Exactly, and therefore - for the granularity - it can be coded in a linear fashion. This is how we used to code decades ago. That was/is the classical SDLC way; from concepts to rules to procedures to tasks. I call that a process of deabstraction because the information grows exponentially with every, next step till it scales out of control at task level. Interestingly, that is exactly where the "cleverness"of the designer/programmer has its maximum effect. Seems though as if we are still somehow talking about compression and decompression algorithms and not design.
Just imagine if N duplicated instances of partitioned processing was initialized for a single task, the extent of machine resources required? This approach has failed so many times over, it's not even worth pursuing anymore. Instead, I think we need to rather imagine the abstraction/deabstraction life cycle (which was published in 2008 already under tacit-knowledge management and even before as medical research) as a 3D atomic component, a pure object having polymorphic characteristics. Each instance of such a component would - by default- become a unique record. Such uniqueness forms the baseline for all matters of traceability throughout any system. It would accommodate everything systems-related being said here. The persistent issue of how 'clever' humans are would actually diminish to the point of becoming almost irrelevant. Last, the implications for reducing the demand on machine resources could theoretically be sufficient to enable a fully-recursive system and real-time reasoning. Rob ________________________________ From: Jim Bromer via AGI <[email protected]> Sent: Tuesday, 30 October 2018 2:54 AM To: AGI Subject: Re: [agi] Abstraction is not simple 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 <[email protected]> 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-M29110ba91c1846667d584655 Delivery options: https://agi.topicbox.com/groups/agi/subscription
