EAI Researcher: Are Language Models Missing Something?

https://ai.northeastern.edu/eai-researcher-are-language-models-missing-something/

Bottoms Up

Judging by Saba’s critique of subsymbolic systems, you would think he would find a home alongside other critics of neural net LLMs like Gary Marcus and Noam Chomsky. And, in the divide between rationalism vs. empiricism, Saba is definitely in the former camp with Marcus and Chomsky. However, he does differ in a way that brings him closer to the empirical camp, and it has to do with data.

For all the limitations of the empirical method, its bottom-up methodology allows for sophisticated data analysis and learning, because the system is working from scratch. It learns from whatever data is at its disposal. Meanwhile, the top-down approach emblematic of symbolic AI is flawed because there is no foundational axiom (or agreed upon general principles) to work from—at least not when it comes to language and how our minds externalize thoughts as language.

“When it comes to language and the mind, we have nothing,” Saba says. “Nobody knows anything about the mind and how the language of thought works. So anybody doing top-down is playing God.”

That being said, language seems to operate on a common understanding between people, which suggests the existence of an underlying system. When a waiter says, “The corner table wants another beer,” we understand that the table itself is not expressing a desire for beer; it’s the people sitting at the table. Computers, to the extent they can be said to understand anything, cannot make that common sense leap. And the ambiguity of language—the messiness of it—is actually a reflection of its efficiency. Saba calls this the “missing text phenomenon:”

“We don't say everything, because I can assume you have common sense and you know the rest.”

So how do we impart that understanding to AI? For Walid Saba, it’s all about melding these two schools: symbolic and subsymbolic. Saba calls his approach, “bottom-up, symbolic reverse engineering of language at scale.” Essentially, the idea is to combine the bottom-up approach from the subsymbolic school with the hierarchy of concepts from the symbolic school. Indeed, despite decades of sluggish growth in symbolic AI research, there has been a recent resurgence. And ChatGPT, with all its flaws, might have something to do with it.

We can still ask the question, are we playing God? And we can still ask if we’re toiling with a basic misunderstanding of what language is. But who knows? Success often seems to follow the middle path.