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.
Secondly how do we get to claim that a particular thing is neuromorphic when we have such a rudimentary understanding of how a biological brain works or how it generates things like a model of the world, understanding of self etc etc.
Estimates put training of gpt4 at something like 2500 gpu years to train, over about 10000 gpus. 20 years would be a big improvement.
What I'm trying to express is we seem to want to cherry pick certain features from nature but ignore others that are inconvenient and that is understandable, but currently because our knowledge of the biological systems is so incomplete we really don't know which (if any) of these features gives rise to intelligence. For all we know we could be doing training in a seemingly-efficient way that completely precludes intelligence actually emerging.
I would highly recommend it to people who love a good "near future" scifi book.
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.
/way out of my area of expertise here
And things have improved a lot since then.
I’ve been thinking the same for a while, but I’m starting to wonder if giant context windows are good enough to get us there. I think recurrency is more neuromorphic, and possibly important in the longer run, but maybe not required for SI.
I’m also just a layman with just a surface level understanding of these things, so I may be completely ignorant and wrong.
FIR filters are way simpler to design and can capture memory without hacks.
> RNNs are particularly suitable for sequence modelling settings such as those involving time series, natural language processing, and other sequential tasks where context from previous steps informs the current prediction.
I would like to draw an analogy to digital signal processing. If you think of the recurrent-style architectures as IIR filters and feedforward-only architectures as FIR filters, you will likely find many parallels.
The most obvious to me being that IIR filters typically require far fewer elements to produce the same response as an equivalent FIR filter. Granted, the FIR filter is often easier to implement/control/measure in practical terms (fixed-point arithmetic hardware == ML architectures that can run on GPUs).
I don't think we get to the exponential scary part of AI without some fundamentally recurrent architecture. I think things like LSTM are kind of an in-between hack in this DSP analogy - You could look at it as FIR with dynamic coefficients. Neuromorphic approaches seem like the best long term bet to me in terms of efficiency.