It is unlikely that many people are going about it in just the same way as I am. Jim Bromer
On Wed, Jun 5, 2019 at 2:25 PM Jim Bromer <[email protected]> wrote: > When someone talks about deep learning they are talking about some kind of > method that 'connects' weights and other functions derived from the weights > all of which are calculated using standard arithmetic. Addition, > multiplication.. I am not dismissing those ideas, in fact, I want to think > about creatine something somewhat similar. However, I believe that it is > worth exploring ideas that work a little differently. Instead of using > arithmetic, which relies on a few simple rules, how about an arithmetic > that relies on hundreds or even thousands of rules (abstractions) that have > to be learned or acquired along with the data objects that the mathematical > objects refer to. What is wrong with this idea? Those kinds of mathematical > methods are not as flexible as traditional arithmetic. As an amateur > mathematician whenever I try something different it never seems to work as > smoothly as traditional arithmetic. Other people have thought about things > like this. It is not original. All that makes me a crackpot. (Thank god!) > But my ideas are unique in this group and it is pretty safe to say that > they are unique in much larger groups as well. There may be some people who > are trying something similar but it is very unlikely that we are going > about it in just way. But maybe it is a little like Occam's Razor. Make it > no more unique than it has to be. > I just realized that your Occam criticism was not completely off. In one > way I am saying let the AI (or semi-AI) program produce as many > abstractions as seems useful, and then following Occam's Razor, pare it > down closer to using only what is necessary. (Occam's Razor looks like an > idealism which means it is therefore imperfect.) > Jim Bromer > > > On Wed, Jun 5, 2019 at 10:44 AM Jim Bromer <[email protected]> wrote: > >> I did not mean to be overly critical in my last email. I was just trying >> to be objective, to the best of my ability. >> A major underlying goal in discrete AI has been to look for a simple set >> of rules and discrete object types which could be used as a basis for all >> knowledge. I believe these goals were driven by a few historical events. >> The importance of arithmetic and logic, both of which could be derived from >> a relatively simple set of rules; the value of arithmetic to humankind >> which could be developed with a minimization of rules; and the severe >> constraints on computer technology in the old days. I think that the >> learned development of many rules may be achieved with modern computers but >> that presupposes that there is more than one level of abstraction. So my >> ideas about AI are based largely on discrete methods, but the abstractions >> of the system have to be acquired and learned alongside other kinds of >> knowledge (like facts and how-to stuff), and made to fit the knowledge that >> is learned. >> No one in this group talks this way about this stuff. Of course, a critic >> can say that is implicit in someone else's ideas - except for the stuff I >> am getting wrong. To put it a little more accurately, the necessity of >> acquiring 'abstractions' along with 'knowledge' 'endpoints' (so to speak) >> is implicit in many if not most AI theories. So how is mine original the >> critic might ask? This would be an example of a dismissive argument. My >> theory is my own because other people do not bother to talk about stuff >> like this. So it may not be startlingly 'original' but it is my own theory >> (it is not based on some white-paper or something that someone else has >> talked about in these groups. You can find theories about 'abstraction' but >> there is less around about the necessity of developing abstractions as >> knowledge is developed and even less that suggests that these relationships >> might be expressed mathematically - at least to some extent. >> While I am interested in developing a fast mathematical index into >> knowledge (based on discrete-based knowledge) I now believe that a great >> deal of meaning can be -learned- and baked into the mathematical indexing >> system. For example, mathematics does not have to be restricted to a >> narrowing process (that produces a single numerical result) but it can also >> be an expanding process (that produces a set of results that might be >> expressed numerically.) >> Again, a critic can take a dismissive attitude and might say, for >> example, that fuzzy logic or any number of weighted methods can do that! >> Ok, but I could defensively reply that I am interested in mathematical >> references to sets that could (in theory) (typically) be derived from the >> values. >> Radical originality is not one of my goals. However, individual >> uniqueness is, because I believe that individuality is needed to develop >> useful computational methods that have yet to be developed. There are many >> unique aspects of my theories and the challenge is to find the theories >> which can be made to function as a whole and to eventually develop computer >> programs that will show their usefulness in future AI and AGI programs. >> Jim Bromer >> >> >> On Tue, Jun 4, 2019 at 6:07 PM <[email protected]> wrote: >> >>> Ok, sorry about that misunderstanding. >>> >>> You need to form the model of the of the working area of the a.i. , its >>> true for us all, but how you do it, is what makes what your doing original. >>> *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/T395236743964cb4b-M0c0dd3364303694d41da54b7> >>> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T395236743964cb4b-Mf59c69ede22aa9d4fd0dc378 Delivery options: https://agi.topicbox.com/groups/agi/subscription
