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DonaldFisk

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Developer of Full Metal Jacket, a pure visual dataflow language. http://www.fmjlang.co.uk/

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I have always assumed that Alan Kay compared Lisp to Maxwell's Equations because of the similarity between the interplay of eval and apply in Lisp on the one hand, and the interplay of the electric and magnetic fields in Maxwell's equations on the other.

I'm unsure what to make of the post you're replying to, but the idea that there's a connexion between consciousness and quantum phenomena isn't just a New Age idea. Eugene Wigner wasn't New Agey, and he wrote Remarks on the Mind-Body Question, suggesting that wave function collapse only occurs when the consciousness of an an observer becomes aware of the result of a measurement, not the measuring apparatus, which is entangled with whatever is being measured, records it.

I'm interested in how they work, but building anything like them, given the hardware I have, would be impractical. I've seen others use them, including to answer some questions I had, but the answers they gave were obvious, unhelpful, or wrong.

Even if they become more reliable, I like to understand and work things out for myself, rather than just be given the answer.

The Dartmouth Summer Research Project on Artificial Intelligence was in 1956, before I was born. AI itself is even older than that (e.g. William Grey Walter's robots, Elmer and Elsie in 1948), but it was called cybernetics back then. I've been doing symbolic AI, on and off, since the 1980s.

I assume, though, you mean LLMs. I haven't used them first hand, but I have fairly recently implemented a multi-layer artificial neural network in C, mostly as a learning exercise, but as I had previously built a speech spectrogram in Lisp, I thought I'd try to use it to recognize phonemes, with one hidden layer. The Lisp communicated with the ANN via a Unix pipe. It worked reasonably well for just vowels, but when I added other sounds (e.g. l, r, s, z), its performance deteriorated. I think the C is bug free, but I don't know an easy way to train the ANN. I've tried adding to the training set, adding an extra layer, changing the number of neurons in the hidden layer. The usual debugging skills don't seem to help there.

It's in the name "Hacker News". Hackers are people who enjoy doing the programming themselves, because they get satisfaction from it. Often they program in their spare time, and sometimes what they develop has little to no practical value, and is done as a learning exercise. Using AI would defeat the purpose for them. If they're forced to use AI in their job, it decreases their job satisfaction.

There are others who just want a problem solved, and it doesn't matter to them how the program which solves it gets written, as long as the program (appears to) work and it's done as quickly as possible, so they outsource the development to AI. That is not hacking.

I wrote something similar here: https://fmjlang.co.uk/blog/GroundBreakingLanguages.html

We agree on Algol, Lisp, Forth, APL, and Prolog. For ground-breaking functional language, I have SASL (St Andrews Static Language), which (just) predates ML, and for object oriented language, I have Smalltalk (which predates Self).

I also include Fortran, COBOL, SNOBOL (string processing), and Prograph (visual dataflow), which were similarly ground-breaking in different ways.

Sadly, not part of this course, though Lisp and Prolog are very useful for other things. C's fine for building neural networks from scratch, and you can glue different subsystems together to make anything more complex than that using Python.

Written Chinese stayed the same while the spoken language evolved from the 5th century BC until the 1911 revolution, after which people began writing Chinese the way it's spoken in Beijing. So there's a sharp dividing line just over 100 years ago; Literary Chinese is still taught in school but without that you'd have trouble understanding it.

Here's Trump's claims debunked in detail: https://www.factcheck.org/2025/08/recapping-trumps-deceptive...

"But we found that Trump’s so-called “reciprocal” tariff rates weren’t based on tariffs that other countries charged on goods coming from the U.S. Instead, the Office of the U.S. Trade Representative came up with the rates by dividing the size of a country’s trade imbalance with the U.S. in goods by how much America imports in goods from that nation. "

Agreed.

General relativity was a completely novel idea. Einstein took a purely mathematical object (now known as the Einstein tensor), and realized that since its coveriant derivative was zero, it could be equated (apart fron a constant factor) to a conserved physical object, the energy momentum tensor (except for a constant factor). It didn't just fall out of Riemannian geometry and what was known about physics at the time.

Special relativity was the work of several scientists as well as Einstein, but it was also a completely novel idea - just not the idea of one person working alone.

I don't know why anyone disputes that people can sometimes come up with completely novel ideas out of the blue. This is how science moves forward. It's very easy to look back on a breakthrough and think it looks obvious (because you know the trick that was used), but it's important to remember that the discoverer didn't have the benefit of hindsight that you have.

It depends whether you want your system to handle all of natural language and give answers which are correct most of the time (but it isn't easy to tell when it's wrong), or to handle a limited subset of natural language and either give answers which are demonstrably correct (once it's fully debugged or proven correct), or tells you when it doesn't know the answer.

These are two opposing approaches to AI. Rule induction is somewhere in between - you use training data and it outputs (usually probabilistic) human-readable rules.

again,

    capital(germany, berlin).
    capital(france, paris).
is clearer.

Someone once told me you need humongous vectors to encode nuance, but people are good at things computers are bad at, and vice-versa. I don't want nuance from computers any more than I want instant, precise floating point calculations from people.

It's telling that king – man + woman = queen is the only example I've ever seen used to explain word2vec.

I prefer the old school

    king(X) :- monarch(X), male(X).
    queen(X) :- monarch(X), female(X).
    queen(X) :- wife(Y, X), king(Y).

    monarch(elizabeth).
    female(elizabeth).
    wife(philip, elizabeth).
    monarch(charles).
    male(charles).
    wife(charles, camilla).

    ?- queen(camilla).
    true.

    ?- king(charles).
    true.

    ?- king(philip).
    false.
where definitions are human readable rules and words are symbols.