calhoun137

Joined 13 years ago

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1,201
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352
Member since
Feb 8
I am a builder, not a breaker.
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Comment · 9 months ago
You can find the live demo here:

https://base-1-srrnmbhh3rkmnk8ygcxhvb.streamlit.app/

It seems that waking up the demo from the iframe doesn't always work, but directly visiting the embed url can wake it up.

By the way I just finished completely rewriting the entire blog post.

Comment · 9 months ago
OP here. this has been a valuable learning experience for me. i was so excited to share what i was working on and i blew it. i will rewrite the blog post and readme later. let me at least briefly explain what i did as a reply to your comment

starting with 3=1+2 we have (1+x)P(x)=3P(x) when x=2. so we lift the problem from n to P(2)=n. this is a known technique of lifting the problem to a polynomial setting. after each itteration of the Collatz map i make sure all coeffients are either 0 or 1 by applying carry operations when a coefficient overflows. since the coefficients are unary strings, this makes it like a fluid dynamics problem (each character in a unary string is analogous to one unit of mass in a list of buckets where the buckets can overflow and spill unary characters over into their left neihgbor)

when x=2, multiplying x by P(x) is a left shift, whereas dividing by x, P(x)/x, is a right shift. (when P(2)=n is even the constant term in P(x) is zero)

the +1 term in 3n+1 effectively induces a non linear carry propoagation.

the new technique i used is based on a realization that the polynomial representation of the Collatz map behaves like an LFSR implementation of a finite field with a missing modulus. in LFSR a finite field is implemented where each element is an array of bits of fixed size corresponding to a polynomial and multiplication of elements is polynomial multiplication taken mod Q(x) where Q(x) is an irreducible polynomial. unlike the finite field LFSR the Collatz map in polynomial form as i have described allows the degree of the polynomial (size of the array of bits) to grow unbounded.

the surprise is when i subtract these two objects the sierpinski gasket appears and this fractal is not destroyed by itterations of the collatz map

this document[1] is a prior result showing a connection between fractals and collatz that i found after posting the OP

[1] https://upcommons.upc.edu/server/api/core/bitstreams/9bad675...

lesson learned! i will never post an ai slop blog post on here ever again. thanks for the feedback i needed to hear it.

Comment · 9 months ago
Comment · 9 months ago
My experience leads to the same conclusion that the models are very good at math reasoning, but you have to really know what you are doing and be aware of the blatant lies that result from poorly phrased queries.

I recently prompted Gemini Deep Research to “solve the Riemann Hypothesis” using a specific strategy and it just lied and fabricated the result of a theorem in its output, which otherwise looked very professional.

Comment · 9 months ago
> Does replacing that lengthy text with "if you aren't sure of the answer say you don't know" have the same exact effect?

i believe it makes a substantial difference. the reason is that a short query contains a small number of tokens, whereas a large “wall of text” contains a very large number of tokens.

I strongly suspect that a large wall of text implicitly activates the models persona behavior along the lines of the single sentence “if you aren't sure of the answer say you don't know” but the lengthy argument version of that is a form of in-context learning that more effectively constrains the models output because you used more tokens.

Comment · 2 years ago
Comment · 2 years ago
my understandinf is that conservation of momentum does not mean momentum is conserved as time passes. it means if you have a (closed) system in a certain configuration (not in an external field) and compute the total momentum, the result is independent of the configuration of the system.
Comment · 2 years ago
Comment · 2 years ago
Very nice article! I recently had a long chat with chatgpt on this topic, although from a slightly different perspective.

A neural network is a type of machine that solves non linear optimization problems, and the principle of least action is also a non linear optimization problem that nature solves by some kind of natural law.

This is the one thing that chatgpt mentioned which surpised me the most and which I had not previously considered.

> Eigenvalues of the Hamiltonian in quantum mechanics correspond to energy states. In neural networks, the eigenvalues (principal components) of certain matrices, like the weight matrices in certain layers, can provide information about the dominant features or patterns. The notion of states or dominant features might be loosely analogous between the two domains.

I am skeptical that any conserved quantity besides energy would have a corresponding conserved quantity in ML, and the Reynolds operator will likely be relevant for understanding any correspondence like this.

iirc the Reynolds operator plays an important role in Noethers theorem, and it involves an averaging operation similar to what is described in the linked article.

Comment · 3 years ago
I don't see any evidence here for "self awareness", among other things, ChatGPT is simultaneously answering a very very large number of queries, and the underlying hardware is just a bunch of servers in the cloud. Furthermore, what would it even mean for "ChatGPT to become self aware" and how could we measure if this had taken place or not? Without a solid definition and method of measurement, it's meaningless to talk about abstract concepts like "self awareness".

Nevertheless, a sensible definition for self awareness is some kind of neural network that becomes aware of its own activity and is in some way able to influence its own function.

After considering these issues for a long time, I came to the conclusions that

1. It's impossible for a program running on a normal computer to have self awareness (or consciousness), because those things are essentially on the hardware level and not the software level

2. In order to create a machine that is capable of self awareness (and consciousness) it is necessary to invent a new type of computer chip which is capable of modifying its own electrical structure during operation.

In other words, I believe that a computer program which models a neural network can never be self aware, but that a physical neural network (even if artificially made) can in principle achieve self awareness.

Comment · 5 years ago
This is so awesome! I have wanted to make something like this for like 20 years, this is much better than anything I made though. Great work

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