Prediction Games (PGs)

[under construction] Human intelligence appears to require many concepts (that work well enough together), for instance to achieve daily 'common sensical' behavior. There are two distinct but inter-related problems here, in the ways I understrood and have approached the issues:

1. [Time snapshot] How does one (a person), efficiently and adequately, figure out (mostly unconsciously) which of one's many concepts are useful at any time point (for instance when looking at a picture)?¹
2. [History/Development] Where do these many concepts come from, in the first place? (and where are they going?!)

A few other ensuing or related questions include: what is a concept? and, for me, how can machine learning (ml), and more broadly, computational thinking, help? (eg with the wealth of learning and inference techniques that continue to be developed)

Cognitive scientists tell us that most concepts (such as: water, chair, my friend's voice, a house, my house, democracy, ...) develop over time, in a sequential and cumulative manner. This development appears to be largely unsuperivsed, ie no explicit teacher. By the time one shows signs of learning a language, if concepts are to be useful, many such must have already been developed (somewhat), as the child already can do a lot in her/his world.² In the 2000s, I proposed Prediction Games (PGs) to develop and study such systems.³ In PGs, a system composed of multiple learning and inferencing parts, given its input stream (broken into episodes), plays the game of prediction on it. The system tries to get better at the prediction task over time: predicting more extensively, into the future or into space, with possibly less (become faster and more powerful). This has plausible survival advantage! Thus, prediction, of one's world, is a unifying task: could it be sufficient for providing the feedback needed to achieve the conceptual complexity of humans (assuming learning is the main vehicle of development)? But of course, we need more details. In order to get better at prediction, the PGs system keeps expanding its hierarchical networked vocabulary of concepts. In PGs, concepts are both the predictors and the predictands (ie the targets of prediction). This symmetry is a major draw of this approach to me.⁴ Furthermore, concepts not only predict one another but are also built out of one another, akin to Lego pieces. This is the cumulative (constructivist!) part of the approach. To start the whole process of learning, the system is given an initial set of primitive (innate/hardwired) concepts, with the capability to break its raw sensed input in an episode into those primitives. So each episode begins with a sequence of primitives in the input buffer. In order to predict with concepts, the system needs to break or map its buffer contents into its current most useful concepts. This process is called interpretation (prediction and interpretation are intertwined). By practicing many interpretation episodes, the system (continually) figures out which concepts (new and old) go together: predict each other, and could perhaps be joined to make larger more useful concepts. PGs involve a continual self-supervised and cumulative online learning process.

I currently believe that the research is most relevant to learning in perception. Over the years, I have built a few versions of such systems that play the game in one dimensional text (see the pointers below). There are many challenges: How do we avoid combinatorial explosion (because such systems work with explicit structures)? These systems make local decisions, eg in determnining which concepts to activate in a given episode, or which concepts to join to make composite concepts, based on noisy indirect information. There is much uncertainty (hence the picture above of a house of cards!): can the system build a robust networked hierarchy of concepts? How do we design algorithms and objectives so that learning can go on and not get stuck in poor local optima? When or why should such systems succeed over time? Questions of (code/engineering) complexity and how to control and understand the dynamics (of subsystems interactings): for instance, how biased will such a system, which is learning sequentially, be? (what it learns now affects its future choices and learning) There are many problems and subproblems to be discovered and defined here. Along the way, problems of philosophical nature arise too, as the system is building its own (biased) reality in a sense (so then, is there 'one unique objective truth out there'?). I think this makes for a good project and research area!

Our (human) thoughts are built on concepts (and embodied!). A few references:

• The Big Book of Concepts. Gregory L. Murphy, MIT press, 2002.
• Philosophy in the Flesh: the Embodied Mind and its Challenge to Western Thought. George Lakeoff and Mark Johnson, Basic Books, 1999.
• Surfaces and Essences: Analogy as the Fuel and Fire of Thinking. Douglas R. Hofstadter and Emmanuel Sander, Basic Books, 2013

[More material (pointer and papers) to go here. ]