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Artificial intelligence helps in the identification of astronomical objects

Classifying celestial objects is a long-standing problem. With sources at near unimaginable distances, sometimes it’s difficult for researchers to distinguish between objects such as stars, galaxies, quasars or supernovae.

Instituto de Astrofísica e Ciências do Espaço’s (IA) researchers Pedro Cunha and Andrew Humphrey tried to solve this classical problem by creating SHEEP, a that determines the nature of astronomical sources. Andrew Humphrey (IA & University of Porto, Portugal) comments: “The problem of classifying is very challenging, in terms of the numbers and the complexity of the universe, and is a very promising tool for this type of task.”

The first author of the article, now published in the journal Astronomy & Astrophysics, Pedro Cunha, a Ph.D. student at IA and in the Dept. of Physics and the University of Porto, says, “This work was born as a side project from my MSc thesis. It combined the lessons learned during that time into a unique project.”

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This March, we, a group of educators, scientists, and psychologists started an educational non-profit (501 c3) Earthlings Hub, helping kids in refugee camps and evacuated orphanages. We are getting lots of requests for help, and are in urgent need to raise funds. If you happen to have any connections to educational and humanitarian charities, or if your universities or companies may be interested in providing some financial support to our program, we would really appreciate that! Please share with everyone who might be able to offer help or advice.

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AI less about ‘killer robots,’ more about Pentagon transformation, Groen says

“We’ve spent a lot of time in education to help people understand that just like an automobile extends your capabilities in the physical domain, artificial intelligence extends your abilities within the data domain and the information domain,” the general said Wednesday.

AI and its traces can be found across the Pentagon and its many enclaves and alcoves. The department has for years recognized its value as well, describing the tech in a 2018 strategy as rapidly changing businesses, industries and military threats. More can be done, Groen said.

“Implementation in the department, of course, is always a challenge, as new technology meets legacy processes, legacy organizations and legacy technology,” he said, later adding: “We believe that a lot of the rules have to change, a lot of the thought processes have been rendered obsolete, and, maybe, the cores of how our organizational processes work have to be reevaluated through the lens of artificial intelligence and data.”

Is diversity the key to collaboration? New AI research suggests so

A new training approach yields artificial intelligence that adapts to diverse play-styles in a cooperative game, in what could be a win for human-AI teaming.

As artificial intelligence gets better at performing tasks once solely in the hands of humans, like driving cars, many see teaming intelligence as a next frontier. In this future, humans and AI are true partners in high-stakes jobs, such as performing complex surgery or defending from missiles. But before teaming intelligence can take off, researchers must overcome a problem that corrodes cooperation: humans often do not like or trust their AI partners.

Now, new research points to diversity as being a key parameter for making AI a better team player.

Real-time AI: Microsoft announces preview of Project Brainwave

The speed of operations leaves manual inspectors with just seconds to decide if the product is really defective, or not.

That’s where Microsoft’s Project Brainwave could come in. Project Brainwave is a hardware architecture designed to accelerate real-time AI calculations. The Project Brainwave architecture is deployed on a type of computer chip from Intel called a field programmable gate array, or FPGA, to make real-time AI calculations at competitive cost and with the industry’s lowest latency, or lag time. This is based on internal performance measurements and comparisons to other organizations’ publicly posted information.

At Microsoft’s Build developers conference in Seattle this week, the company is announcing a preview of Project Brainwave integrated with Azure Machine Learning, which the company says will make Azure the most efficient cloud computing platform for AI.

Neural network-based prediction of the secret-key rate of quantum key distribution

For instance, continuous-variable (CV) QKD has its own distinct advantages at a metropolitan distance36,37 due to the use of common components of coherent optical communication technology. In addition, the homodyne38 or heterodyne39 measurements used by CV-QKD have inherent extraordinary spectral filtering capabilities, which allows the crosstalk in wavelength division multiplexing (WDM) channels to be effectively suppressed. Therefore, hundreds of QKD channels may be integrated into a single optical fiber and can be cotransmitted with classic data channels. This allows QKD channels to be more effectively integrated into existing communication networks. In CV-QKD, discrete modulation technology has attracted much attention31,40,41,42,43,44,45,46,47,48,49,50 because of its ability to reduce the requirements for modulation devices. However, due to the lack of symmetry, the security proof of discrete modulation CV-QKD also mainly relies on numerical methods43,44,45,46,47,48,51.

Unfortunately, calculating a secure key rate by numerical methods requires minimizing a convex function over all eavesdropping attacks related with the experimental data52,53. The efficiency of this optimization depends on the number of parameters of the QKD protocol. For example, in discrete modulation CV-QKD, the number of parameters is generally \(1000–3000\) depending on the different choices of cutoff photon numbers44. This leads to the corresponding optimization possibly taking minutes or even hours51. Therefore, it is especially important to develop tools for calculating the key rate that are more efficient than numerical methods.

In this work, we take the homodyne detection discrete-modulated CV-QKD44 as an example to construct a neural network capable of predicting the secure key rate for the purpose of saving time and resource consumption. We apply our neural network to a test set obtained at different excess noises and distances. Excellent accuracy and time savings are observed after adjusting the hyperparameters. Importantly, the predicted key rates are highly likely to be secure. Note that our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols. Through some open source deep learning frameworks for on-device inference, such as TensorFlow Lite54, our model can also be easily deployed on devices at the edge of the network, such as mobile devices, embedded Linux or microcontrollers.

Amazon and Max Planck Society launch Science Hub

Amazon and Max Planck Society announced the formation of a Science Hub—a collaboration that marks the first Amazon Science Hub to exist outside the United State… See more.


Amazon and Max Planck Society (also known as Max-Planck-Gesellschaft or MPG) today announced the formation of a Science Hub. The collaboration marks the first Amazon Science Hub to exist outside the United States and will focus on advancing artificial intelligence research and development throughout Germany.

The hub’s goal is to advance the frontiers of AI, computer vision, and machine learning research to ensure that research is creating solutions whose benefits are shared broadly across all sectors of society. To achieve that end, the collaboration will include sponsored research; open research; industrial fellowships co-supervised by Max Planck and Amazon; and community events funding to enrich the MPG and Amazon research communities.

The hub opens doors to further scientific collaboration with Max Planck Institutes (MPI), including the MPI for Intelligent Systems, the MPI for Software Systems, the MPI for Informatics, and the MPI for Biological Cybernetics.

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