A team at NASA's Jet Propulsion Laboratory (JPL) is testing a 3D-printed Transformer-like new robot, Shapeshifter, which is capable of morphing into multiple configurations. And it is stated that a similar design could one day be leveraged to explore Saturn's moon Titan. Saturn's moon Titan is one of the most potential targets on any planetary scientist's list for exploration. But any mission to Titan will have to deal with an environment unlike any other – frigid temperatures, cryovolcanoes, caves, and lakes, seas, and rain of liquid hydrocarbons. However, the latest concept could encompass 12 mini robots – cobots (Collaborative Robots) – that can fly or swim, exploring caves and oceans and will go where other robots haven't been able to explore.

A team at NASA's Jet Propulsion Laboratory (JPL) is testing a 3D-printed Transformer-like new robot, Shapeshifter, which is capable of morphing into multiple configurations. And it is stated that a similar design could one day be leveraged to explore Saturn's moon Titan. Saturn's moon Titan is one of the most potential targets on any planetary scientist's list for exploration. But any mission to Titan will have to deal with an environment unlike any other – frigid temperatures, cryovolcanoes, caves, and lakes, seas, and rain of liquid hydrocarbons. However, the latest concept could encompass 12 mini robots – cobots (Collaborative Robots) – that can fly or swim, exploring caves and oceans and will go where other robots haven't been able to explore.

Artificial intelligence (AI) technology can help us fight climate change--but it also comes at a cost to the planet. To truly benefit from the technology's climate solutions, we also need a better understanding of AI's growing carbon footprint, say researchers. AI is changing the way we work, live and solve challenges. It can improve healthcare, protect elephants from poachers, and work out how broadband should be distributed. But it could be most valuable as a range of applications helping humanity fight our biggest threat--climate change.

Climate action is the latest buzzword among industry circles since the many International Panel on Climate Change (IPCC) reports and the recent UN Climate Summit in New York City. Greta Thunberg grabbed the headlines, but industrialists are all wondering: How can we move swiftly and effectively to reduce carbon emissions? How can we use AI and other exponential technologies to do the job better, faster and cheaper? As a business strategist and urban planner, I advise companies to focus on cities since they consume 80% of energy and emit 70% of carbon, so we'll win or lose the carbon battle in the cities. Fortunately, cities can move faster than national governments and, as energy buyers, they can directly negotiate energy types and pricing, giving them enormous economic clout.

The voyage is expected to take about 35 days and could prove that ships never really needed humans in the first place. They call Maxlimer a robot ship. But a more apt name could also be a ghost ship. Because if you came across it during one of its seafaring journeys, no humans would be onboard. SEE ALSO: Is This New Submarine the World's Best Aquatic War Machine?

Inspection of overhead (OH) power lines and their subsequent maintenance is one of the major activities of electric utilities. Patrolling OH lines, which includes both distribution and transmission lines, is still an old-fashioned job and is treated as a tedious work. The traditional visual inspection of OH assets is highly error prone and costly. All the different types of insulators on OH lines may appear perfectly ok to the naked eye, but the presence of cracks and dirt can lead to flashover and subsequent tripping of the OH circuits. Proper detection of cracks and the amount of dust and dirt on insulators is still a challenging task.

The convergence of AI and HPC has created a fertile venue that is ripe for imaginative researchers -- versed in AI technology -- to make a big impact in a variety of scientific fields. From new hardware to new computational approaches, the true impact of deep- and machine learning on HPC is, in a word, "everywhere". Just as technology changes in the personal computer market brought about a revolution in the design and implementation of the systems and algorithms used in high performance computing (HPC), so are recent technology changes in machine learning bringing about an AI revolution in the HPC community. Expect new HPC analytic techniques including the use of GANs (Generative Adversarial Networks) in physics-based modeling and simulation, as well as reduced precision math libraries such as NLAFET and HiCMA to revolutionise many fields of research. Other benefits of the convergence of AI and HPC include the physical instantiation of data flow architectures in FPGAs and ASICs, plus the development of powerful data analytic services.

"Whether businesses are using machine learning to perform predictive maintenance or create better retail shopping experiences, ML has the power to unlock value across a myriad of use cases. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience, to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. Today, we're introducing a number of enhancements to our Vision AI portfolio to help even more customers take advantage of AI," Google product managers Vishy Tirumalashetty and Andrew Schwartz wrote in a post.

THE ECONOMIST'S Open Future essay competition winner was announced in September, beating nearly 2,400 entries from over 110 countries. But how might artificial intelligence tackle the question? Specifically, we fed the essay question and the 58-word description through a natural-language processing algorithm called GPT-2, released publicly in February by OpenAI, a group working on AI research and ethics, based in San Francisco. The result was six roughly 400-word texts. We took the larger parts of three of them and placed them one after another with no other editing.

False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics Gautam Raj Mode, Prasad Calyam, Khaza Anuarul Hoque Department of Electrical Engineering & Computer Science University of Missouri, Columbia, MO, USA gmwyc@mail.missouri.edu, Abstract --Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms (especially deep learning algorithms) and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. T o date, the majority of the published literature focuses on the accuracy of the IoT and DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks (in the form of false data injection attack) on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-T erm Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. Our obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model and apply two different types of false data injection attacks (FDIA), specifically, continuous and interim FDIAs on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. Our results demonstrate that attacks on even a small number of IoT sensors can strongly defect the RUL prediction in all cases. However, the GRU-based PdM model performs better in terms of accuracy and FDIA resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths.