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Algorithms Are Not Enough: Creating General Artificial Intelligence
Why a new approach is needed in the quest for general artificial intelligence. Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough , Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent, nor likely. Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence.
Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.
Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.
336 pages, Hardcover
Published October 13, 2020
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Displaying 1 - 5 of 5 reviews
December 28, 2024
This book was published before the hype caused by LLMs, so it focuses on the older applications of AI and explains them to a great detail.
The main idea presented in the book (that we still do not understand the inner working of intelligence, memory, creativity, and other aspects of human intelligence) is legit and plausible, but a lot of things where the book we are not there yet, were already achieved. I recommend to follow the author on LinkedIn - he is quite active and has quite sensible comments under misleading and over-hyped posts about AI.
Unfortunately, the argumentation in the book often weak. There are many claims that are just laid out, without justification. On another place, the author claims that the speed of learning of autonomous vehicles must be slow because rare events are rare, completely missing the possibility to using high number or cars and sharing what they learn between each other, or simulating rare events instead of having to experience them (people often use similar learning techniques).
Also, book is quite repetitive. I cannot even count how many times the book mentioned playing of chess or go, repeating the same argument over and over.
The main idea presented in the book (that we still do not understand the inner working of intelligence, memory, creativity, and other aspects of human intelligence) is legit and plausible, but a lot of things where the book we are not there yet, were already achieved. I recommend to follow the author on LinkedIn - he is quite active and has quite sensible comments under misleading and over-hyped posts about AI.
Unfortunately, the argumentation in the book often weak. There are many claims that are just laid out, without justification. On another place, the author claims that the speed of learning of autonomous vehicles must be slow because rare events are rare, completely missing the possibility to using high number or cars and sharing what they learn between each other, or simulating rare events instead of having to experience them (people often use similar learning techniques).
Also, book is quite repetitive. I cannot even count how many times the book mentioned playing of chess or go, repeating the same argument over and over.
July 28, 2024
This book has an interesting argument to make - and heaps of evidence behind it. For people interested in artificial intelligence, and the history that led to these 'intelligent' systems' creation, look here first. I found the book grew more interesting and made more sense as it progressed - more from the historical context at the beginning, more from the argument that superintelligence cannot kill us all at the end. Read for information and to be pleasantly surprised about your own psychology, and get ready to question what intelligence actually entails, in both natural and artificial forms.
"A computer that did not interact with the world, no matter how superintelligent, could hardly be an existential threat to humanity. It might just as well sit there and watch cartoons."
"A computer that did not interact with the world, no matter how superintelligent, could hardly be an existential threat to humanity. It might just as well sit there and watch cartoons."
February 11, 2021
I enjoyed this book. It brings attention to areas of cognition that mainstream research is not considering yet. If these problem areas are ever replicated in software, this book would serve as a great framing reference on how to approach testing such implementations.
I would recommend this book to anyone who wants to to have a critical perspective on the current implementations of AI and has an interest in what's not being thought of yet.
I would recommend this book to anyone who wants to to have a critical perspective on the current implementations of AI and has an interest in what's not being thought of yet.
January 19, 2025
A bit repetitive at some point, but I like the nuance that he brings. Obviously, the book misses the last 4 years in trends, but the thinking is clear and helps. Would love to get an updated take on the whole AGI.
February 28, 2021
One of the most underrated books on AI. A definite must-read for anyone who is interested in getting a comprehensive, unhyped view of the current state of AI.
Displaying 1 - 5 of 5 reviews