I’ve been saying this for about a year since seeing the Othello GPT research, but it’s nice to see more minds changing as the research builds up.
Edit: Because people aren’t actually reading and just commenting based on the headline, a relevant part of the article:
New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.
This theoretical approach, which provides a mathematically provable argument for how and why an LLM can develop so many abilities, has convinced experts like Hinton, and others. And when Arora and his team tested some of its predictions, they found that these models behaved almost exactly as expected. From all accounts, they’ve made a strong case that the largest LLMs are not just parroting what they’ve seen before.
“[They] cannot be just mimicking what has been seen in the training data,” said Sébastien Bubeck, a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight.”
I’ve been saying this all along. Language is how humans communicate thoughts to each other. If a machine is trained to “fake” communication via language then at a certain point it may simply be easier for the machine to figure out how to actually think in order to produce convincing output.
We’ve seen similar signs of “understanding” in the image-generation AIs, there was a paper a few months back about how when one of these AIs is asked to generate a picture the first thing it does is develop an internal “depth map” showing the three-dimensional form of the thing it’s trying to make a picture of. Because it turns out that it’s easier to make pictures of physical objects when you have an understanding of their physical nature.
I think the reason this gets a lot of pushback is that people don’t want to accept the notion that “thinking” may not actually be as hard or as special as we like to believe.
The bar always gets raised for what counts as actual “AI” with each advancement too. Back in the 60s, the procedural AI of the 80s and 90s would have fit the bill, but at the time, we said “nope, not good enough”. And so it kept getting better and better, each time surpassing the old tech by leaps and bounds. Still, not “true” AI. Now we have ChatGPT, which some still refuse to call “AI”.
We’re going to eventually have fully sentient artificial beings walking around amongst us and these people are going to end up being an existential threat to them, I can see it now.
Think you’re slightly missing the point. I agree that LLMs will get better and better to a point where interacting with one will be indistinguishable from interacting with a human. That does not make them sentient.
The debate is really whether all of our understanding and human experience of the world comes down to weighted values on a graph or if the human brain is hiding more complex, as-yet-undiscovered, phenomena than that.
This whole argument hinges on consciousness being easier to produce than to fake intelligence to humans.
Humans already anthropomorphise everything, so I’m leaning towards the latter being easier.
I’d take a step farther back and say the argument hinges on whether “consciousness” is even really a thing, or if we’re “faking” it to each other and to ourselves as well. We still don’t have a particularly good way of measuring human consciousness, let alone determining whether AIs have it too.