Adam Hibble

I generate models that generate increasingly weird other stuff.

-- Prev: Founder of Popgun Labs (Techstars), Founder of the @QUTCode Network.

[Adam Hibble @Algomancer]

New followers.

No researcher is to be found here. I am a novel capabilities engineer doing random walk.

(read: unhinged)

[Adam Hibble @Algomancer]

@secemp9 @stochasticchasm @ariaurelium @HeMuyu0327 If you like my posts, lmk what ones specifically and what you'd wanna see more of.

[Adam Hibble @Algomancer]

It’s been refreshing to see ai people starting to take RSI seriously. Look after your wrists, good ergonomics is important. Carpal tunnel sucks.

[Adam Hibble @Algomancer]

code soon, once i get some sleep

[Adam Hibble @Algomancer]

Gap closed.

[Adam Hibble @Algomancer]

@ryu0000000001

[Adam Hibble @Algomancer]

@ryu0000000001 a varient

[Adam Hibble @Algomancer]

@Esychology Great - I’ll dm some details on why I’m using it.

[Adam Hibble @Algomancer]

Kinda cooked on this one.

My new favorite bijection for normalising flows, Predictive Rosenblatt Spline, closed form inverse.

Belief over a gaussian codebook -> posterior predict-> spline knots, all analytic. Exact log-det at any K, zero init is the exact Rosenblatt map.

[Adam Hibble @Algomancer]

gimme dat ω = 2 triton kernel when you find it.

[Adam Hibble @Algomancer]

If your spending tokens on solving math problems. May i suggest matrix multiplication exponent?

Probably a decent economic upside. Current best exponent ≈ 2.37

It has a closed form evaluation suite, so it can be auto researched.

[Adam Hibble @Algomancer]

@francoisfleuret I feel like things like schmidhuber SWRM need a proper revisit, especially if you can formulate it without the rank-1 constraint of delta-net interpretations.

[Adam Hibble @Algomancer]

@francoisfleuret https://t.co/OUz967tlR0

[Adam Hibble @Algomancer]

The search space between transformer fast weights in and hyper networks seems rich. Anything that let’s you learn “faster” than your learning rate is probably worth investigating.

[Adam Hibble @Algomancer]

@francoisfleuret Not for a lack of saying.

We been saying.

https://t.co/rHjJ7BXuHA

[Adam Hibble @Algomancer]

@francoisfleuret Refinement strategies seem to be pretty stable. (residuals)

Fast Weights.

Normalisation.

[Adam Hibble @Algomancer]

I need more compute.

[Adam Hibble @Algomancer]

Wait until they find about about H-Space.

Y'all ready for some curvature.

[Adam Hibble @Algomancer]

@leonard_cor @recurseparadox I’d need to spend more time thinking about it to form a take. Generally tho, I tend to think most things can work, the surface area to correct issues presented by anyone with a “x approach is better than y approach” seems rather non trivial.

[Adam Hibble @Algomancer]

Can we train a family of local differentiable physical laws such that persistent local structures emerge which contain compressed predictive models of their own future environment and exhibit measurable causal control over that environment?

Idk, but i thought it was an interesting question if you try to take it seriously.

First attempt It's pretty at least.

A ~100M-parameter a local energy-conserving Hamiltonian field theory. its local rule is the symplectic leapfrog of a learned Hamiltonian. continuous, second-order, reversible, energy-conserving. Can kinda think of it as a second order in time neural ca, optimised such that cell states causally predict future local state whilst maximising variance, symplectic so it can't just push magnitude.

[Adam Hibble @Algomancer]

@cetusian Not the best measure, but high energy clusters predict the future better than random ones in the current varient.

[Adam Hibble @Algomancer]

Extension:

Not happy with it yet, but playing around with an idea to have the data manifold as a point of equilibrium.

[Adam Hibble @Algomancer]

Can we train a family of local differentiable physical laws such that persistent local structures emerge which contain compressed predictive models of their own future environment and exhibit measurable causal control over that environment?

Idk, but i thought it was an interesting question if you try to take it seriously.

First attempt It's pretty at least.

A ~100M-parameter a local energy-conserving Hamiltonian field theory. its local rule is the symplectic leapfrog of a learned Hamiltonian. continuous, second-order, reversible, energy-conserving. Can kinda think of it as a second order in time neural ca, optimised such that cell states causally predict future local state whilst maximising variance, symplectic so it can't just push magnitude.

[Adam Hibble @Algomancer]

@cetusian So, right now - just rolling out future predicted states and comparing them.