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If we are to realize the potential of self-driving cars, we need to recognize the limits of machine learning. We should not pretend self-driving cars are around the corner: it will still take substantial time and effort to integrate the technology safely and fairly into our societies.
Technology companies have quickly become powerful with their access to large amounts of data and machine learning technologies, but consumers could be empowered too with automated tools to protect their rights.
Juxi Leitner recounts how he and his team took part in — and won — the 2017 Amazon Robotics Challenge and reflects on the importance of solving big picture problems in robotics.
Affordances are ways in which an animal or a robot can interact with the environment. The concept, borrowed from psychology, inspires a fresh take on the design of robots that will be able to hold their own in everyday tasks and unpredictable situations.
Humans infer much of the intentions of others by just looking at their gaze. Similarly, we want to understand how machine learning systems solve a problem. New tools are developed to find out what strategies a learning machine is using, such as what it is paying attention to when classifying images.
Artificial intelligence (AI) has recently re-emerged from the intersection of many fields, directing its collective energy at the building and studying of intelligent machines.
David Oh was lead flight director for the Curiosity Mars rover and is now part of NASA’s mission to Psyche, a 200-km-wide metal asteroid. Our editor Yann Sweeney met with David at SIGGRAPH Asia to discuss whether advances in AI could improve autonomous robots for space exploration.
Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.
To perform complex tasks, robots need to learn the relationship between their bodies and dynamic environments. A biologically plausible approach to hardware and software design shows that a robotic tendon-driven limb can make effective movements based on a short period of learning.
Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.
A bibliometric analysis of the past and present of AI research suggests a consolidation of research influence. This may present challenges for the exchange of ideas between AI and the social sciences.
Preprints provide an efficient way for scientific communities to share and discuss results. We encourage authors to post preprints on arXiv, bioRxiv or other recognized community preprint platforms.
Artificial intelligence (AI) promises to be an invaluable tool for nature conservation, but its misuse could have severe real-world consequences for people and wildlife. Conservation scientists discuss how improved metrics and ethical oversight can mitigate these risks.
A survey of 300 fictional and non-fictional works featuring artificial intelligence reveals that imaginings of intelligent machines may be grouped in four categories, each comprising a hope and a parallel fear. These perceptions are decoupled from what is realistically possible with current technology, yet influence scientific goals, public understanding and regulation of AI.
Generative machine learning models are used in synthetic biology to find new structures such as DNA sequences, proteins and other macromolecules with applications in drug discovery, environmental treatment and manufacturing. Gupta and Zou propose and demonstrate in silico a feedback-loop architecture to optimize the output of a generative adversarial network that generates synthetic genes to produce ones specifically coding for antimicrobial peptides.
The annotation of medical imaging data requires biological expertise. A human–machine interface connects a deep learning image segmentation system with image viewing software to annotate images.
