AGI Progress Report The latest AI models are exciting, but they're far from artificial general intelligence

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IBM Watson wins the television game show Jeopardy!, February 16, 2011

Dear friends,

Here’s a quiz for you. Which company said this?

“It’s always been a challenge to create computers that can actually communicate with and operate at anything like the level of a human mind. . . . What we’re doing is creating here a system that will be able to be applied to all sorts of applications in the world and essentially cut the time to find answers to very difficult problems.”

How about this?

“These creative moments give us confidence that AI can be used as a positive multiplier for human ingenuity.”

These are not recent statements from generative AI companies working on large language models (LLMs) or image generation models! The first is from a 2011 IBM video that promotes the Watson system’s upcoming participation in the TV game show Jeopardy!. The second comes from Google DeepMind webpage about AlphaGo, which was released in 2015.

IBM’s and DeepMind’s work moved AI forward. But it also inspired some people’s imaginations to get ahead of them. Some supposed that the technologies behind Watson and AlphaGo represented stronger AI capabilities than they did. Similarly, recent progress on LLMs and image generation models has reignited speculation about artificial general intelligence (AGI).

Generative AI is very exciting! Nonetheless, today’s models are far from AGI. Here’s a reasonable definition of from Wikipedia:

“Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that human beings or other animals can.”

The latest LLMs exhibit some superhuman abilities, just as a calculator exhibits superhuman abilities in arithmetic. At the same time, there are many things that humans can learn that AI agents today are far from being able to learn.

If you want to chart a course toward AGI, I think the baby steps we’re making are very exciting. Even though LLMs are famous for shallow reasoning and making things up, researchers have improved their reasoning ability by prompting them through a chain of thought (draw one conclusion, use it to draw a more sophisticated conclusion, and so on).

To be clear, though, in the past year, I think we’ve made one year of wildly exciting progress in what might be a 50- or 100-year journey. Benchmarking against humans and animals doesn’t seem to be the most useful question to focus on at the moment, given that AI is simultaneously far from reaching this goal and also surpasses it in valuable ways. I’d rather focus on the exciting task of putting these technologies to work to solve important applications, while also addressing realistic risks of harm.

While AGI may be part of an indeterminate future, we have amazing capabilities today, and we can do many useful things with them. It will take great effort on all of our parts to to find ways to harness them to advance humanity. Let’s get to work on that.

Keep learning!



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