A computer Go program based on deep neural networks defeats a human professional player to achieve one of the grand challenges of artificial intelligence.
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.
The application and development of machine-learning methods used in experiments at the frontiers of particle physics (such as the Large Hadron Collider) are reviewed, including recent advances based on deep learning.
A reinforcement learning approach allows a suitably equipped glider to navigate thermal plumes autonomously in an open field.
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.
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.
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.
Accelerating PALM/STORM microscopy with deep learning allows super-resolution imaging of >1,000 cells in a few hours.
Quantum machine learning software could enable quantum computers to learn complex patterns in data more efficiently than classical computers are able to.
Estimates of parameters of strong gravitational lenses are obtained in an automated way using convolutional neural networks, with similar accuracy and greatly improved speed compared to previous methods.
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.
Embedding a deep-learning model in the known structure of cellular systems yields DCell, a ‘visible’ neural network that can be used to mechanistically interpret genotype–phenotype relationships.
A deep-learning algorithm is developed to provide rapid and accurate diagnosis of clinical 3D head CT-scan images to triage and prioritize urgent neurological events, thus potentially accelerating time to diagnosis and care in clinical settings.
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.
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.
DNA-strand-displacement reactions are used to implement a neural network that can distinguish complex and noisy molecular patterns from a set of nine possibilities—an improvement on previous demonstrations that distinguished only four simple patterns.
Multi-layered neural architectures that implement learning require elaborate mechanisms for symmetric backpropagation of errors that are biologically implausible. Here the authors propose a simple resolution to this problem of blame assignment that works even with feedback using random synaptic weights.
To celebrate the centenary of the year of Alan Turing's birth, four scientists and entrepreneurs assess the divide between neuroscience and computing.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
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.
FreemoVR is a virtual reality system for freely moving animals. The versatile platform is demonstrated in various experiments with Drosophila, zebrafish, and mice.
Leaky integrate-and-fire artificial neurons based on diffusive memristors enable unsupervised weight updates of drift-memristor synapses in an integrated convolutional neural network capable of pattern recognition.
A tiny soft-bodied robot is described that can be magnetically actuated to swim, climb, roll, walk and jump, while carrying a load.
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.
A robot instructed by a machine learning algorithm and coupled with real-time spectroscopic systems provides fast and accurate reaction outcome predictions and reactivity assessments, leading to the discovery of new reactions.
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.
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.
Stephen Cave and Kanta Dihal revisit the extraordinary history of cultural responses to automata.
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.
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.
As machine learning infiltrates society, scientists are trying to help ward off injustice.
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.
Define an international doctrine for cyberspace skirmishes before they escalate into conventional warfare, urge Mariarosaria Taddeo and Luciano Floridi.