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MELBOURNE, Australia — The Japanese Coast Guard has started operations with a newly delivered MQ-9B SeaGuardian drone, while more airborne early warning aircraft have arrived in the country by ship.

The UAV’s manufacturer, General Atomics Aeronautical Systems, said in a news release that the Coast Guard commenced flight operations with a SeaGuardian from the Japan Maritime Self-Defense Force Air Station Hachinohe on Oct. 19.

The American company said the high-altitude, long-endurance unmanned aircraft “will primarily perform Maritime Wide Area Search (MWAS) over the Sea of Japan and the Pacific Ocean. Other missions will include search and rescue, disaster response, and maritime law enforcement.”

Prof. Aleks Farseev is an entrepreneur, research professor, keynote speaker, and the CEO of SoMin.ai, a long-tail ad optimization platform.

Not too long ago, I was asked to present a tool to some of my clients. It was a simple prototype, where a person would type in a few things (i.e., advertising channel, product and occasion), and in turn, the machine would give a number of sample ads. When I clicked the button, in just a few seconds, the machine spat out several ads complete with images and text. The first comment was, “Wow, that was really fast.” What would take a person a few hours to do, this machine did in but a fraction. There were a lot of other interesting comments, some even pointing out that this machine was really creative. Then one person spoke out, a comment that put the room into an uncomfortable silence, “This thing is going to take my job.”

We are in a time of uncertainty. As AI applications become more visible and popular, many will start wondering how they will impact our society. There are the “doomsayers” who think AI will take over the world. Then there are the more “sane” people who think that AI will never be able to replicate humans. After all, how can a machine copy something so intricate and complex? But then again, day by day, the advancements in AI continue to surprise us, as if to challenge our very humanity.

Advancing Space For Humanity — Dr. Ezinne Uzo-Okoro, Ph.D. — Assistant Director for Space Policy, Office of Science and Technology Policy, The White House.

Dr. Ezinne Uzo-Okoro, Ph.D. is Assistant Director for Space Policy, Office of Science and Technology Policy, at the White House (https://www.whitehouse.gov/ostp/) where she focuses on determining civil and commercial space priorities for the President’s science advisor, and her portfolio includes a wide range of disciplines including Orbital Debris, On-orbit Servicing, Assembly, and Manufacturing (OSAM), Earth Observations, Space Weather, and Planetary Protection.

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While automated manufacturing is ubiquitous today, it was once a nascent field birthed by inventors such as Oliver Evans, who is credited with creating the first fully automated industrial process, in flour mill he built and gradually automated in the late 1700s. The processes for creating automated structures or machines are still very top-down, requiring humans, factories, or robots to do the assembling and making.

However, the way nature does assembly is ubiquitously bottom-up; animals and plants are self-assembled at a cellular level, relying on proteins to self-fold into target geometries that encode all the different functions that keep us ticking. For a more bio-inspired, bottom-up approach to assembly, then, human-architected materials need to do better on their own. Making them scalable, selective, and reprogrammable in a way that could mimic nature’s versatility means some teething problems, though.

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Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.

MIT researchers have created a new method for computing directly on these devices, which drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.

The waves are transmitted to a connected device using fiber optics, which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.

Researchers from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences (CAS) have proposed a new artificial intelligence framework for target detection that provides a new solution for fast and high-precision real-time online target detection.

Relevant results were published in Expert Systems with Applications.

In recent years, deep learning theory has driven the rapid development of artificial intelligence technology. Object detection technology based on deep learning theory is also successful in many industrial applications. Current research focuses on improving the speed or accuracy of target detection and fails to take efficiency and accuracy into account. How to achieve fast and accurate object detection has become an important challenge in the field of artificial intelligence.

The artificial intelligence speech translation system can decipher Hokkien, a spoken language.

Meta has created a new speech translator that can translate Hokkien, a predominantly oral language spoken in the diaspora of China and one of the national languages of Taiwan.

Meta.

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UCLA / Flexible Research Group.

The study was published today in the journal Science Robotics.

A Brazilian study published in Scientific Reports shows that artificial intelligence (AI) can be used to create efficient models for genomic selection of sugarcane and forage grass varieties and predict their performance in the field on the basis of their DNA.

In terms of accuracy compared with traditional breeding techniques, the proposed methodology improved predictive power by more than 50%. This is the first time a highly efficient genomic selection method based on machine learning has been proposed for polyploid plants (in which cells have more than two complete sets of chromosomes), including the grasses studied.

Machine learning is a branch of AI and computer science involving statistics and optimization, with countless applications. Its main goal is to create algorithms that automatically extract patterns from datasets. It can be used to predict the performance of a plant, including whether it will be resistant to or tolerant of biotic stresses such as pests and diseases caused by insects, nematodes, fungi or bacteria, and or abiotic stresses such as cold, drought, salinity or insufficient soil nutrients.