Perceptrons
Perceptrons: An Introduction to Computational Geometry is a book written by Marvin Minsky and Seymour Papert and published in 1969. An edition with handwritten corrections and additions was released in the early 1970s. An expanded edition was further published in 1988 (ISBN 9780262631112) after the revival of neural networks, containing a chapter dedicated to countering the criticisms made of it in the 1980s.
Stats for nerds
4recommend
48mentions
39people
2008–2026years
15% recommend81% neutral4% critical
Mentions per year
What Hacker News says
- My arguments are very strong.
Information technology does not operate by the rules of any other technology. It is a technology of math and organization, not particular materials.
The unique value of information technology is that it compounds the value of other information and technology, including its own, and lowers the bar for its own further progress.
And we know with absolute certainty we have barely scratched the computing capacity of matter. Bremermann’s limit : 10^47 operations per second, per gram. See my other comment for other relevant limits.
Do you also expect a wall in mathematics?
And yes, an unbroken historical record of 4.5 billions years of information systems becoming more sophisticated with an exponential speed increase over time, is in fact a very strong argument. Changes that took a billion years initially, now happen in very short times in today's evolution, and essentially instantly in technological time. The path is long, with significant acceleration milestones at whatever scale of time you want to look at.
Your argument, on the other hand, is indistinguishable from cynical AI opinions going back decades. It could be made any time. Zero new insight. Zero predictive capacity.
Substantive negative arguments about AI progress have been made. See "Perceptrons" by Marvin Minksy and Seymour Papert, for an example of what a solid negative argument looks like. It delivered insights. It made some sense at the time.
- > Historical "technological progress" can't be used as argument for any particular technology.
Historical for billions of years of natural information system evolution. Metabolic, RNA, DNA, protein networks, epigenetic, intracellular, intercellular, active membrane, nerve precursors, peptides, hormonal, neural, ganglion, nerve nets, brains.
Thousands of years of human information systems. Hundreds of years of technological information systems. Decades of digital information systems. Now in in just the last few years, progress year to year is unlike any seen before.
Significant innovations being reported virtually every day.
Yes track records carry weight. Especially with no good reason for any reason for a break, while every tangible reason to believe nothing is slowing down, right up to today.
"Past is not a predictor of future behavior" is about asset gains relative to asset prices in markets where predictable gains have had their profitability removed by the predictive pricing of others. A highly specific feedback situation making predicting asset gains less predictable even when companies do maintain strong predictable trends in fundamentals.
It is a narrow specific second order effect.
It is the worst possible argument for anything outside of those special conditions.
Every single thing you have ever learned was predicated on the past having strong predictive qualities.
You should understand what an argument means, before throwing it into contexts where its preconditions don't exist.
> Right now, if we are talking about AI, we're talking about specific technologies, which may just as well fail and remain inconsequential in the grand scheme of things, like most technologies, most things really, did in the past. Even more so since we don't understand much anything in either human or artificial cognition. Again and again, we've been wrong about predicting the limits and challenges in computation.
> Your argument [...] is indistinguishable from cynical AI opinions going back decades. It could be made any time. Zero new insight. Zero predictive capacity.
If I need to be clearer, nobody could know when you wrote that by reading it. It isn't an argument it's a free floating opinion. And you have not made it more relevant today, than it would have been all the decades up till now, through all the technological transitions up until now. Your opinion was equally "applicable", and no less wrong.
This is what "Zero new insight. Zero predictive capacity" refers to.
> Substantive negative arguments about AI progress have been made. See "Perceptrons" by Marvin Minksy and Seymour Papert, for an example of what a solid negative argument looks like. It delivered insights. It made some sense at the time.
Here you go:
- Glenn Gould Reader, a collection of writings by Canadian pianist Glenn Gould
Eye, Brain, Vision by David Hubel, IMO an excellent introduction to visual neuroscience for the layperson and a really, really nice example of good scientific writing
Huygens and Barrow, Newton and Hooke by VI Arnold, for the density of ideas
Perceptrons by Minsky and Papert, mainly as an example of clear mathematical exposition
- Perceptrons by Marvin Minsky and Seymour Papert [1].
https://en.wikipedia.org/wiki/Perceptrons_(book)
[1] http://libgen.rs/book/index.php?md5=8486B416F728B5D1035D2543...
- Another great book by Minsky is Perceptrons. It's really short to boot, IIRC. It is an especially good book for those interested in ML. In it Minsky shows that single layer neural nets can not compute all problems. I remember that he has a caveat somewhere that he hasn't thought about multi-layer neural nets and they they may still be interesting. So great was the impact of this that hardly anyone worked on neural nets for something like a decade afterwards. But it's a brilliant book presented in a really simple style. It's a piece of history, I think.
- This doesn't actually answer the question, but I always think that people who want to study neural nets should read Marvin Minsky's Perceptrons. It's an academic work. It's short. It's incredibly well written and easy to understand. It shaped the history of neural net research for decades (err... stopped it, unfortunately :-) ). You should be able to find it at any university library.
Although this recommendation doesn't really fit the requirements of the poster, I think it is easy to reach first for modern, repackaged explanations and ignore the scientific literature. I think there is a great danger in that. Sometimes I think people are a bit scared to look at primary sources, so this is a great place to start if you are curious.
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