This book is written for lay audience who tend to get carried away by impressive headlines. It is a tale of caution to not get excited by the current progress in AI and communicate the research at its scale; not make an exorbitant story out of it. This is important. Not only news articles but even research paper titles have seen a trend to make bold statements, but proving very little. So this book is a great reminder to call a spade a spade.



However, I think the tone of the book is a little sni

This book is written for lay audience who tend to get carried away by impressive headlines. It is a tale of caution to not get excited by the current progress in AI and communicate the research at its scale; not make an exorbitant story out of it. This is important. Not only news articles but even research paper titles have seen a trend to make bold statements, but proving very little. So this book is a great reminder to call a spade a spade.



However, I think the tone of the book is a little snide. Even though authors mention multiple times that they do not want to rubbish the current research rather reevaluate the future research, the book contains very few success stories of AI and a lot of media hyped achievements which are later proven to be apocryphal. Authors could have demonstrated their pride in AI by bring forward more of the exemplary work.



Despite the tone, I think the authors make a good point about the need for multidisciplinary research. AI researchers have to learn from other disciplines; the progress cannot happens in an island. The need for cognition and common sense is important and well acknowledged. How to achieve it is a question left for future. Perhaps this book is also for the agencies that fund AI research. What should be the next area of focus? Where should the academic community invest their time? Definitely not in end-to-end learning.



Quotes:



“Ultimately what has happened is that people have gotten enormously excited about a particular set of algorithms that are terrifically useful, but that remain a very long way from genuine intelligence—as if the discovery of a power screwdriver suddenly made interstellar travel possible. Nothing could be further from the truth. We need the screwdriver, but we are going to need a lot more, too.”





“What the field really needs is a foundation of traditional computational operations, the kind of stuff that databases and classical AI are built out of: building a list (fast food restaurants in a certain neighborhood) and then excluding elements that belong on another list (the list of various McDonald’s franchises).”





“The reason you can’t count on deep learning to do inference and abstract reasoning is that it’s not geared toward representing precise factual knowledge in the first place. Once your facts are fuzzy, it’s really hard to get the reasoning right”



“it is clear that humans use different kinds of cognition for different kinds of problems... the mind is not one thing, but many. ... The brain is a highly structured device, and a large part of our mental prowess comes from using the right neural tools at the right time. We can expect that true artificial intelligences will likely also be highly structured, with much of their power coming from the capacity to leverage that structure in the right ways at the right time, for a given cognitive challenge.”





“AI researchers must draw not only on the many contributions of computer science, often forgotten in today’s enthusiasm for big data, but also on a wide range of other disciplines, too, from psychology to linguistics to neuroscience. The history and discoveries of these fields—the cognitive sciences—can tell us a lot about how biological creatures approach the complex challenges of intelligence: if artificial intelligence is to be anything like natural intelligence, we will need to learn how to build structured, hybrid systems that incorporate innate knowledge and abilities, that represent knowledge compositionally, and that keep track of enduring individuals, as people (and even small children) do.

Once AI can finally take advantage of these lessons from cognitive science, moving from a paradigm revolving around big data to a paradigm revolving around both big data and abstract causal knowledge, we will finally be in a position to tackle one of the hardest challenges of all: the trick of endowing machines with common sense.”

