For small values of "music"? Really, no. But tbh, neither have more advanced "AI" composition experiments I've encountered over the years, Markov models, linear predictive coding, genetic/evolutionary algs, rule based systems, and now modern diffusion and transormers... they all lack the "spirit of jazz" [0]

[0] https://i.pinimg.com/originals/e4/84/79/e484792971cc77ddff8f...

Thanks for these links. You're right, I think computer-vision "sight reading" is now a fairly done deal. Very impressive progress in the past 30 years.

More typos (LLMs are really good at finding these):

"Press the "Run Simulation" button to start traininng the RBM." ("traininng" -> "training")

"...we want to derivce the contrastive divergence algorithm..." ("derivce" -> "derive")

"A visisble layer..." ("visisble" -> "visible")

> I'm a bit unclear on how that isn't just a multi-layer perceptron. What's different about a Boltzmann machine?

In a Boltzmann machine, you alternate back and forth between using visible units to activate hidden units, and then use hidden units to activate visible units.

> What 0xTJ's [flagged][dead] comment says about it being undesirable to hijack or otherwise attempt to reinvent scrolling is spot on.

The page should be considered a slideshow that is paged discretely and not scrollable continuously. And there should definitely be no scrolling inertia.

It's more accurate to say that businesspeople consider research a waste in our quarter-by-quarter investment climate, since it generally doesn't lead to immediate gains.

And our current leadership considers research a threat, since science rarely supports conspiracy theorists or historical revisionism.

Why do you say that America looks in research as a waste? We spend higher percentage of gdp on R&D than just about any other country in the world:

https://en.wikipedia.org/wiki/List_of_sovereign_states_by_re...

By "undirected structure" I assume you refer to the presence of cycles in the graph? I was taught to call such networks "recurrent" but it seems that that term has evolved to mean something slightly different. Anyway yeah, because of the cycles Gibbs sampling is key to the network's operation. One still employs gradient descent during training, but the procedure to calculate the gradient itself involves Gibbs sampling.

Edit: Actually was talking about the General Boltzmann Machine. For the Restricted Boltzmann Machine an approximation has been assumed which obviates the need for full Gibbs sampling during training. Then (quoting the article, emphasis mine) "after training, it can sample new data from the learned distribution using Gibbs sampling."

We can’t really discuss Descartes without first explaining the horse.

My understanding, which is far from certain, is that problems like this (try a large combination of solutions, cooperate between superpositions to identify the best, then set up the quantum outputs so when they are classically sampled, the best answer is the most likely sample obtained) are solved in four stages:

1. N^2 potential solutions encoded in superposition across N qubuts.

2. Each superposition goes through the quantum version of a normal inference pass, using each superpositions weights to iteratively process all the classical data, then calculates performance. (This is all done without any classical sampling.)

3. Cross-superposition communication that results in agreement on the desired version. This is the weakest part of my knowledge. I know that is an operation type that exists, but I don't know how circuits implement it. But the number of bits being operated on is small, just a performance measure. (This is also done without any classical sampling.)

4. Then the output is sampled to get classical values. This requires N * log2(N) circuit complexity, where N is the number of bits defining the chosen solution, i.e. parameters and architectural settings. This can be a lot of hardware, obviously, perhaps more than the rest of the hardware, given N will be very large.

Don't take anything I say here for granted. I have designed parts of such a circuit, using ideal quantum gates in theory, but not all of it. I am not an expert, but I believe that every step here is well understood by others.

The downside relative to other approaches: It does a complete search of the entire solutions space directly, so the result comes from reducing N^2 superpositions across N qubits, where for interesting models the N can be very large (billions of parameters x parameter bit width), to get N qubits. No efficiencies are obtained from gradient information or any other heuristic. This is the brute force method. So an upper bound of hardware requirements.

Another disadvantage, is since the solution was not obtained by following a gradient, the best solution might not be robust. Meaning, it might be a kind of vertex between robust solutions that manages to do best on the designed data, but small changes of input samples from the design data might not generalize well. This is less likely to be a problem if smoothing/regularization information is taken into account account for determining each superpositions performance.