Name: Mind Over Machine
ISBN: 9780029080610

Mind Over Machine

Human intuition and perception are basic and essential phenomena of consciousness. As such, they will never be replicated by computers. This is the challenging notion of Hubert Dreyfus, Ph. D., archcritic of the artificial intelligence establishment. It's important to emphasize that he doesn't believe that AI is fundamentally impossible, only that the current research program is fatally flawed. Instead, he argues that to get a device (or devices) with human-like intelligence would require them to have a human-like being in the world, which would require them to have bodies more or less like ours, and social acculturation (i.e. a society) more or less like ours. This helps to explain the practical problems in implementing artificial intelligence algorithms.

What Hacker News says

jonsenFeb 2022

My learning strategies are very personal, subtle, and hard to explain. I believe I build most of them a long time ago after reading Mind over Machine [1]. It's a critique of the, back then, glorious promises of AI, but it has a very thorough take on what learning is and is not.

[1] https://www.amazon.com/Mind-Over-Machine-Hubert-Dreyfus/dp/0...

thread

jonsenJun 2021

You might find this interesting:

https://www.amazon.com/Mind-Over-Machine-Hubert-Dreyfus/dp/0...

It’s a critique of AI but gives a thorough take on the progress from novice to expert.

thread

muzaniMar 2020

There's a lot of books written on this. In general, you have to follow the right order of things.

1. Follow rules rigidly, without understanding or judging, similar to how a baby imitates her parents by putting on clothes or drawing on paper. Don't try to be clever. Just copy and pay attention.

2. Be able to treat parts as a step. Bundle together chunks of work - this is how I filter an array, this is how I commit code, this is how compile a site, this is how I add a button to the page.

This phase is the longest. Practice until you can do it without supervision/guidance. Practice until you no longer struggle with it. Then practice some more until it's as natural as typing, where you can sense when it's off. Expect to spend 10 years on this, faster if you practice right and practice hard. Is there a Leetcode problem you can't solve? Solve it. Is there open source code you can't reverse engineer? Reverse engineer it.

Don't try to learn everything though. Just find something you like and build that to fluency.

3. By now, you start experimenting. You start taking risks. You start to get curious and obsessed, instead of needing to motivate yourself. You customize procedures for yourself. Your results will be crude (hacky).

4. You'll start to see lots of exceptions to the rules. You may have multiple mentors and see that they're not aligning. Instead of rejecting maxims, you merge them to guide you. You produce a high standard of work, with little effort.

5. You no longer rely on rules and maxims. You have an intuitive grasp on things, but unlike step 4, you can combine them with data and rationality. You have visions on how things go together.

College puts us in 2, but often not enough, and we get dumped into 3 before we're ready. My advice is to go back to phase 2, keep practicing, keep finding mentors. All of these phases have a plateau that you have to push through, and all of them have different mentors, often those who are stuck on their own plateaus.

If you want books, I recommend, A. Mind Over Machine, Dreyfus; B. Mastery, Greene; C. The Art of Learning, Waitzkin.

thread

unix1Jul 2017

In 1988 Hubert L. Dreyfus and Stuart E. Dreyfus released a paperback version of their previously published "Mind over Machine" book, in which they mostly spend time debunking the myth that expert systems and rule-based programs are ever going to have "intelligence" on par with human brain.

The book is an interesting read in itself, but what I found remarkable is that in the 1988 release they added a "preface to paperback edition" in which they used a couple of pages to give their views on artificial neural networks, which (though not new) was gaining some steam at the time. The conclusions they reached are as relevant now as they were 3 decades ago.

There have been no new breakthroughs in this area. Most of the research being done is in application of what we have known for decades in specific areas, with minor insights into tweaks and uses of combinations of algorithms to better solve specific problems. The big differences between then and now are: (1) technology is more accessible - data is easier to collect, store and output via many input/output methods; and (2) the hardware is significantly faster - we can now go through more data, make algorithms run faster, and appear to perform better.

This inevitably brought a lot of hype, including many predicting human-like artificial intelligence not too far away. But maybe those with experience in 60s and 70s in the field in USA and Japan can draw a parallel between what's happening now and what has happened few times in the past in this area:

- companies perform neat promising demos with unrealistic implicit or explicit promises

- investors pour money in

- media hype ensues

- after awhile - no new breakthroughs: still can't turn ANN or expert system into a human brain

- outcome is improvements in limited use cases

- hype dies down, but we can repeat the cycle after improvements in hardware

Edit: formatting

thread

jonsenAug 2014

A detailed an well argumented framework to be found in

Chapter 1: Five Steps from Novice to Expert

Mind Over Machine by Hubert Dreyfus

http://www.amazon.com/Mind-Over-Machine-Hubert-Dreyfus/dp/00...

thread

f_klemJun 2026

I have the third edition, so I can only speak for it. Being the 3rd edition of the book, I assume that it is the 3rd time the text is revised, so I expect the other two editions (1st and 2nd) to adolesce from the same problem, which I state in the following paragraphs.

The mention to Dreyfus in the 3rd edition of Artificial Intelligence, a Modern Approach, by Stuart Russell and Peter Norvig, is made in 4 different places of the book, referencing four different problems.

The first mention is in page 279, effectively in the bibliographical notes, and it is about something called the 'frame problem'. Dreyfus presents this problem in the 1972 edition of the book, as a problem pertaining 'how to differentiate figure from ground', or 'how to account for what is important and what is not in a specific scenario'. But the solution to the problem that Norvig and Russel cite (Ray Reiter, 1991) is from a paper that _changes the conditions of the problem_, even _change the problem completely_ (by reductionism) to 'how to detect objects that do not change after an action'. They claim the problem solved, but they are actually not addressing Dreyfus criticisms, and misleading the reader to think that the problem is actually solved. The frame problem, by now, is still unsolved (and is one of the most difficult problems to solve).

The second mention is in page 1024, under a section called 'Weak AI: Can machines act intelligently?', and subsection 'The argument from informality'. The section mentions the books What Computers can't do (1972) and What Computers still can't do (1992), as well as Mind over Machine (1986). Unfortunately, this section completely misunderstands the critique of AI that Dreyfus exposes in those books. The whole section is misleading, obfuscating or tergiversing the critique from Dreyfus to fit the purpose of Norvig and Russell (mainly, to show that advances in machine learning and AI can make a solid base for machines that 'act intelligently').

The third mention is in page 1049, and it tries to undermine the first-step fallacy (which is similar to the fallacy of composition). Again, they do it by completely dismissing Dreyfus' critique, not addressing the issue. Then they go on talking about 'rationality' (as explained in chapter 1), but with a trick: only in terms of machines, goal-oriented expectations, computing resources. Dreyfus' critique is about the overall AI enterprise and the search for 'artificial' intelligence, Russell and Norvig discourse in this section first reduce Dreyfus' critique to what they can handle, to their own terms. That is, they evade the issue.

The fourth and final mention, in page 1072, is the bibliographical citation.

Re-reading the non-technical, but more theoretical parts of the book just made me realize how poorly constructed the book is. For example, the definitions given about AI in page 2 are just laughable. Compare with an introductory text on Psychology [0].

[0] https://pressbooks.openeducationalberta.ca/saitintropsycholo...

thread