Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A6639264967746994176

#AI: Beyond #Theory to #Practice , #Reality , and #Causality:
#MIT Computer Science & AI Lab: AI Executive Guide:
AIMLExchange.com/MIT/: https://lnkd.in/eknKzm5 :
These shortcomings have something in common: they exist because AI systems don’t understand #causation.

AI systems don’t understand #meaning or #commonsense either: hence problems are inherent in the #scientific process of #valid & #reliable #knowledge #creationhttps://lnkd.in/eTFfAii

…Driven by #past #data https://lnkd.in/eW9mKjT & oblivious of #dynamic #nonlinear #nonnormal #feedforward loops https://lnkd.in/eapGgy6 underlying #systemic ‘causal textures’ of #dynamic #environments https://lnkd.in/e83Bdd2 generating #futures, current AI systems can’t ‘anticipate’ major #threats & #opportunitieshttps://lnkd.in/e4TPQne

…Compounded by long-known limitations of underlying: #Shannon’s #InformationTheory:
https://lnkd.in/eGHiatP .

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Building Adversarial Command & Control and Counter-Adversarial Command & Control Capabilities of the Future.

https://www.technologyreview.com/s/615189/what-ai-still-cant-do/

Artificial intelligence won?t be very smart if computers don?t grasp cause and effect. That?s something even humans have trouble with.
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