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Sometimes these abstractions are leaky, and I have to worry about colour-spaces, or image resolution, but very seldom do I have to go down to the actual “bottom” representation, a massive 1D array of RGB colour-values for each pixel.In the Finder I worry about file sizes, extensions, names and folders, permissions, etc., but unless things go seriously badly I never have to worry about how these things actually get saved to a disk (again essentially a massive 1D array of bytes).SHRDLU could reason about classes of things and the rules of world (“can a pyramid support a pyramid?”), as well as individual blocks (“where is the in the box”).They say “what places are open for lunch”, and they mean as in a meal, so they want to eat, and probably a restaurant and not a supermarket.They say “make an appointment with my sister for lunch”, and mean the sister in the same city, not the one working in another country. We can provide lists of synonyms, but all this introduces a domain-dependenc.It is however bad, since knowing what you don’t know is often challenging.
We call this an to carry out the three core processes in a chatbot brain: parsing and understanding the input, modelling the conversation and deciding what to say next, and finally actually writing the output sentences.
Even if you know that to the computer is mostly empty tokens, it’s hard to not imbue them with the same semantics you would when talking to another person.
At first sight this may look great: people are easy to fool with simple syntactic tricks!
People could chat with ELIZA, and it used simple pattern matching rules to echo parts of the statements back (“I don’t SHRDLU was much more sophisticated than ELIZA.
Incoming text was parsed and actually understood, and a rich dialog about things in the blocks world was possible.
All of these programs offer useful abstractions that let me do things that I would never be able to do at the lowest level.