3,247 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
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
Tracking humans that are interacting with the other subjects or environment
remains unsolved in visual tracking, because the visibility of the human of
interests in videos is unknown and might vary over time. In particular, it is
still difficult for state-of-the-art human trackers to recover complete human
trajectories in crowded scenes with frequent human interactions. In this work,
we consider the visibility status of a subject as a fluent variable, whose
change is mostly attributed to the subject's interaction with the surrounding,
e.g., crossing behind another object, entering a building, or getting into a
vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
causal-effect relations between an object's visibility fluent and its
activities, and develop a probabilistic graph model to jointly reason the
visibility fluent change (e.g., from visible to invisible) and track humans in
videos. We formulate this joint task as an iterative search of a feasible
causal graph structure that enables fast search algorithm, e.g., dynamic
programming method. We apply the proposed method on challenging video sequences
to evaluate its capabilities of estimating visibility fluent changes of
subjects and tracking subjects of interests over time. Results with comparisons
demonstrate that our method outperforms the alternative trackers and can
recover complete trajectories of humans in complicated scenarios with frequent
human interactions.Comment: accepted by CVPR 201
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Causal reasoning is a crucial part of science and human intelligence. In
order to discover causal relationships from data, we need structure discovery
methods. We provide a review of background theory and a survey of methods for
structure discovery. We primarily focus on modern, continuous optimization
methods, and provide reference to further resources such as benchmark datasets
and software packages. Finally, we discuss the assumptive leap required to take
us from structure to causality.Comment: 35 page
Interactive Visual Reasoning under Uncertainty
One of the fundamental cognitive abilities of humans is to quickly resolve
uncertainty by generating hypotheses and testing them via active trials.
Encountering a novel phenomenon accompanied by ambiguous cause-effect
relationships, humans make hypotheses against data, conduct inferences from
observation, test their theory via experimentation, and correct the proposition
if inconsistency arises. These iterative processes persist until the underlying
mechanism becomes clear. In this work, we devise the IVRE (pronounced as
"ivory") environment for evaluating artificial agents' reasoning ability under
uncertainty. IVRE is an interactive environment featuring rich scenarios
centered around Blicket detection. Agents in IVRE are placed into environments
with various ambiguous action-effect pairs and asked to determine each object's
role. They are encouraged to propose effective and efficient experiments to
validate their hypotheses based on observations and actively gather new
information. The game ends when all uncertainties are resolved or the maximum
number of trials is consumed. By evaluating modern artificial agents in IVRE,
we notice a clear failure of today's learning methods compared to humans. Such
inefficacy in interactive reasoning ability under uncertainty calls for future
research in building human-like intelligence.Comment: Accepted at NeurIPS 2023 (Datasets and Benchmarks
Visual Causal Feature Learning
We provide a rigorous definition of the visual cause of a behavior that is
broadly applicable to the visually driven behavior in humans, animals, neurons,
robots and other perceiving systems. Our framework generalizes standard
accounts of causal learning to settings in which the causal variables need to
be constructed from micro-variables. We prove the Causal Coarsening Theorem,
which allows us to gain causal knowledge from observational data with minimal
experimental effort. The theorem provides a connection to standard inference
techniques in machine learning that identify features of an image that
correlate with, but may not cause, the target behavior. Finally, we propose an
active learning scheme to learn a manipulator function that performs optimal
manipulations on the image to automatically identify the visual cause of a
target behavior. We illustrate our inference and learning algorithms in
experiments based on both synthetic and real data.Comment: Accepted at UAI 201
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