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Category: information science

In an age when digital information can fly around the connected networks of the world in the blink of an eye, it may seem a little old timey to consider delivering messages by hand. But that’s precisely what Panasonic is doing at CEATEC this week. The company is demonstrating a prototype communication system where data is transmitted from one person to another through touch.

There’s very little information on the system available, but Panasonic says that the prototype uses electric field communication technology to move data from “thing-to-thing, human-to-human and human-to-thing.” Data transfer and authentication occurs when the objects or people touch, with digital information stored in a source tag instantaneously moving to a receiver module – kind of like NFC tap to connect technology, but with people in the equation as well as devices.

It has the potential to allow business types to exchange contact information with a handshake, mood lighting in a room to be changed to match or contrast with clothing when a lamp is touched or access to a building granted by placing a hand or object on a lock interface or door handle. And Panasonic suggests that because the data is traveling through the body and not over the air, secure transmission is assured.

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Computation is stuck in a rut. The integrated circuits that powered the past 50 years of technological revolution are reaching their physical limits.

This predicament has computer scientists scrambling for new ideas: new devices built using novel physics, new ways of organizing units within computers and even algorithms that use new or existing systems more efficiently. To help coordinate new ideas, Sandia National Laboratories has assisted organizing the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Rebooting Computing held Oct. 17–19.

Researchers from Sandia’s Data-driven and Neural Computing Dept. will present three papers at the conference, highlighting the breadth of potential non-traditional neural computing applications.

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D-Wave 2000-qubit processor (credit: D-Wave Systems)

D-Wave Systems announced Tuesday (Sept. 28, 2016) a new 2000-qubit processor, doubling the number of qubits over the previous-generation D-Wave 2X system. The new system will enable larger problems to be solved and performance improvements of up to 1000 times.

D-Wave’s quantum system runs a quantum-annealing algorithm to find the lowest points in a virtual energy landscape representing a computational problem to be solved. The lowest points in the landscape correspond to optimal or near-optimal solutions to the problem. The increase in qubit count enables larger and more difficult problems to be solved, and the ability to tune the rate of annealing of individual qubits will enhance application performance.

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IBM delivered on the DARPA SyNAPSE project with a one million neuron brain-inspired processor. The chip consumes merely 70 milliwatts, and is capable of 46 billion synaptic operations per second, per watt–literally a synaptic supercomputer in your palm.

Along the way—progressing through Phase 0, Phase 1, Phase 2, and Phase 3—we have journeyed from neuroscience to supercomputing, to a new computer architecture, to a new programming language, to algorithms, applications, and now to a new chip—TrueNorth.

Fabricated in Samsung’s 28nm process, with 5.4 billion transistors, TrueNorth is IBM’s largest chip to date in transistor count. While simulating complex recurrent neural networks, TrueNorth consumes less than 100mW of power and has a power density of 20mW / cm2.

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This video realized by the AI Lab of SoftBank Robotics shows how Pepper robot learns to play the ball-in-a-cup game (“bilboquet” in French). The movement is first demonstrated to the robot by guiding its arm.

From there, Pepper has to improve its performance through trial-and-error learning. Even though the initial demonstration does not land the ball in the cup, Pepper can still learn to play the game successfully.

The movement is represented as a so-called dynamic movement primitive and optimized using an evolutionary algorithm. Our implementation uses the freely available software library dmpbbo: https://github.com/stulp/dmpbbo.

After 100 trials, Pepper has successfully optimized its behavior and is able to repeatedly land the ball in the cup.

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For 200 years, our knowledge of reproduction has been clear: sperm + egg = baby. But scientists say they may have found a way to create babies with two biological dads. Should we celebrate?

Which came first: the chicken or the egg? It is a question pondered since the time of Ancient Greece, when Aristotle decided that the answer must be both.

Now, scientists say it could be possible to remove the egg from the equation all together. Dr Tony Perry and his team announced this week that they have successfully bred mice without using a normal egg cell. Instead, they used sperm to fertilise a kind of non-viable embryo called a parthenogenote, which multiplies more like a normal cell. Then they ‘tricked’ it into developing into an embryo using special chemicals, planted it into a surrogate, and a new mouse was born. It survived, and has even gone on to have offspring of its own.

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Theory of a mach effect thruster I

The Mach Effect Thruster (MET) is a propellant—less space drive which uses Mach’s principle to produce thrust in an accelerating material which is undergoing mass—energy fluctuations. Mach’s principle is a statement that the inertia of a body is the result of the gravitational interaction of the body with the rest of the mass-energy in the universe. The MET device uses electric power of 100 — 200 Watts to operate. The thrust produced by these devices, at the present time, are small on the order of a few micro-Newtons. Researchers give a physical description of the MET device and apparatus for measuring thrusts. Next they explain the basic theory behind the device which involves gravitation and advanced waves to incorporate instantaneous action at a distance. The advanced wave concept is a means to conserve momentum of the system with the universe. There is no momentun violation in this theory. We briefly review absorber theory by summarizing Dirac, Wheeler-Feynman and Hoyle-Narlikar (HN). They show how Woodward’s mass fluctuation formula can be derived from first principles using the HN-theory which is a fully Machian version of Einstein’s relativity. HN-theory reduces to Einstein’s field equations in the limit of smooth fluid distribution of matter and a simple coordinate transformation.

It is shown that if Mach’s Principle is taken seriously, and the inertia of a body can be described as the interaction of the body with the rest of the universe, then the advanced and retarded fields transmitted between the particle and the universe can be used to explain the thrust observed in the Mach Effect drive experiments. This idea was originally put forward by one of the authors, James Woodward. The idea of inertia being a gravitational effect was first postulated by Einstein. In fact Mach’s principle was the foundation on which Einstein’s general relativity was based.

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Scientists may have found signs that phonons, the very small packets of energy that make up sound waves, were leaking out of sonic black holes, just as Hawking’s equations predicted.

Some 42 years ago, renowned theoretical physicist Stephen Hawking proposed that not everything that comes in contact with a black hole succumbs to its unfathomable nothingness. Tiny particles of light (photons) are sometimes ejected back out, robbing the black hole of an infinitesimal amount of energy, and this gradual loss of mass over time means every black hole eventually evaporates out of existence.

Known as Hawking radiation, these escaping particles help us make sense of one of the greatest enigmas in the known Universe, but after more than four decades, no one’s been able to actually prove they exist, and Hawking’s proposal remained firmly in hypothesis territory.

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Advances in machine learning have been driven by innovations and ideas from many fields. Inspired by the way that humans learn, Reinforcement Learning (RL) is concerned with algorithms which improve with trial-and-error feedback to optimize future performance.

Board games and video games often have well-defined reward functions which allow for straightforward optimization with RL algorithms. Algorithmic advances have allowed for RL to be in real-world problems, such as high degree-of-freedom robotic manipulation and large-scale recommendation tasks, with more complex goals.

Twitter Cortex invests in novel state-of-the-art machine learning methods to improve the quality of our products. We are exploring RL as a learning paradigm, and to that end, Twitter Cortex built a framework for RL development. Today, Twitter is open sourcing torch-twrl to the world.

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