Chaos
The author describes how scientists studying the growth of complexity in nature are discovering order and pattern in chaos. He explains concepts such as nonlinearity, the Butterfly Effect, universal constants, fractals, and strange attractors, and examines the work of scientists such as Mitchell J. Feigenbaum, Edward Lorenz, and Benoit Mandelbrot.
Stats for nerds
45recommend
71mentions
63people
2008–2026years
68% recommend27% neutral6% critical
Mentions per year
What Hacker News says
- So I fell in love with Physics when I read Chaos by James Gleick and I didn't actually know how to program at the time but much of the book was rooted in the concept that chaotic non-deterministic behaviour can emerge from a few simple rules. Probably the best known example is the butterfly effect but there are many others. 20+ years later I am able to vibe code this app to visually demonstrate this simplicity and chaos. Would love to get some feedback on the app. Please be as honest as possible. Or just some chats about Chaos.
- A fascinating potential link between the two famous popular books on early computing and mathematics, 'Hackers', and 'Chaos' by James Gleick (two books that shaped my teenage years fwiw).
Is the link to Edward Lorenz just a guess based on the name and subject matter of the project given in the Hackers extract? Or do you have information from some other source that Margaret Hamilton wrote code for Lorenz?
- So I fell in love with Physics when I read Chaos by James Gleick and I didn't actually know how to program at the time but much of the book was rooted in the concept that chaotic non-deterministic behaviour can emerge from a few simple rules. Probably the best known example is the butterfly effect but there are many others. 20+ years later I am able to vibe code this app to visually demonstrate this simplicity and chaos. Would love to get some feedback on the app. Please be as honest as possible. Or just some chats about Chaos.
- I completely agree, tremendous complexity can arise from simple mechanisms. Gleick's Chaos is a really good introduction to that. I was talking about the article though, and the mechanisms currently used for training and inference in LLMs. Those mechanisms are mathematically precise (unlike Chaotic attractors) and as the author points out, achieve the same function as compressors do in a strict bit pattern minimization role. Sometimes tensor math is pretty complex, like the FFT and DCTs on JPEG compression, but with the same inputs you get the same results. And while a JPEG will never decompress to a different image than the one that was compressed in the first place, LLMs do not 'infer' token streams that haven't been trained in their training process. The big difference here is that if you imagine a JPEG compressor that compresses 100 different images into one 'chunk', you can see how to provoke it to produce any one of the images it previously compressed. And with a bit of creativity you can have it express different images in different parts of the resulting composite. FWIW I looked at patenting something like this for digital cameras to give them more "shots" space for a given amount of SD storage.[1]
Given the way that models work in 'inference' mode (vs 'training' mode) you can't forward bias the result into the correct result when there are multiple forward results that have identical weights. It's the root cause of hallucinations, and you've lost information in the training phase that you can't then use to discriminate between the 'right' answer and an equally valid 'wrong' answer.
[1] FWIW I could never recover enough state to insure that the image it regenerated was all of the same image you took. So you might get the street but one of the houses might be a house that was in a different picture you took. That kind of bug. Mostly arising out of the same kind of problem you have with using hashes to find documents, when you get a hash collision two documents have the same hash, so you don't know which one to return.
- Gleick's "Chaos" got me sent to the principal's office in high school. I went crazy for fractals. Unfortunately all I had at home was an IBM PC XT. Mandelbrot set renderings were agonizingly slow and the CGA palette was too limiting.
Around this time my co-conspirator and I realized the library had 386s that almost no one was using for catalog search. They became our fractal render farm. We'd exit the catalog program, insert a floppy with our latest renderer, kick off a deep zoom, and turn off the monitors to avoid suspicion until we could check back next period. The results were thrilling. What a difference the access to compute made.
You all know the story -- eventually the librarian found us out and reported us for "hacking."
- Tierra was the first thing that came to my mind, too. I'd already played with "Core War" [0] when I learned about Tierra. I so badly wanted to implement something like Tierra myself. I couldn't wrap my head around how I could identify the metes and bounds of a given "organism" to isolate it from the rest of the "soup".
At the time I learned about Tierra I didn't have a way to get at any of Ray's papers or code. Now I do, but I'd kinda forgotten about it. I really should revisit it. There's probably a lot of fun to be had there.
Aside: Steven Levy's "Artificial Life"[1] was where I learned about Tierra. That book, along with Levy's "Hackers"[2], and Gleick's "Chaos"[3] were formative books for young me.
[0] https://en.wikipedia.org/wiki/Core_War
[1] https://www.amazon.com/Artificial-Life-Frontier-Computers-Bi...
[2] https://www.amazon.com/Hackers-Computer-Revolution-Steven-Le...
[3] https://www.amazon.com/Chaos-Making-Science-James-Gleick/dp/...
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