When “The Master Algorithm” was released in 2015, it acted as a window into the field of AI and machine learning, proving to be a great primer, especially for the ‘outside’ world. It was authored by Pedro Domingos, a pioneer in the field and someone who has lived through the evolution of the field, right from the early 90s to now in the 2020s. In a career that has spanned over three decades and counting, Domingos has been bestowed with several recognitions, including the SIGKDD award, which is widely considered the highest honour in data science. He is also credited with the invention of Markov Logic Networks.
Analytics India Magazine was recently in conversation with Domingos, talking about topics ranging from the evolution of machine learning, his book (s), to AGI and Metaverse!
“When I was younger, I came across this book on AI. It made me curious about machine learning – if you could make it work, it had the potential to take over the world. Later, when I decided to pursue a formal degree in this field, there were hardly any colleges offering such courses. The field was very primitive, making it very difficult to make significant contributions, as opposed to more mature fields like Physics or Biology. University of California, Irvine was one of the very few colleges that offered substantial machine learning courses, and that is where I got a PhD,” he said. He counts Geoff Hinton and Tom Mitchell among his earliest heroes of the field.
Times have drastically changed since then. As Domingos points out, the field of machine learning is dramatically changing, and it is growing bigger. The other thing that marks the evolution of the field in the last three decades is the development of a whole new industry for AI. “If you consider the top ten companies of the world, seven of them would say AI is essential to what they do,” Domingos pointed out.
While the progress of AI is encouraging, Domingos points out there is a downside to it. “It is becoming impossible to keep up with the field. It is good that we have a thriving industry because of its impact factor, but on the other hand, it becomes a lot more difficult to change. We will have to fight this inertia,” he said.
“I thought about writing such a book back in the 90s with the then-ongoing data mining explosion. I thought people outside of the field would benefit from knowing the developments. My book was going to be directed toward people who were not necessarily working in the field of AI and machine learning. In that sense, it was a challenge because a popular science book cannot be a list of topics and should instead have a storytelling element to it. And I did not know at that time what that story would be,” said Domingos.
Close to over two decades later, two things persuaded him to finally write this book – the big data boom and the lack of knowledge about it that was causing costly mistakes being made. “There was a sense of urgency,” he said.
“What occurred to me is that machine learning is a quest for the ‘Master Algorithm’. Such an algorithm can learn anything. So I cast the book as being the search for such a Master Algorithm. Through this book, I take people on a tour of different approaches of different ‘tribes’ as I’d like to call them. These tribes are different schools of thought in machine learning – interestingly, each of them thinks that their way is the only right way when actually, all, individually, are just a piece of the puzzle. We need to find a unified theory of machine learning, building a standard like there exists in Physics or Biology,” he explained. The five tribes of machine learning are – Symbolists, Connectionists, Evolutionaries, Bayesians, and Analogies.
