A close look reveals that the newest systems, including DeepMind’s much-hyped Gato, are still stymied by the same old problems.
To the average person, it must seem as if the field of artificial intelligence is making immense progress. According to the press releases, and some of the more gushing media accounts, OpenAI’s DALL-E 2 can seemingly create spectacular images from any text; another OpenAI system called GPT-3 can talk about just about anything; and a system called Gato that was released in May by DeepMind, a division of Alphabet, seemingly worked well on every task the company could throw at it. One of DeepMind’s high-level executives even went so far as to brag that in the quest for artificial general intelligence (AGI), AI that has the flexibility and resourcefulness of human intelligence, “The Game is Over!” And Elon Musk said recently that he would be surprised if we didn’t have artificial general intelligence by 2029.
Don’t be fooled. Machines may someday be as smart as people, and perhaps even smarter, but the game is far from over. There is still an immense amount of work to be done in making machines that truly can comprehend and reason about the world around them. What we really need right now is less posturing and more basic research.
To be sure, there are indeed some ways in which AI truly is making progress—synthetic images look more and more realistic, and speech recognition can often work in noisy environments—but we are still light-years away from general purpose, human-level AI that can understand the true meanings of articles and videos, or deal with unexpected obstacles and interruptions. We are still stuck on precisely the same challenges that academic scientists (including myself) having been pointing out for years: getting AI to be reliable and getting it to cope with unusual circumstances.
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