Main topic: What similarities/differences should we expect between the brain and AI-systems?

On one hand, one might expect evolution and engineering to discover similar solutions to similar computational problems. On the other hand, the two are optimizing under very different constraints: evolution cares about self-assembly, self-repair, learning and low power-consumption, while AI-designers care about simplicity & ease of understanding.

Bonus topics if time permits: 

* Do we need a science of intelligence or merely engineering of intelligence?

* Hilbert questions in AI and how to approach them

* Which questions should we be asking but aren’t? (“unknown unknowns”)

* At what level of structure can we best understand the mind? 

Would an accurate simulation have to include atoms, cells, idealized neurons or merely simplified cortical columns? How much does it matter that there are dozens of neuronal cell types and that synapses are so complicated? While the Navier-Stokes equation lets us understand the motion of a fluid without worrying about it being made up of atoms, there are other effects such as Brownian motion where the atomic details matter. On the other hand, we can simulate a computer program perfectly at the abstract level of bits without knowing anything about transistors or other details of the computational substrate.

*Food and social starts at 3:30 p.m.