What?
On Sat, Dec 21, 2013 at 2:25 PM, Piaget Modeler <[email protected]>wrote: > Whoa!! > > ~PM > > ------------------------------ > Date: Sat, 21 Dec 2013 13:43:19 -0600 > Subject: Re: [agi] A Random Thought... > From: [email protected] > To: [email protected] > > > Ok, which dimension are you attempting to scale up? Triviality corresponds > to minimal representational capacity, both in the environment and the agent > operating within it. Human beings are (currently) at the other end of that > scale, with enormous representational capacity for dealing with a highly > complex environment. The more complex (non-trivial) the environment, the > greater the representational capacity required of agents operating within > it in order to effectively make decisions. It is this dimension that I am > looking at. > > Learning algorithms are easy to understand, design, and implement. They > are just solutions to optimization problems. I do not think learning itself > is where the bottleneck lies. Instead I look at the representational > systems underlying those learning algorithms. The simplest learning > algorithms operate over tables of choices. They tabulate expected returns > or error levels for each choice, over many repetitions, and gradually > settle on the choice(s) with the maximum expected return or minimum > expected error level. Adding layers of sophistication, we begin to see > context matter more and more: Conditional choices and statefulness result > in much more interesting and coherent behavior. Generalizing over choices > and conditions and actions to those that are similar, we see an additional > gain in coherency, with algorithms that can deal with new situations > robustly based on previous experience with other situations. > > What is needed is to increase the expressivity of the underlying > representational schemes used by learning algorithms. Moving up to the > representational complexity level of ontologies, episodic memory, etc., the > representational scheme becomes ever more capable. In order to reason about > things, we need to represent those things effectively. Once we have a fully > capable representational scheme -- a programmatic framework for the > representation of Meaning, in all its forms, with all its inherent > ambiguities -- we can begin writing learning algorithms to extract meaning > from the environment, generate rules for predicting arbitrary unobserved > phenomena from arbitrary observed phenomena, recombining meanings to > produce new ones, choosing contextually appropriate and meaningful > behavior, etc. There is no understanding without meaning, and there is no > intelligence without understanding. > > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/23050605-2da819ff> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
