Lecture by Tomer Ullman at Harvard University. My personal takeaway on auditing the presented content.
Course overview at https://klab.tch.harvard.edu/academia/classes/BAI/bai.html
Biological and Artificial Intelligence
The development of intuitive physics and intuitive psychology
Turing proposed that an AI could be developed very much like a human – from a empty notebook or child to a developed adult. However, early development research in psychology and cognitive science has shown that the note may not be “empty”. There is some core knowledge that seems to be either innate to or very early developing in humans but the notion is generally contested and still under research.
Evolutionarily, it may make sense to kickstart a “new” being with some innate knowledge to give it a head-start instead of having to acquire the knowledge on its own. The core knowledge is very limited to several domains. In core physics knowledge, infants have expectations about objects amongst others permanence, cohesive, solid, smooth paths and contact causality. There is not much more than these principles. At the moment, for these core knowledge expectations, there seems to be more a limitation of how we inquire the knowledge rather than them knowing it. Research is actively conducted to find a lower limit.
Side note: For preverbal infants, surprise is measured by the time they spend looking at something but it can be confused with looking at things or people they are attached to (like parents).
Alternatives to Core Physics?
Physical Reasoning Systems
There could be physical reasoning systems (Luo & Baillargeon, 2005) where visually observable features are evaluated to make a decision what physical result would happen. For infants, it appears that the reasoning system is refined with development. A feedforward deep network by Lerer at al. (2016) trained to evaluate whether a piling of stones is stable but the system did not generalize.
Since 2010 a cognitive revolution of sorts has happened in neural network architecture consisting of decoders, LSTMs, memory, and attention that have become “off the shelves”. Using these systems (Piloto et al., 2018), it is possible to generalize better (51% success classifying surprise).
Mental Game Engine Proposal
Maybe, the human brain works like a game engine that emulates physics to approximate reality (Battaglia et al., 2013, in PNAS). A minimal example is a model, a test stimuli and data. This is an ongoing area of research. A model of physics understanding at 4 months consists of approximate objects, dynamics, priors, re-sampling and memory (Smith et al., 2019) is used to predict the next state which is compared to the real next state. In this context, surprise can be defined as the difference between the prediction and the outcome.
Mental planning engine proposal
There are also expectation about agents. Infants have ideas about agents goals, actions, and planning.
Models have many possible routes.
- Human brains could be the way to model intelligence.
- Intelligence can be modelled in another way and need not be human-like.
- A general/universal function approximator may eventually converge to human behaviour/ability.
- A general/universal function approximator actually represents human behaviour/ability.
The problem is that any input output problem can be represented with a look-up-table and thus have no intelligence. Many models may eventually end up in “look-up-table land” where they don’t learn an actual model but only a simple look-up. These models can be useful to solve some tasks but they do not respond to common sense and fail easily on variation.
At the time, reinforcement learning is only a solution in as much evolution seems to have “used” it to produce human behaviour. But how that worked and what the conditions are to make that work are still unknown and thus reinforcement learning is still not the solution to get human-like-behaviour.