A Symbol Grounding Problem?

I’ve just read a really interesting paper, on the symbol grounding problem [1], I hope I understand it, because I don’t think I’m convinced. When John Humphries (Today, Radio 4) asks an interviewee when will computers be able to think, we’re still thinking about thinking being represented on Turing Machines. But there are problems with this representation.

An artificial symbolic system is the result of a double translation of the world. Firstly, the external symbols are translated by a programmer into the language of human symbols. Secondly, they are transformed into a formal language, which can be implemented in the machine.

This is entirely true. The software engineer is engaged in flattening reality from experience into descriptions, and from descriptions into symbols which can be processed by a machine. This represents a layering issue which is inherent in the Church-Turing model. This is particularly explicit in the process of normalisation in designing a database: the designer knocks the real world out of the system to represent everything in tables. Ultimately, the machine has no understanding of what these symbols mean back in reality. However, I’m not so convinced about the further argument towards a symbol grounding phenomenological world.

The argument is that have agency in the world a machine would have to have the same connection to an experienced world that a human has; to be grounded, the symbolic representations need to be supported by physical evidence. So, when we’re baking a cake, a machine would have to see that we’re up to our elbows in flour; and, it is only once we’ve produced a cake from the oven that we’re no longer baking a cake. But my first issue is that blind people are no less thoughtful because of their lack of a sense dimension. And secondly, can we be certain that the baker isn’t baking bread: can we determine people’s intentions by their actions. Not only do we need evidence, we still need trust in what they say. This is a difficult problem for humans to solve, not just machines!

Peircean Semiotics[2] and the Language Action Perspective [3] are not concerned with producing agency—an AI—they describe how we communicate. What makes us human is that we talk, and therefore interfaces to machines should be provided through voice. Anyone who has problems with screens and keyboards deserves a better interface. This includes the 370M monolingual Hindi speakers who don’t have  a keyboard, and the 55M people who are blind or partially sighted. But what the link between what someone says and what someone means is arbitrary.

Enguage doesn’t break utterances into words, and words into letters, as if cognition were a pseudo-Turing machine. It treats an utterance as a single and unique value. This is not to suggest that while an utterance has one value that it only has one meaning. In natural conversation it is possible to misunderstand, the key is to try to get the most likely interpretation most of the time, and to deal with exceptional cases, again, through voice. This means there is the need to deal with similar utterances in the same way: i need a coffee as a unique value is unlike i need a biscuit, but it needs to be processed in the same way. My examples elsewhere on this site has demonstrations that this is achievable.

But I do have to admit, this may seem like kicking the can down the road as it means we are now wholly dependent on trust, not evidence. Where does this trust come from? As an interface, Enguage allows the user to access computing resources as a windowing system does already.  So it is up to the user to create a useful a system in a cogent manner. Only in such a manner  can the system becomes useful and rewarding.

  1. Sarosiek, A. (2018) Semantics and symbol grounding in Turing machine processes
  2. Peirce, C. S. (1955) Logic as Semiotic, In: The Philosophical Writings of Peirce pp98-
  3. Winograd, T., Flores, F. (1987) Understanding Computers and Cognition