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A group of scientists from the RIKEN Center for Emergent Matter Science in Japan has succeeded in taking repeated measurements of the spin of an electron in a silicon quantum dot (QD) without changing its spin in the process. This type of “non-demolition” measurement is important for creating quantum computers that are fault-tolerant. Quantum computers would make it easier to perform certain classes of calculations such as many-body problems, which are extremely difficult and time-consuming for conventional computers. Essentially, the involve measuring a quantum value that is never in a single state like a conventional transistor, but instead exists as a “superimposed state”—in the same way that Schrodinger’s famous cat cannot be said to be alive or dead until it is observed. Using such systems, it is possible to conduct calculations with a qubit that is a superimposition of two values, and then determine statistically what the correct result is. Quantum computers that use single electron spins in silicon QDs are seen as attractive due to their potential scalability and because silicon is already widely used in electronics technology.

The key difficulty with developing quantum computers, however, is that they are very sensitive to external noise, making error correction critical. So far, researchers have succeeded in developing single electron spins in silicon QDs with a long information retention time and high-precision quantum operation, but quantum non-demolition measurement—a key to effective error correction—has proven elusive. The conventional method for reading out single electron spins in silicon is to convert the spins into charges that can be rapidly detected, but unfortunately, the electron spin is affected by the detection process.

Now, in research published in Nature Communications, the RIKEN team has achieved such non-demolition measurement. The key insight that allowed the group to make the advance was to use the Ising type interaction model—a model of ferromagnetism that looks at how the electron spins of neighboring atoms become aligned, leading to the formation of ferromagnetism in the entire lattice. Essentially, they were able to transfer the spin information—up or down—of an electron in a QD to another electron in the neighboring QD using the Ising type interaction in a magnetic field, and then could measure the spin of the neighbor using the conventional method, so that they could leave the original spin unaffected, and could carry out repeated and rapid measurements of the neighbor.

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Google has released a neural-network-powered chatbot called Meena that it claims is better than any other chatbot out there.

Data slurp: Meena was trained on a whopping 341 gigabytes of public social-media chatter—8.5 times as much data as OpenAI’s GPT-2. Google says Meena can talk about pretty much anything, and can even make up (bad) jokes.

Why it matters: Open-ended conversation that covers a wide range of topics is hard, and most chatbots can’t keep up. At some point most say things that make no sense or reveal a lack of basic knowledge about the world. A chatbot that avoids such mistakes will go a long way toward making AIs feel more human, and make characters in video games more lifelike.

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Some forms of autonomous vehicle watch the road ahead using built-in cameras. Ensuring that accurate camera orientation is maintained during driving is, therefore, in some systems key to letting these vehicles out on roads. Now, scientists from Korea have developed what they say is an accurate and efficient camera-orientation estimation method to enable such vehicles to navigate safely across distances.

A fast camera-orientation estimation algorithm that pinpoints vanishing points could make self-driving cars safer.

John Wallace

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When crossing the street, which way do you first turn your head to check for oncoming traffic? This decision depends on the context of where you are. A pedestrian in the United States looks to the left for cars, but one in the United Kingdom looks right. A group of scientists at Columbia’s Zuckerman Institute has been studying how animals use context when making decisions. And now, their latest research findings have tied this ability to an unexpected brain region in mice: an area called the anterior lateral motor cortex, or ALM, previously thought to primarily guide and plan movement.

This discovery, published today in Neuron, lends new insight into the brain’s remarkable ability to make decisions. Flexible decision making is a critical tool for making sense of our surroundings; it allows us to have different reactions to the same information by taking context into account.

“Context-dependent decision-making is a building block of higher cognitive function in humans,” said neuroscientist Michael Shadlen, MD, PhD, the paper’s co-senior author with Richard Axel, MD. “Observing this process in a motor area of the mouse brain, as we did with today’s study, puts us a step closer to understanding cognitive function at the level of brain cells and circuits.”

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The locusts have no king, and yet they all go forth in ranks, noted King Solomon some three thousand years ago. That a multitude of simple creatures could display coherent collective behavior without any leader caused his surprise and amazement, and it has continued to do so for much of our thinking over the following millennia. Caesar’s legions conquered Europe, Napoleon’s armies reached Moscow: We always think of a great commander telling the thoughtless multitudes what to do.

Statistical physics pioneered an opposite view. When a piece of iron is cooled down to a certain temperature (the Curie temperature), the majority of the atoms align their spins, thereby making it magnetic. No atomic general gives any commands; each atom communicates only with its neighbors, and yet there is an overall alignment. It shows us that local microscopic interactions as such can lead to dramatic global behavior, and this realization brought about a revolution in the understanding of swarm behavior.

Some hundred years ago, serious biologists still thought that the coordination of birds in a flock was reached by telepathy, and the synchronized light emission by fireflies in the Asiatic jungle was attributed to faulty observation by the observer. The introduction of physics concepts in biology has to a large extent resolved these puzzles. Flocks of birds are much more like the atoms in iron than they are like the armies of Napoleon, and the fireflies act much like a laser. Collective behavior in the world of living beings is after all not so different from that in the inanimate world.

The fusion of physics concepts and biological observations has proven fruitful for both sides, and the conceptual transfer worked in both directions. For centuries, physics concentrated on simple systems, since these were solvable by the available techniques. Scientists broke up a large system into many simple little ones, which could be handled. Putting them back together then described the large system. At the turn of the last century, Per Bak, a pioneer of the truly new physics of complexity, noted that “the laws of physics are simple, but nature is complex.” If the Big Bang initially produced an ideal gas of primordial particles, how could this eventually lead to the appearance of Per Bak? A living being is more than a set of molecules, and today we study systems in physics which refuse to be decomposed additively into little subsystems.

The understanding of collective behavior of animal societies can perhaps act as a first step in the search for an answer. Today we can simulate a flock of birds on a computer, allowing each bird to move freely, subject to only two social rules: Follow your neighbor, but don’t crowd him. Putting a large number of such simplistic birds on the computer then produces the behavior observed for flocks of real birds. A primitive way to achieve collective behavior is provided by commands of Caesar or Napoleon; a more subtle and more natural way is to allow a many component system to move subject to the simple clear social rules.

A still more dramatic form of collective behavior appears in insect societies. The whole now no longer consists of identical components. Evolution has found it preferable to have different components designed specifically to carry out particular tasks. In an ant colony, we have workers, nannies, soldiers, drones, and a queen. Each individual carries out specific tasks; it is dependent on the others in order to exist, it cannot survive alone. And no matter how good a worker ant is, it will never have children to whom it can pass on its capabilities. All descendants are produced by the queen and the drones. Charles Darwin’s survival of the fittest now takes on a new and unexpected form. It no longer applies to individuals, but rather to the entire collective system. Insect societies thus in a way precede the pattern of modern industrial societies, in which large firms employ different “species” of workers to carry out dedicated tasks. In most human societies, the caste status is not (yet) inherited, and caste transitions are possible. Hopefully, evolution will consider this as dominant.

Continue reading “The Rules of the Flock” | >

Over the last century, scientists have developed methods to map the structures within the Earth’s crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface.

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