10,862 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
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Artificial intelligence makes computers lazy
This paper looks at the age-old problem of trying to instil some degree of intelligence in computers. Genetic Algorithms (GA) and Genetic Programming (GP) are techniques that are used to evolve a solution to a problem using processes that mimic natural evolution. This paper reflects on the experience gained while conducting research applying GA and GP to two quite different problems: Medical Diagnosis and Robot Path Planning. An observation is made that when these algorithms are not applied correctly the computer seemingly exhibits lazy behaviour, arriving at a suboptimal solutions. Using examples, this paper shows how this 'lazy' behaviour can be overcome
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Towards an Architecture for Semiautonomous Robot Telecontrol Systems.
The design and development of a computational system to support robot–operator collaboration is a challenging task, not only because of the overall system complexity, but furthermore because of the involvement of different technical and scientific disciplines, namely, Software Engineering, Psychology and Artificial Intelligence, among others. In our opinion the approach generally used to face this type of project is based on system architectures inherited from the development of autonomous robots and therefore fails to incorporate explicitly the role of the operator, i.e. these architectures lack a view that help the operator to see him/herself as an integral part of the system. The goal of this paper is to provide a human-centered paradigm that makes it possible to create this kind of view of the system architecture. This architectural description includes the definition of the role of operator and autonomous behaviour of the robot, it identifies the shared knowledge, and it helps the operator to see the robot as an intentional being as himself/herself
Robotic Behavior based on Formal Grammars
Formal grammars, studied by N. Chomsky for the definition of equivalence with languages and models of computing, have been a useful tool in the development of compilers, programming languages, natural language processing, automata theory, etc. The words or symbols of these formal languages can denote deduced actions that correspond to specific behaviors of a robotic entity or agent that interacts with an environment. The primary objective of this paper pretend to represent and generate simple behaviors of artificial agents. Reinforcement learning techniques, grammars, and languages, as defined based on the model of the proposed system were applied to the typical case of the ideal route on the problem of artificial ant. The application of such techniques proofs the viability of building robots that might learn through interaction with the environment
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