21–30 of 68 posts
The tutorial claims that fib(0) = 1, which is wrong. https://en.wikipedia.org/wiki/Fibonacci_sequence This one was easy to spot and would have been easy to get right. Makes me wonder…
> Many writers begin the sequence with 0 and 1, although some authors start it from 1 and 1[1][2] and some (as did Fibonacci) from 1 and 2.
The tutorial claims that fib(0) = 1, which is wrong. https://en.wikipedia.org/wiki/Fibonacci_sequence This one was easy to spot and would have been easy to get right. Makes me wonder…
The tutorial claims that fib(0) = 1, which is wrong. https://en.wikipedia.org/wiki/Fibonacci_sequence This one was easy to spot and would have been easy to get right. Makes me wonder…
> Many writers begin the sequence with 0 and 1, although some authors start it from 1 and 1[1][2] and some (as did Fibonacci) from 1 and 2.
If you like scallop, you are gonna love lobster: https://liby99.github.io/res/papers/lobster.pdf
Unfortunately it doesn't seem to be available yet. Scallop and Lobster are both from UPenn, and the Scallop website says "We are still in the process of open sourcing Scallop," so I assume it's a matter of time.
I honestly have no idea what any of this means. It seems like schizo ramblings to me. But I'm sure there's some merit to it.
But - it's time to do the tutorials and try and see.
I'm really confused. Is this metaprogramming in the sense that, add_relation and add_rule are using an LLM to make an educated guess about what to do based on what went before it? Or is it using some deterministic method or heuristic to evaluate those terms?
I may re-evaluate now, thinking of smoother LLM integration as well as differentiability.
Has anyone here used Scallop for a large application? I ask because in the 1980s I wrote a medium large application in Prolog and it was a nice developer experience.
I wish this website explained what neurosymbolic means.
It's a combination of neural networks and symbolic reasoning. You can use a neurosymbolic approach by combining deep learning and logical reasoning: A neural network (PyTorch) detects objects and actions in the image, recognizing "Jim" and "eating a burger" with a confidence score. A symbolic reasoning system (Scallop) takes this detection along with past data (e.g., "Jim ate burgers 5 times last month") and applies…
I wish this website explained what neurosymbolic means.
It's a combination of neural networks and symbolic reasoning. You can use a neurosymbolic approach by combining deep learning and logical reasoning: A neural network (PyTorch) detects objects and actions in the image, recognizing "Jim" and "eating a burger" with a confidence score. A symbolic reasoning system (Scallop) takes this detection along with past data (e.g., "Jim ate burgers 5 times last month") and applies…