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Would be better to extend to use Llama 2 and break itself away from an open ai dependency. However I think this is striking in the right direction with LLM, emb

Would be better to extend to use Llama 2 and break itself away from an open ai dependency. However I think this is striking in the right direction with LLM, embed them in a goal based agent framework with a variety of abilities and available actions that are mediated by planners, optimizers, constraint systems, etc, using the abductive capabilities of LLM for providing the abstract semantic glue and delegating other functions to classical AI and algorithms. I always find it weird people are obsessed with LLMs being unable to solve quadratic equations or recite pi to some digit or play chess. That’s not the interesting abilities they demonstrate, and we already have techniques that do those tasks as good as you would ever need or want. But we have never had something that can operate in a natural language space and “reason” in that abstract space so effectively. That language alone is sufficient to bring out such amazing capabilities is cool, and mixing models and techniques using LLM as a glue between them will be (IMO) where they really change things.

HanClinto 3y ago
Agreed -- now that the repository is open-source, it feels ripe for adapting to a smaller local model. Is it too much dreaming to imagine that if we can perhaps fine-tune a small LLaMa2 model to perform well in this context, we might even be able to get this small enough to run on consumer hardware in an actual "Sims" type game...?
- krautt 3y ago
  there is no technical reason why you couldn't unbolt openai interface and bolt in llama. Moreover, once you have this, you need only load the model into memory once. emulating different agents would be handled exclusively through the context window sizes that llm's expect. each agent would just have its own evolving context window. roundrobin the submissions. repeat what's crazy to think about is what new things will become possible as the context window sizes creep up.
  - dogcomplex 3y ago
    Or as the model is able to be trained (integrated with new information) on the fly, making a somewhat limitless context window
    - __loam 3y ago
      I think there are actual technical problems in doing this.
      - dogcomplex 3y ago
        Well, cost prohibitive on big models obviously. But even just things like DreamBooth training extra associations to sets of new data images is tuning the model as you go
- gabereiser 3y ago
  My dwarves in DF would be much more predictable. I’m all for it. So long as the model is safely trained and constraints are in place to prevent Actor A from doing something shockingly bad to Actor B in a way that wasn’t designed.
HanClinto 3y ago
Side note -- discussion on adding support for local models is here, along with a preliminary fork that adds support: https://github.com/joonspk-research/generative_agents/issues/3 https://github.com/joonspk-research/generative_agents/issues...
taneq 3y ago
Plenty of people I know can't solve quadratic equations or more than 4-5 digits of pi, and nobody claims they're not intelligent. Once again it's a kind of backhanded milestone for AI that in less than a year the goalposts have moved from "can it even once hold a half-coherent conversation" to "if anyone can think of a question it can't answer then it's dumb and not AI".
- bee_rider 3y ago
  It is definitely a nice milestone, for researchers. I do think, though, that one of the rewards of making LLMs promising was the discovery that the bar for “what is useful” for any task is higher than what the average person can do. That could be seen as moving the goalposts, but another way of looking at it is: we’ve finally bothered to install a scoreboard. Like, if a person can’t solve a quadratic equation, they probably just aren’t useful for manipulating equations. Which is fine, of course, the average person is also not a very good hammer or screwdriver. We’re all specialized, the bar for usefulness is probably something more like “how would a person who’s taken an intro college course on this do it.”
  - fnordpiglet 3y ago
    The goal isn’t to be more or less useful than a person. It’s to expand the capabilities of computers. And computers are exceptionally good at mathematics tasks already. Why do we care if LLMs are good at things that are already exceptionally well done with computers? The point is they’re exceptionally good at things computers to date have been exceptionally bad at. The rest is weird goalpost moving noise - I wouldn’t use ChatGPT to solve a quadratic equation, I would use Mathematica, or any number of numeric libraries and runtimes. But I challenge you to get Mathematica to summarize a book, carry on a conversation, or even call an api based on natural human language without a rigorous specification apriori. These are holy grails of NLP, which is a capability that is enhancing of the existing capabilities offered by computing technologies today.
    - cmpalmer52 3y ago
      What we need are LLMs that can use the tools for mathematics (and other disciplines) that already exist and to write and execute new programs to hopefully solve novel problems, or at least to be able to glue the existing tools together with shells.
Davidzheng 3y ago
Not your point but I'm pretty sure gpt4 is more than capable of solving quadratic equations and playing chess and probably knows pi to quite a few digits... also it knows many great algorithms for computing pi
- fnordpiglet 3y ago
  Yeah, but it’s not nearly as good at playing chess as a basic chess algorithm. Why would I use GPT4 for chess when Chessmaster 2000 can beat it 100/100 times?
- earthboundkid 3y ago
  I would be shocked if GPT4 could reliably solve novel quadratic equations when it struggles with simple arithmetic.
  - flangola7 3y ago
    Why would you be shocked? GPT-4 scores in the 89th percentile on SAT Math.
    - earthboundkid 3y ago
      Because SAT math is all multiple choice, which can be reverse engineered. Ask it to multiply numbers too large to be in the memorization set.
      - earthboundkid 3y ago
        I don't pay for GPT4, but here's Claude falling on its face: Q: Provide the quadratic roots of the following equation. Work out the answer step by step. 21 - 135 x + 19 x^2 = 0 A: Okay, here are the step-by-step workings: 1) Start with the quadratic equation: 21 - 135x + 19x^2 = 0 2) Group the x^2 and x terms together: 19x^2 - 135x + 21 = 0 3) Use the quadratic formula to find the roots: x = (-b ± sqrt(b^2 - 4ac)) / 2a Where: a = 19 b = -135 c = 21 4) Substitute the values into the formula: x = (-(-135) ± sqrt((-135)^2 - 4(19)(21))) / 2(19) 5) Simplify: x = (135 ± sqrt(18225 - 1596)) / 38 6) Evaluate the square root: x = (135 ± 135) / 38 7) The two roots are: x = 5 x = 3 Therefore, the roots of the quadratic equation 21 - 135x + 19x^2 = 0 are x = 5 and x = 3.
avereveard 3y ago
We need a llama2 shim that runs locally and understand openai calls, then we can run all these funny software just changing our host file at no additional complexity for the implementer

HanClinto 3y ago

Agreed -- now that the repository is open-source, it feels ripe for adapting to a smaller local model. Is it too much dreaming to imagine that if we can perhaps fine-tune a small LLaMa2 model to perform well in this context, we might even be able to get this small enough to run on consumer hardware in an actual "Sims" type game...?

krautt 3y ago
there is no technical reason why you couldn't unbolt openai interface and bolt in llama. Moreover, once you have this, you need only load the model into memory once. emulating different agents would be handled exclusively through the context window sizes that llm's expect. each agent would just have its own evolving context window. roundrobin the submissions. repeat what's crazy to think about is what new things will become possible as the context window sizes creep up.
- dogcomplex 3y ago
  Or as the model is able to be trained (integrated with new information) on the fly, making a somewhat limitless context window
  - __loam 3y ago
    I think there are actual technical problems in doing this.
    - dogcomplex 3y ago
      Well, cost prohibitive on big models obviously. But even just things like DreamBooth training extra associations to sets of new data images is tuning the model as you go
gabereiser 3y ago
My dwarves in DF would be much more predictable. I’m all for it. So long as the model is safely trained and constraints are in place to prevent Actor A from doing something shockingly bad to Actor B in a way that wasn’t designed.

HanClinto 3y ago

Side note -- discussion on adding support for local models is here, along with a preliminary fork that adds support: https://github.com/joonspk-research/generative_agents/issues/3 https://github.com/joonspk-research/generative_agents/issues...

taneq 3y ago

Plenty of people I know can't solve quadratic equations or more than 4-5 digits of pi, and nobody claims they're not intelligent. Once again it's a kind of backhanded milestone for AI that in less than a year the goalposts have moved from "can it even once hold a half-coherent conversation" to "if anyone can think of a question it can't answer then it's dumb and not AI".

bee_rider 3y ago
It is definitely a nice milestone, for researchers. I do think, though, that one of the rewards of making LLMs promising was the discovery that the bar for “what is useful” for any task is higher than what the average person can do. That could be seen as moving the goalposts, but another way of looking at it is: we’ve finally bothered to install a scoreboard. Like, if a person can’t solve a quadratic equation, they probably just aren’t useful for manipulating equations. Which is fine, of course, the average person is also not a very good hammer or screwdriver. We’re all specialized, the bar for usefulness is probably something more like “how would a person who’s taken an intro college course on this do it.”
- fnordpiglet 3y ago
  The goal isn’t to be more or less useful than a person. It’s to expand the capabilities of computers. And computers are exceptionally good at mathematics tasks already. Why do we care if LLMs are good at things that are already exceptionally well done with computers? The point is they’re exceptionally good at things computers to date have been exceptionally bad at. The rest is weird goalpost moving noise - I wouldn’t use ChatGPT to solve a quadratic equation, I would use Mathematica, or any number of numeric libraries and runtimes. But I challenge you to get Mathematica to summarize a book, carry on a conversation, or even call an api based on natural human language without a rigorous specification apriori. These are holy grails of NLP, which is a capability that is enhancing of the existing capabilities offered by computing technologies today.
  - cmpalmer52 3y ago
    What we need are LLMs that can use the tools for mathematics (and other disciplines) that already exist and to write and execute new programs to hopefully solve novel problems, or at least to be able to glue the existing tools together with shells.

Davidzheng 3y ago

Not your point but I'm pretty sure gpt4 is more than capable of solving quadratic equations and playing chess and probably knows pi to quite a few digits... also it knows many great algorithms for computing pi

fnordpiglet 3y ago
Yeah, but it’s not nearly as good at playing chess as a basic chess algorithm. Why would I use GPT4 for chess when Chessmaster 2000 can beat it 100/100 times?
earthboundkid 3y ago
I would be shocked if GPT4 could reliably solve novel quadratic equations when it struggles with simple arithmetic.
- flangola7 3y ago
  Why would you be shocked? GPT-4 scores in the 89th percentile on SAT Math.
  - earthboundkid 3y ago
    Because SAT math is all multiple choice, which can be reverse engineered. Ask it to multiply numbers too large to be in the memorization set.
    - earthboundkid 3y ago
      I don't pay for GPT4, but here's Claude falling on its face: Q: Provide the quadratic roots of the following equation. Work out the answer step by step. 21 - 135 x + 19 x^2 = 0 A: Okay, here are the step-by-step workings: 1) Start with the quadratic equation: 21 - 135x + 19x^2 = 0 2) Group the x^2 and x terms together: 19x^2 - 135x + 21 = 0 3) Use the quadratic formula to find the roots: x = (-b ± sqrt(b^2 - 4ac)) / 2a Where: a = 19 b = -135 c = 21 4) Substitute the values into the formula: x = (-(-135) ± sqrt((-135)^2 - 4(19)(21))) / 2(19) 5) Simplify: x = (135 ± sqrt(18225 - 1596)) / 38 6) Evaluate the square root: x = (135 ± 135) / 38 7) The two roots are: x = 5 x = 3 Therefore, the roots of the quadratic equation 21 - 135x + 19x^2 = 0 are x = 5 and x = 3.

avereveard 3y ago

We need a llama2 shim that runs locally and understand openai calls, then we can run all these funny software just changing our host file at no additional complexity for the implementer