The Nature of Consciousness

Piero Scaruffi

(Copyright © 2013 Piero Scaruffi | Legal restrictions )
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These are excerpts and elaborations from my book "The Nature of Consciousness"

Artificial Intelligence

The term “Artificial Intelligence” was coined around 1955 by the US mathematician John McCarthy, but it has never been clarified what it was truly supposed to mean. The reason is simple: there is no consensus on what makes a machine (or, for that matter, a human being) “intelligent”.

If opinions vary on whether Artificial Intelligence is feasible or not, opinions are even more varied on how Artificial Intelligence should be achieved.

At the beginning Artificial Intelligence was often equated with the quest for the “general problem solver”, the program capable of solving all mathematical problems. Because the computer is a symbolic processor, and proving theorems is about processing symbols, it was natural to assume that a computer can prove all theorems. However, scientists soon realized that problem solving is not everything, and in everyday life we can solve problems that are essential to our survival (such as deciding when to cross a street) without ever using the Mathematics we studied in school.

Thus “intelligence” is not commonly defined by the number of theorems one can prove in a second (otherwise machines would already be far more intelligent than the most intelligent humans) but by the ability to move around in the real world and carry on all the tasks that humans carry out more or less effortlessly during the day.

A more realistic view is that intelligence is the result of reasoning about knowledge. Intelligent behavior originates from a base of knowledge and from the ability to carry out inferences on that knowledge base.  Intelligence is essentially knowledge processing. Since a computer is ultimately a symbol processor, the issue is then how to express knowledge in a symbolic form.

The difference between knowledge and information is crucial. Information can be found in books, knowledge comes from experience. Common sense, for example, is a form of knowledge but not a form of information. Anybody can access the information stored in a medical encyclopedia, but only physicians have real knowledge about medicine. The focus of Artificial Intelligence is not in building encyclopedias, in storing huge amounts of information: it is in “cloning” humans who are experts (i.e., have acquired specialized knowledge) in a field or domain. The difference between information and knowledge is, for example, the difference between asking “who is the president of the United States?” and asking “who will be the next president of the United States?” The former question requires only “information” about who is the current president, the latter question requires “knowledge” about the domain of politics.

According to John McCarthy ("Programs with Common Sense", 1958), knowledge representation must satisfy three fundamental requirements: “ontological” (must allow one to state the relevant facts), “epistemological” (allow one to express the relevant knowledge) and “heuristic” (allow one to perform the relevant inference). Artificial Intelligence can then be defined as the discipline that studies what can be represented in a formal manner (epistemology) and computed in an efficient manner (heuristics).  The language of Logic satisfies those requirements: it allows us to express everything we know and it allows us to make computations on what is expressed by it. Each set of knowledge is in fact a mathematical theory.

The underlying assumption of the knowledge-based approach is that symbolic processing per se may lead to human-like intelligence.


Knowledge Representation

One of the crucial steps to build intelligent machines is therefore knowledge representation: first and foremost, one must encode in a machine the knowledge about the world possessed by humans. Every science needs to build a mathematical model of its world before it can perform any inference and draw any conclusions. Physics, for example, represents natural laws with formulas. Then formulas can be combined to yield prescriptions about the effects of actions. The world of Artificial Intelligence is the world of knowledge: what must be represented formally is knowledge.

Knowledge has traditionally been formalized in three forms: facts, stimulus-response pairs (or cause-effect, or premise-action, or antecedent-consequent pairs), and relations between concepts. Facts are easily represented in first-order Predicate Logic in the form of  logical expressions: “Piero is a writer” can be represented as “writer (Piero)”, meaning that Piero satisfies the predicate “writer” (or that Piero belongs to the set of individuals that satisfy the predicate “writer”). For example, if we know that all writers are creative, then we can apply a simple step of deduction and derive that Piero is also creative.

“Production” rules are usually employed to express the causal connection between one fact and another fact (if something is true, then something else must be true too). For example, if somebody is a human being, then she is also a mammal. Whenever the antecedent is true, the consequent is also true. This too can be translated into Predicate Logic, because the “implication” is mathematically equivalent to a logical expression (in Logic, p IMPLIES q is equivalent to NOT p OR q). More rules can therefore be combined according to Predicate Calculus.

Finally, relations between concepts (i.e., complex concepts) can be represented with systems such as “semantic networks” and “frames”. A semantic network represents concepts as nodes, “links” a concept with other concepts, and specifies of what type each link is. For example, the concept of a human being is linked to the concept of a mammal by a link of type “BELONGS TO”. A concept may have many links of many types to other concepts. Ideally, all human knowledge could be represented by a gigantic semantic network.

A “frame” can be used to represent the inner structure of a concept: its attributes, their default values, the actions associated with the attributes, and, again, the links to other concepts. A car’s attributes include that its function is to move, that it has four wheels,  that it costs so much, etc. Both semantic networks and frames can also be reduced to expressions of first-order Predicate Logic.

Anything that can be reduced to Predicate Logic satisfies McCarthy’s requirements.


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