When photons are used as the carriers of quantum information to transmit data, that information is often distorted due to environment fluctuations destroying the fragile quantum states necessary to preserve it. Researchers from Louisiana State University exploited a type of machine learning to correct for information distortion in quantum systems composed of photons. Published in Advanced Quantum Technologies, the team demonstrated that machine learning techniques using the self-learning and self-evolving features of artificial neural networks can help correct distorted information. This results outperformed traditional protocols that rely on conventional adaptive optics. "We are still in the fairly early stages of understanding the potential for machine learning techniques to play a role in quantum information science," said Dr. Sara Gamble, program manager at the Army Research Office, an element of U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory.

The Bank of England has revealed the design for the UK's new £50 note featuring computer scientist and codebreaker Alan Turing. Turing was selected to appear on the note in July 2019 in recognition of his groundbreaking work in mathematics and computer science, as well as his role in cracking the Enigma code used by Germany in World War II. The polymer note will enter circulation from June 23 this year, and incorporates a number of designs linked to Turing's life and legacy. These include technical drawings for the bombe, a decryption device used during WWII; a string of ticker tape with Turing's birthday rendered in binary (23 June 1912); a green and gold security foil resembling a microchip; and a table and mathematical formulae taken from one of Turing's most famous papers. As well as honoring his scientific achievements, Turing was also selected to appear on the bank note in recognition of his persecution by the UK government for homosexuality.

Lee Sedol, a world champion in the Chinese strategy board game Go, faced a new kind of adversary at a 2016 match in Seoul. Developers at DeepMind, an artificial intelligence startup acquired by Google, had fed 30 million Go moves into a deep neural network. Their creation, dubbed AlphaGo, then figured out which moves worked by playing millions of games against itself, learning at a faster rate than any human ever could. The match, which AlphaGo won 4 to 1, "was the moment when the new movement in artificial intelligence exploded into the public consciousness," technology journalist Cade Metz writes in his engaging new book, "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World." Metz, who covers AI for The New York Times and previously wrote for Wired magazine, is well positioned to chart the decades-long effort to build artificially intelligent machines.

All of these are the result of algorithms meant to make our lives better. But what happens when those algorithms aren t fair? And what happens when it s government agencies that are using artificial intelligence to conduct the people s business? Government agencies are also increasingly turning to artificial intelligence, and this might sound like a good thing. Relying on data-based algorithms can remove the potential for human biases when making critical decisions or allocating resources.

A coalition of AI researchers and health care professionals in fields like infectious disease, radiology, and ontology have found several common but serious shortcomings with machine learning made for COVID-19 diagnosis or prognosis. After the start of the global pandemic, startups like DarwinAI, major companies like Nvidia, and groups like the American College of Radiology launched initiatives to detect COVID-19 from CT scans, X-rays, or other forms of medical imaging. The promise of such technology is that it could help health care professionals distinguish between pneumonia and COVID-19 or provide more options for patient diagnosis. Some models have even been developed to predict if a person will die or need a ventilator based on a CT scan. However, researchers say major changes are needed before this form of machine learning can be used in a clinical setting.

While the governments of the United States and China are pushing policies for technological decoupling, private tech firms continue to tap resources from both sides. In the field of autonomous vehicles, it's common to see Chinese startups -- or startups with a strong Chinese link -- keep operations and seek investments in both countries. But as these companies mature and expand globally, their ties to China also come under increasing scrutiny. When TuSimple, a self-driving truck company headquartered in San Diego, filed for an initial public offering on Nasdaq this week, its prospectus flagged a regulatory risk due to its Chinese funding source. On March 1, the Committee on Foreign Investment in the United States (CFIUS) requested a written notice from TuSimple regarding an investment by Sun Dream, an affiliate of Sina Corporation, which runs China's biggest microblogging platform Sina Weibo.

Deep Neural Networks (DNNs) are popularly used for implementing autonomy related tasks in automotive Cyber-Physical Systems (CPSs). However, these networks have been shown to make erroneous predictions to anomalous inputs, which manifests either due to Out-of-Distribution (OOD) data or adversarial attacks. To detect these anomalies, a separate DNN called assurance monitor is often trained and used in parallel to the controller DNN, increasing the resource burden and latency. We hypothesize that a single network that can perform controller predictions and anomaly detection is necessary to reduce the resource requirements. Deep-Radial Basis Function (RBF) networks provide a rejection class alongside the class predictions, which can be utilized for detecting anomalies at runtime. However, the use of RBF activation functions limits the applicability of these networks to only classification tasks. In this paper, we show how the deep-RBF network can be used for detecting anomalies in CPS regression tasks such as continuous steering predictions. Further, we design deep-RBF networks using popular DNNs such as NVIDIA DAVE-II, and ResNet20, and then use the resulting rejection class for detecting adversarial attacks such as a physical attack and data poison attack. Finally, we evaluate these attacks and the trained deep-RBF networks using a hardware CPS testbed called DeepNNCar and a real-world German Traffic Sign Benchmark (GTSB) dataset. Our results show that the deep-RBF networks can robustly detect these attacks in a short time without additional resource requirements.

We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem collapses to the apportionment problem for party-list proportional representation systems (in such case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computation complexity.

Scientists in China have developed a laser that can locate a hidden object from a mile away. Researchers hid a mannequin inside an apartment and fired a laser emitter at its location, determining the dummy's location by calculating how long it took photons to hit different parts of the room and travel back to the laser. The technology, known as non-line-of-sight (NLOS) imaging, could be utilized by the military to find enemy targets or rescue teams to find victims. It could also be beneficial in helping self-driving cars detect pedestrians and other vehicles from behind buildings. A team at the University of Science and Technology of China perfected the new technique.

This week, we will briefly touch upon the topic of artificial intelligence, or AI, discuss what it is, why it is so important to the American empire and national security, and how the ancient Chamorro people of the Marianas can prepare themselves to be ready for future possible job and entrepreneurial opportunities at the intersection of warfare and technology. Part of the answer is found in a report released by the congressionally established National Security Commission on Artificial Intelligence, which outlined the national importance of AI and its application to all facets of American--and by implication, American colonial--society. AI will become more important as Guam continues to move toward technological solutions for future energy, food technology and security opportunities. Now is the time for the governments of Guam and the CNMI to consider creating a Marianas Artificial Intelligence, Security and Emerging Technologies Understanding advisory board to learn and more completely seek to comprehend the nature of AI, how it is currently used and how it presents opportunities and vulnerabilities to every aspect of Pacific island life. A most dangerous aspect of rapidly emerging AI enabled technology and networks is that nation state adversaries such as China and Russia may outpace the United States on this front over the next 10 to 15 years.