21–30 of 42 posts
edit: I spoke too soon, it actually has a module to publish AR model object of the design you created. so this could potentially be directly usable with AR glasses.
Earlier quoted context omitted.
A perfect third is a music term where the periods of two periodic signals (sounds) have a frequency ratio of 4:5. Including this shows the LLM can integrate knowledge from different domains: music, math, flags of the world, and Mathematica.
4:5 is a major third. "Perfect third" isn't really a thing in western music theory. Just found it odd the LLM didn't pick up on that.
Earlier quoted context omitted.
There's no such thing as a perfect third in music theory, only perfect 4ths and 5ths, which explains the 4:5 ratio. There's major/minor (imperfect) thirds. Interesting test of LLMs, this one failed.
While "perfect third" may not exist as a name for an interval, it does make sense to apply the adjective "perfect" to the interval "third" in order to distinguish a just intonation third (4/5) from a well-tempered intonation third (1/2)^(4/12).
Earlier quoted context omitted.
A perfect third is a music term where the periods of two periodic signals (sounds) have a frequency ratio of 4:5. Including this shows the LLM can integrate knowledge from different domains: music, math, flags of the world, and Mathematica.
There's no such thing as a perfect third in music theory, only perfect 4ths and 5ths, which explains the 4:5 ratio. There's major/minor (imperfect) thirds. Interesting test of LLMs, this one failed.
Symbolic AI could never become great because it was missing the connection to the intricacies of the real world that you can only get from data. I think a symbioses of symbolic techniques with LLMs or generally multimodal autoregressive foundation models will lead to our first legit "AGIish" agents. The LLM takes the role of a little gremlin inside the machine that provides the magic sauce, that tiny bit of general i…
I strongly believe the first person that figures out how to interface these language models with some kind of knowledge model or source of truth will win a lot of the pot for AI technology. Human feedback clearly helped GPT get to where it is now, but It's clearly optimized for answers that appear good rather than are good and based on known facts.
Symbolic AI could never become great because it was missing the connection to the intricacies of the real world that you can only get from data. I think a symbioses of symbolic techniques with LLMs or generally multimodal autoregressive foundation models will lead to our first legit "AGIish" agents. The LLM takes the role of a little gremlin inside the machine that provides the magic sauce, that tiny bit of general i…
This is I think quite insightful. Looking at the current AI hype cycle, I haven't actually been that impressed with GPT and the image generation models. They just get things too wrong, too often, and too convincingly to the point that they become dangerous/harmful. There's just no way to evaluate quality. I strongly believe the first person that figures out how to interface these language models with some kind of kno…
It's a bit unclear from this post: is this a locally running LLM or in the cloud and if so whose servers? Does it use an existing service like openai or a completely new model specific to wolfram?
Symbolic AI could never become great because it was missing the connection to the intricacies of the real world that you can only get from data. I think a symbioses of symbolic techniques with LLMs or generally multimodal autoregressive foundation models will lead to our first legit "AGIish" agents. The LLM takes the role of a little gremlin inside the machine that provides the magic sauce, that tiny bit of general i…
This is I think quite insightful. Looking at the current AI hype cycle, I haven't actually been that impressed with GPT and the image generation models. They just get things too wrong, too often, and too convincingly to the point that they become dangerous/harmful. There's just no way to evaluate quality. I strongly believe the first person that figures out how to interface these language models with some kind of kno…
Earlier quoted context omitted.
This is I think quite insightful. Looking at the current AI hype cycle, I haven't actually been that impressed with GPT and the image generation models. They just get things too wrong, too often, and too convincingly to the point that they become dangerous/harmful. There's just no way to evaluate quality. I strongly believe the first person that figures out how to interface these language models with some kind of kno…
It's been already done with LLangchain / Plugins / Function calling by LLMs.
Have a read of https://www.wolfram.com/language/elementary-introduction/3rd... and play with some examples maybe?
The tldr; is “yes, but…” and the “but” is that, no, generating/invoking python functions from an LLM is no where even remotely near the capabilities exposed by their online systems.
Yeah yeah, “but there’s a python library for that…”, no really. Have a play with it. There isn’t, for lots of the stuff it can do.