30,360 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
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
How does language inform our downstream thinking? In particular, how do
humans make meaning from language -- and how can we leverage a theory of
linguistic meaning to build machines that think in more human-like ways? In
this paper, we propose \textit{rational meaning construction}, a computational
framework for language-informed thinking that combines neural models of
language with probabilistic models for rational inference. We frame linguistic
meaning as a context-sensitive mapping from natural language into a
\textit{probabilistic language of thought} (PLoT) -- a general-purpose symbolic
substrate for probabilistic, generative world modeling. Our architecture
integrates two powerful computational tools that have not previously come
together: we model thinking with \textit{probabilistic programs}, an expressive
representation for flexible commonsense reasoning; and we model meaning
construction with \textit{large language models} (LLMs), which support
broad-coverage translation from natural language utterances to code expressions
in a probabilistic programming language. We illustrate our framework in action
through examples covering four core domains from cognitive science:
probabilistic reasoning, logical and relational reasoning, visual and physical
reasoning, and social reasoning about agents and their plans. In each, we show
that LLMs can generate context-sensitive translations that capture
pragmatically-appropriate linguistic meanings, while Bayesian inference with
the generated programs supports coherent and robust commonsense reasoning. We
extend our framework to integrate cognitively-motivated symbolic modules to
provide a unified commonsense thinking interface from language. Finally, we
explore how language can drive the construction of world models themselves
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