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SHARP is the result of a research
started to give answers to some issues raised in the DARPA XAI (eXplainable Artificial
Intelligence) program. |
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The tremendous success that
Deep-Learning has achieved in recent years has, however, highlighted serious
problems related to this technology. In addition to the problems due to the
ease with which this technology can be deceived, also a serious problem has
been highlighted: this technology is of the BLACK-BOX type and therefore the
inference cannot be explained. Although the explainability of the decisions
taken by Artificial Intelligence algorithms is of great interest at
international level (e.g. the GDPR decree of the EU), it is evident how much
this characteristic of AI algorithms is indispensable in human-to-AI
collaborative scenarios in the military and in the context of safety-critical
missions. Our SHARP™ (Systolic Hebb Agnostic
Resonance Perceptron™) neural model allows you to create rules from the data
and map them to the synaptic weights of the neural network itself. When the neural
network makes a decision it is possible to extract the rule or set of rules
that determined the decision, going back to the detail of the original data
learned and the expert's evaluation added as a range of the single variable
during the training phase. The SHARP algorithm has L2 (Lifelong Learning) and
OSL (One Shot Learning) properties. With our algorithms we can extract rules
also from RBF neural networks with RCE learning algorithm. |
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