AGI refers to artificial general intelligence. Once realized, potentially in the near future (before 2026), AGIs will learn and accomplish all intellectual tasks currently associated with humans. Sure, this will be an important moment in the history of AI, but focusing on AGI underestimates the real potential of AI. Imagine you just invented the jet engine and the popular press kept asking you when finally, jet planes will become just like birds? You would be confused – sure, birds are fascinating, beautiful, and inspiring, but jet planes are a much better solution for things most people care about, such as getting from one continent to another.
Simple parity of silicon-based computers with massively-parallel electrochemical computers (aka humans) is not particularly interesting; rather the long term goal of AI must be to dramatically outperform humans across all relevant metrics and tasks. We already have, or are close to, non-human computers able to formulate scientific hypotheses based on all 27 million papers in pubmed, write new novels knowing everything humans have ever written, or generate mathematical proofs and game strategies that are at least as beautiful and creative than what humans have created so far.
Silicon-based computers (such as those that run LLMs, specialized chess algorithms, and game playing AIs) are already better at many tasks once believed to require “special” powers somehow reserved to the human brain. Tasks that are hard for most people – e.g. outperforming a typical student on the MCAT – are already within easy reach of contemporary LLMs. The reality is that the human brain is a computer governed by physics, and it is therefore inescapable that other compute architectures with faster upgrade cycles will soon outperform our brains. Yes, the human brain has many interesting properties (low energy consumption, graceful degradation when connections are pruned, excellent at recognizing wolves and other dangers) but it also has many important limitations, such as limited working memory (when’s the last time you memorized a 800 digit number?), limited and cumbersome paths to high bandwidth interfaces with silicon-based computers, and complete absence of regular performance upgrades. Wouldn’t we all like 10x compute and IO upgrades every other year?
Focusing on “When AGI?” is certainly relevant to businesses wishing to 1:1 replace humans with “drop-in” silicon-based employees, and therefore relevant to the pressing and long overdue debate about the societal implications of AI. However, if your dream is a more equitable world, with better medicines, better stewardship of the environment, better education for all regardless of their background, then (the precise moment of) AGI is not important. Rather, your focus should be on harnessing (the hopefully benign) computers that are smarter than we are, and, more importantly, will keep getting smarter and smarter.
Robots, Data, and Networks 