New MIT study shows what you already knew about AI: it doesn't actually understand anything
The latest generative AI models are capable of astonishing, magical human-like output. But do they actually understand anything? That'll be a big, fat no according to the latest study from MIT (via Techspot).
More specifically, the key question is whether the LLMs or large language models at the core of the most powerful chatbots are capable of constructing accurate internal models of the world. And the answer that MIT researchers largely came up with is no, they can't.
To find out, the MIT team developed new metrics for testing AI that go beyond simple measures of accuracy in responses and hinge on what's known as deterministic finite automations, or DFAs.
A DFA is a problem with a sequence of interdependent steps that rely on a set of rules. Among other tasks, for the research navigating the streets of New York City was chosen.
The MIT team found some generative AI models are capable of very accurate turn-by-turn driving directions in New York City, but only in ideal circumstances. When researchers closed some streets and added detours, performance plummeted. In fact, the internal maps implicitly generated by the LLMs by their training processes were full of nonexistent streets and other inconsistencies.
“I was surprised by how quickly the performance deteriorated as soon as we added a detour. If we close just 1 percent of the possible streets, accuracy immediately plummets from nearly 100 percent to just 67 percent,” says lead author on the research paper, Keyon Vafa.
The core lesson here is that the remarkable accuracy of LLMs in certain contexts can be misleading. «Often, we see these models do impressive things and think they must have understood something about the world. I hope we can convince people that this is a question to think very carefully about, and we don’t have to rely on our own intuitions to answer it,» says senior paper author Ashesh Rambachan.
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