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What Hacker News says
- A characteristic of the field since the beginning. Reading What Computers Can't Do in college (early 2000s) was an important contrast for me.
> A great misunderstanding accounts for public confusion about thinking machines, a misunderstanding perpetrated by the unrealistic claims researchers in AI have been making, claims that thinking machines are already here, or at any rate, just around the corner.
> Dreyfus' last paper detailed the ongoing history of the "first step fallacy", where AI researchers tend to wildly extrapolate initial success as promising, perhaps even guaranteeing, wild future successes.
https://en.wikipedia.org/wiki/Hubert_Dreyfus's_views_on_arti...
- The gold standard for thinking about the limits of AI is the work of Herbert Dreyfus, specifically the books What Computers Can't Do: The Limits of Artificial Intelligence published in 1972, and the revised What Computers Still Can't Do: A Critique of Artificial Reason, published in 1992, and Mind over Machine, from 1986.
- An original source for the Heideggerian critique of symbolic AI projects is Hubert Dreyfus, a philosophy professor at MIT who specialized in Heidegger and argued that his colleagues in the CS department were codifying just the kind of naive views on cognition that Heidegger spent his life criticizing.
See his books “What Computers Can’t Do”, “Being-in-the-world”, and the paper “Why Heideggerian AI Failed and Why Fixing It Would Require Making it More Heideggerian” (something like that).
A basic point is that ordinary human coping does not involve conceptual thinking, schematic rules, or the manipulation of symbols. It’s sort of like “Thinking Fast and Slow”.
We do not fundamentally live by constantly consulting our inner symbolic representation of the world, though we do that too. The more fundamental way of being is to just cope and care directly without explicit cognitive representation.
So I could attempt to codify an “expert system” for my way of coping with and caring for my cat, let’s say. But it would only be a kind of symbolic ghost of my real way of being, and it would never be sufficient. The more precise I tried to make it, the more complex it would become, until it became a gigantic mess, because it’s fundamentally an inaccurate model.
Dreyfus “Being-in-the-world” brings up many examples of the way the intelligence of daily life is informal, unconscious, and nonsymbolic. The way we maintain distance from other bodies which is only roughly approximated by the idea of a “personal space”, or the ways in which we live out masculinity and femininity.
“There are no beliefs to get clear about; there are only skills and practices. These practices do not arise from beliefs, rules, or principles, and so there is nothing to make explicit or spell out. We can only give an interpretation of the interpretation already in the practices.”
“Being and Time seeks to show that much of everyday activity, of the human way of being, can be described without recourse to deliberate, self-referential consciousness, and to show how such everyday day activity can disclose the world and discover things in it without containing any explicit or implicit experience of the separation of the mental from the world of bodies and things.”
“The traditional approach to skills as theories has gained attention with the supposed success of expert systems. If expert systems based on rules elicited from experts were, indeed, successful in converting knowing-how into knowing-that, it would be a strong vindication of the philosophical tradition and a severe blow to Heidegger's contention that there is no evidence for the traditional claim that skills can be reconstructed in terms of knowledge. Happily for Heidegger, it turns out that no expert system can do as well as the experts whose supposed rules it is running with great speed and accuracy. Thus the work on expert systems supports Heidegger's claim that the facts and rules ‘discovered’ in the detached attitude do not capture the skills manifest in circumspective coping.”
- Yes.
The critiques of AI from Hubert Dreyfus have stood the test of time, those who want to understand or challenge them directly can read What Computers Can't Do (1972, 1979), or even better the updated reprint What Computers Still Can't Do (1992). He's a Heideggarian Philosopher but all you need to know is that modern AI is ignorant of vast swathes of 20th Century investigation into the human mind, and state of being.
Hence, I think the big idea you're talking about is in AI that takes Hubert Dreyfus's critiques seriously.
Luckily for anyone reading about this for the first time, that process has already started. Dreyfus wrote a 2007 paper on the successes and failures of the first few steps of what he called Heideggarian AI with the snappy name of "Why Heideggerian AI failed and how fixing it would require making it more Heideggerian".
The "fixing" refers to work of a Neuroanatomist, with a suitable philosophical background, called Walter Freeman III, and is broadly described in the paper, but properly investigated in Freeman's (also eminently readable) book How Brains Make Up Their Minds (2000).
A note of caution, you'll be introduced to concepts that blur the line between body and environment, subject and object, intention and influence, and eventually things like relinquising your belief in causality and an objective "out there" universe (or at least any value in such a belief), all whilst staying perfectly scientific and evidence based.
Finally, bear in mind that if we do create an intelligence worthy of the name, we have reason to believe it will take about 18 years of "raising" by two adult humans, after which it will want to do its own thing, and not your dumb image classification tasks.
- Haha, this is arguably the article in AI.
Other landmark events in the field include the Dartmouth Conference, when John McCarthy named the field "Artificial Intelligence", defined the goals, and set out to achieve them, unfortunately it's not easy to point to a single paper on the subject, though do read the conference proposal, posted on HN recently [https://news.ycombinator.com/item?id=12080269 ]; then you have the Lighthill report where the UK Government essentially lost interest in the project, the conclusions are debatable, but clearly and entertainingly argued, a video of the presentation of the report, with McCarthy present and responding, is available online also [https://www.youtube.com/watch?v=yReDbeY7ZMU&list=PLhThm05V6b...].
Finally I'd cite the 1970s era book What Computers Can't Do, and its 90s reprint, What Computers Still Can't Do as all you need to know about the current state of AI, its ultimate aims and the fatal flaws in its fundamental assumptions. The fact that it was written in the 70s and applies to today's discussions on AI should be enough to indicate its prescience.
Modern AI ignores or is unaware of many of the critiques that have gone before, only time will tell if they will soon hit the same historical obstacles.
The points I've mentioned here don't really cover the history and development of Neural Networks, but they went through a similar process of discovery, critique, near-dormant research, and finally a return to popularity, but with neglect of the critiques rather than addressing them.
Welcome to the field!
- The so-called data-driven science have not understand the notion of science. In a minimal sense, science is to produce knowledge. There are two things to it: hypothesis generation; testing the hypothesis. As the history and philosophy of sciences have shown, there is no algorithmic way of generating hypotheses. Or if you generate hypotheses algorithmically, you are still left to figure out whether these hypotheses are ad hoc or not. After all, the history of sciences have given powerful heuristics to reduce the solution space to generate hypotheses to solve or explain problems or facts. Here, whether one picks 'solve' or 'explain' depends on which philosophy of science one picks up.
Whenever I see statistics and data-sciences, I see tons of adhoc bullshit masquerading as sciences/knowledge. It is always easy to come up with a hypothesis to explain a set of chosen facts; in order for that hypothesis to be non ad hoc, it has to predict surprising facts.
As the fad continues, we may hear like robots replacing scientists to produce knowledge about various phenomena. For a best critique of AI, check the book by UCBerkeley philosopher Hubert Dreyfus: what computers can't do, a critique of artificial reason.
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