[alankarmisra]

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 logical rules like:

        likes(X, Food) :- frequently_eats(X, Food).

        frequently_eats(Jim, burgers) if Jim ate burgers > 3 times recently.

The system combines the image-based probability with past symbolic facts to infer: "Jim likely likes burgers" (e.g., 85% confidence).

This allows for both visual perception and logical inference in decision-making.

[f1shy]

Also can be used to verify NN decisions. In autonomous driving, a NN can make “instinctive” decisions, and a GOFAI system can verify they work and don’t break civil or physical laws. You can have many parallel NN giving recommendations, and let a symbolic system take the final decision.

[eternauta3k]

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.

[alankarmisra]

In your specific example, time of day, weather (foggy, sunny, over-cast) along with images of cars with different colors, models, makes, from different angles will all be training parameters to begin with so the neural net can do this on its own without needing specific symbolic processing apriori or downstream. Training data input into neural nets is usually sanitized and transformed to some extent but whether this sanitization / preprocessing requires symbolic programming depends on the use case. For example, with the car example, you preprocess car images to color them differently, hide random sections of it, clip it in different ways so only partial sections are showing, turn them upside down, introduce fake fog, darken, lighten, add people, signs, fire, etc and use each of these images for training so that the neural net can recognize cars under different situations (even after accidents where they are upside down and on fire). Eventually the neural net will recognize a car in most circumstances without symbolic programming/intervention.

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.

[andoando]

Is this really all they different from writing some functions in any language that use a neural net to make these predictions?

Why is this a language and not just some say, Java/Rust library?

It's interesting but doesnt seem like fundamentally anything new.