Sublime

With all the challenges in ethics and computation, and the knowledge needed from fields like linguistics, psychology, anthropology, and neuroscience, and not just mathematics and computer science, it will take a village to raise to an AI.

For at least four reasons, hybrid AI, not deep learning alone (nor symbols alone) seems the best way forward:

1. So much of the world’s knowledge, from recipes to history to technology is currently available mainly or only in symbolic form.


2. Deep learning on its own continues to struggle even in domains as orderly as arithmetic. A hybrid system may have

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Classical computer science, of the sort practiced by Turing and von Neumann and everyone after, manipulates symbols in a fashion that we think of as algebraic, and that’s what’s really at stake.

But symbols on their own have had problems; pure symbolic systems can sometimes be clunky to work with, and have done a poor job on tasks like image recognition and speech recognition; the Big Data regime has never been their forté. As a result, there’s long been a hunger for something else.

o, alternative pathways to building Type-2 reasoning-capable AI systems, likely using neurosymbolic approaches, have become much more attractive. People like Gary Marcus have argued for neurosymbolic approaches for decades. Such approaches combine the pattern recognition of neural nets, like LLMs, with symbolic reasoning’s logic and rules. Vinod Kh... See more