As always, great article. Perhaps we would need a Kuhn's paradigm shift for AI, where Neuroscience, AI and Cognitive Psychology could converge to a new foundation to reach AGI. You raised a much-needed review in the AI field, which is still a long way to go. I would like to add to your discussion the underrated field of Computational Psychology which explores the computational cognitive modeling, a possibly key factor for an AGI that would almost certainly interact somehow with human beings. (BTW, a good reference on this matter is the book: The Cambridge Handbook of Computational Psychology).
Have you read Jeff Hawkins’ book A Thousand Brains? Its arguments are very much in-line with your own. A major theme of the book is built around his belief that our modern understanding of neurons should serve as a guide for the design or AI systems. The book might be too basic for someone well-versed in these fields, but as a non-computer scientist I found it accessible and interesting. Regardless, I do agree with your position that we should look at biological systems for ideas of new ways to model intelligence... at least until we figure out a superior method.
Thanks Alberto for this insightful text. In particular one key point is important:
"""
Right now, neuroscience (and AI) is at a similar stage as physics was pre-Galileo and pre-Newton.
We lack well-established explanatory theories of intelligence, brain function/structure, and how the former emerges from the latter. AI should evolve in parallel with neuro until we develop those.
"""
As a long-term neuroscientist, this is exactly how I see it, too. There are three major issues in neuroscience which have hindered faster progress:
1) Most neuroscientist have no math training, and those who have have often shallow training in biology. We need both to understand biological signal processing. This said, a neuroscientist does not benefit much from math as long as you get your output from new experimental techniques, and do not care about modeling the biology.
2) Evolving modeling and simulation software is a key towards quantitative understanding of complex systems, such as the brain. It is not so far back the best prediction of weather tomorrow was weather today. In this sense we are living in a golden era with rapidly evolving infrastructure for modelers.
3) There is an innate brake for interdisciplinary fields in professional science. Due to continuous hard competition of funding, the top groups need to optimize publication output. The publication output then dictates their future probability of funding. This means that you should not diverge unless you are certain of returns and absolutely not play deep in uncertain forests.
As you Alberto pointed out, we are in pre-Newton era in neuroscience.
First, defining humans as intelligent requires some imagination. Please let us recall that we are the species with a massive loaded gun in it's mouth (nuclear weapons), that we typically find too boring to bother discussing. A single human being can destroy modern civilization in just minutes, and we aren't interested. Intelligent??
Instead of studying neuroscience to make AI more powerful, we should be studying thousands of years of human behavior, an investigation which would provide us with a pretty credible look at what powerful AI is likely to be used for, more killing, more conquest, more domination of the weak by the strong, more concentration of wealth at the top of society etc.
If we're going to ignore logic, common sense, and human history, and continue to develop AI anyway, modeling it on the human brain seems a very questionable proposition.
As always, great article. Perhaps we would need a Kuhn's paradigm shift for AI, where Neuroscience, AI and Cognitive Psychology could converge to a new foundation to reach AGI. You raised a much-needed review in the AI field, which is still a long way to go. I would like to add to your discussion the underrated field of Computational Psychology which explores the computational cognitive modeling, a possibly key factor for an AGI that would almost certainly interact somehow with human beings. (BTW, a good reference on this matter is the book: The Cambridge Handbook of Computational Psychology).
Great article. Thanks for sharing it.
Have you read Jeff Hawkins’ book A Thousand Brains? Its arguments are very much in-line with your own. A major theme of the book is built around his belief that our modern understanding of neurons should serve as a guide for the design or AI systems. The book might be too basic for someone well-versed in these fields, but as a non-computer scientist I found it accessible and interesting. Regardless, I do agree with your position that we should look at biological systems for ideas of new ways to model intelligence... at least until we figure out a superior method.
Thanks Alberto for this insightful text. In particular one key point is important:
"""
Right now, neuroscience (and AI) is at a similar stage as physics was pre-Galileo and pre-Newton.
We lack well-established explanatory theories of intelligence, brain function/structure, and how the former emerges from the latter. AI should evolve in parallel with neuro until we develop those.
"""
As a long-term neuroscientist, this is exactly how I see it, too. There are three major issues in neuroscience which have hindered faster progress:
1) Most neuroscientist have no math training, and those who have have often shallow training in biology. We need both to understand biological signal processing. This said, a neuroscientist does not benefit much from math as long as you get your output from new experimental techniques, and do not care about modeling the biology.
2) Evolving modeling and simulation software is a key towards quantitative understanding of complex systems, such as the brain. It is not so far back the best prediction of weather tomorrow was weather today. In this sense we are living in a golden era with rapidly evolving infrastructure for modelers.
3) There is an innate brake for interdisciplinary fields in professional science. Due to continuous hard competition of funding, the top groups need to optimize publication output. The publication output then dictates their future probability of funding. This means that you should not diverge unless you are certain of returns and absolutely not play deep in uncertain forests.
As you Alberto pointed out, we are in pre-Newton era in neuroscience.
First, defining humans as intelligent requires some imagination. Please let us recall that we are the species with a massive loaded gun in it's mouth (nuclear weapons), that we typically find too boring to bother discussing. A single human being can destroy modern civilization in just minutes, and we aren't interested. Intelligent??
Instead of studying neuroscience to make AI more powerful, we should be studying thousands of years of human behavior, an investigation which would provide us with a pretty credible look at what powerful AI is likely to be used for, more killing, more conquest, more domination of the weak by the strong, more concentration of wealth at the top of society etc.
If we're going to ignore logic, common sense, and human history, and continue to develop AI anyway, modeling it on the human brain seems a very questionable proposition.