Neuromorphic mostly just means "like how the brain works". It encompasses a variety of software & hardware approaches.

The most compelling and obvious one to me is hardware purpose-built to simulate spiking neural networks. In the happy case, SNNs are extremely efficient. Basically consuming no energy. You could fool yourself into thinking we can just do this on the CPU due to the sparsity of activations. I think there is even a set of problems this works well for. But, in the unhappy cases SNNs are impossible to simulate on existing hardware. Neuronal avalanches follow power law distribution and meaningfully-large ones would require very clever techniques to simulate with any reasonable fidelity.

> the system isn't just simulating neurons but involves a variety of methods and interactions across "agents" or sub-systems.

I think the line between "neuron" and "agent" starts to get blurry in this arena.

Neuromorphic has been an ongoing failure (for general purpose processors or even AI accelerators), ever since Carver Mead introduced (and quickly abandoned them) them nearly half a century ago. Bill Dally (NVidia CTO) concurs: "I keep getting those calls from those people who claim they are doing neuromorphic computing and they claim there is something magical about it because it's the way that the brain works ... but it's truly more like building an airplane by putting feathers on it and flapping with the wings!" From: Hardware for Deep Learning, HotChips 2023 keynote.

We have NO idea how the brain produces intelligence, and as long as that doesn't change, "neuromorphic" is merely a marketing term, like Neurotypical, Neurodivergent, Neurodiverse, Neuroethics, Neuroeconomics, Neuromarketing, Neurolaw, Neurosecurity, Neurotheology, Neuro-Linguistic Programming: the "neuro-" prefix is suggesting a deep scientific insight to fool the audience. There is no hope of us cracking the question of how the human brain produces high-level intelligence in the next decade or so.

Neuromorphic does work for some special purpose applications.

I love this book and have it sitting on my shelf right now! Read it when I was a kid and was amazed at the ideas in it, nowadays it's clearer to me that the author only had a grasp of how things like that would be built but still cool nonetheless.

I would highly recommend it to people who love a good "near future" scifi book.

I don't think so. FIR filters can be unrolled and parallelized over the data. These are definitely possible to do on GPU to great effect. But, IIR filters constantly depend on the output of the prior time step, so you can't unroll anything. These would probably be faster to simulate on the CPU.

Yes. See this paper: http://cs.txstate.edu/~mb92/papers/asplos18.pdf

And things have improved a lot since then.

I'm not proposing a change in model size; rather, I'm suggesting a higher dimensionality within the current structure. There’s an interesting paper on LLM explainability, which found that individual neurons often represent a superposition of various data elements.

What I’m advocating is a substantial increase in this aspect—keeping model size the same while expanding dimensionality. The "curse of dimensionality" illustrates how a modest increase in dimensions leads to a significantly larger volume.

While I agree that backpropagation isn’t a complete solution, it’s ultimately just a stochastic search method. The key point here is that expanding the dimensionality of a model’s space is likely the only viable long-term direction. To achieve this, backpropagation needs to work within an increasingly multidimensional space.

A useful analogy is training a small model on random versus structured data. With structured data, we can learn an extensive amount, but with random data, we hit a hard limit imposed by the network. Why is that?

I see what you are saying, and I made a similar comment.

However it's still an interesting observation that many architectures can arrive at the same performance (even though the training requirements are different).

Naively, you wouldn't expect eg 'x -> a * x + b' to fit the same data as 'x -> a * sin x + b' about equally well. But that's an observation from low dimensions. It seems once you add enough parameters, the exact model doesn't matter too much for practical expressiveness.

I'm faintly reminded of the Church-Turing Thesis; the differences between different computing architectures are both 'real' but also 'just an optimisation'.

> When you start layering in normalisation techniques to minimise overfitting, and especially once you start thinking about more agentic architectures (eg. Deep Q Learning, some of the search space design going into OpenAI's o1), then I don't think the just-an-optimisation perspective can hold much water at all - more computing power simply couldn't solve those problems with older architectures.

You are right, these normalisation techniques help you economise on training data, not just on compute. Some of these techniques can be done independent of the model, eg augmenting your training data with noise. But some others are very model dependent.

I'm not sure how the 'agentic' approaches fit here.

Wait! We certainly did NOT have huge datasets (like current internet) for ages. Not even decades. I’ve seen a lecture by a MIT professor (which I cannot find now) where he asserted categorically, that the advances in AI are mostly because of the huge data that we now have and we didn’t before. And that was an old video.

I think by far the biggest advances are related to compute power. The amount of processing needed to run training algorithms on the amounts of data needed for the latest models was just not possible even five years ago, and definitely not ten years ago.

I'm sure there are optimizations from the model shape as well, but I don't think that running the best algorithms we have today with hardware from five-ten years ago would have worked in any reasonable amount of time/money.

Isn’t bogosort transformer and quicksort proposed modified rnn (175 times faster training for 500 seq) here?

> Even if many types of architectures converge to the same loss over time, finding the one that converges the fastest is quite valuable given the cost of running GPU's at scale.

This! Not just fastest but with the lowest resources in total.

Fully connected neural networks are universal functions. Technically we don’t need anything but a FNN, but memory requirements and speed would be abysmal far beyond the realm of practicality.

> finding the one that converges the fastest is quite valuable given the cost of running GPU's at scale

Not to him, he runs the ARC challenge. He wants a new approach entirely. Something capable of few-shot learning out of distribution patterns .... somehow

This is true. This is the reason, in many of our experiments we find that using a new algorithm, KESieve, we actually find the planes much faster than the traditional deep learning training approaches. The premise is, a neaural network builds planes which separate the data and adjusts these planes through an iterative learning process. What if we can find a non iterative method which can draw these same planes. We have been trying this and so far we have been able to replace most network layers using this approach. haven't tried for transformers though yet.

Some links if interested:

[1] https://gpt3experiments.substack.com/p/understanding-neural-...

[2] https://gpt3experiments.substack.com/p/building-a-vector-dat...

This and also the way models are trained has to be rethought. BPP is good for figuring out complex function mappings, but not for storing information.

Sounds like something that has unsustainable energy costs.

> I remember one of the initial transformer people saying in an interview that they didn't think this was the "one true architecture" but a lot of the performance came from people rallying around it and pushing in the one direction.

You may be referring to Aidan Gomez (CEO of Cohere and contributor to the transformer architecture) during his Machine Learning Street Talk podcast interview. I agree, if as much attention had been put towards the RNN during the initial transformer hype, we may have very well seen these advancements earlier.

Reminds me of the "Considered Harmful" articles:

https://meyerweb.com/eric/comment/chech.html

Starting of course with the classic paper from Lennon and McCartney, 1967.

> can conceivably fit a curve with a single, huge layer

I think you need a hidden layer. I’ve never seen a universal approximation theorem for a single layer network.

No. An RNN has an arbitrarily-long path from old inputs to new outputs, even if in practice it can't exploit that path. Transformers have fixed-size input windows.

One reason why I'm excited about o1 is that it seems like OpenAI have cracked the nut of effective RL during training time, which takes us out of the domain of just fitting to the curve of "what a human would have said next." I just finished writing a couple blog posts about this; the first [1] covers some problems with that approach and the second [2] talks about what alternatives might look like.

[1] https://www.airtrain.ai/blog/how-openai-o1-changes-the-llm-t... [2] https://www.airtrain.ai/blog/how-openai-o1-changes-the-llm-t...

> After reading this paper, I am now

Is this your paper?

Author: @fandzomga Username: fsndz

Why try to funnel us to your paywalled article?

I would like to read it, but it's under a paywall.

paper is paywalled; just logging into Medium won't do it

TLDR: “statistically fitting token output is not the same as human intelligence, and human intelligence and AGI are contradictory anyways (because humans make mistakes)”

Saved you the paywall click to the poorly structured medium article :)

The "squashing function" necessarily is nonlinear in multilayer nueral networks. A single layer of a neural network can be quite simply written a weight matrix, times an input vector, equalling an output vector, like so

Ax = y

Adding another layer is just multiplying a different set of weights times the output of the first, so

B(Ax)= y

If you remember your linear algebra course, you might see the problem: that can be simplified

(BA)x = y

Cx = y

Completely indistinguishable from a single layer, thus only capable of modeling linear relationships.

To prevent this collapse, a non linear function must be introduced between each layer.

> It's astounding that we DID get the emergent intelligence from just doing this "curve fitting" onto "lines" rather than actual "curves".

In Ye Olden days (the 90’s) we used to approximate non-linear models using splines or seperate slopes models - fit by hand. They were still linear, but with the right choice of splines you could approximate a non-linear model to whatever degree of accuracy you wanted.

Neural networks “just” do this automatically, and faster.

> This is no different than a transformer, which, after all, is bound by a finite state, just organized in a different manner.

It's not just a matter of organizing things differently. Suppose your network dimension and sequence length are both X.

Then your memory usage (per layer) will be O(X^2), while your training update cost will be O(X^3). That's for both Transformers and RNNs.

However, at the end of the sequence, a Transformer layer can look back see O(X^2) numbers, while an RNN can only see O(X) numbers.

Well, that's what Transformer already does... One problem with the scaling you're describing is that there would be a massive amount of redundant information stored in hidden activations during training the RNN. The hidden state at each time step t in the sequence would need to contain all info that (i) could be useful for predicting the token at time t and (ii) that could be useful for predicting tokens at times >t. (i) is obvious and (ii) is since all information about the past is transferred to future predictions through the current hidden state. In principle, Transformers can avoid storing redundant info in multiple hidden states at the cost of having to maintain and access (via attention) a larger hidden state at test/eval time.

It's necessary for arbitrary information processing if you can forget and have no way to "unforget".

A model can decide to forget something that turns out to be important for some future prediction. A human can go back and re-read/listen etc, A transformer is always re-reading but a RNN can't and is fucked.

Also, a lightweight network could do a first pass to identify tasks, instructions, constraints etc, and then a second pass could use the RNN.

Consider the flood fill algorithm or union-find algorithm, which feels magical upon first exposure.

https://en.wikipedia.org/wiki/Hoshen%E2%80%93Kopelman_algori...

Having 2 passes can enable so much more than a single pass.

Another alternative could be to have a first pass make notes in a separate buffer while parsing the input. The bandwidth of the note taking and reading can be much much lower than that required for fetching the billions of parameters.

People did something similar to what you are describing 10 years ago: https://arxiv.org/abs/1409.0473

But it's trained on translations, rather than the whole Internet.

You could theoretically run the input twice, allowing the model to correlate later tokens with previous ones. It would fix the problem with not knowing what information to retain. A more complicated approach would train the RNN to request replaying some earlier data when needed.

A great thing about RNNs is they can easily fork the state and generate trees, it would be possible to backtrack and work on combinatorial search problems.

Also easier to cache demonstrations for free in the initial state, a model that has seen lots of data is not using more memory than a model starting from scratch.

> They were solved by LSTMs first proposed in 1997.

I see this stuff everywhere online and it's often taught this way so I don't blame folks for repeating it, but I think it's likely promulgated by folks who don't train LSTMs with long contexts.

LSTMs do add something like a "skip-connection" (before that term was a thing) which helps deal with the catastrophic vanishing gradients you get from e.g. Jordan RNNs right from the jump.

However (!), while this stops us from seeing vanishing gradients after e.g. 10s or 100s of time-steps, when you start seeing multiple 1000s of tokens, the wheels start falling off. I saw this in my own research, training on amino acid sequences of 3,000 length led to a huge amount of instability. It was only after tokenizing the amino acid sequences (which was uncommon at the time) which got us down to ~1500 timesteps on average, did we start seeing stable losses at training. Check-out the ablation at [0].

You can think of ResNets by analogy. ResNets didn't "solve" vanishing gradients, there's a practical limit of the depth of networks, but it did go a long way towards dealing with it.

EDIT: I wanted to add, while I was trying to troubleshoot this for myself, it was super hard to find evidence online of why I was seeing instability. Everything pertaining to "vanishing gradients" and LSTMs were blog posts and pre-prints which just merrily repeated "LSTMs solve the problem of vanishing gradients". That made it hard for me, a junior PhD at the time, to suss out the fact that LSTMs do demonstrably and reliably suffer from vanishing gradients at longer contexts.

[0] https://academic.oup.com/bioinformatics/article/38/16/3958/6...

Agreed, Ilya Sutskever himself has spent a long time with lstm and published papers like this one while working at Google. http://proceedings.mlr.press/v37/jozefowicz15.pdf

Recent comments from him have said that any architecture can achieve transformer accuracy and recall, but we have devoted energy to refining transformers, due to the early successes.

LSTM and GRU did not quite solve the issue, but they made it less bad. Overall, recurrent units are nutritiously prone to vanishing and exploding gradients.

I don't want to downplay the value of these models. Some people seem to be under the perception that transformers replaced or made them obsolete, which is faar from the truth.

Counterpoint: the hidden state at the beginning of ([the][cat][is][black]) x 3 is (probably) initialized to all zeros, but after seeing those first 4 tokens, it will not be all zeros. Thus, going into the second repetition of the sentence, the model has a different initial hidden state, and should exhibit different behavior. I think this makes it possible for the model to learn to recognize repeated sequences and avoid your proposed pitfall.

yes, that is clearer indeed. However S4 and Mamba class models have also performed well at small scale and started lagging with larger models and larger context sizes, or at particular tasks.

I think there are two distinct areas. One is the building of the representations, which is achieved by fitting. The other area is loosely defined as "computing" which is some kind of searching for a path through representation space. All of that is wrapped in a translation layer that can turn those representations into stuff we humans can understand and interact with. All of that is achieved to some extent by current transformer architectures, but I guess some believe that they are not quite as effective at the "computation/search" stage.

During that time, your brain would do far more than just that text generation though, beyond what we even know scientifically.

But yes, food energy could be useful for AI. A little dystopian potentially too, if you think about it. Like DARPA's EATR robot, able to run on plant biomass (although potentially animal biomass too, including human remains):

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

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

There has been some work, but the problem is that its such a massive search space. Philosophically speaking, if you look at how humans came into existence, you could make an argument that the process of evolution from basic lifeforms can be represented as one giant compute per minute across of all of earth, where genetic selection happens and computation proceeds to the next minute. Thats a fuckload of compute.

In more practical terms, you would imagine that an advanced model contains some semblance of a CPU to be able to truly reason. Given that CPUs can be all NAND gates (which take 2 neurons to represent), and are structured in a recurrent way, you fundamentally have to rethink how to train such a network, because backprop obviously won't work to capture things like binary decision points.

Program synthesis is a generalization of this. I’m not sure that many ML researchers have thought about the connections yet.

The search space is all off too wide, difficult to parameterize, and there is a wide gap between effective and ineffective architectures - ie: a very small change can make a network effectively DOA.

Neural networks are Turing complete, i.e. there is a universal neural network that can compute any effectively computable function¹. Incidentally, when this is combined with Rice's theorem² it means that safety research is essentially an unsolvable problem because any non-trivial property of a sufficiently complex neural network, e.g. one that can simulate a Turing machine, will have properties which can not be predicted with finite computation.

1: https://www.sciencedirect.com/science/article/pii/0893965991...

2: https://en.wikipedia.org/wiki/Rice%27s_theorem?useskin=vecto...

Surely the universe is all you need though

Not really, because

1) Yoshua's reputation would take a hit if this paper were bullshit, so he has extrinsic motivation to make it good 2) Yoshua has enough experience in the field to know what is going on in the field, you don't have to ask if he forgot about a certain architecture or the work of a certain research group which would contradict his findings-- if such work exists and is credible, it is very likely to be discussed in the paper. 3) This test answers something a leader in the field thinks is important enough for them to work on, else he wouldn't be involved.

Also note, the poster said the paper shouldn't be taken lightly. That doesn't mean we need to take it blindly. It only means we cannot dismiss it out of hand, if we have a different view we would need substantive arguments to defend our view.

I've overturned the field leader several times in science, but that's only because I acknowledged what they got right and that they were indeed the person who got it right.

Sad state of affairs, people are incentivized to get more papers and citations at all costs, and quality be damned.

An AI Winter is not a great an idea, but an AI Autumn may be beneficial.

Just have no major AI conferences for ‘25, perhaps only accept really high tier literature reviews.

Not always, I think?

Opinions probably differ, for example, on John Backus's paper "Can programming be liberated from the Von Neumann style?" Many fans of functional programming would say the answer is yes, but Backus himself expressed less enthusiasm in interviews later in his life.

I think the important point, though, is that academic papers and newspaper articles are not the same, and titles in the form of questions function differently in the two domains. Journalists tend to use titles like these to dissemble and sensationalize. When academics use these kinds of titles for peer-reviewed articles, it's because they really are asking an honest question. Backus was doing it in his paper. The authors of this paper are doing the same. They end the paper by re-iterating the question before launching into a discussion of the limitations that prevent them from reaching any firm conclusions on the answer to this question.

More like "we aren't sure, but we have good reasons not to exclude the possibility"

I'm not sure that's true at all. There are several well known researchers that say LLMs are in fact not doing reasoning.