I would point to the problem that chatbots fail not at having a “spark” but at things ordinary computer software does well. The other day somebody pointed out in an HN conversation that I had gotten the 1984 Superbowl confused with the 1986 Superbowl.
That’s a very human mistake, I’m sure somebody can tell you who played in every Superbowl and what the score was but people do misremember things frequently and we don’t call it a hallucination. (which is a defect in perception)
“Superhuman intelligence” is easy to realize for sports statistics if you do the ontology and data entry work and put the data in a relational or related database.
The thing is that chatbots get 90% accuracy for cases where you can get 99.99% accuracy (sometimes the data entry is wrong) with conventional technology. There is a kind of faith that we can go to 10^17 or 10^30 parameters or something and at some point perfect performance will “emerge” but no I think it is more like it will approach some asymptote, say 95% and you will try harder and harder and it will like pushing a bubble around other a rug. It’s a common situation in failing technology projects, quite well documented in
but boy people are seduced by those situations and have a hard time recognizing that they are in them.
In a certain sense chatbots already have superhuman powers of seduction that, I think, come from not having a “self” which makes mirroring easier to attain. People wouldn’t ordinarily be impressed by a computer program that can sort a list of numbers 90% correctly but give it the ability to apologize and many people will think it is really sincere and think it is really promising, it just needs a few trillion more transistors. (See the story Trurl’s Machine in Stanislaw Lem’s excellent Cyberiad except that machine is belligerent and not agreeable)
Now an obvious path is to have the chatbot turn a question into a SQL query and then feed the results into conversation and that’s a great idea and an active research area, but I’d point out the dialogues between Achilles and the Tortoise in
which people mistakenly think is about symbolic A.I. but that is really about the problems of solving problems where the correct solution has a logical aspect. Even though logic isn’t everything, the formulation of most problems (like “Who won the soccer game at Cornell last night?”) is fundamentally logical and leads you straight to paradoxes that can have you forever pushing a bubble under the rug and thinking “just one more” little hack will fix it…
LLMs are just one tool in a collection. Intelligence is based on many models, not just the language parts of our brain - and I expect AI to incorporate more models in a system approach. Why does it matter if LLMs are able to play chess at a grandmaster level or not? They can delegate the actual chess optimization problem to a chess optimizing program. While it’s interesting language alone is as powerful as it is, it’s very myopic to judge the tool alone and not as a part of a toolbox.
Exactly
It is NOT all about LLM's.
There are a lot of other successful models.
From AlphaGo, to visions systems, robotics.
LLM is just the latest shiny thing.
At some point they will all be tied together, and at that point it will start to look a lot more like sections of our brain, one is vision, one is language, one is movement. etc....
I think it's already been made clear that the main reason for the "asymptote" is wrong data input. These models attempt to learn from random internet text ... and this turns out to not be all that accurate.
Also, I've observed a model I was training having the same problem as I do myself. If I at any point learn wrong data, which happens of course, then getting that wrong data back out is very hard and requires 10 or 50 times the effort I spent learning the initial data. In fact I strongly suspect I never unlearn bad data, I just additionally learn "if I say X, it's wrong, say Y instead".
Brains suck for exact work such as database work or precise calculations over longer chains. But they excel at approximate work, and that's a very useful skill to have as long as if you have to you can fire up the pencil and paper and do your precise calculations that way. And paper works fine for database work as well and will remember all of those sports stats for as long as you care (and even after you're dead).
Brains are so powerful because they are universal, they can use auxiliary data stores and co-processors just fine.
So basically we have to give the LLM access to (both read and add to) a tool that can deal with structured knowledge/state strictly, same thing we have to do for humans- like calculators, databases, clocks/alarms, computer language executors… That way if we tell it “remember that my birthday is April 5” it can enter it into a calendar tool in such a way that it can quickly retrieve it later to confirm its “LLM guesswork” or get triggered a reminder of it on that date.
I’ve been experimenting with prompting to get GPT4 to realize it has a “memory” (just a flat file for now) which it can contextually retrieve and write to, coupled with a process that interprets any requests it makes of this “memory” and adds them to the conversation. Limited success so far. End goal is a “life agent” that does things like remind me of things in a human-like way, sums up my emails, etc.
That’s a very human mistake, I’m sure somebody can tell you who played in every Superbowl and what the score was but people do misremember things frequently and we don’t call it a hallucination. (which is a defect in perception)
“Superhuman intelligence” is easy to realize for sports statistics if you do the ontology and data entry work and put the data in a relational or related database.
The thing is that chatbots get 90% accuracy for cases where you can get 99.99% accuracy (sometimes the data entry is wrong) with conventional technology. There is a kind of faith that we can go to 10^17 or 10^30 parameters or something and at some point perfect performance will “emerge” but no I think it is more like it will approach some asymptote, say 95% and you will try harder and harder and it will like pushing a bubble around other a rug. It’s a common situation in failing technology projects, quite well documented in
https://www.amazon.com/Friends-High-Places-Livingston-1990-1...
but boy people are seduced by those situations and have a hard time recognizing that they are in them.
In a certain sense chatbots already have superhuman powers of seduction that, I think, come from not having a “self” which makes mirroring easier to attain. People wouldn’t ordinarily be impressed by a computer program that can sort a list of numbers 90% correctly but give it the ability to apologize and many people will think it is really sincere and think it is really promising, it just needs a few trillion more transistors. (See the story Trurl’s Machine in Stanislaw Lem’s excellent Cyberiad except that machine is belligerent and not agreeable)
Now an obvious path is to have the chatbot turn a question into a SQL query and then feed the results into conversation and that’s a great idea and an active research area, but I’d point out the dialogues between Achilles and the Tortoise in
https://en.wikipedia.org/wiki/G%C3%B6del,_Escher,_Bach
which people mistakenly think is about symbolic A.I. but that is really about the problems of solving problems where the correct solution has a logical aspect. Even though logic isn’t everything, the formulation of most problems (like “Who won the soccer game at Cornell last night?”) is fundamentally logical and leads you straight to paradoxes that can have you forever pushing a bubble under the rug and thinking “just one more” little hack will fix it…