Computers Math

The Artificial Intelligence industry should create a global community of hackers and “threat modellers” dedicated to stress-testing the harm potential of new AI products in order to earn the trust of governments and the public before it’s too late.
This is one of the recommendations made by an international team of risk and machine-learning experts, led by researchers at the University of Cambridge’s Centre for the Study of Existential Risk (CSER), who have authored a new “call to action” published today in the journal Science.
They say that companies building intelligent technologies should harness techniques such as “red team” hacking, audit trails and “bias bounties” — paying out rewards for revealing ethical flaws — to prove their integrity before releasing AI for use on the wider public.
Otherwise, the industry faces a “crisis of trust” in the systems that increasingly underpin our society, as public concern continues to mount over everything from driverless cars and autonomous drones to secret social media algorithms that spread misinformation and provoke political turmoil.
The novelty and “black box” nature of AI systems, and ferocious competition in the race to the marketplace, has hindered development and adoption of auditing or third party analysis, according to lead author Dr Shahar Avin of CSER.
The experts argue that incentives to increase trustworthiness should not be limited to regulation, but must also come from within an industry yet to fully comprehend that public trust is vital for its own future — and trust is fraying. More

Black holes are one of the greatest mysteries of our Universe — for example, a black hole with the mass of our Sun has a radius of only 3 kilometers. Black holes in orbit around each other give off gravitational radiation — oscillations of space and time predicted by Albert Einstein in 1916. This causes the orbit to become faster and tighter, and eventually, the black holes merge in a final burst of radiation. These gravitational waves propagate through the Universe at the speed of light, and are detected by observatories in the USA (LIGO) and Italy (Virgo). Scientists compare the data collected by the observatories against theoretical predictions to estimate the properties of the source, including how large the black holes are and how fast they are spinning. Currently, this procedure takes at least hours, often months.
An interdisciplinary team of researchers from the Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen and the Max Planck Institute for Gravitational Physics (Albert Einstein Institute/AEI) in Potsdam is using state-of-the-art machine learning methods to speed up this process. They developed an algorithm using a deep neural network, a complex computer code built from a sequence of simpler operations, inspired by the human brain. Within seconds, the system infers all properties of the binary black-hole source. Their research results were published in the flagship journal of Physics, Physical Review Letters.
“Our method can make very accurate statements in a few seconds about how big and massive the two black holes were that generated the gravitational waves when they merged. How fast do the black holes rotate, how far away are they from Earth and from which direction is the gravitational wave coming? We can deduce all this from the observed data and even make statements about the accuracy of this calculation,” explains Maximilian Dax, first author of the study Real-Time Gravitational Wave Science with Neural Posterior Estimation and Ph.D. student in the Empirical Inference Department at MPI-IS.
The researchers trained the neural network with many simulations — predicted gravitational-wave signals for hypothetical binary black-hole systems combined with noise from the detectors. This way, the network learns the correlations between the measured gravitational-wave data and the parameters characterizing the underlying black-hole system. It takes ten days for the algorithm called DINGO (the abbreviation stands for Deep INference for Gravitational-wave Observations) to learn. Then it is ready for use: the network deduces the size, the spins, and all other parameters describing the black holes from data of newly observed gravitational waves in just a few seconds. The high-precision analysis decodes ripples in space-time almost in real-time — something that has never been done with such speed and precision. The researchers are convinced that the improved performance of the neural network as well as its ability to better handle noise fluctuations in the detectors will make this method a very useful tool for future gravitational-wave observations.
“The further we look into space through increasingly sensitive detectors, the more gravitational-wave signals are detected. Fast methods such as ours are essential for analyzing all of this data in a reasonable amount of time,” says Stephen Green, senior scientist in the Astrophysical and Cosmological Relativity department at the AEI. “DINGO has the advantage that — once trained — it can analyze new events very quickly. Importantly, it also provides detailed uncertainty estimates on parameters, which have been hard to produce in the past using machine-learning methods.” Until now, researchers in the LIGO and Virgo collaborations have used computationally very time-consuming algorithms to analyze the data. They need millions of new simulations of gravitational waveforms for the interpretation of each measurement, which leads to computing times of several hours to months — DINGO avoids this overhead because a trained network does not need any further simulations for analyzing newly observed data, a process known as ‘amortized inference’.
The method holds promise for more complex gravitational-wave signals describing binary — black-hole configurations, whose use in current algorithms makes analyses very time-consuming, and for binary neutron stars. Whereas the collision of black holes releases energy exclusively in the form of gravitational waves, merging neutron stars also emit radiation in the electromagnetic spectrum. They are therefore also visible to telescopes which have to be pointed to the respective region of the sky as quickly as possible in order to observe the event. To do this, one needs to very quickly determine where the gravitational wave is coming from, as facilitated by the new machine learning method. In the future, this information could be used to point telescopes in time to observe electromagnetic signals from the collisions of neutron stars, and of a neutron star with a black hole.
Alessandra Buonanno, director at the AEI, and Bernhard Schölkopf, a Director at the MPI-IS, are thrilled with the prospect of taking their successful collaboration to the next level. Buonanno expects that “going forward, these approaches will also enable a much more realistic treatment of the detector noise and of the gravitational signals than is possible today using standard techniques,” and Schölkopf adds that such “simulation-based inference using machine learning could be transformative in many areas of science where we need to infer a complex model from noisy observations.”
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Researchers in Finland have developed a circuit that produces the high-quality microwave signals required to control quantum computers while operating at temperatures near absolute zero. This is a key step towards moving the control system closer to the quantum processor, which may make it possible to greatly increase the number of qubits in the processor.
One of the factors limiting the size of quantum computers is the mechanism used to control the qubits in quantum processors. This is normally accomplished using a series of microwave pulses, and because quantum processors operate at temperatures near absolute zero, the control pulses are normally brought into the cooled environment via broadband cables from room temperature.
As the number of qubits grows, so does the number of cables needed. This limits the potential size of a quantum processor, because the refrigerators cooling the qubits would have to become larger to accommodate more and more cables while also working harder to cool them down — ultimately a losing proposition.
A research consortium led by Aalto University and VTT Technical Research Centre of Finland has now developed a key component of the solution to this conundrum. ‘We have built a precise microwave source that works at the same extremely low temperature as the quantum processors, approximately -273 degrees,’ says Mikko Möttönen, Professor at Aalto University and VTT Technical Research Centre of Finland, who led the team.
The new microwave source is an on-chip device that can be integrated with a quantum processor. Less than a millimetre in size, it potentially removes the need for high-frequency control cables connecting different temperatures. With this low-power, low-temperature microwave source, it may be possible to use smaller cryostats while still increasing the number of qubits in a processor.
‘Our device produces one hundred times more power than previous versions, which is enough to control qubits and carry out quantum logic operations,’ says Möttönen. ‘It produces a very precise sine wave, oscillating over a billion times per second. As a result, errors in qubits from the microwave source are very infrequent, which is important when implementing precise quantum logic operations.’
However, a continuous-wave microwave source, such as the one produced by this device, cannot be used as is to control qubits. First, the microwaves must be shaped into pulses. The team is currently developing methods to quickly switch the microwave source on and off.
Even without a switching solution to create pulses, an efficient, low-noise, low-temperature microwave source could be useful in a range of quantum technologies, such as quantum sensors.
‘In addition to quantum computers and sensors, the microwave source can act as a clock for other electronic devices. It can keep different devices in the same rhythm, allowing them to induce operations for several different qubits at the desired instant of time,’ explains Möttönen.
The theoretical analysis and the initial design were carried out by Juha Hassel and others at VTT. Hassel, who started this work at VTT, is currently the head of engineering and development at IQM, a Finnish quantum-computing hardware company. The device was then built at VTT and operated by postdoctoral research Chengyu Yan and his colleagues at Aalto University using the OtaNano research infrastructure. Yan is currently an associate professor at Huazhong University of Science and Technology, China. The teams involved in this research are part of the Academy of Finland Centre of Excellence in Quantum Technology (QTF) and the Finnish Quantum Institute (InstituteQ).
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Materials provided by Aalto University. Note: Content may be edited for style and length. More

In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without having to actually build each new variation for testing, the process remains slow.
Now, researchers at MIT and Google Brain have developed a system that makes it possible not just to evaluate one proposed design at a time, but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of new, improved configurations.
The new system, called a differentiable solar cell simulator, is described in a paper published in the journal Computer Physics Communications, written by MIT junior Sean Mann, research scientist Giuseppe Romano of MIT’s Institute for Soldier Nanotechnologies, and four others at MIT and at Google Brain.
Traditional solar cell simulators, Romano explains, take the details of a solar cell configuration and produce as their output a predicted efficiency — that is, what percentage of the energy of incoming sunlight actually gets converted to an electric current. But this new simulator both predicts the efficiency and shows how much that output is affected by any one of the input parameters. “It tells you directly what happens to the efficiency if we make this layer a little bit thicker, or what happens to the efficiency if we for example change the property of the material,” he says.
In short, he says, “we didn’t discover a new device, but we developed a tool that will enable others to discover more quickly other higher performance devices.” Using this system, “we are decreasing the number of times that we need to run a simulator to give quicker access to a wider space of optimized structures.” In addition, he says, “our tool can identify a unique set of material parameters that has been hidden so far because it’s very complex to run those simulations.”
While traditional approaches use essentially a random search of possible variations, Mann says, with his tool “we can follow a trajectory of change because the simulator tells you what direction you want to be changing your device. That makes the process much faster because instead of exploring the entire space of opportunities, you can just follow a single path” that leads directly to improved performance. More

UBC researchers have created what could be the first battery that is both flexible and washable. It works even when twisted or stretched to twice its normal length, or after being tossed in the laundry.
“Wearable electronics are a big market and stretchable batteries are essential to their development,” says Dr. Ngoc Tan Nguyen, a postdoctoral fellow at UBC’s faculty of applied science. “However, up until now, stretchable batteries have not been washable. This is an essential addition if they are to withstand the demands of everyday use.”
The battery developed by Dr. Nguyen and his colleagues offers a number of engineering advances. In normal batteries, the internal layers are hard materials encased in a rigid exterior. The UBC team made the key compounds — in this case, zinc and manganese dioxide — stretchable by grinding them into small pieces and then embedding them in a rubbery plastic, or polymer. The battery comprises several ultra-thin layers of these polymers wrapped inside a casing of the same polymer. This construction creates an airtight, waterproof seal that ensures the integrity of the battery through repeated use.
It was team member Bahar Iranpour, a PhD student, who suggested throwing the battery in the wash to test its seal. So far, the battery has withstood 39 wash cycles and the team expects to further improve its durability as they continue to develop the technology.
“We put our prototypes through an actual laundry cycle in both home and commercial-grade washing machines. They came out intact and functional and that’s how we know this battery is truly resilient,” says Iranpour.
The choice of zinc and manganese dioxide chemistry also confers another important advantage. “We went with zinc-manganese because for devices worn next to the skin, it’s a safer chemistry than lithium-ion batteries, which can produce toxic compounds when they break,” says Nguyen.
An affordable option
Ongoing work is underway to increase the battery’s power output and cycle life, but already the innovation has attracted commercial interest. The researchers believe that when the new battery is ready for consumers, it could cost the same as an ordinary rechargeable battery.
“The materials used are incredibly low-cost, so if this is made in large numbers, it will be cheap,” says electrical and computer engineering professor Dr. John Madden, director of UBC’s Advanced Materials and Process Engineering Lab who supervised the work. In addition to watches and patches for measuring vital signs, the battery might also be integrated with clothing that can actively change colour or temperature.
“Wearable devices need power. By creating a cell that is soft, stretchable and washable, we are making wearable power comfortable and convenient.”
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Materials provided by University of British Columbia. Note: Content may be edited for style and length. More

The future of computing may be analog.
The digital design of our everyday computers is good for reading email and gaming, but today’s problem-solving computers are working with vast amounts of data. The ability to both store and process this information can lead to performance bottlenecks due to the way computers are built.
The next computer revolution might be a new kind of hardware, called processing-in-memory (PIM), an emerging computing paradigm that merges the memory and processing unit and does its computations using the physical properties of the machine — no 1s or 0s needed to do the processing digitally.
At Washington University in St. Louis, researchers from the lab of Xuan “Silvia” Zhang, associate professor in the Preston M. Green Department of Electrical & Systems Engineering at the McKelvey School of Engineering, have designed a new PIM circuit, which brings the flexibility of neural networks to bear on PIM computing. The circuit has the potential to increase PIM computing’s performance by orders of magnitude beyond its current theoretical capabilities.
Their research was published online Oct. 27 in the journal IEEE Transactions on Computers. The work was a collaboration with Li Jiang at Shanghai Jiao Tong University in China.
Traditionally designed computers are built using a Von Neuman architecture. Part of this design separates the memory — where data is stored — and the processor — where the actual computing is performed. More

Engineers at Caltech, ETH Zurich, and Harvard are developing an artificial intelligence (AI) that will allow autonomous drones to use ocean currents to aid their navigation, rather than fighting their way through them.
“When we want robots to explore the deep ocean, especially in swarms, it’s almost impossible to control them with a joystick from 20,000 feet away at the surface. We also can’t feed them data about the local ocean currents they need to navigate because we can’t detect them from the surface. Instead, at a certain point we need ocean-borne drones to be able to make decisions about how to move for themselves,” says John O. Dabiri (MS ’03, PhD ’05), the Centennial Professor of Aeronautics and Mechanical Engineering and corresponding author of a paper about the research that was published by Nature Communications on December 8.
The AI’s performance was tested using computer simulations, but the team behind the effort has also developed a small palm-sized robot that runs the algorithm on a tiny computer chip that could power seaborne drones both on Earth and other planets. The goal would be to create an autonomous system to monitor the condition of the planet’s oceans, for example using the algorithm in combination with prosthetics they previously developed to help jellyfish swim faster and on command. Fully mechanical robots running the algorithm could even explore oceans on other worlds, such as Enceladus or Europa.
In either scenario, drones would need to be able to make decisions on their own about where to go and the most efficient way to get there. To do so, they will likely only have data that they can gather themselves — information about the water currents they are currently experiencing.
To tackle this challenge, researchers turned to reinforcement learning (RL) networks. Compared to conventional neural networks, reinforcement learning networks do not train on a static data set but rather train as fast as they can collect experience. This scheme allows them to exist on much smaller computers — for the purposes of this project, the team wrote software that can be installed and run on a Teensy — a 2.4-by-0.7-inch microcontroller that anyone can buy for less than $30 on Amazon and only uses about a half watt of power.
Using a computer simulation in which flow past an obstacle in water created several vortices moving in opposite directions, the team taught the AI to navigate in such a way that it took advantage of low-velocity regions in the wake of the vortices to coast to the target location with minimal power used. To aid its navigation, the simulated swimmer only had access to information about the water currents at its immediate location, yet it soon learned how to exploit the vortices to coast toward the desired target. In a physical robot, the AI would similarly only have access to information that could be gathered from an onboard gyroscope and accelerometer, which are both relatively small and low-cost sensors for a robotic platform.
This kind of navigation is analogous to the way eagles and hawks ride thermals in the air, extracting energy from air currents to maneuver to a desired location with the minimum energy expended. Surprisingly, the researchers discovered that their reinforcement learning algorithm could learn navigation strategies that are even more effective than those thought to be used by real fish in the ocean.
“We were initially just hoping the AI could compete with navigation strategies already found in real swimming animals, so we were surprised to see it learn even more effective methods by exploiting repeated trials on the computer,” says Dabiri.
The technology is still in its infancy: currently, the team would like to test the AI on each different type of flow disturbance it would possibly encounter on a mission in the ocean — for example, swirling vortices versus streaming tidal currents — to assess its effectiveness in the wild. However, by incorporating their knowledge of ocean-flow physics within the reinforcement learning strategy, the researchers aim to overcome this limitation. The current research proves the potential effectiveness of RL networks in addressing this challenge — particularly because they can operate on such small devices. To try this in the field, the team is placing the Teensy on a custom-built drone dubbed the “CARL-Bot” (Caltech Autonomous Reinforcement Learning Robot). The CARL-Bot will be dropped into a newly constructed two-story-tall water tank on Caltech’s campus and taught to navigate the ocean’s currents.
“Not only will the robot be learning, but we’ll be learning about ocean currents and how to navigate through them,” says Peter Gunnarson, graduate student at Caltech and lead author of the Nature Communications paper.
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Materials provided by California Institute of Technology. Original written by Robert Perkins. Note: Content may be edited for style and length. More

When you think of a robot, images of R2-D2 or C-3PO might come to mind. But robots can serve up more than just entertainment on the big screen. In a lab, for example, robotic systems can improve safety and efficiency by performing repetitive tasks and handling harsh chemicals.
But before a robot can get to work, it needs energy — typically from electricity or a battery. Yet even the most sophisticated robot can run out of juice. For many years, scientists have wanted to make a robot that can work autonomously and continuously, without electrical input.
Now, as reported last week in the journal Nature Chemistry, scientists at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and the University of Massachusetts Amherst have demonstrated just that — through “water-walking” liquid robots that, like tiny submarines, dive below water to retrieve precious chemicals, and then surface to deliver chemicals “ashore” again and again.
The technology is the first self-powered, aqueous robot that runs continuously without electricity. It has potential as an automated chemical synthesis or drug delivery system for pharmaceuticals.
“We have broken a barrier in designing a liquid robotic system that can operate autonomously by using chemistry to control an object’s buoyancy,” said senior author Tom Russell, a visiting faculty scientist and professor of polymer science and engineering from the University of Massachusetts Amherst who leads the Adaptive Interfacial Assemblies Towards Structuring Liquids program in Berkeley Lab’s Materials Sciences Division.
Russell said that the technology significantly advances a family of robotic devices called “liquibots.” In previous studies, other researchers demonstrated liquibots that autonomously perform a task, but just once; and some liquibots can perform a task continuously, but need electricity to keep on running. In contrast, “we don’t have to provide electrical energy because our liquibots get their power or ‘food’ chemically from the surrounding media,” Russell explained. More