--- Peter Voss <[EMAIL PROTECTED]> wrote: > My question: What specifically is Cyc unable to do? What are the tests, and > how did it fail?
Cyc has not solved the user interface problem. It does not understand natural language. It does not learn. It requires users to enter data manually in an obscure, structured language. This is simply not a practical approach. They have been manually entering common sense information since 1984. In 1994 Lenat predicted that Cyc would be on every computer and would solve the software brittleness bottleneck in 5 years. But if you play the FACTory game at http://www.cyc.com/ you will get a sense of how shallow this database still is, compared to what the average human knows. Its ability to reason deductively mostly generates useless facts, such as (from the game), "most shirts weigh more than most appendixes". Logic is a poor model of human knowledge. Cyc, like a lot of AGI projects, seems to lack a well defined goal. "Let's build it and see what happens. We will know AGI when we see it". No we won't. Like Russell Wallace said, intelligence is not a scalar quantity. We already have machines that are vastly more intelligent than humans in some areas and less in others. If the goal of Cyc is to make computers more usable, it has certainly not done that. Common sense is useless without natural language. Solve the language problem first. I believe this has to be done by modeling childhood development. Human knowledge is like a broad pyramid with sensory and motor I/O at the base and abstract, adult level knowledge at the tip. Cyc and other expert systems skip right to the abstract knowledge for the sake of computational efficiency. But to communicate with users you need the whole pyramid. Natural language has a structure that allows it to be learned bottom up, layer by layer: phonemes (one month), then word segmentation (7 months), then word semantics (12 months), then grammatical structures (2 years), and only then can you teach it logical connectives (and, or, if) and arithmetic (5 years). In NLP there are many examples of successes using bottom up models (e.g. information retrieval), and many examples of failures using top down models (e.g. parsers, expert systems). -- Matt Mahoney, [EMAIL PROTECTED] ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
