AI & machine learning are becoming part of today's products more than ever before. It is now even more important for businesses to have a clear vision of how artificial intelligence intersects with their mission. This is the reading list we recommend to our partners to get insights into the current ecosystem. It is important to say that while this list is completely relevant for 2020, it does not mean that all books were published recently.
Max Tegmark is a physicist and cosmologist and the author of some well-known cosmological books like Our Mathematical Universe. However, he has been recently focusing more on artificial intelligence research. He co-founded the Future of Life Institute and also has organized conferences with speakers such as Elon Musk, Demis Hassabis, Stuart Russell, and others. The Future of Life Institute has already received donations from various business leaders to investigate risks posed by advanced artificial intelligence. In Life 3.0, we can read a fictitious story about Prometheus, advanced artificial general intelligence (AGI) created by Team Omega. In this story, Tegmark illustrates what could happen if such AGI is developed and how Team Omega can use Prometheus to take over the world. The book also refers to the world's leading companies which are focusing on creating AGI like DeepMind or OpenAI. It is a must-read for everyone who would like to see how our life could look in the future.
Ray Kurzweil is a well-known inventor and entrepreneur, currently a Chief Engineer at Google. He accepted an offer from Google after publishing his book called How to Create a Mind (2012). In his theory, he explains that the brain consists of a hierarchy of pattern recognizers and suggests that techniques like the Hidden Markov models and genetic algorithms will be used for creating brain-like AGI architecture in the future. He is also known for successfully predicting many technological breakthroughs. He first mentioned a couple of predictions in The Age of Intelligent Machines (1990). Many of his predictions were successful and he continued to make more such predictions in The Singularity is Near. It is important to mention, however, that some of his predictions were not correct. That said, it is very impressive to successfully predict a number of well-defined events 15-20 years in the future. It is a very interesting and thought-provoking read.
This is one of the most important books for every business leader in the current AI ecosystem. Kai-Fu Lee is one of the few entrepreneurs who have experience in both Silicon Valley and China. He has worked at both Microsoft and Google and provides very useful insights into today's AI race between the US and China. This is a must-read for everyone serious about building AI-driven products.
This is a slightly more technical book than the others listed above. Pedro Domingos is a professor at the University of Washington and a machine learning researcher. He is known for his work on Markov logic networks. He describes the primary machine learning philosophies such as inductive reasoning, connectionism, evolutionary approaches, approaches based on Bayes' theorem, and others. He uses a lot of examples and analogies to the real world, and the book is a very helpful read for people without a technical background who are trying to understand the main directions the field is taking.
This is the most technical book on the list, but it is still very accessible to beginners. I can see this book as a great fit for people with only a software engineering background who would like to get more technical insights into the machine learning field. Andriy Burkov is a lead data scientist at Gartner. Fitting the basics of the main machine learning methods in 100-150 pages is a very difficult task, but Andriy did it successfully. There are, however, much better choices for gaining in-depth knowledge in the field. This includes classic titles like Deep Learning (Adaptive Computation and Machine Learning Series) or Artificial Intelligence - A Modern Approach.
This list of books is great to get up to speed with the current AI ecosystem. It is important to recognize and be careful about being too optimistic about current AI capabilities. We might, for example, get easily caught up in the idea of exponential growth, which to a certain extent, is valid. Nonetheless, our prediction models are just an approximation of the world and reality is usually much more complex.