What’s missing obviously is a viable third option that would disrupt and transform the status quo by leaning into and operating from an awareness of the emerging future

An Active Inference Model of Collective Intelligence

Kevin Mitchell says in one of his books free agents he talks about I 00:27:10 move therefore I am is that yeah yeah no that's that's that's that's exactly right and all the work on um uh uh active inference

so now finally we get to active inference all this discussion and we're finally getting to the point here right for his lab so um i had and i had already touched on 01:47:35 some of this before but um it would you know today if you're going to develop a really good ai system you're and you're going to have a you have a robot saying the robot has to act 01:47:47 in some environment it is pretty well understood that that if you program that robot to you give it a you give it a i mean traditionally you'll give it a a a fitness function or some kind of 01:47:59 valuation function and it's for example it's good if it it you know you lose points if you fall through a trap door and is and you get points if you uh you know whatever 01:48:11 find find the piece of cake or something well that's uh that's fine for extremely simple universes that your robot might work in but as soon as you get beyond you know as soon as you get to any kind of more realistic uh 01:48:24 universe that your robot has to work in that pre-programming pre-programming concept just kind of falls apart it is you you it would require the the the practitioner to think ahead of all the 01:48:37 things that the robot might encounter and then how to value certain you know value those situations in certain ways uh and that is really uh what active inference 01:48:49 offers is a is a kind of a cognitive understanding or a mechanism by which an organism will uh uh where its 01:49:00 fitness score is in a sense involves both uh you know achieving goals and exploring its world to for for for epistemic gain so 01:49:16 um that's what we would like the that's how we would like to program the robot in a sense so that it can learn from it can learn on the fly from its experiences it can it can alter its actions and 01:49:30 goals as it be as it becomes clear as it gathers more information from its universe as it as it meets new situations that were never never conceived of by the by the 01:49:42 programmer that it through through an active inference or an active inference like uh you know mechanism it can learn and explore and and critically balance exploration with 01:49:54 exploitation and then we come right back to that whole concept of criticality so you know what you would really like your robot to do is remain at that critical uh phase between 01:50:06 exploring what's out there and making use and gold directed behavior of what's in front of it and um and uh you know that's how you could program this world this robot to act in the world and be pretty good at 01:50:20 it you know if you if you build it well so that's what the systems of a society can help a society to do you you don't you it's worth talking about building new systems i think it would not be wise to say 01:50:32 this checklist of like we wanted this level of education we want to want this you know to react this way in this situation react this way in this situation and this level of uh you know whatever money and this level of this and this 01:50:45 level of that while those kinds of preferences can be a useful start society has to be alive in its moment you know in the moment as society is alive it's cognating it's 01:50:57 it's it's it's actively uh you know comparing what it's the result of its actions to the model that is in its head and uh so active inference offers this way 01:51:09 to uh to balance uh exploration and and uh and uh exploitation and remain critical and remain optimally cognitive right so that's part of it 01:51:24 uh and then part of it i mean and for me this the the the idea of the embodied uh you know the three four e's uh this is what i really am attracted to in 01:51:46 active inference is in a sense it's kind of a simple concept it's not really very complicated you know if you've studied bayesian uh theory it all it's kind of straight you know in a way it's kind of straightforward 01:51:58 but the the you know the way fristen has connected the dots and and and and uh extended that into the bigger picture of life kind of it it to me it is uh it is rich 01:52:11 there's a there's a lot yet to be learned and gained and explored in this umbrella of active inference