Logic Explained Networks and Tsetlin Machines: the future of (X)AI?
What if AI were fully explainable? What if black-box models could be interpreted in their reasoning? What if we replaced the hefty numerical computations with propositional logic? Over the last decade, academics and tech startups have attempted to answer these questions, introducing the concepts of Logic Explained Networks and Tsetlin Machines. Caught between promising benefits and lack of funding, these solutions may be a game changer for the future of Explainable AI.
Logic Explained Networks
Logic Explained Networks are a family of interpretable models purporting to bridge the gap between “black-box” neural networks and human-understandable reasoning.
The origins
In August 2021, a group of researchers from the Italian Universities of Florence and Siena, the French University of Côte d’Azur and the University of Cambridge published a paper titled Logic Explained Networks. Highlighting the intrinsic limitation of neural networks – i.e., providing human-understandable rationales for the model’s decisions - they...
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