Most interesting. Thanks for sharing. From the little I understand about this large, body of work, this makes sense to me. However, I would contend that by adopting - what is called by some - a network structure (closing loops in a 3-entity structure) would lead to confusing results.
For example, one cannot reliably infer a vertex from that, which may then skew the rest of the structural results. . I think it's a classical "copout" in systems design; when in doubt, then to close the loop to open the associative option i.e., A=> B and C and B => C. Result: A indirectly causing C, but it was already inferred that A directly caused C. Did it, or didn't it? This would present as a self-made paradox, not so? ________________________________ From: Robert Levy via AGI <[email protected]> Sent: Thursday, 13 September 2018 10:08 PM To: AGI Subject: [agi] Judea Pearl on AGI I don't think I've seen a discussion on this mailing list yet about Pearl's hypothesis that causal inference is the key to AGI. His breakthroughs on causation have been in use for almost 2 decades. The new Book of Why, other than being the most accessible presentation of these ideas to a broader audience, is interesting in that it expressly goes into applying causal calculus to AGI. Artificial General Intelligence List<https://agi.topicbox.com/latest> / AGI / see discussions<https://agi.topicbox.com/groups/agi> + participants<https://agi.topicbox.com/groups/agi/members> + delivery options<https://agi.topicbox.com/groups/agi/subscription> Permalink<https://agi.topicbox.com/groups/agi/T0f9fecad94e3ce7e-M9e6c354c9f8ac56c414a651f> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T0f9fecad94e3ce7e-M4e059f68d10346e680f74b75 Delivery options: https://agi.topicbox.com/groups/agi/subscription
