41–50 of 68 posts
By the way I wish there were more real-life examples, both basic and advanced to show what it may be especially useful for, maybe even compare it to other languages like Prolog. I expected the tutorial to have examples for what "neurosymbolic" means, because I am not entirely sure what it means in practice.
A bit over my head - but can't Prolog achieve similar results?
Very pleasant branding though. Great work! :)
The problem with scallop is that no one has ever shown a single program that wasn't easier to write without it. Their papers usually contain also no examples, and the ones that do are a heck of a lot more complicated than asking the model to do it directly. The programming languages world let's them get away with this because they're excited to be relevant in the LLM universe. But I wouldn't accept any of this work i…
It's not about performance, but safety.
Making safe decisions becomes exponentially more important as ML / agents evolve, to avoid "performant" but ultimately inefficient/dangerous/wasteful inferences.
Ever since I learned about category theory and its relationship with symbolic reasoning I've suspected that AGI will come from elegantly combining symbolic reasoning and probabilistic reasoning. This is the first project I've seen that seems to be positioned that way. Very cool.
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Yes, this seems to be what the symbolists always forget. We don’t use symbolism like this, we just have very dense neural connections that emerge from scale and approximate it
So if inventing the airplane how long should one stick with a flapping bird wing design?
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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…
Is the reasoning strictly downstream of the image recognition? Or can prior knowledge impact how objects are recognized? E.g. I'm driving on the road at night so the two incoming lights are probably a car.
So when would you use symbolic programming? To generate quality data for the neural network. For example, maybe the neural net reports it read the speed limit to be 1000 km/h on a sign because of someone's shenanigans. A symbolic programming aid which knows potential legal limits will flag this data as potentially corrupt and pass it back to the network as such allowing the neural network to take more sensible decisions.
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So if inventing the airplane how long should one stick with a flapping bird wing design?
That is not a good analogy. Symbolism has given us lots of useful things, including SAT/SMT and theorem provers.
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…
Why is this a language and not just some say, Java/Rust library?
It's interesting but doesnt seem like fundamentally anything new.