Jim, You are making some rookie errors...
1. You fail to appreciate the speed issue. Computers are WAY too slow to even be able to experiment in the domains you are speculating. In short, you are at least decades too early to start. Note (for example) my fast parsing, where I FINALLY proposed a fast-enough method of parsing English, using ideally-constructed tables, and NOT using the sort of expensive learning you are talking about. People hear "gigahertz" and their eyes cross, their knees weaken, and they think they can do ANYTHING. WATSON comes a little closer, still understands nothing, but uses 2,880 processors to do it. 2. There is a belief/condition in people's minds that they can arbitrarily discard entire dimensions, often more than one dimension, and still make a working learning system. I might bet a week of my time testing such a highly questionable presumption, but certainly not years. In any case, computers are still too slow for your approach, even with discarded dimensions. Note what I did with my Scanning UV Fluorescent Microscope. Here is something I first came up with ~50 years ago - and it was WAY ahead of its time. As late as ~2 years ago it was rejected as being "off topic" by the AGI conference. Now, Obama is calling for just such a machine in his BRAIN Initiative. I am now scrambling to get my SUVFM considered because it IS the best of the several competing approaches. I suspect that you may end up doing the same. Once we know how brains work, and you can buy a petascale machine from Best Buy for ~$1K, then you can dust off your proposal and forge on ahead. You will then be government funded (via Social Security) and have your medical insurance covered (by Medicare) as I now am. Mine is a "success" story, as most good designs that are ahead of their time end up lost to history, often because their creators have also been lost to history (died). Once you have finished your design, your next job will be to stay alive for another ~50 years, to be around to promote it when the "missing pieces" have become readily available. Steve ================== On Sat, Apr 13, 2013 at 3:39 AM, Jim Bromer <[email protected]> wrote: > Part 1 > > I feel that complexity is a major problem facing contemporary AGI. It is > true, that for most human reasoning we do not need to figure out > complicated problems precisely in order to take the first steps toward > competency but so far AGI has not been able to get very far beyond the > narrow-AI barrier. > > I am going to start with a text-based AGI program. I agree that more > kinds of IO modalities would make an effective AGI program better. However, > I am not aware of any evidence that sensory-based AGI or multi-modal > sensory based AGI or robotic based AGI has been able to achieve something > greater than other efforts. The core of AGI is not going to be found in the > peripherals. And it is clear that starting with complicated IO > accessories would make AGI programming more difficult. It seems obvious > that IO is necessary for AI/AGI and this abstraction is a probably more > appropriate basis for the requirements of AGI. > > My AGI program is going to be based on discreet references. I feel that > the argument that only neural networks are able to learn or are able to > incorporate different kinds of data objects into an associative field is > not accurate. I do, however, feel that more attention needs to be paid to > concept integration. And I think that many of us recognize that a good > AGI model is going to create an internal reference model that is a kind of > network. The discreet reference model more easily allows the program to > retain the components of an agglomeration in a way in which the traditional > neural network does not. This means that it is more likely that the > parts of an associative agglomeration can be detected. On the other > hand, since the program will develop its own internal data objects, these > might be formed in such a way so that the original parts might be difficult > to detect. With a more conscious effort to better understand concept > integration I think that the discreet conceptual network model will prove > itself fairly easily. > > I am going to use weighted reasoning and probability but only to a limited > extent. > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10443978-6f4c28ac> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Full employment can be had with the stoke of a pen. Simply institute a six hour workday. That will easily create enough new jobs to bring back full employment. ------------------------------------------- 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
