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Comment by pmarreck | Hacker News Reader

> It is only scale at this point.

I see, this is basically the "AI of the gaps" argument. :P

It is NOT "only scale" at this point. But at the current rate, you will see this soon enough (I just happen to see it already). We'll have some very intelligent-seeming, very useful, but relatively uncreative zombies missing a "spark" (or any willpower, or any real source of what is valuable to them, or any sense of what is aesthetically pleasing or preferable or enjoyable or beautiful, or any consciousness for that matter). It will allow us to redefine what it means to be human. But our distinct human-ness will stand out even more at that point.

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Our distinct humanness will be found in the narcissism of small differences.

The only narcissists here are the computer scientists who have convinced themselves they've made god.

Nobody is claiming they have made god today.

That is on the roadmap for 2030.

Great work, please put this on a tile :D

LOL. Falling back on metaphysical arguments about 'sparks' and 'uniqueness', is NOT you 'seeing' something nobody else is able too.

Agency is a key part of that spark, but we have done all sorts of research into agency and providing goal based agents into an AI model framework incorporating LLMs as well as other optimizers and solvers I think will provide the majority of that spark. The process of creativity depends on both internal agency and goal setting unmoored by external dictation and semantic synthesis of abductively reasoned concepts, with an aesthetic that feels into the goal based optimizer. These are things that can be simulated to the point that while there may be an uncanny valley somewhere it’ll be close enough to be hard to distinguish.

But I do wonder if the practical utility of such an entity is worth the amount of effort and capital required to build and sustain it. I suspect it’ll be more a novelty than a practical tool.

I think much of that spark is actually contextual language generation. We're relying on learned language patterns for most of our sparks.

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

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…

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.

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