The European Commission has chosen Time Machine as one of the six proposals retained for preparing large-scale research initiatives to be strategically developed in the next decade. Time Machine foresees to design and implement advanced new digitisation and Artificial Intelligence (AI) technologies to mine Europe's vast cultural heritage, providing fair and free access to information that will support future scientific and technological developments in Europe. The Time Machine Project, which involves FAU as well as several other institutions, will create advanced AI technologies to make sense of vast amounts of information from complex historical data sets. This will enable the transformation of fragmented data – with content ranging from medieval manuscripts and historical objects to smartphone and satellite images – into useable knowledge for industry. In essence, a large-scale computing and digitisation infrastructure will map Europe's entire social, cultural and geographical evolution.

In 2011, a friend of mine in college asked me if I'd read The Happiness Hypothesis: Finding Modern Truth in Ancient Wisdom, by Jonathan Haidt. Haidt's aim was to probe and distill--and "savor"--the moral precepts of antiquity in the light of modern science. The 2006 book was an answer to an overabundance of too-little-appreciated advice. "We might have already encountered the Greatest Idea, the insight that would have transformed us had we savored it, taken it to heart, and worked it into our lives," Haidt wrote." My friend was happy to encounter it: Haidt helped him through a difficult breakup. I hadn't heard of the book, but I had heard of its author. A paper of Haidt's, "The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment," had been assigned in my moral psychology course, and I was in the middle of writing an essay that argued against its conclusion. Haidt wrote that reason, compared to emotion, typically matters little to what we believe is ...

Are you ready for artificial intelligence in schools? You may already know that researchers believe AI is likely to predict the onset of diseases in future and that you're already using AI every day when you search online, use voice commands on your phone or use Google Translate. Maybe you heard the Canadian government has invested millions of dollars in AI research during the past few years and is emerging as one of the global leaders in AI research. But did you know that some companies are developing AI for use in schools, for example in forms such as AI tutoring systems? Such systems can engage students in dialogue and provide feedback in subjects where they need extra help.

Purpose: Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to address the inverse problem of converting pixel-wise multispectral reflectance measurements to underlying tissue parameters, such as oxygenation. Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed. Methods: We present a novel approach to the assessment of optical imaging modalities, which is sensitive to the different types of uncertainties that may occur when inferring tissue parameters. Based on the concept of invertible neural networks, our framework goes beyond point estimates and maps each multispectral measurement to a full posterior probability distribution which is capable of representing ambiguity in the solution via multiple modes. Performance metrics for a hardware setup can then be computed from the characteristics of the posteriors. Results: Application of the assessment framework to the specific use case of camera selection for physiological parameter estimation yields the following insights: (1) Estimation of tissue oxygenation from multispectral images is a well-posed problem, while (2) blood volume fraction may not be recovered without ambiguity. (3) In general, ambiguity may be reduced by increasing the number of spectral bands in the camera. Conclusion: Our method could help to optimize optical camera design in an application-specific manner.

Quantum graphical models (QGMs) extend the classical framework for reasoning about uncertainty by incorporating the quantum mechanical view of probability. Prior work on QGMs has focused on hidden quantum Markov models (HQMMs), which can be formulated using quantum analogues of the sum rule and Bayes rule used in classical graphical models. Despite the focus on developing the QGM framework, there has been little progress in learning these models from data. The existing state-of-the-art approach randomly initializes parameters and iteratively finds unitary transformations that increase the likelihood of the data. While this algorithm demonstrated theoretical strengths of HQMMs over HMMs, it is slow and can only handle a small number of hidden states. In this paper, we tackle the learning problem by solving a constrained optimization problem on the Stiefel manifold using a well-known retraction-based algorithm. We demonstrate that this approach is not only faster and yields better solutions on several datasets, but also scales to larger models that were prohibitively slow to train via the earlier method.

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system and disturbances. This paper examines two different algorithms to solve the game, Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play, and compares performance on an autonomous driving scenario. We extend the game formulation to a semi-competitive setting and demonstrate that the resulting adversary better captures meaningful disturbances that lead to better overall performance. The resulting robust policy exhibits improved driving efficiency while effectively reducing collision rates compared to baseline control policies produced by traditional reinforcement learning methods.

For those of us who worry that Facebook may have serious boundary issues when it comes to the personal information of its users, Mark Zuckerberg's recent comments at Harvard should get the heart racing. Zuckerberg ostensibly dropped by the university last month as part of a year of conversations with experts about the role of technology in society, "the opportunities, the challenges, the hopes, and the anxieties." His nearly two-hour interview with the Harvard law school professor Jonathan Zittrain in front of Facebook cameras and a classroom of students centered on the company's unprecedented position as a town square for perhaps two billion people. To hear the young CEO tell it, Facebook was taking shots from all sides--either it was indifferent to the ethnic hatred festering on its platforms or it was a heavy-handed censor deciding whether an idea was allowed to be expressed. Zuckerberg confessed that he hadn't sought out such an awesome responsibility.

The Northern Virginia Technology Council (NVTC) announced today it will host a first-ever Impact AI summit on March 21, 2019. Impact AI will showcase companies in our region that are making inroads in the advancement of technology around Artificial Intelligence. Artificial Intelligence is gaining momentum in broad applications from manufacturing to healthcare, energy to telecommunications. The application of machine learning, natural language processing, and robotics to challenges in virtually every industry provide significant motivation for companies to exploit the competitive and strategic advantages that exist. Impact AI 2019 will feature outstanding and informative content from keynotes, informative panels and "Tech Talks," our version of the popular Ted Talks.

In an attempt to attract and retain the best of the talent, the UK government has rolled out a number of scholarships for Indian and EU students. As part of the programme, 1,000 students from India and the European Union will get an opportunity to enhance their skills in artificial intelligence through their 16 dedicated centres for Research and Innovation AI Centres for Doctoral Training (CDTs) in universities across England, Scotland and Wales. The £110 million packages by the UK government will offer 200 AI Masters places at UK universities which is also funded by the likes of Infosys, Deepmind, QuantumBlack, Cisco and BAE Systems. Types of scholarship: Turing Senior AI Fellowships: For existing leaders in the field and applicants should already be demonstrating leadership equivalent to a full professorial position. Location: Fellows will be based in the UK and hosted by a UK organisation with significant ability to carry out research.

Implicit in his comment is the notion that, someday, these systems will be ready. But work by Adina Sterling, an assistant professor of organizational behavior at Stanford Graduate School of Business, questions this optimism, linking it to a deep–and deeply problematic–misconception of hiring's strategic role. In a new paper coauthored with Daniel W. Elfenbein of Washington University in St. Louis and published in Strategy Science, Sterling articulates how smart hiring is inextricable from long-term corporate strategy; she also explains why delegating the responsibility of hiring to machines, at least in the near future, is likely to undermine its strategic potential. "With technology increasingly stepping into this role, we're at a moment in which these questions of higher-level strategy ought to be of great importance," she says. The use of machines in hiring became widespread roughly a quarter-century back, when career platforms like Monster.com emerged on the web.