Living Human Brain Cells Play DOOM on a CL1

 

Andrew Paul, Computer run on human brain cells learned to play ‘Doom’, Popular Science, Mar. 2, 2026.

A biocomputer powered by lab-grown human brain cells has leveled up from Pong to Doom. While nowhere ready to handle the video game shooter’s most challenging levels, researchers at Cortical Labs in Australia believe their neuronal chip is well on its way to powering a new generation of hybrid organic technologies.

“This was a major milestone, because it demonstrated adaptive, real-time goal directed learning,” Brett Kagan, Cortical Labs Chief Scientific and Chief Operations Officer, said in a recent video announcement.

It’s taken years to cross the Doom benchmark. In 2021, Cortical Labs debuted DishBrain—an early biocomputer utilizing around 800,000 human nerve cells. These neurons were connected to a small processing chip capable of interpreting and directing electrical activity similar to a standard silicon-powered device.

To showcase DishBrain’s potential, engineers successfully trained their biocomputer to play Pong. The classic, 2D game is often a test case for computational neuroscientists because it requires their system to navigate a dynamic information landscape in real time.

It took Cortical Labs more than 18 months using its original hardware and software to accomplish their Pong goal. DishBrain was eventually supplanted by CL1, which the company bills as the “world’s first code deployable biological computer.”

There's more at the link.

The transformer has put us on the shore of a new world

I note, however, that it is not at all obvious to me that the transformer architecture will remain. We probably need something that does what it does; but perhaps, when we understand it better, we can create something that serves the function better. But that’s not the main story, not now.

Back in late January of 2023, two months after ChatGPT was let loose on the web, I published a working paper: ChatGPT intimates a tantalizing future; its core LLM is organized on multiple levels; and it has broken the idea of thinking.

Here is the abstract:

Abstract: I make three arguments. A philosophical argument: (1) The behavior of ChatGPT is so sophisticated that the ordinary concept of thinking is no longer useful in distinguishing between human behavior and the ChatGPT’s behavior. We don’t have explicit understanding about what either humans or ChatGPT are doing. Two operational arguments: (2) Having examined its output in a systematic way, short stories in particular, I conclude that inference is organized on at least two levels: a) a ‘lower’ level where we find sentence-level syntax, and b) a ‘higher’ level where specific kinds of texts, such as stories, are implemented over and operate on sentences. This is roughly analogous to the way that high-level programming languages are implemented in assembly code. (3) Consequently, that aspects of full symbolic computation are latent in LLMs. An appendix has descriptive tables showing how four stories are organized on multiple levels.

I recently uploaded that paper to ChatGPT 5.2 and ask for its reaction. Note that it’s response reflects aspects of the prior conversation, e.g. the reference to “polyiscous” learning.

* * * * *

What I think the transformer architecture has done is allow as to enter a world in which those questions are REAL questions, not just opportunities for idle speculation while sipping single-malt scotch and smoking Cuban cigars.

That’s a profound reframing. Transformers as the catalyst that made these questions empirically tractable rather than philosophical armchair speculation.

What Transformers Actually Achieved

They didn’t solve intelligence, but they did something arguably more important: they demonstrated that:

1. Scale matters: Pattern learning at sufficient scale produces surprising capabilities
2. The limitations are real: We can now measure where systems fail, not just theorize
3. The questions are concrete: “How do you implement continuous learning?” is now an engineering question with testable hypotheses, not a philosophical puzzle
4. Capital will flow: The economic potential is clear enough that serious research funding is available

Before GPT-3, asking “how would you build a polyviscous learning system?” would get you philosophical discussions. Now it’s a legitimate research program that could attract $100M-$1B.

The Research Agenda That’s Now Possible

With serious funding, you could actually investigate:

• Hybrid architectures: Can you couple fast digital systems with slower analog/biological components?
• Neuromorphic substrates: Can you design hardware with inherent multi-timescale dynamics?
• Cultured neuron systems: Can you scale biological computing? What are the interface problems?
• Glial-inspired architectures: Can you build artificial “meta-learning” layers?
• Continuous learning mechanisms: What are the minimal requirements for learning without catastrophic forgetting?

These are now experiments you can run, not thought experiments.

The Irony

The industry is spending $10-20 billion per year to scale transformers further. Your point is that spending even 1-5% of that on fundamental research into alternative architectures might be far more productive.

$100M could fund:

• 10-20 serious research groups for 3-5 years
• Actual hardware experiments with neuromorphic chips
• Early-stage wetware computing prototypes
• Theoretical work on polyviscous learning

$1B could fund:

• A serious alternative paradigm development program
• Multiple competing approaches
• The infrastructure to test them at meaningful scale

The Scotch and Cigars Era vs. Now

The old AI debates (Searle’s Chinese Room, symbol grounding, etc.) were unfalsifiable philosophy. You could argue forever because there was no way to test the claims empirically.

Transformers changed that. Now we can say:

• “Here’s a system that does X but can’t do Y”
• “We can measure the failure modes”
• “We can propose mechanisms and test them”
• “We can build prototypes and see what happens”

Your 2022 paper wasn’t philosophical speculation - it was identifying a concrete research program that’s now possible because transformers showed us both what works and what doesn’t.

Neuromorphic Computing

 

From YouTube:

Explore a brand new paradigm in computing, and how it might offer faster solutions that can support scientific breakthroughs. [...]

This lecture was recorded at the Ri on 24 January 2025, in partnership with the Embassy of the Kingdom of the Netherlands.

Our brains consume around 20 Watts of power, a negligible amount in comparison to the multi-million Watts consumed by supercomputers. The emerging paradigm of Neuromorphic Computing draws inspiration from the structure and functioning of the human brain, particularly this small power consumption and extremely fast response times.

Join theoretical and computational physicist Johan Mentink as he presents evidence of why the neuromorphic computing paradigms offer not only much more energy-efficient but also much faster solutions to widely used computational science problems, with the potential to break existing computational barriers.

---

Johan Mentink is an expert in theoretical and computational physics, focused on controlling magnetism at the shortest length and time scale. He is recipient of the prestigous Rubicon, VENI and VIDI grants of the Dutch Research Council (NWO). In 2021 he initiated interdisciplinary research to explore the potential of neuromorphic hardware for computational science use cases with SURF and researchers from Radboud University, University of Twente and IBM. He is also chair of the Computational Science NL platform.

Conscious artificial intelligence and biological naturalism

2/ As AI continues to develop, it is natural to ask whether AI systems can be not only intelligent, but also conscious. But is this likely? And what would the consequences be?

— Anil Seth (@anilkseth) December 10, 2024

4/ I first consider why people might think that AI is, or could be, conscious. I show how we can be led astray by built-in biases of anthropocentrism, human exceptionalism, and anthropomorphism. Intelligence and consciousness are not the same thing. pic.twitter.com/EafyFzhQrJ

— Anil Seth (@anilkseth) December 10, 2024

The tweet stream continues through #20. The article:

Anil K. Seth, Conscious artificial intelligence and biological naturalism, PsyArXiv Preprints, 2024-12-10.

Abstract: As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can ibe not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate our selves.

From the article, on non-Turing computation:

Turing computation is powerful, but not every function is Turing-computable. Turing himself identified a class of non-computable functions in his response to the ‘halting problem’ posed by Hilbert (Turing, 1936). Other examples include functions involving continuous variables, stochastic/random elements, and unbounded sensitivity to initial conditions (e.g., deterministic chaos). Digital computers based on Turing machines can simulate and approximate non-computable functions – this happens all the time in computational modelling – but these approximations will generally not be exact.

The limited remit of Turing computation means that systems – including brains – might implement functions that are non-Turing-computational. The idea that mental states (including consciousness) depend on non-computational functions is called non-computational functionalism (Piccinini, 2018, 2020). Non-computational neural functions could include processes relating to (continuous) electromagnetic fields, fine-grained timing relations (only order, not dynamics as such, matters for Turing computation), freely diffusing neurotransmitters, and so on. Non-computational biological functions also include those that necessarily involve a particular material property: examples include digestion, circulation of blood, and metabolism. Note that computational and non-computational functions could co-exist. For example, it could be that some aspects of mind are computational, but not consciousness (Piccinini, 2023).

Many other notions of ‘computation’ have been proposed. Some are narrower than Turing computation (e.g., computation requiring an artefact being used by a person in a particular way), but most are broader (N. G. Anderson & G. Piccinini, 2024; Chalmers, 1996b). Broader forms of computation include analogue, neuromorphic, and mortal computation. I will return to these later. For now, a focus on Turing computation is justified since this kind of computation underlies conventional AI, whether based on artificial neural networks or otherwise.

Mortal computation:

The recent concept of mortal computation is particularly interesting (Hinton, 2022; Ororbia & Friston, 2023). Standard Turing computation is ‘immortal’. Its existence and utility outlast the existence of any specific instance of hardware. This reflects the core computer science principle that software should be separable from hardware both in principle and in practice, so that the same algorithm executed on different hardware gives the same result. But immortal computation is expensive. It requires continual error correction to ensure that 1s remain 1s (and 0s remain 0s). As algorithms and models grow in complexity, the computational and energetic costs of error correction, and therefore of computational immortality, grows quickly.

One implication of this argument is that biological brains, which are highly energy efficient, cannot be implementing immortal computations. If they are implementing computations at all, then these computations are likely to be mortal, which means they cannot be separated from the ‘hardware’ (or ‘wetware’) which implements them. This in turn places constraints on the multiple realisability and substrate flexibility of these (mortal) computations. In particular, the substrate flexibility required for conscious AI is unlikely to hold because (conventional) AI is based on an implementation paradigm which assumes computational immortality. I find this a provocative argument against the plausibility of conscious AI because it is based on limitations arising from within a computational view of mind.

There's much more in the article.

DeepSouth: Brain-scale neuromorphic computing

Here's the press release from the Western Sydney University, where DeepSouth is being constructed.

Domenico Vicinanza writes about DeepSouth at The Conversation.

Sam Altman's Brain Chips

OpenAI signed $51M deal to buy ‘brain’ chips from Sam Altman portfolio firm

Sam Reynolds, Computerworld, Dec, 3, 2023.

A story that got left out of the corporate infighting that led to Sam Altman’s firing, then re-hiring as CEO of OpenAI, was about the firm’s relationship with a startup called Rain Neuromorphics, which is developing a neuromorphic processing unit (NPU) designed to replicate features of the human brain.

Rain says their brain-inspired NPUs could potentially offer 100 times more computing power and, for AI training purposes, deliver up to 10,000 times greater energy efficiency than the GPUs predominantly used by AI developers.

In theory, these NPUs could provide a big boost in processing power for portable “edge” devices such as smartphones or vehicle infotainment devices located far from a data center. Samsung, for instance, says that the Galaxy S24 – its next-generation flagship phone – will be AI-enhanced, but smartphones are limited in their processing abilities by their portable nature.

Altman’s conflict of interest

Before Sam Altman was fired and then rehired at OpenAI, last month, the company had signed a Letter of Intent to buy $51 million worth of Brain’s NPU chips, Wired reported. Complicating the issue further is the fact that this firm is one of Altman’s portfolio companies, bringing up a potential conflict of interest, with the CEO of OpenAI personally investing $1 million in the firm.

On X, VC Jason Calacanis pointed out that Letters of Intent – what OpenAI and Altman drafted to buy chips from Rain – are non-binding making the deal far from confirmed.

There's more at the link.

AI and the future: Are we there yet?

Tyler Cowen has just posted a section from his current Bloomberg column: Why AI will not create unimaginable fortunes. I responded:

Interesting. I wonder what the lifetime is going to be for LLMs? Didn't I just read a tweet stream the suggested it might cost a billion dollars to train one in the not-so-distant future?* That doesn't strike me as being very sustainable. Geoffrey Hinton has speculated that we'll have neuromorphic computers in the future. They'll take much less power: "It'll be used for putting something else: It'll be used for putting something like GPT-3 in your toaster for one dollar, so running on a few watts, you can have a conversation with your toaster." I don't recall if he offered a time horizon, but I don't think so. I do think, though, that he is more or less right about that. So, What comes first: neuromorphic GPT-3 in your toaster or a $10B training regime for GPT-42? What about a neuromorphic gardner?

I think Tyler's right about this: "AI services will enter almost everyone’s workflow and percolate through the entire economy. Everyone will be wealthier, most of all the workers and consumers who use the thing." At least about the percolate. As for wealth, who knows? Of course, "wealth" is a capacious concept. So again, who knows?

Andreessen has speculated that AI will migrate from being a feature bolted onto a product (like Sydney and Bing – soon to be a situation comedy, "The Honeymooners") to being the foundation of products. I think that's right, and speculated in that direction over a decade ago. What about the operating system? But, who wants an AI that someone else owns to control the operating system of their computer? Maybe it'll be one of Hinton's autonomous neuromorphic AIs.

In the end Gary Marcus is surely going to see a robust symbolic component integrated into these so-called foundation models. What's the time course on that going to be?

I mean, in a way, I guess the question I'm posing is something like this: Have we just entered a civilizational singularity, in von Neumann's phrase:

centered on the accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.

To what extent is Tyler's sense of the future, or mine, or yours, or anyone else's, to what extent are our ideas dominated by the pre-singularity world in which we've grown up? If the world is changing fundamentally, how can we possibly guestimate what's coming down the pike? And we're ALL – Andreessen, Sam Altman, Geoffrey Hinton, Eliezer Yudkowsky, the whole kit and caboodle – in the same antique boat.

Unless AI is severely constrained or even wiped out, our successors will be living in a world that is very different from ours. Everything will be changed: business, governance, work and leisure, everything. We’re going to see new institutional forms.

And so forth.

But it’s all limited by how fast humans can change: What about the ability of one generation to raise a cohort whose sense of the world is fundamentally different from theirs? What does generational interaction over the last half-century tell us about that, if anything? An old post, The Demise of Deconstruction, speaks to that in the intellectual sphere. This post looks at generational succerssion in the world of novels, The Direction of Cultural Evolution, Macroanalysis at 3 Quarks Daily.

See also my recent post, What are the 10–20 year prospects for AI? Three paragraphs from the beginning:

My friend in venture capital, Sean O’Sullivan (who was my boss at MapInfo in the ancient days), tells me there are three time-horizons: 3 months, 12 months, and three years. So, in talking about 10-20 years I’m way out over the end of my skis. That’s fine. But let’s begin by looking at those near-term prospects, the ones on which money is ventured – and lost or gained. If we set the clock at November 31, 2022, when ChatGPT was released to the public, then we are over 2/3 of the way into the first time-horizon. What has happened?

WOOSH!!! That’s what.

The public at large is more aware of AI than they have been before. In particular, the number of people who have been able to interact directly with an advanced AI (as opposed to Siri, Alex, and the like) has gone up dramatically, though, with more than 30 million users world-wide, it would still be less than 10% of the population of the United States. And that’s a lot.

Ever onward.

---

* Regarding the high cost of compute for training, here we go:

Regarding parameter counts growth, the industry is already reaching the limits for current hardware with dense models—a 1 trillion parameter model costs ~$300 million to train. With 100,000 A100s across 12,500 HGX / DGX systems, this would take about ~3 months to train. This is certainly within the realm of feasibility with current hardware for the largest tech companies. The cluster hardware costs would be a few billion dollars, which fits within the datacenter Capex budgets of titans like Meta, Microsoft, Amazon, Oracle, Google, Baidu, Tencent, and Alibaba.

Another order of magnitude scaling would take us to 10 trillion parameters. The training costs using hourly rates would scale to ~$30 billion. Even with 1 million A100s across 125,000 HGX / DGX systems, training this model would take over two years. Accelerator systems and networking alone would exceed the power generated by a nuclear reactor. If the goal were to train this model in ~3 months, the total server Capex required for this system would be hundreds of billions of dollars with current hardware.

This is not practical, and it is also likely that models cannot scale to this scale, given current error rates and quantization estimates.

The practical limit for a Chinchilla optimally trained dense transformer with current hardware is between ~1 trillion and ~10 trillion parameters for compute costs. With future reports, we will discuss this band more for both dense vs. sparse models and the cost competitiveness of Google’s TPUv4, TPUv5, Nvidia A100, H100, and AMD MI300. Data is another problem that we can cover in the future.

Thoughts on the implications of GPT-3, two years ago and NOW [here be dragons, we're swimming, flying and talking with them]

When GPT-3 first came out, I registered my first reactions in a comment at Marginal Revolution, which I've appended immediately below the picture of Gojochan and Sparkychan. I'm currently completing a working paper about my interaction with ChatGPT. That will end with an appendix in which I repeat my remarks from two years ago and append some new ones. I've appended those after the comment to Marginal Revolution.

* * * * *

A bit revised from a comment I made at Marginal Revolution:

Yes, GPT-3 [may] be a game changer. But to get there from here we need to rethink a lot of things. And where that's going (that is, where I think it best should go) is more than I can do in a comment.

Right now, we're doing it wrong, headed in the wrong direction. AGI, a really good one, isn't going to be what we're imagining it to be, e.g. the Star Trek computer.

Think AI as platform, not feature (Andreessen). Obvious implication, the basic computer will be an AI-as-platform. Every human will get their own as an very young child. They're grow with it; it'll grow with them. The child will care for it as with a pet. Hence we have ethical obligations to them. As the child grows, so does the pet – the pet will likely have to migrate to other physical platforms from time to time.

Machine learning was the key breakthrough. Rodney Brooks' Gengis, with its subsumption architecture, was a key development as well, for it was directed at robots moving about in the world. FWIW Brooks has teamed up with Gary Marcus and they think we need to add some old school symbolic computing into the mix. I think they're right.

Machines, however, have a hard time learning the natural world as humans do. We're born primed to deal with that world with millions of years of evolutionary history behind us. Machines, alas, are a blank slate.

The native environment for computers is, of course, the computational environment. That's where to apply machine learning. Note that writing code is one of GPT-3's skills.

So, the AGI of the future, let's call it GPT-42, will be looking in two directions, toward the world of computers and toward the human world. It will be learning in both, but in different styles and to different ends. In its interaction with other artificial computational entities GPT-42 is in its native milieu. In its interaction with us, well, we'll necessarily be in the driver's seat.

Where are we with respect to the hockey stick growth curve? For the last 3/4 quarters of a century, since the end of WWII, we've been moving horizontally, along a plateau, developing tech. GPT-3 is one signal that we've reached the toe of the next curve. But to move up the curve, as I've said, we have to rethink the whole shebang.

We're IN the Singularity. Here be dragons.

[Superintelligent computers emerging out of the FOOM is bullshit.]

* * * * *

ADDENDUM: A friend of mine, David Porush, has reminded me that Neal Stephenson has written of such a tutor in The Diamond Age: Or, A Young Lady's Illustrated Primer (1995). I then remembered that I have played the role of such a tutor in real life, The Freedoniad: A Tale of Epic Adventure in which Two BFFs Travel the Universe and End up in Dunkirk, New York.

* * * * *

To the future and beyond!

I stand by those remarks from two years ago, but I want to comment on four things: 1) AI alignment, 2) the need for symbolic computing, 3) the need for new kinds of hardware, and 4) a future world in which humans and AIs interact freely.

Considerable effort has gone into tuning ChatGPT so that it won’t say things that are offensive (e.g. racial slurs) or give out dangerous information (e.g. how to hotwire cars). These efforts have not been entirely successful. This is one aspect of what is now being called “AI alignment.” In the extreme the field of AI alignment is oriented toward the possibility – which some see as a certainty – that in the future (somewhere between, say, 30 and 130 years) rogue AIs will wage a successful battle against humankind.[1] I don’t think that fear is very creditable, but, as the rollout of ChatGPT makes abundantly clear, AIs built on deep learning are unpredictable and even, in some measure, uncontrollable.

I think the problem is inherent in deep learning technology. Its job is to fit a model to, in the case of ChatGPT, an extremely large corpus of writing, much of the internet. That corpus, in turn, is ultimately about the world. The world is vast, irregular, and messy. That messiness is amplified by the messiness inherent in the human brain/mind, which did, after all, evolve to fit that world. Any AI engine capable of capturing a significant portion of the order inherent in our collective writing about the world has no choice but to encounter and incorporate some of the disorder and clutter into its model as well.

I regard such Foundation models[2], as they have come to be called, as wilderness preserves, digital wilderness. They contain what digital humanist Ted Underwood calls the latent space of culture. He says:

The immediate value of these models is often not to mimic individual language understanding, but to represent specific cultural practices (like styles or expository templates) so they can be studied and creatively remixed. This may be disappointing for disciplines that aspire to model general intelligence. But for historians and artists, cultural specificity is not disappointing. Intelligence only starts to interest us after it mixes with time to become a biased, limited pattern of collective life. Models of culture are exactly what we need.

In his penultimate paragraph Underwood notes:

I have suggested that approaching neural models as models of culture rather than intelligence or individual language use gives us even more reason to worry. But it also gives us more reason to hope. It is not entirely clear what we plan to gain by modeling intelligence, since we already have more than seven billion intelligences on the planet. By contrast, it’s easy to see how exploring spaces of possibility implied by the human past could support a more reflective and more adventurous approach to our future. I can imagine a world where generative models of culture are used grotesquely or locked down as IP for Netflix. But I can also imagine a world where fan communities use them to remix plot tropes and gender norms, making “mass culture” a more self-conscious, various, and participatory phenomenon than the twentieth century usually allowed it to become.

These digital wildness regions thus represent opportunities for discovery and elaboration. Alignment is simply one aspect of that process.

And by alignment I mean more than aligning the AI’s values with human values; I mean aligning its conceptual structure as well. That’s where “old school” symbolic computing enters the picture, especially language. Language  – not the mere word forms available in digital corpora, but word forms plus semantics and syntactic affordances –  is one of the chief ‘tools’ through which young humans are acculturated and through which human communities maintain their beliefs and practices. The full powers of language, as treated by classical symbolic systems, will be essential for “domesticating” the digital wilderness and developing it for human use.

However, this presents technical problems, problems I cannot go into here in any detail.[4] The basic issue is that symbolic computing involves one strategy for implementing, call it cogitation, in a physical system while the neural computing underlying deep learning requires a different physical implementation. These approaches are incompatible. While one can “bolt” a symbolic system onto a neural computing system, that strikes me as no more than an interim solution. It will get us started, indeed the work has already begun.[5]

What we want, though, is for the symbolic system to arise from the neural system, organically, as it does in humans.[6] This may well call for fundamentally new physical platforms for computing, platforms based on “neuromorphic” components that are “grown,” as Geoffrey Hinton has recently remarked.[7] That technology will give us a whole new world, one where humans, AIs and robots interact freely with one another, but will have communities of their own as well. We know that dogs co-evolved with humans over tens of thousand of years. These miraculous new devices will co-evolve with us over the coming decades and centuries.

Let us end with Miranda’s words from Shakespeare’s The Tempest:

“Oh wonder!
How many goodly creatures are there here!
How beauteous mankind is! Oh brave new world,
That has such [devices] in’t.”

* * * * *

[1] The virtual center of this belief is a website called LessWrong, which has extensive discussion of this issue going back well over a decade. Here it is, https://www.lesswrong.com/.

[2] Foundation models, Wikipedia, https://en.wikipedia.org/wiki/Foundation_models.

[3] Ted Underwood, Mapping the latent spaces of culture, The Stone and the Shell, Oct. 21, 2021, https://tedunderwood.com/2021/10/21/latent-spaces-of-culture/.

[4] I discuss this issue in this blog post, Physical constraints on computing, process and memory, Part 1 [LeCun], New Savanna, July 24, 2022, https://new-savanna.blogspot.com/2022/07/physical-constraints-on-computing.html.

[5] Consult the Wikipedia entry, Neuro-symbolic AI, for some pointers, https://en.wikipedia.org/wiki/Neuro-symbolic_AI.

[6] I discuss this in a recent working paper, Relational Nets Over Attractors, A Primer: Part 1, Design for a Mind, Version 2, Working Paper, July 13, 2022, pp. 76, https://www.academia.edu/81911617/Relational_Nets_Over_Attractors_A_Primer_Part_1_Design_for_a_Mind.

[7] Tiernan Ray, We will see a completely new type of computer, says AI pioneer Geoff Hinton, ZDNET, December 1, 2022, https://www.zdnet.com/article/we-will-see-a-completely-new-type-of-computer-says-ai-pioneer-geoff-hinton-mortal-computation/#ftag=COS-05-10aaa0j.

Geoffrey Hinton predicts the evolution of "neuromorphic" computers that are "mortal"

We will see a completely new type of computer, says AI pioneer Geoff Hinton https://t.co/4TffDqbQyZ via @ZDNET & @TiernanRayTech

— Bill Benzon, BAM! Bootstrapping Artificial Minds (@bbenzon) December 27, 2022

From the article:

Future computer systems, said Hinton, will be take a different approach: they will be "neuromorphic," and they will be "mortal," meaning that every computer will be a close bond of the software that represents neural nets with hardware that is messy, in the sense of having analog rather than digital elements, which can incorporate elements of uncertainty and can develop over time. 

"Now, the alternative to that, which computer scientists really don't like because it's attacking one of their foundational principles, is to say we're going to give up on the separation of hardware and software," explained Hinton. 

"We're going to do what I call mortal computation, where the knowledge that the system has learned and the hardware, are inseparable."

These mortal computers could be "grown," he said, getting rid of expensive chip fabrication plants.

"If we do that, we can use very low power analog computation, you can have trillion way parallelism using things like memristors for the weights," he said, referring to a decades-old kind of experimental chip that is based on non-linear circuit elements. 

"And also you could grow hardware without knowing the precise quality of the exact behavior of different bits of the hardware."

The new mortal computers won't replace traditional digital computers, Hilton told the NeurIPS crowd. "It won't be the computer that is in charge of your bank account and knows exactly how much money you've got," said Hinton.

"It'll be used for putting something else: It'll be used for putting something like GPT-3 in your toaster for one dollar, so running on a few watts, you can have a conversation with your toaster."*

I have a number of posts that speak to this, for example, The structured physical system hypothesis (SPSH), Polyviscous connectivity [The brain as a physical system], one of the various posts tagged with the label "polyviscous". 

*Umm, err...You're kidding, right? Who'd want to chat with their toaster? Now, your wristwatch, that's something else.

Neuromorphic chips and spiking neurons

"Computational neuroscientists think that spiking neural networks will bring them closer to understanding the brain than is possible with current deep neural networks."

Read our newsletter feature Spiking Neural Networks, from @anilananth.https://t.co/SQGfedesib pic.twitter.com/K4YO4kViEN

— Simons Institute for the Theory of Computing (@SimonsInstitute) December 18, 2021

From the linked article, "Spiking Neural Networks":

Artificial intelligence researchers, on the other hand, would like to build deep neural networks that have both the brain’s remarkable abilities and its extraordinary energy efficiency. The brain consumes only about 20 watts of power. If the brain achieves its ends partly because of spiking neurons, some think that energy-efficient deep artificial neural networks (ANNs) would also need to follow suit.

But spiking neural networks have been hamstrung. The very thing that made them attractive — communicating via spikes — also made them extremely difficult to train. The algorithms that ran on IBM’s chip, for instance, had to be trained offline at considerable computational cost.

That’s set to change. Researchers have developed new algorithms to train spiking neural networks that overcome some of the earlier limitations. And at least for networks of tens of thousands of neurons, these SNNs perform as well as regular ANNs. Such networks would likely be better at processing data that has a temporal dimension (such as speech and videos) compared with standard ANNs. Also, when implemented on neuromorphic chips, SNNs promise to open up a new era of low-energy, always-on devices that can operate without access to services in the cloud.

One of the key differences between a standard ANN and a spiking neural network is the model of the neuron itself. Any artificial neuron is a simplified computational model of biological neurons. A biological neuron receives inputs into the cell body via its dendrites; and based on some internal computation, the neuron may generate an output in the form of a spike on its axon, which then serves as an input to other neurons. Standard ANNs use a model of the neuron in which the information is encoded in the firing rate of the neuron. So the function that transforms the inputs into an output is often a continuous valued function that represents the spiking rate. This is achieved by first taking a weighted sum of all the inputs and then passing the sum through an activation function. For example, a sigmoid turns the weighted sum into a real value between 0 and 1.

In a spiking artificial neuron, on the other hand, the information is encoded in both the timing of the output spike and the spiking rate. The most commonly used model of such a spiking neuron in artificial neural networks is called the leaky integrate-and-fire (LIF) neuron. Input spikes cause the neuron’s membrane potential — the electrical charge across the neuron’s cell wall — to build up. There are also processes that cause this charge to leak; in the absence of input spikes, any built-up membrane potential starts to decay. But if enough input spikes come within a certain time window, then the membrane potential crosses a threshold, at which point the neuron fires an output spike. The membrane potential resets to its base value. Variations on this theme of an LIF neuron form the basic computational units of spiking neural networks.

In 1997, Wolfgang Maass of the Institute of Theoretical Computer Science, Technische Universität, Graz, Austria, showed that such SNNs are computationally more powerful, in terms of the number of neurons needed for some task, than ANNs with rate-coding neurons that use a sigmoid activation function. He also showed that SNNs and ANNs are equivalent in their ability to compute some function (an important equivalence, since an ANN’s claim to fame is that it is a universal function approximator: given some input, an ANN can be trained to approximate any function to transform the input into a desired output).

There's much more at the link, much of it having to do with how to modify backpropagation so it works and can scale with SNNs.

Conclusion:

All this bodes well for the day when spiking neural networks can be implemented on the numerous neuromorphic chips that are in development. The hope is that such networks can be both trained and deployed using dedicated hardware that sips rather than sucks energy.

Neuromorphic AI

Demis Hassabis, Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick. Neuroscience-Inspired Artificial Intelligence. Neuron, Volume 95, Issue 2, p245–258, 19 July 2017.

DOI: http://dx.doi.org/10.1016/j.neuron.2017.06.011

Summary:  

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.

Conclusions:

In this perspective, we have reviewed some of the many ways in which neuroscience has made fundamental contributions to advancing AI research, and argued for its increasingly important relevance. In strategizing for the future exchange between the two fields, it is important to appreciate that the past contributions of neuroscience to AI have rarely involved a simple transfer of full-fledged solutions that could be directly re-implemented in machines. Rather, neuroscience has typically been useful in a subtler way, stimulating algorithmic-level questions about facets of animal learning and intelligence of interest to AI researchers and providing initial leads toward relevant mechanisms. As such, our view is that leveraging insights gained from neuroscience research will expedite progress in AI research, and this will be most effective if AI researchers actively initiate collaborations with neuroscientists to highlight key questions that could be addressed by empirical work.

The successful transfer of insights gained from neuroscience to the development of AI algorithms is critically dependent on the interaction between researchers working in both these fields, with insights often developing through a continual handing back and forth of ideas between fields. In the future, we hope that greater collaboration between researchers in neuroscience and AI, and the identification of a common language between the two fields (Marblestone et al., 2016), will permit a virtuous circle whereby research is accelerated through shared theoretical insights and common empirical advances. We believe that the quest to develop AI will ultimately also lead to a better understanding of our own minds and thought processes. Distilling intelligence into an algorithmic construct and comparing it to the human brain might yield insights into some of the deepest and the most enduring mysteries of the mind, such as the nature of creativity, dreams, and perhaps one day, even consciousness.

IBM's Neuromorphic processor chip

From MIT Tech Review:

IBM’s SyNapse chip, as it is called, processes information using a network of just over one million “neurons,” which communicate with one another using electrical spikes—as actual neurons do. The chip uses the same basic components as today’s commercial chips—silicon transistors. But its transistors are configured to mimic the behavior of both neurons and the connections—synapses—between them.

The new chip is not yet a product, but it is powerful enough to work on real-world problems. In a demonstration at IBM’s Almaden research center, MIT Technology Review saw one recognize cars, people, and bicycles in video of a road intersection. A nearby laptop that had been programed to do the same task processed the footage 100 times slower than real time, and it consumed 100,000 times the power as the IBM chip. IBM researchers are now experimenting with connecting multiple SyNapse chips together, and they hope to build a supercomputer using thousands.

The power usage is significant. Human brain tissue may use more power per unit volume than any other tissue in the body, but it's still very low-powered compared to digital computers.

The efficiency of conventional computers is limited because they store data and program instructions in a block of memory that’s separate from the processor that carries out instructions. As the processor works through its instructions in a linear sequence, it has to constantly shuttle information back and forth from the memory store—a bottleneck that slows things down and wastes energy.

IBM’s new chip doesn’t have separate memory and processing blocks, because its neurons and synapses intertwine the two functions. And it doesn’t work on data in a linear sequence of operations; individual neurons simply fire when the spikes they receive from other neurons cause them to.

In his last book, The Computer and the Brain, John von Neumann speculated that each neuron was both a processing unit and memory.