One cornerstone of making AI work is machine learning - the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it's been the explosion in research and application of machine learning that's made AI the recent hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning for data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data. Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. In many cases, these bad models are easy to spot since they perform poorly.

This position requires a U.S. person or the ability to obtain an Export Authorization from the appropriate government agency for non-U.S. Raytheon BBN Technologies (BBN) is looking for creative, talented individuals to join our world-class Speech, Language, and Multimedia group and to help us advance the state-of-the-art in our areas of operation. Our work ranges from seminal research and development to advanced fielded solutions. Our research activities drive the development of industry-leading 24 7 solutions and their deployment into demanding user environments. At BBN, Staff Scientists work with a team of experienced staff to design and implement new techniques in a variety of technologies, including speech recognition, speaker ID, language ID, machine translation, information extraction, question-answering, machine learning, NLP, document image processing, and video analysis.

One cornerstone of making AI work is machine learning - the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it's been the explosion in research and application of machine learning that's made AI the hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning to data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data. Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. In many cases, these bad models are easy to spot since they perform poorly.

Fairness concerns about algorithmic decision-making systems have been mainly focused on the outputs (e.g., the accuracy of a classifier across individuals or groups). However, one may additionally be concerned with fairness in the inputs. In this paper, we propose and formulate two properties regarding the inputs of (features used by) a classifier. In particular, we claim that fair privacy (whether individuals are all asked to reveal the same information) and need-to-know (whether users are only asked for the minimal information required for the task at hand) are desirable properties of a decision system. We explore the interaction between these properties and fairness in the outputs (fair prediction accuracy). We show that for an optimal classifier these three properties are in general incompatible, and we explain what common properties of data make them incompatible. Finally we provide an algorithm to verify if the trade-off between the three properties exists in a given dataset, and use the algorithm to show that this trade-off is common in real data.

From public CCTV cameras to biometric identification systems in airports, facial recognition technology is now common in a growing number of places around the world. In its most benign form, facial recognition technology is a convenient way to unlock your smartphone. At the state level though, facial recognition is a key component of mass surveillance, and it already touches half the global population on a regular basis. Today's visualizations from SurfShark classify 194 countries and regions based on the extent of surveillance. Click here to explore the full research methodology.

Machine intelligence has been a military research goal for decades, but is it even worth it? Artificial intelligence research reaches toward long-held visions of human-machine symbiosis, and all the benefits this would have for military might. Even if scientists fall short of these lofty ambitions, or even if they prove impossible to fully achieve, aiming for them may move humanity further along the path of scientific progress -- but are small increments of progress worth billions of taxpayer dollars? Such ambitions for generic AI systems have fueled research programs across the defense landscape since the late 1960s. The Strategic Computing Program grew out of the context of the early 1980s --an optimism about the ability of computers to solve military problems coupled with the Reagan administration's Cold War push to bolster the United States through technology advancement and big defense budgets.

Washington – U.S. Secretary of State Mike Pompeo on Friday condemned China's effort to take over national security legislation in Hong Kong, calling it "a death knell for the high degree of autonomy" that Beijing had promised the territory. Pompeo called for Beiing to reconsider the move and warned of an unspecified U.S. response if it proceeds. Meanwhile, White House economic adviser Kevin Hassett said China risked a major flight of capital from Hong Kong that would end the territory's status as the financial hub of Asia. Shortly afterward, the Commerce Department announced new restrictions on sensitive exports to China. The contentious measure, submitted Friday on the opening day of China's national legislative session, is strongly opposed by pro-democracy lawmakers in semi-autonomous Hong Kong.

Delaying the reopening of primary schools in England on 1 June by two weeks could halve the risk to each child of being exposed to an infectious classmate, according to a report by the Independent Scientific Advisory Group for Emergencies, a recently-formed group of scientists that is seeking to provide alternative advice to the UK government. The group say that modelling suggests that waiting until September would reduce this risk further, to less than the risk to children of road traffic accidents. The group is chaired by former government chief scientific advisor David King and is separate from the official SAGE committee that advises the UK government. "The crucial factor allowing school reopening around the world has been the presence of well-functioning local test, trace and isolate protocols – something that is now accepted will not be in place in England by early June," the report says. It adds that before schools can reopen, it is important to confirm that daily new ...

Recent years have witnessed the emergence and development of graph neural networks (GNNs), which have been shown as a powerful approach for graph representation learning in many tasks, such as node classification and graph classification. The research on the robustness of these models has also started to attract attentions in the machine learning field. However, most of the existing work in this area focus on the GNNs for node-level tasks, while little work has been done to study the robustness of the GNNs for the graph classification task. In this paper, we aim to explore the vulnerability of the Hierarchical Graph Pooling (HGP) Neural Networks, which are advanced GNNs that perform very well in the graph classification in terms of prediction accuracy. We propose an adversarial attack framework for this task. Specifically, we design a surrogate model that consists of convolutional and pooling operators to generate adversarial samples to fool the hierarchical GNN-based graph classification models. We set the preserved nodes by the pooling operator as our attack targets, and then we perturb the attack targets slightly to fool the pooling operator in hierarchical GNNs so that they will select the wrong nodes to preserve. We show the adversarial samples generated from multiple datasets by our surrogate model have enough transferability to attack current state-of-art graph classification models. Furthermore, we conduct the robust train on the target models and demonstrate that the retrained graph classification models are able to better defend against the attack from the adversarial samples. To the best of our knowledge, this is the first work on the adversarial attack against hierarchical GNN-based graph classification models.

Governor Northam announced that Virginians can now use COVIDCheck, a new online risk-assessment tool to check their symptoms and connect with the appropriate health care resource, including COVID-19 testing. "If you are feeling sick or think you may have been exposed to someone with COVID-19, it is important that you take action right away," said Governor Northam. "This online symptom-checking tool can help Virginians understand their personal risk for COVID-19 and get recommendations about what to do next from the safety of their homes. As we work to flatten the curve in our Commonwealth, telehealth services like this will be vital to relieving some of the strains on providers and health systems and making health care more convenient and accessible." COVIDCheck is a free, web-based, artificial intelligence-powered telehealth tool that can help individuals displaying symptoms associated with COVID-19 self-assess their risk and determine the best next steps, such as self-isolation, seeing a doctor, or seeking emergency care.