| Next: | 2.4: Thoughts | Bookmark | |
| Up: | 2: Mind | Monolithic | |
| Prev: | 2.3: Concepts |
Rational reasoning is very large, and very complicated. In trying to duplicate the functionality of a line of rational reasoning, it's easy to bite off too much, and despair - or worse, oversimplify. The remedy is an understanding of precedence, a sequence that tells you when you're getting ahead of yourself and building the roof before you've laid the foundations; heuristics that tell you when to slow down and build the tools to build the tools. Before you can create a thing, there must be the potential for that thing to exist, and sometimes you have to recurse on creating the potential.
Drew McDermott, in the classic article "Artificial Intelligence Meets Natural Stupidity", pointed out that the first task, in AI, is to get the AI to notice its subject. Not "understand". Notice. If a classical AI has a LISP token named "hamburger", that doesn't mean the token is a symbol, or that there's any hamburgerness about it. For an AI to notice something, its internal behavior must change because of what is noticed. A LISP token named "hamburger" has no attached hamburgerness. A philosopher of classical AI would say that the LISP token has semantics because it refers to hamburgers in external reality, but the AI has no way of noticing this alleged reference. The "reference" does not influence the AI's behavior - neither external behavior, nor the internal flow of program causality.
I've extended McDermott's heuristic to describe a sequence called RNUI, which stands for Represent, Notice, Understand, and Invent. Represent comes before Notice; before you can write feature-detectors in a modality, you need data structures (or non-crystalline equivalents thereof) for the data being examined and the features being perceived. Understand comes before Invent; before an AI can design a good bicycle, it needs to be able to tell good bicycles from bad bicyles - perceive the structure of goals and subgoals, understand a human designer's explanation of why a bicycle was designed a particular way, be capable of Representing the explanation and Noticing the difference between explanations and random babbling. Only then can the AI independently invent a bicycle and explain it to someone else.
Represent is when the skeleton of a cognitive structure, or the input and output of a function, or a flat description of a real thought, can be represented within the AI. Represent is about static data, what remains after dynamic aspects and behaviors have been subtracted. Represent can't tell the difference between data constituting a thought, and data that was provided by a random-number generator.
Notice provides the behaviors that enforce internal relations and internal coherence. Notice adds the dynamic aspect to the data. Applied to the modality-level, Notice describes the feature-extractors that annotate the data with simple facts about relations, simple bits of causal links, obvious similarities, temporal progressions, small predictions and expectations, and other features created by the "laws of physics" of that domain. The converse of modality-level Notice perception is Notice manipulation, the availability of choices and actions that manipulate the cognitive representations in direct ways. The RNUI sequence also applies to higher levels, and to the AI as a whole; it's possible to be capable of Representing and Noticing threeness without Understanding it, or being able to do anything useful with it.
Understand is about intentionality and external relations. Understand is about coherence with respect to other cognitive structures, and coherence with respect to both upper context and underlying substance (the upper and lower levels of the reductholistic representation). Understanding means knowledge and behaviors that reflect the goal-oriented aspects of a cognitive structure, and the purpose of a design feature. Understanding reflects the use of heuristics that can bind high-level characteristics to low-level characteristics. Understanding means being able to distinguish a good design from a bad one. Understanding is the ability to fully represent the cognitive structures that would be created in the course of designing a bicycle or inventing an explanation, and to verify that these cognitive structures represent a good design or a good explanation.
Invent is the ability to design a bicycle, to invent a heuristic, to analyze a phenomenon, to create a plan for a chess game - in short, to think.
If you have trouble getting an AI to design a bicycle, ask yourself: "Could this AI understand a design for a bicycle if it had one? Could it tell a good design for a bad design?" If you have trouble getting an AI to understand the design for a bicycle, ask yourself: "Can this AI notice the pieces of a bicycle? Could it tell the difference between a bicycle and random static?" If you have trouble getting an AI to notice the pieces, ask yourself: "Can this AI represent the pieces of the bicycle? Can it represent what is being noticed about them?"
| Next: | 2.4: Thoughts |
| Up: | 2: Mind |
| Prev: | 2.3: Concepts |