For large corporates and other entities of any size, the threat of the core of your infrastructure getting suddenly disabled because of something like this is going to be untenable. I predict the pressures for on-prem, offline access (whether by licensing weights or getting them in a restricted setting like TEE/CC) will be overwhelming and one the players will fill the need.
Thinking that on prem models will be a halfway decent solution against what can be served out of a data center is a fools take... One that is more common than it should be on here...
The point is not to be as good as the multi-trillion parameter model you can host in across 72 GPUs (or whatever).
I'm running a 248B model on a paltry amount of hardware and getting plenty of good use out of it.
Sure, the most demanding tasks will demand the best models (and always will). There's still less demanding tasks for other models.
I think some people are fooling themselves that coding of all tasks is always going to requires the biggest models ever. Again, maybe some coding tasks will, but the majority of business CRUD apps probably don't. Same goes for virtually any other type of task. The biggest models are really only useful for the most complex tasks.
If you wouldn't mind, could you explain a bit what the 248B model is good for, and where it breaks down and you need something better? I hear this take often, but it is always a fleeting remark so I have no idea what the 'useful' looks like - at all.
To answer this and my sibling, it's DeepSeek V4 Flash at native FP4 quantization, on two Nvidia DGX Sparks. Which is a bit of kit but still paltry relative to the data centre. ~40 TPS generation, ~2000 TPS prompt processing, which makes it feel approximately as fast as typical APIs.
I primarily use it with my own harness for coding. I'm not going to say it will compete with Opus in the most challenging domains, because it won't, but I will say that there's a reasonable likelihood that Opus is used for tasks that a model like Flash could comfortably handle at 1/100th the cost.
So far I've only seen it struggle at tasks that I myself would struggle with. Tasks that I can describe the shape of the solution for, it has a high success rate at implementing.
Useful is going to be different for everyone. I'm not working on the hardest problems, I don't need the best models.
In my experience they require much more hand holding and more specific directions with less possibilities to interpret a command in several ways. You do the planning, keep on eye on that they're producing and they do the legwork. It's not that their knowledge of Java or PHP or what have you is lacking, it's the long horizon planning that you have to do yourself. Technically they're good. You just have to do more thinking and more reviewing yourself. YMMV.
Depending on quantization I figure they need at least a p4 and likely a p5 EC2 (or similar instance in another provider) for a model with that many parameters. Maybe they are hosting on bare metal but I imagine not. Those instance types (assuming not using spot) are quite expensive to run.
It’s perfectly reasonable to believe that a law of marginal decreasing returns will kick in at some point (if it hasn’t already), and that what one point looked like an exponential may start looking like an s-curve.
I do not see how being experienced in engineering, or having higher studies in computer science and economics should make that view less common.
Some will take greater risks and win (or lose); others will play it safer and slowly accumulate wins (or be obsoleted).
Never mind the threat of letting these models write code that runs your business, or operate it agentically. Models trained by actors (corporate or nationstate) diametrically opposed to your interests.
Lots to take into account now, interesting time to be in business.
Or abstract i.e. openrouter, that reduces the risk vector to "all implementations have been simultaneously banned".
If a government entity bans a LLM provider due to a jailbreak concern, they can also ban an on-prem solution under the same guise. The jailbreak risk exists regardless of where it's hosted. You could defensibly argue the on-prem risk is higher since frontier model companies can justify safety spend due to their size, it's more difficult to combat bad actors if you're company is the only one using the model and you don't have economies of scale.
This is ignoring the fact that the government is the foundation of society (I know some will disagree with that, but the end result is just government with more steps).
Private models in a low trust society means the government will come and seize the models. Competitive business will only be allowed through cronyism.
The better option is to opt for high trust. Yes the Gman can rip your servers apart, but they know they'll face consequences, legal and political. Laws and regulations are the answer, not locking down into smaller fiefdoms.
You get high trust through social norms, not by more "laws and regulations". Social norms can't be imposed by fiat, they arise spontaneously, often for unclear reasons. That's why they're so fragile and precious. With Trump's destruction of social norms around the presidency and the federal government generally, the US is now just another country where bribery is the cost of doing business.
This is precisely why I expect that Chinese open models are going to win in the long run. The capability difference isn't dramatic in the grand scheme of things, but the fact that you can run your own is a huge selling point. Even if you rent an open model from a Chinese company, you can switch to on prem if they decided to yank access or change terms in the way you don't like. It might be a pain, but it wouldn't be existential. On the other hand, if you become dependent on a closed model and it gets yanked then you're in a world of hurt.
And infrastructure dominance is really the big picture here. Chinese models are going to become the standard setters because they're going to be what people are using. That means more research, more tooling, and a whole ecosystem developing around them.
After this action, I have no doubt that this administration will try to ban Chinese models. Of course, doing so will be futile, we'll figure out ways to get around it, but now I'm pretty sure they're going to try.
It is almost certain that the CCP will impose constraints on access to their models at some point too. But Trump is doing it to extort cash from Anthropic, and China will be doing it to leverage political and economic concessions.
Remember that there are degrees of banning. Slower tokens, dumber models, token caps, KYC for each model consumer, hurting specific companies that are not capitulating in a deal with a Chinese company, etc.
A big difference with open models is that anybody can run and tune them any way they like. The real difference in philosophy is that Americans companies treat the model as the product, while Chinese companies see models at infrastructure you build products on top of. You amortize the cost of deploying it at scale by sharing knowledge and iterating quickly to bring the cost down.
I see absolutely no reason why CPC would choose to kneecap themselves the way the USG just did. Keeping open access to the models means that the whole world will be using Chinese based AI stack going forward. Only a government run by absolute imbeciles would do what the US did.
Even if everyone uses Chinese open weight models at somw point, how do you make money creating them?
This is just typical Chinese behavior. Flood the market with cheap or free stuff and wait for your competitors to die off. Then you have a monopoly. (Maybe you were implying that would happen, dunno)
I mean you could ask the exact same question about other foundational tech like Linux. And the real question here is what stops Americans companies from producing things cheaply and at scale the way Chinese companies do. You frame it as some nefarious tactic, but the reality is that they're just more efficient and American companies are unable to compete with that.
I'm waiting for that to happen as well since the price difference makes it very difficult for companies like Anthropic and OpenAI to compete. And we already have precedent for this with stuff like EVs, phones, and so on. As soon as Chinese companies start making a product that's more popular, they get banned on some national security pretext.
The tricky part with banning Chinese models is that they're open. It'll be easy to ban access to service providers, but preventing people from running these models on prem is going to be really tough. Like are they going to go after Cursor for example given that their model is based on Kimi?
I very much agree it's going to be a futile endeavour in the end. It kind of reminds me of the time Microsoft tried to get Linux and open source banned when Linux started encroaching on Windows server market. This is going to end the same way.
I'm going to guess they'll go after sites like Huggingface that host downloads. I suspect we'll be torrenting Chinese models in the not-too-distant future. Or we'll have to geo-spoof with VPN to download from other countries.
Great point. That is what all the Fortune 500 CEO's are frothing at the mouth about. Having LLM's replace their payroll. So yeah, they deserve to fail.
Personally knowing four people with GBM and not being an oncologist is exceptional and worrying. That feels like it should be raised to… someone. It is very possible something bad is happening and a commonality needs to be tracked down urgently.
It is indeed very strange, none of these people lived in the same place or got diagnosed at the same time (some 20 years ago). Most are fairly remote to me though... Like : a good friend's mom, or my wife's cousin's husband, etc.
I knew two people (one adult, one child) who lived in the same building and got diagnosed with leukemia (~14 per 100,000) shortly after each other, maybe within a few weeks.
It's anecdotal, of course, but I've always thought that there could have been a connection.
It could be birthday paradox, but it could potentially be enough to get someone to come check for contaminants.
Looks like benzene, some pesticides, and formaldehyde are the common workplace exposures that can trigger leukemia. But some of those can turn up near housing.
> benzene, some pesticides, and formaldehyde are the common workplace exposures that can trigger leukemia.
That rings a bell. I remember that someone mentioned a recent repainting of the building. The incident happened at least a decade ago, so I can't remember all details.
I just finished Silent Spring last fall and was shocked to learn they already knew DDT and other pesticides were causing leukemia back in 1960. I guess PR from the chemical companies is working, because I would have guessed 1979-1985.
We have a gaussian splat component (same format that it looks World lab uses) for A-Frame that should work in VR on Quest and Vision Pro in their respective Web browsers. Quest FPS might not be ideal yet
I was searching mostly under the News section of google although this seems to just be a Tiktok feature "restricted mode" that Tiktok may have accidentally enabled for some users?
Fun story I heard recently: apparently a bunch of pigs were placed on various small islands in the pacific in the 19th century so in case sailors were running out of food, they would have a known self-sustaining backup option.
Many of those pigs were completely isolated for over a hundred years and entirely missed out on the globalization of tons of infectious diseases.
A while ago a group of them were picked up and brought to a special protected refuge in New Zealand, where they are being used for artificial organ research (https://nzeno.nz/) basically for the reasons you highlight.
Am I just crazy? Do others not wonder that if pigs and humans have remarkably similar biology, then this increases the chance that pigs and humans might also have similar mental features? (I'm not claiming we're equally intelligent, just that it's well known that pigs are [quite intelligent](https://en.wikipedia.org/wiki/Pig#Behavior):
> Pigs are highly intelligent animals,[64] on par with dogs,[65] and according to David DiSalvo's writing in Forbes, they are "widely considered the smartest domesticated animal in the world. Pigs have demonstrated the ability to move a cursor on a video screen with their snouts and understand what is happening onscreen, and have learned to distinguish between the scribbles they had seen before and those they were seeing for the first time."[66][a][70]
But forget about the capacity to process information. How do we expect to improve as a society, in terms of treating other humans in a non-disposable fashion, if we regularly treat animals in a disposable fashion, given that a standard tactic in feeling okay about mistreating humans involves reducing ("de-anthropomorphizing") humans (via comparisons, propaganda, statistics, etc.) to be like "other animals"?
Suppose aliens were to visit us one day, and sci-fi-magically were to give certain animals a voice and a say, and those animals advocated for giving humans what they got from them: putting humans on little islands, where they multiply, and then a few years later, animals/aliens/etc. come by to use them as transplant sources. Clearly, the chances of that happening are next to zero. However, equally clearly, if this were to happen, the fact that humanity is reduced to such a state would be horrific. Yet the animals are giving us tit-for-tat: does it only become horrific for party A, when party B is able to do the same in return?
Damn. My heart goes out to those pigs. Sleep peacefully, this world isn't a nice place anyway.
You should find a camp to join that can help coordinate tickets. (Whether Directed Group Sale or just having the network for overflow tickets.) Unless you’re prepared to spend $$$ and suffer through figuring out a lot to truly DIY it, it’ll be a much better experience to be part of a camp that knows what it is doing anyway.
Right, if our best regular conductors (used in your ohmmeter) are ~10^-8 and superconductivity is (by convention) less than 10^-11, one can see right away the simple regular methods won’t work and some cleverness is needed.
The conductors of your ohmmeter are not that important, though. You can work around that by using four-terminal sensing, and you can of course also calibrate your probes by directly touching them together. Even if your ohmmeter conductors have a resistance of several ohm, you could still get an accurate measurement if your tool has a high enough resolution.
A bigger issue is going to be sample size. A 1mm-diameter 1mm-long rod of silver has a resistance of about 20 μΩ (or 2e-5) at room temperature. That's already getting tricky to measure with lab-grade equipment without pushing insane currents through it, let alone anything even smaller. If you want to measure a 1m-diameter 1m-long silver rod (which would be 0.02μΩ or 2e-8) you could just push a few thousand amps through it and reliably measure that using a household multimeter in the mV range - but do that with a small sample and it'll evaporate.
> Even if your ohmmeter conductors have a resistance of several ohm, you could still get an accurate measurement if your tool has a high enough resolution.
Not that low in range though, you will end up seeing thermal noise that dwarfs your measurement.
> superconductivity is (by convention) less than 10^-11,
Ah, so you're saying that superconductivity is not actual zero resistance, but something close to it, and in fact only a factor of 1000x less resistive than the best conductor?
If that is so, this is something that I had previously thought would make a lot more sense to me.
But in that case it's not intuitive to me how SMES is possible with a 0% discharge rate. Shouldn't a significant fraction of the electrons looping around the coils be lost after many loops? (I know very little about electricity, as you can probably tell, never mind superconductors).
After running into these issues a few others and I wrote a typescript agent framework that I think significantly improves on LangChain in many ways: https://github.com/sciencecorp/buildabot/
It’s still very early days for software composing AI models and we almost certainly don’t have all the right metaphors yet. And I think there is a lot to be said for strong typing and simple, robust code!
I've played with langchain now for a couple weeks (with some of the llama-derivative local models and Oobadooba's native & openai apis + TextGen https://python.langchain.com/docs/modules/model_io/models/ll... ) and find it not-too-insanely-hard for an idiot like myself to figure out, though I'm just experimenting at this point with different models, esp. using tools, etc. I've found that some of the recommended prompts in the demos that, while perhaps working well with chatgpt/gpt4, need a lot of tweaking to work with with say WizardLM. But then I can get them working, so that's kinda neat.
I also played with huggingface's transformer agent (https://huggingface.co/docs/transformers/transformers_agents ) and thought it was a lot easier to useas far as the tools go, though is perhaps less capable for other things. I may go back to playing with that actually.
Interestingly, this is not a new result; people have been doing stuff like this since at least the 90s, most notably Steve Potter at GA Tech and Tom DeMarse in Florida.[1][2] (I built a shitty counterstrike aimbot using a cultured neural network in college based on their papers.)
There was a lot of coverage back in 2004 when DeMarse hooked it up to a flight simulator and claimed it was flying an F-22 [3] (lol, but I don't blame him too much...)
The basic idea is that if you culture neurons on an electrode array (not that hard) you can pick some electrodes to be "inputs" and some to be "outputs" and then when you stimulate both ends the cells wire together more or less according to Hebb's rule[4] and can learn fairly complex nonlinear mappings.
On the other hand, these cultures have essentially no advantage over digital computers and modern machine learning models. Once you get through the initial cool factor, you realize it's a pain to keep the culture perfectly sterile, fed, supplied with the right gases, among many other practical problems, for a model which is just much less powerful, introspectable, and debuggable than is possible on digital computers.
Once AI gets over the initial cool factor that humans are wet-tech, they'll realize it's a pain to keep the human culture perfectly sterile, fed, supplied with the right gases, among many other practical problems, only for a model which is just much less powerful, introspectable, and debuggable than is possible on digital computers.
I was with you up to “once you get over the cool factor.” It seems impossible to get over how cool it is to have a minibrain playing video games. Having one of those at home must really impress the girls.
"played video games" is overstatement. There was a slight increase in the performance with the particular setup that they used. It was not as straightforward as it sounds. This kind of science is still in its infancy
One of the early CorticalLabs founders here. This is like dissing AlphaZero because "This is not a new result; computers have been playing chess since the 50s!". We are standing, as always, on the shoulders of giants. Steve Potter is one of our advisors.
We've improved on every axis 10x. We process over 1000 signal channels in real-time and respond with sub-millisecond latency from our simulated environment. We've recorded thousands of hours of play time from mouse and human neurons. We're investigating biological learning with top neuroscientists from around the globe. This is by far the most rigorous, extensive and technologically advanced work on in-vitro learning ever produced.
Our work goes well beyond Hebbian, "fire together, wire together", We have follow up papers in the pipeline that study internal non-linear dynamics and show how whole-network dynamics changes during game play and learning. Being able to observe and measure cognition has huge applications to drug testing and discovery.
For background, frisco (the above commenter) helped start NeuralLink. Consider this, our DishBrain is a completely reproducible, highly controlled test bed for brain computer interfaces. This will massively accelerate neural interface development, all without sacrificing any chimpanzees.
> On the other hand, these cultures have essentially no advantage over digital computers and modern machine learning models
The brain is the single existing example of general intelligence. A human brain can do more computation than our largest super computers with 20W of power (a million times more efficient). Trillions of interacting synaptic circuits, rewiring themselves on the molecular level. Biological learning is the only game in town, honed by a eons of evolution. There are fundamental physical limits to hot slabs of silicon. Do you have a single credible proposal for building such a machine that isn't growing one?
> (I built a shitty counterstrike aimbot using a cultured neural network in college based on their papers.)
Nice humble brag. I trained neural networks from my bedroom in highschool in 2002. There is a long road between a cool university project and building a world class neuroscience R&D company, you know that!
CoriticalLabs is always open to collaborations. We're here to talk when you want
to integrate some of our cutting-edge neuroscience technology with your work.
Instead grumbling about the 90's, let's look forward to what neuroscience looks like in the 2030's
> The brain is the single existing example of general intelligence.
This is incorrect. It is not pedantic to point out that we have never interacted with a "brain" in isolation: the human brain is an organ of the human organism. The human being is the single existing example of general intelligence.
> let's look forward to what neuroscience looks like in the 2030's
This is very interesting science without question. Are there existing ethical and moral frameworks guiding the development of your field?
All I’m saying is that I think it will be challenging to produce a commercial product that achieves product-market fit for an application other than basic neuroscience research. It’s a cool tool but the practical drawbacks are myriad, and when you say “the brain is the single existing example of general intelligence,” that’s true of the whole thing, with glial ion buffering, ephaptic coupling, global oscillations, and so much more. We should be honest here: the system being studied in DishBrain is very far removed from that, so it’s tough to use the existence proof like you are doing.
I hope I don’t come across as uncivil, but you guys alienated a lot of people both in how you talked about “sentience” and also seemed to heavily hype this as totally novel.
I would never root against cool progress in neural engineering, but I would be curious as to what you think your first big product will be based on this. Past attempts have usually ended up pivoting to stuff like artificial noses.
Edit: I tried to ignore it but the bad faith attack on neuralink, which, look, I have complicated feelings about too — you should know the animal use data in the press is extremely out of context (to the point of simply being wrong) and also neuralink has had zero chimpanzees in its entire history.
> “the brain is the single existing example of general intelligence,” that’s true of the whole thing, with glial ion buffering, ephaptic coupling, global oscillations, and so much more. We should be honest here: the system being studied in DishBrain is very far removed from that, so it’s tough to use the existence proof like you are doing.
Our vision is incredibly ambitious. We can't build a whole brain yet, only small 2D fragments. We have a roadmap that goes all the way to a complete synthetic biological intelligence. The short and medium term milestones are concrete, achievable and valuable. The long term goals are more speculative, we're clear about that. It's a path, a tightrope, but still a path.
> [...] but you guys alienated a lot of people both in how you talked about “sentience” and also seemed to heavily hype this as totally novel.
We clearly defined our terms, our paper was accepted via a long peer review process into a prestigious academic journal. We coauthored with multiple top neuroscientists from around the world. Our discussion section alone has more citations than
most entire papers. If scientists are "alienated" by this, it's a grievance that we cannot remedy.
Our work was hyped, we hyped it, it deserves to be hyped. Can you cite an example in our own words where we claim our work is totally novel?
> I tried to ignore it but the bad faith attack on neuralink, which, look, I have complicated feelings about too — you should know the animal use data in the press is extremely out of context (to the point of simply being wrong) and also neuralink has had zero chimpanzees in its entire history.
Please accept my apologies; it was meant to be more collaborative. I really do think that our system could be used to reduce the need for animal sacrifices and this is a good thing. I also believe you take making animal sacrifices seriously.
Unfortunately I can’t share specifics about Neuralink. But the general points I will make are:
- in this field, monkeys are high value animals and experimenters will often work with the same ones for many years; they are not, generally speaking, a high throughput model.
- to the extent a company does need to go through a large number of animals for a study, the way this works is you start by figuring out all of the problems you might be worried about, and choosing some rarity threshold to verify absence of (safety against), and then animal numbers are derived from the power calculation. For example, to rule out a potential complication to no more than 1% of patients with 95% confidence… you need a lot of animals, especially considering multiple study arms. This is the values tradeoff we as a society have chosen to make and empower our regulators to enforce. There is often a negotiation for the least controversial species to use that will satisfy the scientific goals.
We can estimate the amount of information processed. Visual is like 10mbit [1] plus other senses it might be up to 100mbit. Only doing similar sensor fusion and extracting features in realtime on computer requires more power. But there's also the symbolic processing, doing something similar too requires much more power on computer. Then there is other stuff such as maintaining homeostasis we don't really know how to compute yet.
You can estimate the amount of information being processed in a few different ways. But that's not really relevant; the whole point of solving this problem is that you can do the same job with less computation than it looks like you need.
There is no particular connection between "amount of information processed" and "amount of computation performed".
There is a connection. How can any computation be done without moving information around? In absence of better measure, we can roughly estimate the computational complexity of a black box from looking on the input and output.
If the brain's job could be hypothetically done by some optimized system using an picowatt is irrelevant. We don't have such a system.
> On the other hand, these cultures have essentially no advantage over digital computers and modern machine learning models.
Absolutely false. While it's indeed hard to keep it alive, real neurons are far more sophisticated than what AI researchers think they are. Modern digital so called neural networks are built on the outdated and oversimplified knowledge of neuron model, almost a century-old by now.
Modern CMOS transistors are extremely sophisticated devices (you need a very complicated model with hundreds of parameters to simulate all kinds of quantum effects to predict its behavior). Yet all it does is one simple function - it's an on/off switch.
Evolution would not allow waste of energy and complexity for just one on/off switch. Inefficient things die out in the course of millions of years. Neural tissue on itself is far older than humanity, so it had much more time to perfect.
> Evolution would not allow waste of energy and complexity for just one on/off switch. Inefficient things die out in the course of millions of years. Neural tissue on itself is far older than humanity, so it had much more time to perfect.
Sorry but that's not how evolution works at all. You are essentially postulating that evolution results in efficient outcomes given enough time whereas there are many, many examples of evolution delivering results but clearly sub-optimal ones. It's not a given that evolution will lead to an efficient solution, it's not even a given that it will lead to a solution at all.
Out there food and energy are often scarce. Evolution does care about efficiency in that particular case a lot. True, there are ecosystems with plenty of free food but they are rarity.
At least in plants there is some evidence that mutations produced and then acted on by natural selection are not fully random. It is a long held assumption this should not be possible, but there are interesting lines of evidence suggesting it may be. It would open the chance of there being a kind of (limited) underlying logic.
This is an example. Epigenetically driven evolution in arabodopsis, which protects certain regions from mutation. In a very limited sense, evolution might be said to "care" about something here, as it is kind of taking direction from the environment, not simply acting on uniformly random mutations. Nothing like this is known in animals or most anywhere else afaik.
sophisticated is not a scientific word. they are complex and complicated, and the voltage dynamics across their elaborate membrane takes a lot of computers to simulate. But we don't really know what it is doing or if it is particularly sophisticated. Nature has found a lot of complex solutions to simple problems because it does not know better. We don't know how well it did with intelligence
There are different studies proposing 2 or 3 layer network for representing the input-firing curve of neurons (Usually hippocampal). Of course, neural networks are abritrary approximators so the size of the network determines the fidelity of the reproduction. But it 's not clear what the firing does and what amount of complexity in the firing code is reduntant or useful for making AI systems
Even that is not clear. A model like GPT-4 can read an entire book in seconds, and produce an intelligent answer about its content [1]. A human would need at least several hours to perform the same task.
Unlike the capacity of human brains the capacity of ML models has been growing very fast in the last 10 years. The number of tasks AI cannot do is shrinking fast.
Power consumption is what's relevant to this discussion thread here. We're not talking about possible capacity but possible efficiency for given capacity as implemented in analog vs digital circuits.
If a "given capacity" is the ability to read books and answer questions about the content, then LLMs are more power efficient (4kW for 20 seconds beats 20W for 3 hours). That's on standard power hungry GPUs (8xA100 server consumes about 4kW). If we switch to analog deep learning accelerators, we gain even better power efficiency. There's simply no chance for brains to compete with electronic devices - as long as we match brain's "capacity".
Ok so it's clear you are not familiar with the vocabulary of this domain. "Capacity" means something specific (if extremely hard to measure precisely) about the informational throughput of a system, not just "the ability to do high-level task X as judged by a naive human observer".
I urge you to study more seriously about the things you seem so eager to speculate about before publishing underinformed opinions in public spaces.
Is that what you mean when you say "capacity"? Does not seem very relevant in the context of our discussion. If it's not what you have in mind, I'd appreciate a link to the Wikipedia article on "ML model capacity" or whatever specific term experts use to represent the concept.
> The basic idea is that if you culture neurons on an electrode array (not that hard) you can pick some electrodes to be "inputs" and some to be "outputs" and then when you stimulate both ends the cells wire together more or less according to Hebb's rule[4] and can learn fairly complex nonlinear mappings.
This is fascinating, can you clarify it a bit? Do you 'stimulate', e.g. apply electrical potential to both the inputs and outputs to represent each instance of training data, without any physical distinction between input and output at that stage? And then if you apply the potential only the inputs, you can then read predictions on the outputs?
What I always wonder about with these systems is how feedback was delivered to the cultured neurons. How do we tell them they're doing things correctly? Or is this some form of unsupervised learning with them?
They used a specific region of the electrode array to deliver the "reward" signal which was a regular predictable pulse pattern . An error was represented with unpredictable activity
The paper used unfortunate terminology everywhere even if there were negative comments about in the preprint. It caused a number of reactions
https://pubmed.ncbi.nlm.nih.gov/36863319/
Tangent: was thinking it would be cool if you had a bio mass that could connect to a pcie slot and act as a graphics card. That would be some really impressive tech. Build circuits in the goo with floating particles.
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