What do you get when you combine neural networks with symbolic AI? The answer is: Neuro-symbolic artificial intelligence. Tom Merritt lists five things you should know about it.
You hear a lot about neural networks and deep learning these days. They can do a lot of impressive things like create pictures of cats that never existed and other potentially more useful examples. But symbolic AI is starting to get some attention too and when you combine the two, you get neuro-symbolic AI which may just be something to watch. Here are five things to know about neuro-symbolic AI.
- Neural networks and symbolic logic systems both have roots in the 1960s. Both have benefitted by the huge increase in capacity and capability brought about by modern computers, GPUs, and the Internet.
- You can't interpret neural networks results well. You can't completely rely on the results. This shows up in bias creeping into the results based on the data set for instance.
- Neural networks can't do it all. They lack strong generalization being constrained by their training data. They're great at using what is known, but can't be creative. So you need huge data sets created by people.
- Symbolic algorithms use an artificial logic system. This is great at abstractions, but it doesn't do well with massive data streams.
- Neuro-symbolic AI combines the two approaches to use what's powerful about each. As an example Cisco and SigularityNET used the OpenCog AGI engine with deep neural networks to analyze street scenes. Each camera in the system is modeled by a neural network and the symbolic layer integrates it to make sense of the whole. For instance, routing an ambulance in a way that doesn't run into or cause traffic congestion.
Neuro-symbolic networks are hybrids that take what neural networks can do and do it faster, easier, and sometimes in a way that might not have been able to be done before.
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