An Enactivist account of Mind Reading in Natural Language Understanding

In this paper we apply our understanding of the radical enactivist agenda to a classic AI-hard problem. Natural Language Understanding is a sub-field of AI research that looked easy to the pioneers. Thus the Turing Test, in its original form, assumed that the computer could use language and the challenge was to fake human intelligence. It turned out that playing chess and formal logic were easy compared to the necessary language skills. The techniques of good old-fashioned AI (GOFAI) assume symbolic representation is the core of reasoning and human communication consisted of transferring representations from one mind to another. But by this model one finds that representations appear in another's mind, without appearing in the intermediary language. People communicate by mind reading it seems. Systems with speech interfaces such as Alexa and Siri are of course common but they are limited. Rather than adding mind reading skills, we introduced a "cheat" that enabled our systems to fake it. The cheat is simple and only slightly interesting to computer scientists and not at all interesting to philosophers. However, reading about the enactivist idea that we "directly perceive" the intentions of others, our cheat took on a new light and in this paper look again at how natural language understanding might actually work between humans.

Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.

Constraint Exploration and Envelope of Simulation Trajectories

The implicit theory that a simulation represents is precisely not in the individual choices but rather in the 'envelope' of possible trajectories - what is important is the shape of the whole envelope. Typically a huge amount of computation is required when experimenting with factors bearing on the dynamics of a simulation to tease out what affects the shape of this envelope. In this paper we present a methodology aimed at systematically exploring this envelope. We propose a method for searching for tendencies and proving their necessity relative to a range of parameterisations of the model and agents' choices, and to the logic of the simulation language. The exploration consists of a forward chaining generation of the trajectories associated to and constrained by such a range of parameterisations and choices. Additionally, we propose a computational procedure that helps implement this exploration by translating a Multi Agent System simulation into a constraint-based search over possible trajectories by 'compiling' the simulation rules into a more specific form, namely by partitioning the simulation rules using appropriate modularity in the simulation. An example of this procedure is exhibited. Keywords: Constraint Search, Constraint Logic Programming, Proof, Emergence, Tendencies