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The multidisciplinary nature of machine intelligence

Machine Intelligence is a highly multidisciplinary and active field, combining computer science, robotics and cognitive science, with potentially transformative applications in many areas of science, industry and society. Current research aims to develop AI systems with broad applicability that will safely interact with humans and the physical world. Different concepts and approaches – machine learning, symbolic reasoning, cognitive science, developmental psychology, robot control engineering, human-machine interactions among others – are increasingly brought together for such goals. To mark the impending launch of Nature Machine Intelligence, this collection explores recent developments in the field and its wide impact in other areas.

Machine Learning

Image reconstruction is reformulated using a data-driven, supervised machine learning framework that allows a mapping between sensor and image domains to emerge from even noisy and undersampled data, improving accuracy and reducing image artefacts.

Letter | | Nature

The success of machine learning techniques in handling big data sets proves ideal for classifying condensed-matter phases and phase transitions. The technique is even amenable to detecting non-trivial states lacking in conventional order.

Letter | | Nature Physics

An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, achieving a performance comparable to that of an expert human player; this work paves the way to building general-purpose learning algorithms that bridge the divide between perception and action.

Letter | | Nature

Quantum machine learning software could enable quantum computers to learn complex patterns in data more efficiently than classical computers are able to.

Review Article | | Nature

Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake triggering.

Letter | | Nature

A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list.

Letter | | Nature Physics

Bio-inspired Intelligence

This Review Article examines the development of organic neuromorphic devices, considering the different switching mechanisms used in the devices and the challenges the field faces in delivering neuromorphic computing applications.

Review Article | | Nature Electronics

Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

Article | open | | Nature Communications

FreemoVR is a virtual reality system for freely moving animals. The versatile platform is demonstrated in various experiments with Drosophila, zebrafish, and mice.

Article | | Nature Methods

Robotics

An intelligent trial-and-error learning algorithm is presented that allows robots to adapt in minutes to compensate for a wide variety of types of damage.

Letter | | Nature

Tactile sensors provide robots with the ability to interact with humans and the environment with great accuracy, yet technical challenges remain for electronic-skin systems to reach human-level performance.

Commentary | | Nature Materials

Microrobots are envisioned to revolutionize microsurgery and targeted drug delivery. Their design, operation, locomotion and interaction with the environment are inspired by microorganisms. This Review highlights soft, responsive and active materials for the development of (semi-)autonomous microrobots.

Review Article | | Nature Reviews Materials

Machine Intelligence and Society

Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.

Article | open | | Nature Communications

Self-driving cars offer a bright future, but only if the public can overcome the psychological challenges that stand in the way of widespread adoption. We discuss three: ethical dilemmas, overreactions to accidents, and the opacity of the cars’ decision-making algorithms — and propose steps towards addressing them.

Comment | | Nature Human Behaviour

The development of autonomous weapon systems, by removing the human element of warfare, could make war crimes and atrocities a thing of the past. But if these systems are unable to respect the principles of humanitarian law, we might create a super-intelligent predator that is beyond our control.

Comment | | Nature Human Behaviour