There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Automated Driving (AD) features. These range from a need for road determination (<5 meters), lane determination (<1.5 meters), and determining where the vehicle is within the lane (<0.3 meters). This work examines the performance of Global Navigation Satellite Systems (GNSS) on 30,000 km of North American highways to better understand the automotive positioning needs it meets today and what might be possible in the near future with wide area GNSS correction services and multi-frequency receivers. This includes data from a representative automotive production GNSS used primarily for turn-by-turn navigation as well as an Inertial Navigation System which couples two survey grade GNSS receivers with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth. The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections delivered over a cellular modem in real-time. We assess on-road GNSS accuracy, availability, and continuity. Availability and continuity are broken down in terms of satellite visibility, satellite geometry, position type (RTK fixed, RTK float, or standard positioning), and RTK correction latency over the network. Results show that current automotive solutions are best suited to meet road determination requirements at 98% availability but are less suitable for lane determination at 57%. Multi-frequency receivers with RTK corrections were found more capable with road determination at 99.5%, lane determination at 98%, and highway-level lane departure protection at 91%.

IBM Research labs are part of a tradition where large tech companies had extensive research labs. IBM Research, along with the original Bell labs and the Xerox Palo Alto Research Center (PARC), have developed many innovations. And IBM Research continues that tradition to today. I got to visit IBM's Almaden Research center, nestled in a bucolic part of the south San Jose area, up on a hillside, surrounded with fields of grazing cattle and a thin fog from the Pacific Ocean just over the Santa Cruz mountains. But in that lab a lot of amazing research is underway. This lab is also noted for a critical invention - the Winchester disk drive - that revolutionized storage in mainframe computers, and which eventually scaled down to personal computers.

The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves. The Cannon-Delivered Area Effects Munition (C-DAEM) system will use GPS to identify enemy tanks and armoured shells, which will be scanned in advance from the skies. According to sources, the Pentagon will invest vast sums into the AI-guided munitions, which could be ready by 2021. They will replace the Dual-Purpose Improved Conventional Munition (DPICM) artillery rounds, which were introduced in the 1980s. Cannon-Delivered Area Effects Munition system: The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves C-DAEM is a 155-millimeter artillery shell, and will be available for the M777 towed howitzer, the M109A6 Paladin self-propelled howitzer, and the new XM1299 self-propelled howitzer, which has a range of up to 43 miles.

Indian cab aggregator Ola on Tuesday said it had acquihired Bengaluru-based artificial intelligence startup Pikup.ai. As part of the deal, the team at Pikup.ai will join Ola. Speaking on the acqui-hiring, Inder Singh, Co-founder, Pikup.ai, said, By bringing deep domain expertise to Ola, this acquihire will also deliver innovations that continue to improve safety and transform customer experience. The cab aggregator also said it was increasing its focus on using advanced analytics and deep technology to build on its mobility solutions. With the availability of rich data, the application of machine learning and AI will enable the ride-hailing giant to identify deep insights that can lead to improved mobility outcomes.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."

Reno, NV, July 30, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE -- Artificial Intelligence Technology Solutions, Inc., (AITX:OTCPK) is pleased to announce that Robotic Assistance Devices, Inc., (RAD) its wholly owned subsidiary, has received a new purchase order and subsequently deployed three SCOT units at a new multi-billion dollar real estate client. This follows the deployment of the SCOT mentioned in the February 5, 2019 press release, which was for a San Francisco Bay area commercial real estate customer. This client has indicated they are likely to expand their RAD system quickly. "We are gaining good momentum in the real estate vertical," said Steve Reinharz, President and CEO of RAD. "This is a new commercial real estate order and the first in the Northeast of the U.S. This is a large sector that has the potential to greatly increase sales for the company in the future."

A lot of startups in the San Francisco Bay Area claim that they're planning to transform the world. San-Francisco-based, Elon Musk-founded OpenAI has a stronger claim than most: It wants to build artificial general intelligence (AGI), an AI system that has, like humans, the capacity to reason across different domains and apply its skills to unfamiliar problems. Today, it announced a billion dollar partnership with Microsoft to fund its work -- the latest sign that AGI research is leaving the domain of science fiction and entering the realm of serious research. "We believe that the creation of beneficial AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity," Greg Brockman, chief technology officer of OpenAI, said in a press release today. Existing AI systems beat humans at lots of narrow tasks -- chess, Go, Starcraft, image generation -- and they're catching up to humans at others, like translation and news reporting.

As the world descends into geopolitical competition, other powers increasingly challenge European countries' ability to defend their interests and values. Russia is willing to weaponise energy supplies, cyber capabilities, and disinformation; China invests strategically and uses state capitalism to skew the market; Turkey instrumentalises migration; Saudi Arabia leverages its energy resources. And the Trump administration is willing to exploit European dependence on the transatlantic security alliance and the dollar to achieve short-term policy goals. What unites these disparate powers is their unwillingness to separate the functioning of the global economy from political and security competition. The EU has the market power, defence spending, and diplomatic heft to end this vulnerability and restore sovereignty to its member states.

Something stood out of the ordinary during a speech by China's president, Xi Jinping, in January 2018. Behind Xi, on a bookshelf, were two books on artificial intelligence (AI). Why were those books there? Similar to 2015, when Russia "accidentally" aired designs for a new weapon, the placement of the books may not have been an accident. Was China sending a message?

Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. However, most studies in this area use a constrained problem setup, comparing performance when speakers overlap almost completely, at artificially low sampling rates, and with no external background noise. In this paper, we strive to move the field towards more realistic and challenging scenarios. To that end, we created the WSJ0 Hipster Ambient Mixtures (WHAM!) dataset, consisting of two speaker mixtures from the wsj0-2mix dataset combined with real ambient noise samples. The samples were collected in coffee shops, restaurants, and bars in the San Francisco Bay Area, and are made publicly available. We benchmark various speech separation architectures and objective functions to evaluate their robustness to noise. While separation performance decreases as a result of noise, we still observe substantial gains relative to the noisy signals for most approaches.