588 research outputs found
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Building machines that adapt and compute like brains
Building machines that learn and think like humans is essential not only for
cognitive science, but also for computational neuroscience, whose ultimate goal
is to understand how cognition is implemented in biological brains. A new
cognitive computational neuroscience should build cognitive-level and neural-
level models, understand their relationships, and test both types of models
with both brain and behavioral data.Comment: Commentary on: Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. (2017)
Building machines that learn and think like people. Behavioral and Brain
Sciences, 4
Can Machines Think in Radio Language?
People can think in auditory, visual and tactile forms of language, so can
machines principally. But is it possible for them to think in radio language?
According to a first principle presented for general intelligence, i.e. the
principle of language's relativity, the answer may give an exceptional solution
for robot astronauts to talk with each other in space exploration.Comment: 4 pages, 1 figur
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