Classification: given a set of inputs return a distinct output that compresses the information of the input into a smaller set of values. Classification tasks can be done with neural networks, fuzzy logic, case based reasoning, specialized compression, etc. Construction: given an initial state, a set of operations, and a goal state, return a sequence of operations that transforms the initial state into the goal state. Construction tasks can be done with planning algorithms (state space search, plan space search, hierarchical search, etc.).
Both approaches ARE used in complex AI applications. ~PM Date: Tue, 17 Feb 2015 09:03:40 +0100 Subject: Re: [agi] Couple thoughts From: [email protected] To: [email protected] On Tue, Feb 17, 2015 at 8:42 AM, Piaget Modeler via AGI <[email protected]> wrote: I was taught that in AI there are two primary tasks, Classification and Construction. Please correct me where I'm wrong, anyone. I like to learn. There has always been a lot of debate about what AI is. We don't even have anything close to a consensus on a good definition of "intelligence". This leads me to suspect that the main problem with AI is that we don't have a well-defined problem to tackle, but that's a broader issue. Sure "Classification and Construction" is not so bad. It's not a matter of being right or wrong. There are thousands of plausible alternatives to this. You pick a model and run with it, but let's not pretend we are dealing with some super-objective definition. Deep Learning and (many other methods) are good at classification tasks. We also need methods good at construction tasks (i.e. plan generation). This "also need" mentality could be the problem. Maybe what we need is something that can holistically perform both types of tasks. Suppose you take deep blue. It can play chess really well, a skill that was up to then associated with humans. But then someone says: wait humans are also usually good at driving cars. Then you merge Google cars and deep blue and claim to be closer to AGI? Does this make any sense? Do you see the problem? Best,Telmo. ~PM > Date: Mon, 16 Feb 2015 16:09:00 -0800 > Subject: [agi] Couple thoughts > From: [email protected] > To: [email protected] > > I had a couple of things running through my mind -- > > 1) "Deep learning algorithms are very good at one thing today: > learning input and mapping it to an output. X to Y. Learning concepts > is going to be hard." Andrew Ng. > > I guess I take that to be an acid test of where the big guys are with > concepts. > > 2) "brain inspired", "physics inspired", "math inspired," X-inspired, > etc-inspired, hybird-inspired... > > It seems all AGI approaches take the "inspired by" approach. The only > approach that is not deliberately inspired by some discipline, but > aspires to the actual thing: Colin Hayes' approach. > > There is nothing wrong with the "inspired by" approach, of course. > > Mike > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/19999924-4a978ccc > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com AGI | Archives | Modify Your Subscription AGI | Archives | Modify Your Subscription ------------------------------------------- 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
