The U.S. National Institute of Standards and Technology (NIST) is developing a plan for federal engagement in the development of AI technical standards and related tools. This is in response to President Trump's Executive Order on Maintaining American Leadership in Artificial Intelligence.

If enemy cruise missiles, helicopter gunfire and even fighter-jet launched bombs close in on Navy surface ships at sea, service commanders could employ a range of time-sensitive layered defenses to include interceptor missiles, deck-mounted guns, electronic warfare tactics and even lasers. Navy preparations for this kind of scenario include the use of radar, long-range sensors and coordinated surveillance with surface, undersea and air assets - all seemingly operated for rapid response-enabled destruction of incoming enemy fire. Virtually all of these contingencies rely upon an often overlooked area of maritime warfare -- information warfare. Targeting data for pretty much any defensive weapons system would need to precede or inform fire control systems and certain kinds of sensor-weapons fusion. For this reason, the Navy is revving up its focus on training a new generation of information warriors to surge into future decades, hopefully, armed with the technical skills needed to counter enemy attacks today and 20 years from now.

In his new book Advances in Financial Machine Learning, noted financial scholar Marcos López de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the financial world today. He points out that not only are business-as-usual approaches largely impotent in today's high-tech finance, but in many cases they are actually prone to lose money. But López de Prado does more than just expose the mathematical and statistical sins of the finance world. Instead, he offers a technically sound roadmap for finance professionals to join the wave of machine learning. What is particularly refreshing is the author's empirical approach -- his focus is on real-world data analysis, not on purely theoretical methods that may look pretty on paper but which in many cases are largely ineffective in practice. The book is geared to finance professionals who are already familiar with statistical data analysis techniques, but it is well worth the effort for those who want to do real state-of-the-art work in the field.

INTRODUCTION My idea is related to the application of machine learning, based on rudimentary pattern analysis and matching, onto the field of forensic analysis of the financial markets. I hope to raise money to support the creation of a body of work in software and machine learning that (1) performs forensic analysis of financial markets via the application of my concepts, theories, and findings on what I refer to market consensus (2) while at the same time, create the opportunity to rebuild my credentials to allow me to return to work. INTERNATIONAL ATTENTION AND INTEREST This work has WORLDWIDE public interest as well as international value. Specifically, in the forensic mode, anomalies can be detected and understood whereas in the predictive mode, they can be used to help understand market behavior and evaluate hedges. The work is not limited to forensic analysis but also predictive modeling but at this time, I prefer to focus not its predictive power.

A wide range of experts representing academics, private sector, disaster managers, nongovernmental organizations (NGOs), and United Nations agencies gathered recently to examine how artificial intelligence (AI) and big data can be used in disaster risk reduction to save lives. "As humanitarians and disaster managers, we don't want to be only spectators in this big data industry but want to learn from this industry and shape its work for our needs," said Ms. Adelina Kamal, Executive Director of the ASEAN Coordination Centre for Humanitarian Assistance (AHA Centre). She added, "Having a data intelligence system will allow the AHA Centre to quickly analyze data and transform it to sharp, accurate, and reliable information, increasing scale and solidarity for One ASEAN One Response." The ASEAN Workshop on Disaster Reporting and Big Data for Disaster Management held in Jakarta, Indonesia, March 18-19, organized by the AHA Centre, was attended by about one hundred participants, including 30 representatives from the association's national disaster management organizations. AHA Centre is responsible for coordinating humanitarian response and risk reduction for ASEAN's ten member states.

A new priest named Mindar is holding forth at Kodaiji, a 400-year-old Buddhist temple in Kyoto, Japan. Like other clergy members, this priest can deliver sermons and move around to interface with worshippers. But Mindar comes with some ... unusual traits. A body made of aluminum and silicone, for starters. Designed to look like Kannon, the Buddhist deity of mercy, the $1 million machine is an attempt to reignite people's passion for their faith in a country where religious affiliation is on the decline.

Newswire) Patriot One Technologies Inc., (TSX: PAT) (OTCQB: PTOTF) (FRANKFURT: 0PL) ("Patriot One" or the "Company"), developer of the PATSCAN Multi-Sensor Covert Weapon Detection Platform ("PATSCAN Platform"), is pleased to announce the acquisition of XTRACT Technologies Inc. ("XTRACT") and all its related interests, assets, and intellectual property for consideration of $6,000,000 and the issuance of 9,422,956 Patriot One common shares (the "Consideration Shares") to XTRACT's former shareholders (the "Transaction"). Subject to certain exceptions based on the business of the Company or the performance of the Company's common share price, the Consideration Shares will be subject to escrow restrictions and released on a quarterly basis over the next twelve consecutive fiscal quarters of Patriot One. The Transaction follows demonstrated success of an ongoing collaborative project between the two firms that was announced in late July 2019. The XTRACT acquisition is part of an ongoing strategic initiative by the Company to bring together leading-edge technologies and services that will enhance the power of its PATSCAN Platform, which offers a layered, multi-sensor approach to concealed threat and disturbance detection. "Just over a month ago, we announced a development project with XTRACT, and during this period we've already seen great progress in significant opportunities for enhanced detection accuracy" said Martin Cronin, Patriot One's CEO and President.

It used to be that only Hollywood production companies with deep pockets and teams of skilled artists and technicians could make deepfake videos, realistic fabrications appearing to show people doing and saying things they never actually did or said. Not anymore -- software freely available online lets anyone with a computer and some time on their hands create convincing fake videos. Whether used for personal revenge, to harass celebrities or to influence public opinion, deepfakes render untrue the age-old axiom that "seeing is believing." My research team and I at the University of Southern California Information Sciences Institute are developing ways to tell the difference between realistic-looking fakes and genuine videos that show actual events as they happened. Our recent research has found a new and apparently more accurate way to detect deepfake videos.

A friend from The Royal Society and Simple Reminders, (founders, Bryant McGill and Jenni Young) recently posted a Facebook video about "Perspective." In her video, she moved her phone from one side, showing busy traffic, then, as she turned 90 degrees, Lake Michigan was viewed with its tranquil shoreline. I thought of all the people I have met over the years and were "kindred spirits" or "empaths" like myself. I feel there is a heightened emotional and mental acuity (some call a 6th sense) that I have always had. I have a problem with coupling Neurodivergence with Autism or Disability.

Bad data and poorly designed AI systems can lead you to spurious conclusions and hurt customers, your products, and your brand. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Over the past decade, organizations have begun to rely on an ever-growing number of algorithms to assist in making a wide range of business decisions, from delivery logistics, airline route planning, and risk detection to financial fraud detection and image recognition. We're seeing the end of the second wave of AI, which began several decades ago with the introduction of rule-based expert systems, and moving into a new, third wave, termed perception AI. It's in this next wave where a specific subset of AI, called deep learning, will play an even more critical role.