My understanding is that the Harmonium (Smolensky) was the first restricted Boltzmann machine, but maximized “harmony” instead of minimizing “energy.” When Smolensky, Hinton and Rummelhart collaborated, they instead called it “goodness of fit.”
The harmonium paper [1] is a really nice read. Hinton obviously became the superstar and Smolensky wrote long books about linguistics.
The key takeaways are that there are lots of people involved with making these breakthroughs.
The value of grad students is often overlooked, they contribute so much and then later on advance the research even more.
Why does America look on research as a waste, when it has move everything so far?
macintux · 3h ago
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.
itissid · 6h ago
IIUC, we need gibbs sampling(to compute the weight updates) instead of using the gradient based forward and backward passes with today's NNetworks that we are used to. Any one understand why that is so?
ebolyen · 3h ago
Not an expert, but I have a bit of formal training on Bayesian stuff which handles similar problems.
Usually Gibbs is used when there's no directly straight-forward gradient (or when you are interested in reproducing the distribution itself, rather than a point estimate), but you do have some marginal/conditional likelihoods which are simple to sample from.
Since each visible node depends on each hidden node and each hidden node effects all visible nodes, the gradient ends up being very messy, so its much simpler to use Gibbs sampling to adjust based on marginal likelihoods.
oac · 2h ago
I might be mistaken, but I think this is partly because of the undirected structure of RBMs, so you can't build a computational graph in the same way as with feed-forward networks.
Nevermark · 3h ago
I mistook the title for "A Tiny Boltzmann Brain"! [0]
My own natural mind immediately solved the conundrum. Surely this was a case where a very small model was given randomly generated weights and then tested to see if it actually did something useful!
After all, the smaller the model, the more likely simple random generation can produce something interesting, relative to its size.
I stand corrected, but not discouraged!
I propose a new class of model, the "Unbiased-Architecture Instant Boltzmann Model" (UA-IBM).
One day we will have quantum computers large enough to simply set up the whole dataset as a classical constraint on a model defined with N serialized values, representing all the parameters and architecture settings. Then let a quantum system with N qubits take one inference step over all the classical samples, with all possible parameters and architectures in quantum superposition, and then reduce the result to return the best (or near best) model's parametesr and architecture in classical form.
Anyone have a few qubits laying around that want to give this a shot? (The irony that everything is quantum and yet so slippery we can hardly put any of it to work yet.
(Sci-fi story premise: the totally possible case of an alien species that evolved one-off quantum sensor, which evolved into a whole quantum sensory system, then a nervous system, and subsequently full quantum intelligence out of the gate. What kind of society and technological trajectory would they have? Hopefully they are in close orbit around a black hole, so the impact of their explosive progress has not threatened us yet. And then one day, they escape their gravity well, and ...)
Poor quantum beings. They don't have access to a computation model that exceeds the speeds of their own thoughts and they are forever doomed to be waiting a long time for computations to happen
immibis · 1h ago
That isn't how quantum computers work.
Nevermark · 19m ago
My understanding, which is far from certain, is that a superposition of circuit states with a superposition of output values can be sampled with N x log2(N) hardware to most likely return the optimal solution.
Granted, N x log2(N) is very a large amount of hardware for sampling, since N is the number of parameters and architectural setting bits.
I could be wrong, but I believe that is the case.
A more efficient, i.e. practical system would require significant other complexities and iteration.
There are a lot of classical computations that can be done in one pass too - if hardware is free, with enough available for the problem size.
This takes me back. 1990, building Boltzman machines and Perceptrons
from arrays of void pointers to "neurons" in plain C. What did we use
"AI" for back then? To guess the next note in a MIDI melody, and to
recognise the shape of a scored note, minim, crotchet, quaver on a 5 x
9 dot grid. 85% accuracy was "good enough" then.
gopalv · 20m ago
> recognise the shape of a scored note, minim, crotchet, quaver on a 5 x 9 dot grid
Reading music off a lined page sounds like a fun project, particularly to do it from scratch like 3Blue1Brown's number NN example[1].
Mix with something like Chuck[2] and you can write a completely clientside application with today's tech.
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]
Oh, this is a neat demo. I took Geoff Hinton's neural networks course in university 15 years ago and he did spend a couple of lectures explaining Boltzmann machines.
> A Restricted Boltzmann Machine is a special case where the visible and hidden neurons are not connected to each other.
This wording is wrong; it implies that visible neurons are not connected to hidden neurons.
The correct wording is: visible neurons are not connected to each other and hidden neurons are not connected to each other.
Alternatively: visible and hidden neurons do not have internal connections within their own type.
CamperBob2 · 6h ago
Alternatively: visible and hidden neurons do not have internal connections within their own type.
I'm a bit unclear on how that isn't just an MLP. What's different about a Boltzmann machine?
Edit: never mind, I didn't realize I needed to scroll up to get to the introductory overview.
What 0xTJ's [flagged][dead] comment says about it being undesirable to hijack or otherwise attempt to reinvent scrolling is spot on.
nayuki · 6h ago
> 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.
vanderZwan · 8h ago
Lovely explanation!
Just FYI: mouse-scrolling is much too sensitive for some reason (I'm assuming it swipes just fine in mobile contexts, have not checked that). The result is that it jumped from first to last "page" and back whenever I tried scrolling. Luckily keyboard input worked so I could still read the whole thing.
nickvec · 5h ago
Great site! Would be cool to be able to adjust the speed at which the simulation runs as well.
tomrod · 4h ago
Great read!
One nit, a misspelling in the Appendix: derivce -> derive
rollulus · 5h ago
Now the real question: is it you enjoying that nice page or is it a Boltzmann Brain?
It's Decartes demon all over again. Problem solved centuries ago. You can skin it however you want, it's the same problem.
taneq · 1h ago
We can’t really discuss Descartes without first explaining the horse.
alganet · 52m ago
I don't think there is anything to discuss about Decartes.
bbstats · 7h ago
anyone got an archived link?
antidumbass · 5h ago
The section after the interactive diagrams has no left padding and thus runs off the screen on iOS.
No comments yet
nickvec · 5h ago
> Here we introduce introduction to Boltzmann machines and present a Tiny Restricted Boltzmann Machine that runs in the browser.
nit: should "introduction" be omitted?
No comments yet
BigParm · 6h ago
That font with a bit of margin looks fantastic on my phone specifically. Really nailing the minimalist look. What font is that?
mac9 · 6h ago
"font-family: ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";"
from the css so odds are it's whatever your browser or OS's default sans font is, in my case it's SF Pro which is an Apple font though it may vary if you use a non Apple device.
tambourine_man · 7h ago
Typo
“They can be used for generating new data that…”
munchler · 7h ago
Another typo (or thinko) in the very first sentence:
"Here we introduce introduction to Boltzmann machines"
croemer · 7h ago
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")
djulo · 8h ago
that's soooo coool
pawanjswal · 6h ago
Love how this breaks down Boltzmann Machines—finally makes this 'energy-based model' stuff click!
thingamarobert · 4h ago
This is very well made, and so nostalgic to me! My whole PhD between 2012-16 was based on RBMs and I learned so much about generative ML through these models. Research has come so far and one doesn't hear much about them these days but they were really at the heart of the "AI Spring" back then.
The harmonium paper [1] is a really nice read. Hinton obviously became the superstar and Smolensky wrote long books about linguistics.
Anyone know more about this history?
[1] https://stanford.edu/~jlmcc/papers/PDP/Volume%201/Chap6_PDP8...
Do check out his T2 Tile Project.
The value of grad students is often overlooked, they contribute so much and then later on advance the research even more.
Why does America look on research as a waste, when it has move everything so far?
And our current leadership considers research a threat, since science rarely supports conspiracy theorists or historical revisionism.
Usually Gibbs is used when there's no directly straight-forward gradient (or when you are interested in reproducing the distribution itself, rather than a point estimate), but you do have some marginal/conditional likelihoods which are simple to sample from.
Since each visible node depends on each hidden node and each hidden node effects all visible nodes, the gradient ends up being very messy, so its much simpler to use Gibbs sampling to adjust based on marginal likelihoods.
My own natural mind immediately solved the conundrum. Surely this was a case where a very small model was given randomly generated weights and then tested to see if it actually did something useful!
After all, the smaller the model, the more likely simple random generation can produce something interesting, relative to its size.
I stand corrected, but not discouraged!
I propose a new class of model, the "Unbiased-Architecture Instant Boltzmann Model" (UA-IBM).
One day we will have quantum computers large enough to simply set up the whole dataset as a classical constraint on a model defined with N serialized values, representing all the parameters and architecture settings. Then let a quantum system with N qubits take one inference step over all the classical samples, with all possible parameters and architectures in quantum superposition, and then reduce the result to return the best (or near best) model's parametesr and architecture in classical form.
Anyone have a few qubits laying around that want to give this a shot? (The irony that everything is quantum and yet so slippery we can hardly put any of it to work yet.
(Sci-fi story premise: the totally possible case of an alien species that evolved one-off quantum sensor, which evolved into a whole quantum sensory system, then a nervous system, and subsequently full quantum intelligence out of the gate. What kind of society and technological trajectory would they have? Hopefully they are in close orbit around a black hole, so the impact of their explosive progress has not threatened us yet. And then one day, they escape their gravity well, and ...)
[0] https://en.wikipedia.org/wiki/Boltzmann_brain
Granted, N x log2(N) is very a large amount of hardware for sampling, since N is the number of parameters and architectural setting bits.
I could be wrong, but I believe that is the case.
A more efficient, i.e. practical system would require significant other complexities and iteration.
There are a lot of classical computations that can be done in one pass too - if hardware is free, with enough available for the problem size.
It brings up a lot of memories! Shameless plug: I made a visualization of an RBM being trained years ago: https://www.youtube.com/watch?v=lKAy_NONg3g
Reading music off a lined page sounds like a fun project, particularly to do it from scratch like 3Blue1Brown's number NN example[1].
Mix with something like Chuck[2] and you can write a completely clientside application with today's tech.
[1] - https://www.3blue1brown.com/lessons/neural-networks
[2] - https://chuck.stanford.edu/
[0] https://i.pinimg.com/originals/e4/84/79/e484792971cc77ddff8f...
> A Restricted Boltzmann Machine is a special case where the visible and hidden neurons are not connected to each other.
This wording is wrong; it implies that visible neurons are not connected to hidden neurons.
The correct wording is: visible neurons are not connected to each other and hidden neurons are not connected to each other.
Alternatively: visible and hidden neurons do not have internal connections within their own type.
I'm a bit unclear on how that isn't just an MLP. What's different about a Boltzmann machine?
Edit: never mind, I didn't realize I needed to scroll up to get to the introductory overview.
What 0xTJ's [flagged][dead] comment says about it being undesirable to hijack or otherwise attempt to reinvent scrolling is spot on.
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.
Just FYI: mouse-scrolling is much too sensitive for some reason (I'm assuming it swipes just fine in mobile contexts, have not checked that). The result is that it jumped from first to last "page" and back whenever I tried scrolling. Luckily keyboard input worked so I could still read the whole thing.
One nit, a misspelling in the Appendix: derivce -> derive
https://en.m.wikipedia.org/wiki/Boltzmann_brain
It's Decartes demon all over again. Problem solved centuries ago. You can skin it however you want, it's the same problem.
No comments yet
nit: should "introduction" be omitted?
No comments yet
from the css so odds are it's whatever your browser or OS's default sans font is, in my case it's SF Pro which is an Apple font though it may vary if you use a non Apple device.
“They can be used for generating new data that…”
"Here we introduce introduction to Boltzmann machines"
"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")