A World Inside the Mind

Short post today, but a few things occurred to me as I was reading the paper on Bayesian Program Learning:

  • This form of recursive program induction starts to look suspiciously like simulation – something we do in our minds all the time.
  • Simulation may be a better framing for concept formation than via the classification route.
  • Mapping the ‘inner world’ to the ‘outer world’ seems a more sensible approach to understanding what’s going on. If you look at the paper, you also see some thought-provoking examples of new concept generation, such as the single-wheel motorbike example (in the images). This is the most exciting point of all.

A final design?

Combine all the elements together, along with ideas from my last post, and you get something that:

  1. Simulates an internal version of the world
  2. Is able to synthesize concepts and simulate the results, or literally ‘imagine’ the results – much like we do
  3. Is able to learn concepts from few examples
  4. Has memories of events in its lifetime / runtime, and can reference those events to recall the specific context of what else was happening at that time. That is, memories have deep linkage to one another.
  5. Is able to act of its own volition, i.e. in the absence of external stimulus. It may choose to kick off imagination routines – ‘dreaming’, if you will – optimize its internal connections, or do some other maintenance work in its downtime. Again, similar to how our brains do it while we sleep.

This starts to look like a pretty solid recipe for a complete cognitive architecture. Every requirement has been covered in some way or another, though in different models and in different situations. To really put the pieces together into a robust architecture will require many years of work, but it is worth exploring multi-model cognitive approaches.

If it results in a useful AI, then I’m all in.