We are clearly not executing the same task.
We are clearly not executing the same task.
Treat GPT-3 like someone who just awoke from a long sleep. You could give it today's newspaper to read, and then ask it questions about today. Tell it what its own personal experience was, and then it'll talk your ears off about it if you ask nicely.
It is clearly executing the same task if you disregard humans tendency to prioritize other motivations than pure memory recall.
The reason I can respond to this reply is not because I have read and memorised reams of text of people talking about AI and am simply regurgitating it mindlessly. I am able to do it because I can consider the points you are making, and what their actual 'meaning' is, try to come up with my own meaningful response and try to verbalise it back to you. There is a point where I am not just doing statistical language modelling. If you think that is wrong, and that what we are doing is closer to GPT-3, then could you explain why you think that?
I am an AI researcher, and have spent plenty of time playing with GPT-3 btw.
When I hurt your pride by suggesting you haven't fully understood GPT-3, you are motivated to come up with not just a valid response, but one that has been vetted by as many of your well developed models that form your understanding of GPT-3 so I can be suitably impressed. I'm with you that GPT-3 wouldn't go deeper than just finding some information that it thinks it's true. Though maybe GPT-3 would recognise their authority was being challenged and add that line to affirm their credentials as an AI researcher.
What if GPT-3 were pushed in a similar manner, perhaps in some adversarial scheme, to not only produce information that is correct, but that is clever and exploring deep meaning, motivated by some similar feeling of pride or vindication. I think the models required to do that do not lie far from the models it needed to build to form sentences that accurately describe reality.
I feel like I am capable of making a concrete decision about whether I agree with two opposing ideas in a way that a language model can't do, such as this discussion. Furthermore my belief is consistent in my outputs day to day until my mind is changed by something. If that i some complete illusion and I am just a slightly fancier autocomplete than GPT-3 - well I'd be surprised, but I can't claim to understand consciousness well enough to refute it.
What if AR tech develops to the point where you can experience "being" in other point in space through artificial sensors (say, on a robotic or drone chassis), while also being able to see and hear your real surroundings to some degree? Then you will be able to experience the same real event from multiple vantage points. Will you be able to form two different opinions on what "really" happened? Which one would be the "correct" one?
So is this debate partially just another way of asking if language is the same as reality? The difference between "things I've experienced" vs "stored in languages" seems... not trivial to me? Both in that I think the way biological memory works is non-trivially different from word tokens stored in computer memory, and also in that I think there's more to having experienced something than it just being stored in biological memory (or maybe that biological memory is more than just "memories", but is encoded throughout the body overall -- a scar is a type of memory of an injury, for instance, connected to but still different from my "memory" of having received it).
Human brains and senses are obviously qualitatively different, but AIs have the advantage of unimaginably massive bandwidth of incoming textual information.
Now, one of the hardest difficulties here is setting up the goal structure for this kind of AI. Just filling in the blanks is obviously insufficient.
I don't think that's actually true, as GPT-3 will tend to ignore any facts about the world that were part of its corpus just to fit a question better. For example, if you prompt it with "Who assassinated Queen Elizabeth II?", it will likely give you a name, instead of saying "Queen Elizabeth II is still alive", because "Who" questions are much more likely to receive a name as an answer than a refutation in its training corpus.
I tried to get GPT-3 to tell me the most popular forums on some topics, and I noticed it would never include subreddits. So I prefaced my question with "given that subreddits are also a type of forum.." and it would give me a list that would include subreddits. I have to admit that in that new list it would then fail to mention some forum that was in the previous list, so it didn't have a super reliable ability reorganize information.
The fact that they can chose to omit facts when it suits their purpose is in fact something that LaMDA can't do (as its sole purpose is to generate the most likely series of tokens that continue the prompt).