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What Computers Still Can't Do: A Critique of Artificial Reason
When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field.
At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.
Hubert L. Dreyfus, who is Professor of Philosophy at the University of California, Berkeley, is also the author of Being-in-the-World. A Commentary on Heidegger's Being and Time, Division I.
At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.
Hubert L. Dreyfus, who is Professor of Philosophy at the University of California, Berkeley, is also the author of Being-in-the-World. A Commentary on Heidegger's Being and Time, Division I.
407 pages, Hardcover
First published January 1, 1972
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2104 people want to read
About the author
Hubert L. Dreyfus
56 books188 followersHubert Lederer Dreyfus was professor of philosophy at the University of California, Berkeley, where his interests include phenomenology, existentialism, the philosophy of psychology and literature, and the philosophical implications of artificial intelligence.
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Displaying 1 - 17 of 17 reviews
September 12, 2013
Using philosophical arguments from Merleau-Ponty and Heidegger, Dreyfus convincingly demonstrates that there are things people can do, sometimes even without great effort, but which computers are simply incapable of ever being able to achieve. He ends with a list of 20 such items. Thirty-odd years after initial publication, computers still can't do 18 of them - it turns out that Dreyfus wasn't quite right about Grandmaster-level chess and large-vocabulary continuous speech recognition. Maybe there was a bug in Merleau-Ponty's conceptual analysis.
Oh well... if one out of two ain't bad, surely eighteen out of twenty is pretty darn good?
February 22, 2021
“Being is essentially different from a being, from beings” - stated Heidegger. In other words, what gives beings their being is not itself a being. Translated into AI language, this means that what makes some things programmable is not itself programmable and moreover it cannot be specified, described, or even named.
When the digital computer was invented, an entire generation of programmers from MIT and other leading institutions optimistically postulated that General AI was within reach and rushed to accomplish it. Unfortunately, they naively followed the standard metaphysics and its excessively rational definitions of what human and intelligence mean - metaphysics developed and perpetuated by most of the philosophers over the last two thousands of years. This first AI attempt failed; however some byproducts were created. The second AI wave also failed; while we are now in the middle of the third wave.
Dreyfus's philosophical “critique of artificial reason” was perfectly on the mark, devastating, and fulfilled almost completely in several years. According to Dreyfus, none of the four assumptions employed by the AI workers (biological, psychological, epistemological, and ontological) were justified and compared the AI workers with the old alchemists. Instead of paying any attention to him, the AI workers – most of them his peers at MIT – insulted and completely avoided him on the campus. According to Dreyfus, the digital computer logic and its working requires these particular four assumptions – since others cannot be implemented on it. Consequently, the AI workers adopted the four assumptions as self-evident and built their careers and dreams on them. It seemed obvious to them that a philosopher cannot understand their work and cannot criticize them; consequently, they refused to listen to him and optimistically persevered in their work despite increasing difficulties and failures. Dreyfus was exactly in the right place to point out their basic metaphysical assumptions; that is, he was a continental philosophy professor at MIT and he was just invited into the most advanced AI program at that time - RAND.
Pointing out metaphysical assumptions - deeply rooted into our cultural, scientific, and technological worldviews for hundreds of years - to programmers is inevitably going to lead to huge and insanely funny miss-communications as presented in this book and in its reception by the AI community. I never laugh so much while reading a book as technical and philosophical as this one.
But here is a sober reflection from this book: while it is not possible to create a digital program or machine to match humans, this constant and ubiquitous programming and digital worldview may reduce humans to match the existing digital programs and machines. In Heidegger's terms, this is called “enframing”: as everything else that is, man will be eventually turned into a resource by technology and for the further use of technology.
It seems to me that most of the present AI work dropped the original strong pretense to build a General AI and they are instead focusing on limited, but highly practical and successful use of neural networks trained on big data. However, there are some old-fashioned AI workers out there that still predict that “the singularity is near”; that is in a never-changing 20 years horizon (i.e. not short enough to compromise themselves with a failed prediction, but long enough to sustain enthusiasm and to build a career for themselves).
The AI field changed a lot since the first wave - called “good old-fashioned AI” - that Dreyfus criticized in this book. Since some assumptions and approaches were dropped in the AI field for good, the corresponding critiques in this book no longer apply. However, I believe that the main arguments presented in this book still stand against General AI understood as a “rational conscience”/“singularity” and prove its impossibility.
Interestingly enough, Nature published an article against General AI a few months ago; the author used some of Dreyfus's old arguments to prove its impossibility (https://www.nature.com/articles/s4159...).
This is just the old 1972 book with a “new”/1979 introduction.
When the digital computer was invented, an entire generation of programmers from MIT and other leading institutions optimistically postulated that General AI was within reach and rushed to accomplish it. Unfortunately, they naively followed the standard metaphysics and its excessively rational definitions of what human and intelligence mean - metaphysics developed and perpetuated by most of the philosophers over the last two thousands of years. This first AI attempt failed; however some byproducts were created. The second AI wave also failed; while we are now in the middle of the third wave.
Dreyfus's philosophical “critique of artificial reason” was perfectly on the mark, devastating, and fulfilled almost completely in several years. According to Dreyfus, none of the four assumptions employed by the AI workers (biological, psychological, epistemological, and ontological) were justified and compared the AI workers with the old alchemists. Instead of paying any attention to him, the AI workers – most of them his peers at MIT – insulted and completely avoided him on the campus. According to Dreyfus, the digital computer logic and its working requires these particular four assumptions – since others cannot be implemented on it. Consequently, the AI workers adopted the four assumptions as self-evident and built their careers and dreams on them. It seemed obvious to them that a philosopher cannot understand their work and cannot criticize them; consequently, they refused to listen to him and optimistically persevered in their work despite increasing difficulties and failures. Dreyfus was exactly in the right place to point out their basic metaphysical assumptions; that is, he was a continental philosophy professor at MIT and he was just invited into the most advanced AI program at that time - RAND.
Pointing out metaphysical assumptions - deeply rooted into our cultural, scientific, and technological worldviews for hundreds of years - to programmers is inevitably going to lead to huge and insanely funny miss-communications as presented in this book and in its reception by the AI community. I never laugh so much while reading a book as technical and philosophical as this one.
But here is a sober reflection from this book: while it is not possible to create a digital program or machine to match humans, this constant and ubiquitous programming and digital worldview may reduce humans to match the existing digital programs and machines. In Heidegger's terms, this is called “enframing”: as everything else that is, man will be eventually turned into a resource by technology and for the further use of technology.
It seems to me that most of the present AI work dropped the original strong pretense to build a General AI and they are instead focusing on limited, but highly practical and successful use of neural networks trained on big data. However, there are some old-fashioned AI workers out there that still predict that “the singularity is near”; that is in a never-changing 20 years horizon (i.e. not short enough to compromise themselves with a failed prediction, but long enough to sustain enthusiasm and to build a career for themselves).
The AI field changed a lot since the first wave - called “good old-fashioned AI” - that Dreyfus criticized in this book. Since some assumptions and approaches were dropped in the AI field for good, the corresponding critiques in this book no longer apply. However, I believe that the main arguments presented in this book still stand against General AI understood as a “rational conscience”/“singularity” and prove its impossibility.
Interestingly enough, Nature published an article against General AI a few months ago; the author used some of Dreyfus's old arguments to prove its impossibility (https://www.nature.com/articles/s4159...).
This is just the old 1972 book with a “new”/1979 introduction.
Read
December 28, 2012If one earns one's bread in the world of Internet People too long, one will encounter a large number of people who seem inherently suspicious of the concept of humanity and go into long diatribes disparaging the weakness of the human mind without technological augmentation. Turns out that not only are they assholes who ruin your lunch break, they are also on very epistemologically shaky ground.
Dreyfus' argument, along with John Searle's critique, are both devastating attacks on the concept of artificial intelligence. Granted, Dreyfus has gotten some egg on his face as some of the things he considered impossible back in the '70s have since been proven attainable, but the majority of his argument remains sound: that a computer, while it can be trained to learn tasks heuristically, cannot conceive of meaning (among other failings) and is therefore not an intelligence.
It also casts serious doubt over the entire program of cognitive science as it is now practiced. Look out kids, a lot of those Ted talks aren't as accurate as they seem.
Dreyfus' argument, along with John Searle's critique, are both devastating attacks on the concept of artificial intelligence. Granted, Dreyfus has gotten some egg on his face as some of the things he considered impossible back in the '70s have since been proven attainable, but the majority of his argument remains sound: that a computer, while it can be trained to learn tasks heuristically, cannot conceive of meaning (among other failings) and is therefore not an intelligence.
It also casts serious doubt over the entire program of cognitive science as it is now practiced. Look out kids, a lot of those Ted talks aren't as accurate as they seem.
April 22, 2014
What Computers Still Can’t Do (1992) is an evolution of Hubert Dreyfus’s original work, What Computers Can’t Do (1972). Today, the ideas coming out of GOFAI research (Good Old Fashion Artificial Intelligence), which is based on the notion of using symbolic representations to replicate intelligence, are being replaced by more complex models of the brain/mind. In the revised edition, Dreyfus has added an introduction presenting an overview of the developments that have occurred in the field of Artificial Intelligence (AI) since the publication of his original manifesto. Dreyfus also assesses how the perspectives of neural networks and connectionism have transformed the field. That said, What Computers Still Can’t Do presents a similar philosophical analysis to the one contained in the original work, which triggered an avalanche of outrage in the AI community upon its release in the seventies.
Dreyfus's philosophical inquiry tracks the history of developments in AI and is concerned with exposing the incorrect assumptions (psychological, epistemological, and ontological) made by researchers working in the field. Throwing light on the kind of discursive moves used to understand the human brain/mind as reducible to the processes of a digital computer, Dreyfus concentrates on the two subfields of Artificial Intelligence: Cognitive Simulation and Artificial Intelligence. These two fields, he argues, have led to the examination of two distinct but interrelated questions: (1) Does a human being in “processing information” actually follow normal rules like a digital computer? (2) Can human behavior, no matter how generated, be described in a formalism which can be manipulated by a digital machine?
Dreyfus notes that given the difficulties AI experienced during Phase I and Phase II of its development (e.g. failure of GPS), cognitive simulation nevertheless assumed that the information processes of a computer revealed “the hidden information processes” of a human being. He also asks a pertinent question still relevant today: Why do those working on artificial intelligence assume that there must be a digital way of performing human tasks? He writes:
Dreyfus puts forward four models of human information processing to highlight the differences between human information processing and that of a computer: fringe consciousness, ambiguity tolerance, essential/inessential discrimination, and perspicuous grouping. In the context of GOFAI research, for example, each of these kinds of human processing includes a symbolic analogue that cannot be mapped directly onto the essential intelligence of human beings who are able to demonstrate the four models mentioned above. The symbolic analogues relevant to digital computers are as follows: heuristically guided search, context-free precision, trial and error search, and character lists. Dreyfus claims that descriptive or phenomenological evidence, when considered separately from traditional philosophical prejudices, suggests that non-programmable human capacities are involved in all forms of intelligent behavior.
The key point Dreyfus makes in this text is that the complex information processing humans are capable of doing, and human cognitive processes more generally, cannot be reduced to the systematic rule-driven workings of a digital computer. In this capacity, Dreyfus argues, AI is limited by its assumption that the world can be explained in terms of elementary atomistic concepts, a view that dates back to the Greeks (Plato). That said, today AI has evolved by leaps and bounds. Developing computer systems that are increasingly “intelligent” (e.g., Angelina), the field of AI has become one of the most significant components in technological research and development. Although Dreyfus was incorrect about the implementation of GPS, his arguments are still relevant for contemporary analyses of the tendency toward the mechanization of the human brain/mind in all things neuro.
What Computers Can’t Do (1972) and What Computers Still Can’t Do (1992) are necessary reads for those who are quick to jump on the “intelligence explosion” bandwagon (e.g., Ray Kurzweill) that predicts society is headed for a technological singularity (or the singularity), a hypothetical moment in history (2045) when AI has evolved to surpass human intelligence.
Dreyfus's philosophical inquiry tracks the history of developments in AI and is concerned with exposing the incorrect assumptions (psychological, epistemological, and ontological) made by researchers working in the field. Throwing light on the kind of discursive moves used to understand the human brain/mind as reducible to the processes of a digital computer, Dreyfus concentrates on the two subfields of Artificial Intelligence: Cognitive Simulation and Artificial Intelligence. These two fields, he argues, have led to the examination of two distinct but interrelated questions: (1) Does a human being in “processing information” actually follow normal rules like a digital computer? (2) Can human behavior, no matter how generated, be described in a formalism which can be manipulated by a digital machine?
Dreyfus notes that given the difficulties AI experienced during Phase I and Phase II of its development (e.g. failure of GPS), cognitive simulation nevertheless assumed that the information processes of a computer revealed “the hidden information processes” of a human being. He also asks a pertinent question still relevant today: Why do those working on artificial intelligence assume that there must be a digital way of performing human tasks? He writes:
Those who think that a formalization of intelligent behavior must be possible, seem to base their arguments on the ontological assumption that the world can be analyzed into independent logical elements and an epistemological assumption that our understanding of the world can then be reconstructed by combining these elements according to heuristic rules.
Dreyfus puts forward four models of human information processing to highlight the differences between human information processing and that of a computer: fringe consciousness, ambiguity tolerance, essential/inessential discrimination, and perspicuous grouping. In the context of GOFAI research, for example, each of these kinds of human processing includes a symbolic analogue that cannot be mapped directly onto the essential intelligence of human beings who are able to demonstrate the four models mentioned above. The symbolic analogues relevant to digital computers are as follows: heuristically guided search, context-free precision, trial and error search, and character lists. Dreyfus claims that descriptive or phenomenological evidence, when considered separately from traditional philosophical prejudices, suggests that non-programmable human capacities are involved in all forms of intelligent behavior.
The key point Dreyfus makes in this text is that the complex information processing humans are capable of doing, and human cognitive processes more generally, cannot be reduced to the systematic rule-driven workings of a digital computer. In this capacity, Dreyfus argues, AI is limited by its assumption that the world can be explained in terms of elementary atomistic concepts, a view that dates back to the Greeks (Plato). That said, today AI has evolved by leaps and bounds. Developing computer systems that are increasingly “intelligent” (e.g., Angelina), the field of AI has become one of the most significant components in technological research and development. Although Dreyfus was incorrect about the implementation of GPS, his arguments are still relevant for contemporary analyses of the tendency toward the mechanization of the human brain/mind in all things neuro.
What Computers Can’t Do (1972) and What Computers Still Can’t Do (1992) are necessary reads for those who are quick to jump on the “intelligence explosion” bandwagon (e.g., Ray Kurzweill) that predicts society is headed for a technological singularity (or the singularity), a hypothetical moment in history (2045) when AI has evolved to surpass human intelligence.
July 20, 2012
The book is a bit dated, and it really shows when Dreyfus talks about the conteporary limitations of computers. He disparages chess playing computers in a time before Deep Blue, and so it is important to keep in mind that there are large portions of the critique that seem to have overstepped the appropriate boundaries, and that some of those criticisms have been scaled back in the wake of contemporary successes in certain forms of artificial intelligence.
For those interested in the take that many continental philosophers of mind have on the older-school understandings of A.I. and computational approaches to mind in general, Dreyfus is a good introduction. His colleague, John Searle, is arguably more widely read on the subject and more influential in the philosophical literature, but Dreyfus is much more clear in this particular area, expressing a level of comfort with and awareness of his philosophical history, as well as his current context in the continental tradition.
The context isn't totally articulated, though, in the sense that there has been some movement in what is meant by "A.I." since Dreyfus originally wrote the book. It used to mean something fairly overtly computational, centered on input and output; it used to mean, almost exclusively, software. IT no longer means that, and part of the reason is an understanding of the commentaries on context-free approaches. This is one such commentary, but Dreyfus often commits himself to the position that computers will, in principle, never be able to do certain things, which turns out to be predicated on an oversimplistic concept of "computer." This can hardly been seen as the fault of Dreyfus, though perhaps it is a failure of imagination.
On the other hand, Dreyfus manages to simultaniously provide an interesting view of continental philosophy of mind that is explicitly understood in terms contemporary technology. This makes for a deeply relevant account of an area of thought that is often disparaged as frustratingly limited in terms of its relevance; Dreyfus, and several of the thinkers who started pubishing around the same time or not long after this book, gave continental philosophy of mind a new sense of importance in the philosophical community, and in academia more broadly, and this is a nice way to become introduced to that in the context of some thoughts on technology.
For those interested in the take that many continental philosophers of mind have on the older-school understandings of A.I. and computational approaches to mind in general, Dreyfus is a good introduction. His colleague, John Searle, is arguably more widely read on the subject and more influential in the philosophical literature, but Dreyfus is much more clear in this particular area, expressing a level of comfort with and awareness of his philosophical history, as well as his current context in the continental tradition.
The context isn't totally articulated, though, in the sense that there has been some movement in what is meant by "A.I." since Dreyfus originally wrote the book. It used to mean something fairly overtly computational, centered on input and output; it used to mean, almost exclusively, software. IT no longer means that, and part of the reason is an understanding of the commentaries on context-free approaches. This is one such commentary, but Dreyfus often commits himself to the position that computers will, in principle, never be able to do certain things, which turns out to be predicated on an oversimplistic concept of "computer." This can hardly been seen as the fault of Dreyfus, though perhaps it is a failure of imagination.
On the other hand, Dreyfus manages to simultaniously provide an interesting view of continental philosophy of mind that is explicitly understood in terms contemporary technology. This makes for a deeply relevant account of an area of thought that is often disparaged as frustratingly limited in terms of its relevance; Dreyfus, and several of the thinkers who started pubishing around the same time or not long after this book, gave continental philosophy of mind a new sense of importance in the philosophical community, and in academia more broadly, and this is a nice way to become introduced to that in the context of some thoughts on technology.
February 13, 2019
I'm probably biased towards Dreyfus' perspective in this book because I've grown fond of him from listening to his recorded Heidegger lectures at UC Berkeley. Despite his harsh and occasionally smug tone in this book, I've always found him a joy to listen to. He's clearly an expert on both AI technology and on continental European thought--not an easy mix to find!) What surprises me about this book is that it isn't more widely read, given that I believe it to be the most destructive critique on the possibility of human-level AI, hands-down. Maybe it's because the book is somewhat dated, but I find Dreyfus' critique far more convincing than Searle's famous Chinese Room, which is taught far more often.
Want to read
September 27, 2007Bought this a while back and keep meaning to get to it, but I a bit ignorant in Cognitive Science and computers, so it's been too intimidating so far. This is primarily a critique of AI research back 30-40 years ago, from what I hear, though it has been updated for this edition (though this edition is old by now as well, considering the speed with which research advances in the sciences compared to philosophy).
July 26, 2012
Nobody should even talk about Artificial Intelligence without having read thi book!
Furthermore, I found very strong and original the way he uses both phylosophical arguments and historical facts to make his points across.
Furthermore, I found very strong and original the way he uses both phylosophical arguments and historical facts to make his points across.
January 17, 2009
Dreyfus I think is correct, and he is the most endearing person in interviews.
May 2, 2012
Best philosophy of cognitive science book I've read. Dreyfus is harsh, but his words proved prophetic.
January 15, 2021
An update of the original incendiary critique of AI, still completely relevant
July 20, 2023
really good book. dreyfus has done the impossible -- making me want to read heidegger.
Read
September 6, 2022Went back to this classic due to the recent claims about Google's LaMDA and its sentience.
Holds up surprisingly well and is a necessary and humbling read for any AI work.
Holds up surprisingly well and is a necessary and humbling read for any AI work.
January 10, 2024
If I read this book in the 70s or 80s or 90s, I would have given it 4 stars. But since I read it in 2023/2024, post-ChatGPT, I’m giving it 3 stars. The critiques of AI in the 70s are remarkably accurate. That situation has changed in the past year, though.
The main critique is that GOFAI in the 70s was too based on enumeration of formal rules, which Dreyfus argues (mostly via Heidegger and Wittgenstein) does not match with how human intelligence works. Human intelligence relies on much more “gestalt” reasoning which takes the whole picture into account and cannot be easily explained by a sequence of formal rules. The success of neural networks in the past decade seems to vindicate this critique! After all, neural networks do produce answers via a vast network of relations that account for every possible available feature and combination of features in an opaque and difficult to explain way, which also cannot be written down in a sequence of rules.
The new intro that Dreyfus wrote in 1992 does address neural networks and to his credit he does acknowledge the advantages of NNs over rules-based GOFAI. But he still criticizes them. There are still plenty of things to criticize about NNs but I don’t think Dreyfus’ critique of NNs has turned out to be correct, even though his 70s critique of AI was so prescient and so correct.
There is now room for someone to write a new critique of the present state of AI, in the LLM era. Spoiler alert: (Justin Timberlake voice) it’s gonna be me.
The main critique is that GOFAI in the 70s was too based on enumeration of formal rules, which Dreyfus argues (mostly via Heidegger and Wittgenstein) does not match with how human intelligence works. Human intelligence relies on much more “gestalt” reasoning which takes the whole picture into account and cannot be easily explained by a sequence of formal rules. The success of neural networks in the past decade seems to vindicate this critique! After all, neural networks do produce answers via a vast network of relations that account for every possible available feature and combination of features in an opaque and difficult to explain way, which also cannot be written down in a sequence of rules.
The new intro that Dreyfus wrote in 1992 does address neural networks and to his credit he does acknowledge the advantages of NNs over rules-based GOFAI. But he still criticizes them. There are still plenty of things to criticize about NNs but I don’t think Dreyfus’ critique of NNs has turned out to be correct, even though his 70s critique of AI was so prescient and so correct.
There is now room for someone to write a new critique of the present state of AI, in the LLM era. Spoiler alert: (Justin Timberlake voice) it’s gonna be me.
January 14, 2014
Clever
June 13, 2025
Hubert Dreyfus was a brilliant philosopher who was also one of the most notoriously bearish of AI skeptics. He had good reason to be in his day, citing the limited (and oversold) successes of AI up to that point. When he wrote “What Computers Still Can’t Do,” chess engines were still only so-so, and machine translation had trouble with basic natural language idioms.
Flash forward roughly three decades and Deep Blue can spank Grandmaster Gary Kasparov with one robotic arm tied behind its back. Not only has machine translation improved, there is software that can give on-the-spot translation from one language to the other. This, by the way, can be done with a speech in real-time with only the slightest lag, and with a good accent in the target language.
Impressive stuff, and scary.
Still, “trees do not grow to the sky,” and there is still a way to go toward Skynet being live and enslaving humanity. Maybe.
That makes a revisit to this skeptical and thoughtful book well-worth the investment of a few hours. Even if Dreyfus ends up being dead-wrong, his argument was ultimately sound and well-reasoned.
His main argument is that artificial intelligence works mostly by programmed algorithm while humans work by intuition, instinct, and heuristics. Intelligence is not just processing power, but involves consciousness and cognition in ways we still can’t define, let alone replicate. We can certainly build a model of the human brain, but a model is not the thing it’s modeling, and AI will only have achieved parity with humans when it can build its own models.
That’s the argument in a nutshell, or at least that’s as close as I can come in a short review to summarizing such a complex argument.
It helps if one has some background in philosophy, though you could probably navigate your way through this book by simply reading an “Intro to Philosophy” text in tandem with this one. Dreyfus was very big on Heidegger, so ontology is very much his bread and butter. This, in simple terms, is the branch of philosophy related to states of being. In the simplest terms (again, hard to do) it deals with the problem of how one can observe anything when one is in that thing. You are in the world, which limits and taints your ability to draw conclusions about it. Ditto, for your body. Dreyfus seems to think that these limits placed on us become advantages when comparing us to machines. For while a machine is also in the world, and in a body—assuming it’s a cyborg—it might not be conscious of itself as such. This is assuming it’s conscious, at all.
There’s a lot to unpack here, and those already sold or oversold on AI are likely to sneer and roll their eyes at some of Dreyfus’s quainter outdated predictions. Still, it could very well be that Dreyfus, while wrong about the small stuff, gets the big stuff right. And that a lot of our worries about AI end up being wasted. In which case he gets the last laugh from beyond the grave (do the dead laugh?)
Yes, AI is almost undoubtedly going to leave lots of white collar workers unemployed (and pissed, with nothing but time on their hands.) But this does not suggest that the machines themselves are going to enslave humanity. I’d be more worried about the people programming the machines and investing in the companies that build the machines. And what kind of guerilla conflicts might erupt between the disinherited luddites chucking their shoes into the loom and those dedicating their lives to building and maintaining the loom. Maybe, in the case of people like Musk, even worshipping the loom, and maybe make sacrifices to it like a Moloch. Recommended, though I didn’t enjoy this one quite as much as Mind Over Machine, the book Dreyfus cowrote with his brother.
Flash forward roughly three decades and Deep Blue can spank Grandmaster Gary Kasparov with one robotic arm tied behind its back. Not only has machine translation improved, there is software that can give on-the-spot translation from one language to the other. This, by the way, can be done with a speech in real-time with only the slightest lag, and with a good accent in the target language.
Impressive stuff, and scary.
Still, “trees do not grow to the sky,” and there is still a way to go toward Skynet being live and enslaving humanity. Maybe.
That makes a revisit to this skeptical and thoughtful book well-worth the investment of a few hours. Even if Dreyfus ends up being dead-wrong, his argument was ultimately sound and well-reasoned.
His main argument is that artificial intelligence works mostly by programmed algorithm while humans work by intuition, instinct, and heuristics. Intelligence is not just processing power, but involves consciousness and cognition in ways we still can’t define, let alone replicate. We can certainly build a model of the human brain, but a model is not the thing it’s modeling, and AI will only have achieved parity with humans when it can build its own models.
That’s the argument in a nutshell, or at least that’s as close as I can come in a short review to summarizing such a complex argument.
It helps if one has some background in philosophy, though you could probably navigate your way through this book by simply reading an “Intro to Philosophy” text in tandem with this one. Dreyfus was very big on Heidegger, so ontology is very much his bread and butter. This, in simple terms, is the branch of philosophy related to states of being. In the simplest terms (again, hard to do) it deals with the problem of how one can observe anything when one is in that thing. You are in the world, which limits and taints your ability to draw conclusions about it. Ditto, for your body. Dreyfus seems to think that these limits placed on us become advantages when comparing us to machines. For while a machine is also in the world, and in a body—assuming it’s a cyborg—it might not be conscious of itself as such. This is assuming it’s conscious, at all.
There’s a lot to unpack here, and those already sold or oversold on AI are likely to sneer and roll their eyes at some of Dreyfus’s quainter outdated predictions. Still, it could very well be that Dreyfus, while wrong about the small stuff, gets the big stuff right. And that a lot of our worries about AI end up being wasted. In which case he gets the last laugh from beyond the grave (do the dead laugh?)
Yes, AI is almost undoubtedly going to leave lots of white collar workers unemployed (and pissed, with nothing but time on their hands.) But this does not suggest that the machines themselves are going to enslave humanity. I’d be more worried about the people programming the machines and investing in the companies that build the machines. And what kind of guerilla conflicts might erupt between the disinherited luddites chucking their shoes into the loom and those dedicating their lives to building and maintaining the loom. Maybe, in the case of people like Musk, even worshipping the loom, and maybe make sacrifices to it like a Moloch. Recommended, though I didn’t enjoy this one quite as much as Mind Over Machine, the book Dreyfus cowrote with his brother.
Want to read
August 12, 2021Source: Referred by Gary Klein: Source of Power (inspired Think Slow and Fast and mentioned Daniel K) , See what others don't
This entire review has been hidden because of spoilers.
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