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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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WHO_WE_ARE WHAT_WE_DO TECHNOLOGY General Synaptics Aerospace_&_Defence_Machine_Learning_Company VAT NUMBER:_IT0267070992 REA NUMBER: GE-503104 Email:_luca.marchese@synaptics.org |
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