This paper reports on the results of a qualitative survey of 25 experts conducted in 2019 and early 2020, on the possibility of deep learning leading to high-level machine intelligence (HLMI), defined here as an “algorithmic system that performs like average adults on cognitive tests that evaluate the cognitive abilities required to perform economically relevant tasks”. Experts disagreed strongly on whether deep learning could lead to HLMI. Optimists tended to focus on the importance of scale, while pessimists tended to emphasize the need for additional insights.
Based on the interviews, the paper gives a list of 40 limitations of deep learning that some expert pointed to, and a more specific list of five areas that both optimists and pessimists pointed to as in support of their views (and thus would likely be promising areas to resolve disagreements). The five areas are (1) abstraction, (2) generalization, (3) explanatory, causal models, (4) emergence of planning, and (5) intervention.
Planned summary for the Alignment Newsletter: