Artificial neural networks are sufficient mathematical representations of biological cortices. there a huge amount of evidence that concludes this is the case. All that’s left to do is compare human and animal brains to our Ai models. The human brain doesn’t use all 100 trillion parameters on any one task. In fact the brain is divided into regions that allocate compute resources to vision, language, audio etc.. Not even half our brain devotes that many resources to one major region. The upper bound would be 50 trillion parameters. 1 trillion is too small. There aren’t 100 different major cortical regions. There are 10 . All working on the same architecture but processing different modalities. Conservatively 10 trillion parameters are allocated to each major region. Lets take a language model with 10 trillion weights. At that capacity it should be understand language completely. Then, having read all of pub med for example, it would be more knowledgeable than all medical professionals on the planet. A 100 trillion parameter model, I’ve calculated, would be more than a billion times more intelligent than the 10 trillion parameter, in terms of iq, while also having the benefits of of all human knowledge and never being tired and being immortal.