In May of last year, after a 13-month slumber, the ground beneath Washington's Puget Sound rumbled to life. The quake began more than 20 miles below the Olympic mountains and, over the course of a few weeks, drifted northwest, reaching Canada's Vancouver Island. It then briefly reversed course, migrating back across the U.S. border before going silent again. All told, the monthlong earthquake likely released enough energy to register as a magnitude 6. By the time it was done, the southern tip of Vancouver Island had been thrust a centimeter or so closer to the Pacific Ocean.

The dreaded El Niño strikes the globe every 2 to 7 years. As warm waters in the tropical Pacific Ocean shift eastward and trade winds weaken, the weather pattern ripples through the atmosphere, causing drought in southern Africa, wildfires in South America, and flooding on North America's Pacific coast. Climate scientists have struggled to predict El Niño events more than 1 year in advance, but artificial intelligence (AI) can now extend forecasts to 18 months, according to a new study. The work could help people in threatened regions better prepare for droughts and floods, for example by choosing which crops to plant, says William Hsieh, a retired climate scientist in Victoria, Canada, who worked on early El Niño forecasts but who was not involved in the current study. Longer forecasts could have "large economic benefits," he says.

The dreaded El Niño strikes the globe every 2 to 7 years.

Artificial intelligence is learning how to predict El Niño climate cycles. The hope is that the technology could be used to improve climate predictions and give policy-makers more time to prepare. El Niño can cause severe weather and devastating damage. A phase of the El Niño-Southern Oscillation, it occurs when water warms over the tropical Pacific Ocean, shifting east and increasing rainfall and cyclones over the Americas while pulling rain away from Indonesia and Australia. Strong El Niño events are associated with intense storms and flooding in some areas, and drought and fires in others.

On August 29, 2019 I put on a workshop on Emerging Memories and Artificial Intelligence at Stanford University put on by the Stanford Center for Magnetic Nanotechnology and Coughlin Associates. We had several interesting speakers talking about various types of artificial intelligence and the role that new non-volatile memories will play in both training AI models and implementing them in the field using inference engines. This piece will talk about some of the material presented at this workshop. Dr. Shan Wang, co-organizer of the event gave a introduction, talking about emerging non-volatile memories and in particular on Magnetic Random Access Memory (MRAM). He spoke about how various new memories work--in particular Resistive RAM (RRAM), Phase Change Memory (PCM), MRAM and Ferrroelectic RAM (FRAM).

PACAF's Integration Division Deputy Chief Ryan Raber revealed at the Genius Machines event on 3 September that the aim is to better and more rapidly predict a potential enemy's possible intentions. The planned mechanism will be used to improve PACAF's decision-making process and will focus on events taking place in the Pacific region, which means Russia and China could be the potential objects of study, the media outlet Defense One pointed out. PACAF itself didn't specify which countries' actions the planned system will try to predict. The system is expected to forecast a potential adversary's actions by detecting irregularities in its routines based on analysis of past and current actions. The process takes days when done by humans, but by computer with AI it can theoretically handle the result in "just minutes".

The world's major powers are in the middle of an artificial intelligence (AI) arms race. Over the next several years, China expects to deploy a fleet of unmanned submarines in contested waters like the South China Sea. Russia has tested its robotic tank on the battlefield in Syria and is reportedly working on developing autonomous nuclear submarines. For its part, the United States is in the process of testing autonomous swarming drones. This development has major implications--not just for how wars are fought, but also for the future of American foreign policy.

Many setups in the San Francisco Bay Area boast that they are planning to change the world. However, OpenAI founded by Elon Musk has made a bigger promise than the rest: It wants to build artificial general intelligence (AGI), an AI system that like humans, can reason across many different domains and apply its skills to unfamiliar problems. For this reason, it announced a billion-dollar partnership with Microsoft to fund its work. This hints that AGI research is leaving the field of science fiction and entering the territory of serious research. "We believe that the creation of AGI will be the most important tech development in human history, with the potential to chaneg and shape the trajectory of humanity," Greg Brockman, chief technology officer (CTO) of OpenAI, informed the press.

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.