If you listen to recent talks by Hinton (Capsule networks), LeCun (self-supervised learning), and Bengio (system 2 deep learning), as well as others, you'll find plenty of references to neuroscience, psychology, cognitive science, etc. There are always implementation differences, but the inspiration from brains is always there. The point of the book (which might be wrong, btw) is that the brain itself is an agent of the gene, which has evolved out of the need for better survival mechanisms. Therefore, it is suggesting that anything that has been modeled after the brain is—by extension—an agent of the main source of human intelligence (because it serves the goals of humans) and not intelligent by itself.
For me, nothing will replace the original GoF book:
"Design Patterns: Elements of Reusable Object-Oriented Software"
It's still worth reading. But I also recommend "Object-Oriented Analysis and Design with Applications" by Grady Booch. It's not strictly design patterns, but it's a classic that explores a lot of the fundamental concepts you'll need for DP.
Agreed. It is as much a management challenge as an engineering one. Consolidating disparate data silos, iterating on the right algorithm architectures, setting up APIs in the right places, finding the right balance/split between AI and human efforts... You have to bring the right pieces together rather than just injecting the latest DL innovation into your enterprise (it won't work).
I know it's not open to the public. I'm trying to estimate the costs of running/inference based on what we know about the computation costs of transformers and scaling them to 175 billion params