion very thoroughly the social aspects of artificial intelligence. He arrives at very similar optimistic conclusions as myself: artificial intelligence will become an extension of us humans just like all other technologies, such as smartphones and search engines, have already become. Humans will work in cooperation with intelligent systems and devices. These will make our lives easier in many ways. Of course it is inevitable, that there will be less and less economically productive work left for humans to do. This is why we as a society must move towards softer values and be prepared to share more and pay extra for services provided by humans. Both Domingos and I support a basic income that guarantees a certain level of sustenance for everyone.
In my personal opinion, technology should be seen as an achievement of all of humanity. Even though some people may become rich as a result of their own work and entrepreneurship, no one can anymore claim to have achieved everything alone. Behind all success there is the work of generations of humans and the fruits of the global economy. Hard work and risk taking should be rewarded, of course, but wealthy people must be prepared to share more to others as well. The value of a human being cannot be measured by economical measures alone.
With his students, Domingos has developed a relational learner called Alchemy. My own dissertation research is close to this, and I hope to introduce this method in this blog later along with some kind of practical demonstration. The same I wish to do naturally with my own methods as well.
This is the first review article I have written, and it starts a new series of blog articles. I hope to keep publishing it monthly.
(Updates 18 Oct 2016 6:24 pm: clarification to sentence structure, some minor corrections)