WASHINGTON – The United States is eager to spur innovation in its defense industry at a time when China is investing in advanced technologies including fifth-generation, or 5G, high-speed communications equipment, artificial intelligence systems and hypersonic weapons. In contrast to a vibrant U.S. industry, Japanese defense-related companies have long been part of a "lukewarm environment where business opportunities are guaranteed but profits are limited," a Japanese defense industry source said. In March, the U.S. Air Force held a new event to hear presentations from about 60 small and startup businesses, with contract money available on the spot to those with innovative ideas. Speed, as well as originality and innovation, is essential in competition between major powers, according to then-Air Force Secretary Heather Wilson. In particular, the United States is alarmed by China's development of hypersonic weapons, as its existing missile defense equipment is unable to shoot such weapons down.

Singapore's digital national trade platform has launched Tradeteq's AI-based credit scoring system to enable its users to better assess counterparty risk in trade deals. The system is being used to leverage various data sources to provide thorough credit reports for users of Singapore's Networked Trade Platform (NTP), including data on each company in the supply chain as well as each receivable. The NTP is a digital national trade information management system backed by the Singaporean government, which aims to make trade flowing through Singapore more efficient. Launched in September last year, the NTP brings the entire trade ecosystem to a single online location, digitising the trade processing process. It replaced two existing trade facilitation platforms in Singapore, TradeXchange and TradeNet.

The goal of the article is to help you find a dataset from public data that you can use for your machine learning pipeline, whether it be for a machine learning demo, proof-of-concept, or research project. It may not always be possible to collect your own data, but by using public data, you can create machine learning pipelines that can be useful for a large number of applications. Without data you cannot be sure a machine learning model works. However, the data you need may not always be readily available. Data may not have been collected or labeled yet or may not be readily available for machine learning model development because of technological, budgetary, privacy, or security concerns. Especially in a business contexts, stakeholders want to see how a machine learning system will work before investing the time and money in collecting, labeling, and moving data into such a system. This makes finding substitute data necessary. This article wants to provide some light into how to find and use public data for various machine learning applications such as machine learning demos, proofs-of-concept, or research projects.

The potential use of drones to cripple as much as half of Saudi national oil production this week highlights a growing threat in modern-day conflict. The attack has shown that Saudi Arabia -- the world's third largest defence spender -- is incapable of defending arguably its most protected non-military installation in Abqaiq. It is estimated to have halted around 5 per cent of international crude output, has shocked markets and spiked prices globally. Only a decade ago, such an attack by a low-cost, remote weapon systems was largely unthinkable. And players on the world stage have seized on the shift, with groups such as Islamic State and Mexican drug cartels creating their own improvised explosive vehicles from rudimentary hobby kits purchased online and in stores.

For several years, the satellite and commercial space sector has sought ways to automate equipment construction, foster innovation, and boost profitability. Artificial intelligence (AI) can support these efforts. More specifically, the technology can change the way global satellite operators and space agencies process data and transform how the sector operates across four key areas: manufacturing, imaging, telemetry, and spectrum usage. AI has the potential to significantly improve the satellite manufacturing process, particularly when meticulous engineering is required to assemble multiple pieces. Newly developed AI technologies can perform tedious, time-consuming yet necessary tasks, such as cleaning satellite parts.

A person receives a test for diabetes during Care Harbor LA free medical clinic in Los Angeles, California September 11, 2014. DreaMed Diabetes, the Petah Tikva-based developer of personalized diabetes management solutions, has received US Food and Drug Administration (FDA) clearance for its artificial intelligence-powered insulin recommendations technology. The company's AI-based insulin dosing decision-support software, DreaMed Advisor Pro, aims to assist people with Type 1 diabetes (T1D) using insulin-pump therapy with continuous glucose sensors or blood glucose meters.

Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak. But for all the authentic and exciting ways it's transforming the tasks computers can perform, there's a lot of hype, too. As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: "Everyone knows you have to have machine learning in your story or you're not sexy." The inherently broad term gets bandied about so often that it can start to feel meaningless and can be trotted out by companies to gussy up even simple data analysis. To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. One makes robots that can whir around shoppers to help workers restock shelves. Another scans recruiting pitches for unconscious bias. A third analyzes massive data sets to make street-by-street weather predictions. To be included on the list, companies needed to show that techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see") are a core part of their business model and future success. Find all the details on our methodology here. The honorees span categories like human resources, security, insurance, and finance, with healthcare, transportation, and infrastructure startups best represented on the list.

How many of you believe robots and artificial intelligence will take jobs? How many of you believe machines will take your job? Robots, AI and other disruptive new technologies are expected to displace a significant number of office and manual labor jobs that pay $20 to $40 an hour, according to a 2014 Pew Research Center report. It won't happen all at once, and that's a blessing and a challenge. Slowly, then quickly, machines will replace certain human jobs. We might not hear about most of them because they'll happen in pockets of geographies and industries. But in the not too distant future, we'll see the great extent that robots are among us, and that people will no longer be able to apply for the jobs that some humans work at today. How robotics and AI will change jobs has been top of my mind since the national election's emphasis on bringing back jobs to the USA.

DUBAI, UNITED ARAB EMIRATES – The assault on the beating heart of Saudi Arabia's vast oil empire follows a new and dangerous pattern that's emerged across the Persian Gulf this summer of precise attacks that leave few obvious clues as to who launched them. Beginning in May with the still-unclaimed explosions that damaged oil tankers near the Strait of Hormuz, the region has seen its energy infrastructure repeatedly targeted. Those attacks culminated with Saturday's assault on the world's biggest oil processor in eastern Saudi Arabia, which halved the oil-rich kingdom's production and caused energy prices to spike. Some strikes have been claimed by Yemen's Houthi rebels, who have been battling a Saudi-led coalition in the Arab world's poorest country since 2015. Their rapidly increasing sophistication fuels suspicion among experts and analysts however that Iran may be orchestrating them -- or perhaps even carrying them out itself as the U.S. alleges in the case of Saturday's attack.

First principles modeling of physical systems has led to significant technological advances across all branches of science. For nonlinear systems, however, small modeling errors can lead to significant deviations from the true, measured behavior. Even in mechanical systems, where the equations are assumed to be well-known, there are often model discrepancies corresponding to nonlinear friction, wind resistance, etc. Discovering models for these discrepancies remains an open challenge for many complex systems. In this work, we use the sparse identification of nonlinear dynamics (SINDy) algorithm to discover a model for the discrepancy between a simplified model and measurement data. In particular, we assume that the model mismatch can be sparsely represented in a library of candidate model terms. We demonstrate the efficacy of our approach on several examples including experimental data from a double pendulum on a cart. We further design and implement a feed-forward controller in simulations, showing improvement with a discrepancy model.