[xvilka]

> 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 Modern digital so called neural networks are built on the outdated and oversimplified knowledge of neuron model, almost a century-old by now.

[p1esk]

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.

[xvilka]

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.

[jacquesm]

> 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.

[lukebitts]

That’s not true at all. If something is a waste but doesn’t meaningfully change yours odds of suvival, “evolution” won’t care

[xvilka]

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.

[jacquesm]

Evolution doesn't care about anything.

[runsWphotons]

it is possible this is not quite true

[golergka]

Peacocks, antlers, art.

[Lerc]

It is arguable all three have evolved for the same reason.

[seydor]

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

[xvilka]

We already do know[1] the a single neuron has the same level of complexity as multilayered digital "neural network".

[1] https://www.youtube.com/watch?v=hmtQPrH-gC4

[seydor]

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

[uoaei]

What is clear however is the evident power savings in implementing cultured neural networks vs digital ones for a given network capacity.

[p1esk]

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.

[1] https://www.anthropic.com/index/100k-context-windows

[uoaei]

> for a given network capacity

You'd be hard-pressed to find an expert who believes any of the current crop of LLMs have a similar capacity to human brains.