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To better extract meaning from natural language, some less informative words can be removed before a model is trained, which is usually done by using manually curated lists of stopwords. A new information theoretic approach can identify uninformative words automatically and more accurately.
Artificial intelligence and machine learning are increasingly seen as key technologies for building more decentralized and resilient energy grids. However, researchers must consider the ethical and social implications of these developments.
Robots are making a transition into human environments, where they can directly interact with us, in shops, hospitals, schools and more. Transparency about robots’ capabilities and level of autonomy should be integrated into the design from the start.
In cooperative games, humans are biased against AI systems even when such systems behave better than our human counterparts. This raises a question: should AI systems ever be allowed to conceal their true nature and lie to us for our own benefit?
The first Smart Cities Robotics Challenge, organized by the European Robotics League, took place from 18–21 September at the Centre:MK shopping centre in Milton Keynes. The competition tested the ability of robots to interact with humans in everyday tasks as well as with the digital infrastructure of a smart city.
Algorithms and bots are capable of performing some behaviours at human or super-human levels. Humans, however, tend to trust algorithms less than they trust other humans. The authors find that bots do better than humans at inducing cooperation in certain human–machine interactions, but only if the bots do not disclose their true nature as artificial.
Human face recognition is robust to changes in viewpoint, illumination, facial expression and appearance. The authors investigated face recognition in deep convolutional neural networks by manipulating the strength of identity information in a face by caricaturing. They found that networks create a highly organized face similarity structure in which identities and images coexist.
Photonic computing devices have been proposed as a high-speed and energy-efficient approach to implementing neural networks. Using off-the-shelf components, Antonik et al. demonstrate a reservoir computer that recognizes different forms of human action from video streams using photonic neural networks.
Deep learning is currently transforming digital pathology, helping to make more reliable and faster clinical diagnoses. A promising application is in the recognition of malignant white blood cells—an essential step for detecting acute myeloid leukaemia that is challenging even for trained human examiners. An annotated image dataset of over 18,000 white blood cells is compiled and used to train a convolutional neural network for leukocyte classification. The network classifies the most important cell types with high accuracy and can answer clinically relevant binary questions with human-level performance.
Current national cybersecurity and defence strategies of several governments mention explicitly the use of AI. However, it will be important to develop standards and certification procedures, which involves continuous monitoring and assessment of threats. The focus should be on the reliability of AI-based systems, rather than on eliciting users’ trust in AI.
Adversarial attacks make imperceptible changes to a neural network’s inputs so that it recognizes it as something entirely different. This flaw can give us insight into how these networks work and how to make them more robust.
Deep neural networks can be led to misclassify an image when minute changes that are imperceptible to humans are introduced. While for some networks this ability can cast doubt on the reliability of the model, it also offers explainability for networks that use more robust regularization.
AI ethics initiatives have seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite this, Brent Mittelstadt highlights important differences between medical practice and AI development that suggest a principled approach may not work in the case of AI.
Artificial intelligence systems copy and amplify existing societal biases, a problem that by now is widely acknowledged and studied. But is current research of gender bias in natural language processing actually moving towards a resolution, asks Marta R. Costa-jussà.
The organizers of Cognitive Computational Neuroscience, a relatively new AI-themed meeting held recently in Berlin, are dedicated to encouraging informal interactions and conversations to tackle the challenge of bridging scientific cultures.
Robots and machines are generally designed to perform specific tasks. Unlike humans, they lack the ability to generate feelings based on interactions with the world. The authors propose a new class of machines with evaluation processes akin to feelings, based on the principles of homeostasis and developments in soft robotics and multisensory integration.
To keep radiation therapy from damaging healthy tissue, expert radiologists have to segment CT scans into individual organs. A new deep learning-based method for delineating organs in the area of head and neck performs faster and more accurately than human experts.
Neural network force fields promise to bypass the computationally expensive quantum mechanical calculations typically required to investigate complex materials, such as lithium-ion batteries. Mailoa et al. accelerate these approaches with an architecture that exploits both rotation-invariant and -covariant features separately.
