zlacker

At Leela AI we're developing hybrid symbolic-connectionist constructivist AI, combining "neat" neural networks with "scruffy" symbolic logic, enabling unsupervised machine learning that understands cause and effect and teaches itself, motivated by intrinsic curiosity.

Leela AI was founded by Henry Minsky and Cyrus Shaoul, and is inspired by ideas about child development by Jean Piaget, Seymour Papert, Marvin Minsky, and Gary Drescher (described in his book “Made-Up Minds”).

https://mitpress.mit.edu/9780262517089/made-up-minds/

https://leela.ai/leela-core

>Leela Platform is powered by Leela Core, an innovative AI engine based on research at the MIT Artificial Intelligence Lab. With its dynamic combination of traditional neural networks for pattern recognition and causal-symbolic networks for self-discovery, Leela Core goes beyond accurately recognizing objects to comprehend processes, concepts, and causal connections.

>Leela Core is much faster to train than conventional NNs, using 100x less data and enabling 10x less time-to-value. This highly resilient AI can quickly adjust to changes and explain what it is sensing and doing via the Leela Viewer dashboard. [...]

The key to regulating AI is explainability. The key to explainability may be causal AI.

https://leela.ai/post/the-key-to-regulating-ai-is-explainabi...

>[...] For example, the Leela Core engine that drives the Leela Platform for visual intelligence in manufacturing adds a symbolic causal agent that can reason about the world in a way that is more familiar to the human mind than neural networks. The causal layer can cross-check Leela Core's traditional NN components in a hybrid causal/neural architecture. Leela Core is already better at explaining its decisions than NN-only platforms, making it easier to troubleshoot and customize. Much greater transparency is expected in future versions. [...]

>>DonHop+(OP)
I think this is an interesting approach. A child may collectively spend hours looking at toy blocks, to train themselves to understand how what they see maps to an object in three dimensional space. But later on, the child may see a dog for only a few seconds, and be able to construct an internal model for what a dog is. So the child may initially see the dog standing and pointing to the left, but later the child will be able to recognize a dog laying on the floor pointing to the right. And do that without thousands of training examples, because they have constructed an internal mental model of what is a dog. This model is imperfect, and if they child has never seen a cat before, that might be recognized as a dog too.