Abstraction can also be used to categorize abstracted characteristics. Abstractions are not just non-dynamic data objects, an abstraction can also represent a process or operation-like steps and rules. Jim Bromer
On Mon, Oct 29, 2018 at 12:56 AM Stanley Nilsen <senil...@ghvalley.net> wrote: > > > On 10/28/18 6:14 PM, Jim Bromer via AGI wrote: > > ... thinking > of how abstraction might be used to produce recognition. First of all, > a useful abstraction might rely on an algorithm not only to get it out > of a data (or a 'text') but the data or some characteristic of the > data might need to be put through a transformation by the algorithm. > Although this is not radically different it is a new way of looking at > 'abstraction.' Secondly, different kinds of transformative > abstractions of data-text might be needed to build a configuration of > abstractions that could then be used in recognition and subsequent > analyses. I think that is a new and interesting way of looking at the > concept of abstraction. > Jim Bromer > > Hi Jim, I believe that abstraction is important for the goal of making an > intelligent entity, one that will recognize and make good choices with the > facts. My idea of abstraction is fairly simple, probably incomplete. > > With that in mind, a few thoughts and examples: > > I'm thinking abstraction has to do with taking away detail - loss of > information for a reason. > > For example, the IQ score is an abstraction. It doesn't tell you much about > the person with a specific IQ. But it serves a purpose (perhaps) of allowing > one to make a comparison of one person to another (dubious.) > > Encoding is more like what we do with text. The text requires that there be a > device that decodes the symbols, and after decode, ends up with richer > concepts. This happens because there is agreement about what the encoder and > decoder do with the symbols. > > Abstraction is very different than decoding. It uses some formula to arrive > at an end result, but the end result doesn't contain nearly the information > that was originally involved in performing the abstraction. This is the > beauty of the abstraction, and the beast in the abstraction. Beauty in that > we have something simple to work with, but a beast if we try to use the > abstraction for the wrong purpose. > > For an example of the beast of IQ, consider… We agree that a person's IQ > reflects something about their ability to be aware of the meaning behind a > few example questions or problems. This is good, but it isn't really a > predictor of a person's suitability for any given task. When it comes to > people, IQ doesn't tell you if a person has sustainable interest in a > specific domain. > > Another example: > > Compare an apple and a peach. We want to totally abstract both objects and > say which one is preferable. First problem is what does preferable mean? Lets > say we can take one or the other but can only choose one. And lets say that > we have an agent who acts as an intermediary. The operation involves the > agent producing a number for one object and a number for the other object > (fruit objects.) Using the abstracting process, the agent acquires the object > with the higher number. > > Lets say we train the agent to know that we prefer peaches to apples, but > then again, many times a store bought peach is far inferior to a ripe picked > peach. So we start to give the abstractor rules… > > 1) if a peach, value starts as 1 > > 2) if apple, value starts as .9 > > 3) if fruit is spoiled, value is reduced to .1 percent of original. > > 4) if peach is tree ripened, value increased by 30 percent > > 5) if peach is store bought value decreased by 16 percent > > 6) if apple is tree ripened, value is reduced by 20 percent > > 7) if apple is golden delicious increase value by 5 percent > > … and so on, we accumulate a bunch of rules for the abstractor to use in the > evaluation. > > A couple of things we can say about this process. First, the abstractor works > best with lots of information - ONLY if the abstractor has rules for how to > “use” the information. If the abstractor is sophisticated, it will take many > things into consideration. Second, the considerations will be based on who > the abstractor is working for – sort of a “knowing” the end consumer. A chef > may look at fruit differently than a worm farmer or a pig feeding operation. > > Finally, it might be said that we have a good abstracting agent if it was > trained with appropriate rules and ends up guiding our acquisition as we hope > it would. > > And, yes I agree with you Jim, abstraction could aid in recognition. If one > looks at recognition as being a process of making a choice between multiple > possibilities. The abstraction phase could compare the known facts against a > set of facts that are typical of each possible item. Rules would modify the > abstraction number for each item and a highest ranking “winner” emerges. > > Notice that in the real world, the items to be recognized could be numerous. > The abstractor would have lots of work to do to come up with an abstraction > number for every object. This is where parallel processing saves the day. We > distributed the facts to thousands of processors and each one has a dozen or > so rules to consider and then “promotes” it's match number. The highest > promoted number wins, we have our match. > > The “recognition” is made according to comparison of magnitude of numbers. > The numbers are basically totally abstract, (with very little resemblance to > anything,) but, that is what is needed to arrive at the choice. > > In my architecture for an intelligent machine, I separate the intelligence of > doing abstraction from the moment by moment operation of promoting - > promoters are simple components duplicated by the thousands and each one can > only consider a few rules. The abstractor is the genius and does the > programming of promoters. > > Stan > > > Artificial General Intelligence List / AGI / see discussions + participants + > delivery options Permalink ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T586df509299da774-M039e7839f1d1add58e1870a7 Delivery options: https://agi.topicbox.com/groups/agi/subscription
