Just remembered another great book kind of bridging both topics that’s a great introduction to the field. Its called Models of the Mind by Grace Lindsay. It follows the history of computational/theoretical neuroscience and how we got to our modern understanding. Its written for a more general audience than Neuronal Dynamics and gives a great overview of the field.

Good question! Unfortunately, I haven’t found a good source myself. I’ve been trying to piece it together based on my theoretical/mathematical neuroscience background and self-study about transformer architectures. The problem is the point neurons in transformers don’t map to neurons in cortex. You have to instead look at the bigger functions that are being computed by each system.

The best single source to learn more about theoretical neuroscience is Neuronal Dynamics: From Single Neurons to Networks and Models of cognition. But its super math heavy. If you want to easier to digest videos I recommend Artem Kirsanov on youtube. For transformer architecture 3 Blue 1 Brown has a great series of videos about it on YouTube as well.

There are positives and negatives to using cells for computing. There are very complex systems that are responsible for making sure cells grow in the correct architecture and are supported property via blood vessels and glial cells. There is also definitely something to be said about glial cells themselves having a role in computation (whole other topic I won’t get into) not just neurons.

If were just looking at the input output relationship with neurons we can model it pretty well using neuromorphic hardware which is designed to respond like a neuron. There are still gaps but and there are scientists who believe it isn’t close enough.

Our current hardware can model neurons closely as well but its less efficient at it. The current AI models use point neurons which takes a lot less computing power but I don’t think they’re the way to AGI. Personally I’m a fan of the adaptive exponential leaky integrate and fire model or the spike response model

I’m just going to throw in my 2 cents on the AGI issue since I have a better understanding than average. I’m currently a computational neuroscience PhD student. I don’t work in theoretical neuroscience currently but that’s my hope after I finish my doctorate.

I don’t believe current transformer architectures are capable of AGI and all big AI companies just seem to be adding more parameters to established transformer architectures.

If you’re just talking about a strand of DNA with no epigenetic info I think 90% is being very optimistic.

Hadn’t heard of this before but I’m more than a little skeptical, especially if its being used for policing

"More recently, companies such as Parabon NanoLabs and Identitas have begun offering forensic DNA phenotyping services for U.S. and international law enforcement. However, the science behind the commercial services offered by Parabon NanoLabs has been criticized as it has not been subjected to scrutiny in peer-reviewed scientific publications. It has been suggested that it is not known "whether their ability to estimate a face’s appearance is better than chance, or if it’s an approximation based on what we know about ancestry” -DNA phenotyping wiki

You’re joking, right? There’s a reason genotype and phenotype aren’t synonyms.

It’s worse than just using a new tool though. By definition LLMs use the statistically most likely option (with minor variation for flavor). People are literally asking a statistics bot what password can I use that is the most likely?