From statistical learning to acting and thinking in an imagined space

"If we really want to build a machine on the verge of human-level intelligence, we need to ditch current statistical and data-driven learning paradigm in favour of a causal-based approach." In the 1970s and early 1980s, computer scientists believed that the manipulation of symbols provided a priori by humans was sufficient for computer systems to exhibit intelligence and solve seemingly hard problems. This hypothesis came to be known as the symbol-rule hypothesis. However, despite some initial encouraging progress, such as computer chess and theorem proving, it soon became apparent that rule-based systems could not solve problems that appear seemingly simple to humans. "It is comparatively easy to make computers exhibit adult level performance […] and difficult or impossible to give them the skills of a one-year-old".