In what is perhaps the tensest scene in Stanley Kubrick's 2001: A Space Odyssey, the spaceship's onboard artificial intelligence system HAL-9000 reads the lips of hiding crew members. Discovering their plans to turn it off, the system turns against all humans onboard for the sake of protecting the mission. What was once science fiction is now inching closer to reality thanks to artificial intelligence--but in the wrong hands it could open up terrifying possibilities. In 2018, Chinese researchers crafted a new dataset and surveillance application benchmarks for lip-reading in real-world settings. Though this tech was meant to aid the hearing-impaired, the Chinese are likely to use it to broaden their already expansive surveillance state.

That most famous characterization of the complexity causality, a butterfly beating its wings and causing a hurricane on the other side of the world, is thought-provoking but ultimately not helpful. What we really need is to look at a hurricane and figure out which butterfly caused it -- or perhaps stop it before it takes flight in the first place. DARPA thinks AI should be able to do just that. A new program at the research agency is aimed at creating a machine learning system that can sift through the innumerable events and pieces of media generated every day and identify any threads of connection or narrative in them. It's called KAIROS: Knowledge-directed Artificial Intelligence Reasoning Over Schemas.

All are endurance tests to see who can code through the night and subsist on bland food and soda without collapsing. Some are genuinely engendering brilliant innovation. The best are relatively inexpensive hiring fests for the sponsoring organizations, giving potential candidates an invaluable insight into hiring patterns. I've reported on several hackathons in the past year for PCMag, each disparately themed: voice assistants for those with disabilities at USC; a health hackathon at Stanford University; and--a double whammy-- DARPA at NASA hacking software-defined radio drones. The latter, as you'd expect, featured lots of uniformed personnel with security services earpieces against a backdrop of rocketry.

Turing researchers recently made a written submission in which they answered key questions like these from the House of Lords inquiry into artificial intelligence on the implications and future of AI in the UK. Here we look at some of the highlights. Algorithms, according to Turing Visiting Researcher Simon DeDeo, "have the potential to transform the material, social, and political landscape… and alter the basic rhythms of human life in a fashion last seen at the beginning of the Industrial Revolution." A key example of this transformation effect is health, as raised by Turing Fellow Maria Liakata who discussed the way it can combine different data sources and transform the way diseases are diagnosed, monitored and treated. What is the future of AI? Turing Fellow David Barber was called in front of the Committee to give oral evidence, and said that: "where we are right now is what we call perceptual AI. If somebody speaks, the machine can transcribe into words what you are saying, but the machine does not understand what you are saying. It does not understand who you are or the relationship of objects in this environment. The bigger fruit out there is the reasoning AI; really understanding what these objects are, being able to query this machine and get sensible answers back. That is the biggest and most exciting challenge that all the tech giants are currently desperately seeking to solve. For whoever solves that, the world is their oyster."

Daniel Britt is the Pegasus professor of astronomy and planetary sciences at the University of Central Florida. He has served on the science teams of four NASA missions, including New Horizons. NASA's New Horizons spacecraft, now exploring the vast region of our solar system beyond Neptune known as the Kuiper belt, completed yet another trip full of superlatives: Last week, it celebrated its closest approach to Ultima Thule, the farthest object ever visited by spacecraft. Ultima Thule is 1 billion miles past Pluto, more than 4 billion miles from Earth, and radio signals take more than six hours to travel from the spacecraft back to NASA's receivers. At this distance, the sun is just the brightest star in sight, and the local temperature is a balmy minus-390 degrees Fahrenheit.

Police will be handed extra powers to combat drones after the mass disruption at Gatwick airport in the run-up to Christmas. Gatwick was repeatedly forced to close between 19 and 21 December due to reported drone sightings, affecting about 1,000 flights. In response the government has announced a package of measures which include plans to give police the power to land, seize and search drones. The Home Office will also begin to test and evaluate the use of counter-drone technology at airports and prisons. The exclusion zone around airports will be extended to approximately a 5km-radius (3.1 miles), with additional extensions from runway ends.

The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the seafloor. Approximately 70.8% of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean-resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the Ocean Network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the paper are as follows: (1) a CONet architecture is proposed and described in detail; (2) important and useful demonstration applications of the CONet are proposed; and (3) future trends in CONet research are presented.

Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging the UAVs visual sensors and operational simplicity. To fill this gap and enable research in wider application areas, we present a UAV gesture signals dataset recorded in an outdoor setting. We selected 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. We provide 119 high-definition video clips consisting of 37151 frames. The overall baseline gesture recognition performance computed using Pose-based Convolutional Neural Network (P-CNN) is 91.9 %. All the frames are annotated with body joints and gesture classes in order to extend the dataset's applicability to a wider research area including gesture recognition, action recognition, human pose recognition and situation awareness.

Abstract--Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way,where deep representation and clusters are learned jointly by optimizing a single objective function. Both strategies improve clustering performance, however the robustness of these approaches is impeded by several deep autoencoder setting issues, among which the weights initialization, the width and number of layers or the number of epochs. To alleviate the impact of such hyperparameters setting on the clustering performance, we propose a new model which combines the spectral clustering and deep autoencoder strengths in an ensemble learning framework. Extensive experiments on various benchmark datasets demonstrate thepotential and robustness of our approach compared to state-of-the art deep clustering methods. I. INTRODUCTION Learning from large amount of data is a very challenging task. Several dimensionality reduction and clustering techniques thatare well studied in the literature aim to learn a suitable and simplified data representation from original dataset; see for instance [1-3]. While many approaches have been proposed to address the dimensionality reduction and clustering tasks, deep learning-based methods recently demonstrate promisingresults.

Recent systems for natural language understanding are strong at overcoming linguistic variability for lookup style reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations, addressing the ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. Our formal model uses two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic symbol space that captures a noisy grounding of the meaning space in the symbols or words of a language. We apply this framework to study the connectivity problem in undirected graphs---a core reasoning problem that forms the basis for more complex multi-hop reasoning. We show that it is indeed possible to construct a high-quality algorithm for detecting connectivity in the (latent) meaning graph, based on an observed noisy symbol graph, as long as the noise is below our quantified noise level and only a few hops are needed. On the other hand, we also prove an impossibility result: if a query requires a large number (specifically, logarithmic in the size of the meaning graph) of hops, no reasoning system operating over the symbol graph is likely to recover any useful property of the meaning graph. This highlights a fundamental barrier for a class of reasoning problems and systems, and suggests the need to limit the distance between the two spaces, rather than investing in multi-hop reasoning with "many" hops.