Economists view the world economy and society, especially in the United States, differently from intelligence analysts, with varying degrees of skill and accuracy. In 2004, the year Facebook was founded, the U.S. was already wondering what the world would look like 16 years later in 2020. A 119-page report by the National Intelligence Council titled “Mapping the Global Future”, which was released in 2004, showed some of these forecasts.
First, the authors of the report sensed that the world in 2020 will be approaching an inflection point. Aware of America’s waning influence, they wrote: “At no time since the formation of the Western alliance system in 1949 have the shape and nature of international alignments been in such a state of flux.” 16 years ago, they believed that by 2020, the trend would be one that “dramatically altered (America’s) alliances and relationships with Europe and Asia,” as European allies prioritized the European Union over NATO and Asian allies adjusted to the rise of China and India.
Second, the rise of the “America first” movement. The report stated that “a lot of Americans are getting tired of playing the world’s policeman” and shouldering the burden of securing allies is a rotten deal. Even leaving the United Nations (UN) in the United States is a bad deal. This prediction, made 16 years ago, now seems completely correct, because there are “America Firster” groups calling for the UN to leave New York, and the current U.S. president himself is a strong supporter of the “America First” movement.
Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science Advances Tyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.
The map indicated the possibility of training physical wave systems to learn complex features in temporal data using standard training techniques used for neural networks. As proof of principle, they demonstrated an inverse-designed, inhomogeneous medium to perform English vowel classification based on raw audio signals as their waveforms scattered and propagated through it. The scientists achieved performance comparable to a standard digital implementation of a recurrent neural network. The findings will pave the way for a new class of analog machine learning platforms for fast and efficient information processing within its native domain.
The recurrent neural network (RNN) is an important machine learning model widely used to perform tasks including natural language processing and time series prediction. The team trained wave-based physical systems to function as an RNN and passively process signals and information in their native domain without analog-to-digital conversion. The work resulted in a substantial gain in speed and reduced power consumption. In the present framework, instead of implementing circuits to deliberately route signals back to the input, the recurrence relationship occurred naturally in the time dynamics of the physics itself. The device provided the memory capacity for information processing based on the waves as they propagated through space.
Astronomers from CSIRO and Curtin University have used pulsars to probe the Milky Way’s magnetic field. Working with colleagues in Europe, Canada, and South Africa, they have published the most precise catalogue of measurements towards mapping our Galaxy’s magnetic field in 3D.
The Milky Way’s magnetic field is thousands of times weaker than Earth’s, but is of great significance for tracing the paths of cosmic rays, star formation, and many other astrophysical processes. However, our knowledge of the Milky Way’s 3D structure is limited.
Dr. Charlotte Sobey, the lead author of the research paper, said “We used pulsars (rapidly-rotating neutron stars) to efficiently probe the Galaxy’s magnetic field in 3D. Pulsars are distributed throughout the Milky Way, and the intervening material in the Galaxy affects their radio-wave emission.”
This article originally appeared in the Aug. 19, 2019 issue of SpaceNews magazine.
When the Aerospace Corp. launched the Optical Communications and Sensor Demonstration in 2017, one mission objective was to test water-fueled thrusters. At the time, the idea was fairly novel. Two years later, water-based propulsion is moving rapidly into the mainstream.
Capella Space’s first radar satellite and HawkEye 360’s first cluster of three radio-frequency mapping satellites move in orbit by firing Bradford Space’s water-based Comet electrothermal propulsion system. Momentus Space and Astro Digital are testing a water plasma thruster on their joint El Camino Real mission launched in July. And an updated version of the water-fueled cold gas thrusters the Aerospace Corp. first flew in 2017 launched in early August.
Our brain has 86 billion neurons connected by 3 million kilometers of nerve fibers and The Human Brain Project is mapping it all. One of the key applications is neuromorphic computing — computers inspired by brain architecture that may one day be able to learn as we do.
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As an astronomer, there is no better feeling than achieving “first light” with a new instrument or telescope. It is the culmination of years of preparations and construction of new hardware, which for the first time collects light particles from an astronomical object.
This is usually followed by a sigh of relief and then the excitement of all the new science that is now possible.
With the Skydio 2 Dock, a drone-in-a-box solution, the California startup wants to let companies rely on its obstacle-dodging, self-flying drone for automated mapping and surveillance — no humans needed.