Agreed. Could be though that the causal-inference engine (for lack of a better word) may be but one-of-many reasoning subsystems in the overall AI application. Perhaps, for specific functionality, such a subsystem would be activated as a temporary, primary node of an AI schema. However, was that the primary basis used for learning and adapting? Unless we viewed the logical architecture and traced the adaptive competency throughout such architecture, we would not know.
________________________________ From: EdFromNH via AGI <[email protected]> Sent: Friday, 14 September 2018 12:06 AM To: [email protected] Subject: Re: [agi] Judea Pearl on AGI If Demis Hassabis, the current leader of Google's DeepMind AI subsidiary, was able several years ago to create an artificially intelligent program that could learn to play each of many different video games much better than human players -- just from feedback from from playing each such game -- his program obviously had to be able to model the causal inference inherent in whatever videogame it was learning. So obviously there already has been a lot of success in AI's being able to do a good job at automatically learning causal inference. On Thu, Sep 13, 2018 at 3:45 PM Nanograte Knowledge Technologies via AGI <[email protected]<mailto:[email protected]>> wrote: 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]<mailto:[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-Mf8d761b549558b23eeb9b432> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T0f9fecad94e3ce7e-M1dc3bae706850f689ec4b574 Delivery options: https://agi.topicbox.com/groups/agi/subscription
