Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but suffer from approximating a potentially indefinite Hessian with a positive-definite matrix. Hessian-free methods leverage the ability to perform Hessian-vector multiplication without needing the entire Hessian matrix, but each iteration's complexity is significantly greater than quasi-Newton methods. In this paper we propose an alternative approach for solving ML problems based on a quasi-Newton trust-region framework for solving large-scale optimization problems that allow for indefinite Hessian approximations. Numerical experiments on a standard testing data set show that with a fixed computational time budget, the proposed methods achieve better results than the traditional limited-memory BFGS and the Hessian-free methods.

In recent years a number of large-scale triple-oriented knowledge graphs have been generated and various models have been proposed to perform learning in those graphs. Most knowledge graphs are static and reflect the world in its current state. In reality, of course, the state of the world is changing: a healthy person becomes diagnosed with a disease and a new president is inaugurated. In this paper, we extend models for static knowledge graphs to temporal knowledge graphs. This enables us to store episodic data and to generalize to new facts (inductive learning). We generalize leading learning models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx, DistMult) to temporal knowledge graphs. In particular, we introduce a new tensor model, ConT, with superior generalization performance. The performances of all proposed models are analyzed on two different datasets: the Global Database of Events, Language, and Tone (GDELT) and the database for Integrated Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph embeddings might be models also for cognitive episodic memory (facts we remember and can recollect) and that a semantic memory (current facts we know) can be generated from episodic memory by a marginalization operation. We validate this episodic-to-semantic projection hypothesis with the ICEWS dataset.

In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score. We show that this score has statistically significant racial associations, and that by modeling trust directly we find stronger disparities in care than by stratifying on race. We further demonstrate that mistrust is indicative of worse outcomes, but is only weakly associated with physiologically-created severity scores. Finally, we describe sentiment analysis experiments indicating patients with higher levels of mistrust have worse experiences and interactions with their caregivers. This work is a step towards measuring fairer machine learning in the healthcare domain.

Under dynamic conditions on bridges, we need a real-time management. To this end, this paper presents a rule-based decision support system in which the necessary rules are extracted from simulation results made by Aimsun traffic micro-simulation software. Then, these rules are generalized by the aid of fuzzy rule generation algorithms. Then, they are trained by a set of supervised and the unsupervised learning algorithms to get an ability to make decision in real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun and WEKA data mining software is used to execute the learning algorithms. Based on this experiment, the accuracy of the supervised algorithms to generalize the rules is greater than 80%. In addition, CART decision tree and sequential minimal optimization (SMO) provides 100% accuracy for normal data and these algorithms are so reliable for crisis management on bridge. This means that, it is possible to use such machine learning methods to manage bridges in the real-time conditions.

Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past students' data. In particular, the Hint Factory (Barnes & Stamper, 2008) recommends edits that are most likely to guide students from their current state towards a correct solution, based on what successful students in the past have done in the same situation. Still, the Hint Factory relies on student data being available for any state a student might visit while solving the task, which is not the case for some learning tasks, such as open-ended programming tasks. In this contribution we provide a mathematical framework for edit-based hint policies and, based on this theory, propose a novel hint policy to provide edit hints in vast and sparsely populated state spaces. In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states. Because the space of possible weighted averages is continuous, we call this approach the Continuous Hint Factory. In our experimental evaluation, we demonstrate that the Continuous Hint Factory can predict more accurately what capable students would do compared to existing prediction schemes on two learning tasks, especially in an open-ended programming task, and that the Continuous Hint Factory is comparable to existing hint policies at reproducing tutor hints on a simple UML diagram task.

It may sound like something from an episode of Game of Thrones, but Japanese researchers have revealed an indoor robot called DRAGON that can'shapeshift' in mid air. DRAGON, short for'Dual-rotor embedded multilink Robot with the Ability of multi-deGree-of-freedom aerial transformatiON,' can transform into multiple shapes including a square and a snake. There are not many indoor aerial robots due to constraints coming from door frames, as well as various hazards including windows and furniture. Roboticists in Tokyo developed a robot called DRAGON that can fly indoors. DRAGON counters these hazards by having technology that allows it to transform autonomously based on the constraints of space it needs to pass through.

Automatica 2018 is one of Europe's largest robotics and automation-related trade shows and a destination for global roboticists and business executives to view new products. It was held June 19-22 in Munich and had 890 exhibitors and 46,000 visitors (up 7% from the previous show). The International Symposium on Robotics (ISR) was held in conjunction with Automatica with a series of robotics-related keynotes, poster presentations, talks and workshops. The ISR also had an awards dinner in Munich on June 20th at the Hofbräuhaus, a touristy beer hall and garden with big steins of beer, plates full of Bavarian food and oompah bands on each floor. From left: Stefan Lampa, CEO, KUKA; Prof Dr Bruno Siciliano, Dir ICAROS and PRISMALab, U of Naples Federico II; Ken Fouhy, Moderator, Editor in Chief, Innovations & Trend Research, VDI News; Dr. Kiyonori Inaba, Exec Dir, Robot Business Division, FANUC; Markus Kueckelhaus, VP Innovations & Trend Research, DHL; and Per Vegard Nerseth, Group Senior VP, ABB.

HANGZHOU, China/SHANGHAI (Reuters) – In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralized computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.

Think about how much we need cognitive technologies that enable us to make accurate estimates. There is a need for knowledge to save the potato from speculators, to predict elections and the weather accurately, i.e. cognitive computing. Will it hail, when will it hail, how long will it last, how big will the hailstones be and where will it hail in Istanbul from Tekirdağ to Kocaeli? Not Nostradamus but big data and cognitive computing technology lets you find the right answers. Weather forecasting It was not in vain that IBM bought The Weather Company, which has the world's most sensitive, precise and reliable weather data, at the beginning of 2016.

WHEN TAY MADE HER DEBUT in March 2016, Microsoft had high hopes for the artificial intelligence–powered "social chatbot." Like the automated, text-based chat programs that many people had already encountered on e-commerce sites and in customer service conversations, Tay could answer written questions; by doing so on Twitter and other social media, she could engage with the masses. But rather than simply doling out facts, Tay was engineered to converse in a more sophisticated way--one that had an emotional dimension. She would be able to show a sense of humor, to banter with people like a friend. Her creators had even engineered her to talk like a wisecracking teenage girl. When Twitter users asked Tay who her parents were, she might respond, "Oh a team of scientists in a Microsoft lab. They're what u would call my parents." If someone asked her how her day had been, she could quip, "omg totes exhausted."