What if there was no such thing as "real"? What if food could be made from thin air? What if electronics could last forever? These are some of the questions being tackled by Europe's top tech innovators identified by our team here at Sifted, in association with the co-working space Second Home and their Breakthrough event this month. This is not your ordinary innovator list. You may not have heard of these startups.

Jiawei Han, a professor of computer science at the University of Illinois at Urbana-Champaign, was recently named a Michael Aiken Chair, one of the University's highest awards. The endowed chair is the latest honor in Han's distinguished and pioneering career, with notable accomplishments including creating core data mining algorithms and co-authoring the textbook that is considered by many to have defined the field. Professor Han is also a busy and successful teacher with a love for "train[ing] the younger generation, whether at UIUC or all over the world on Coursera." Professor Han had three PhD students graduate in May, with one becoming a professor at Georgia Tech, one joining Google, and one joining Facebook. Students taking his classes as part of the Online Master of Computer Science in Data Science degree have an opportunity to learn from him through videos and can ask him questions directly during live office hours.

Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the nonlinear filtering problem in continuous time. The distinguishing feature of these algorithms is that the Bayesian update step is implemented using a feedback control law. It has been noted in the literature that the control law is not unique. This is the main problem addressed in this paper. To obtain a unique control law, the filtering problem is formulated here as an optimal transportation problem. An explicit formula for the (mean-field type) optimal control law is derived in the linear Gaussian setting. Comparisons are made with the control laws for different types of EnKF algorithms described in the literature. Via empirical approximation of the mean-field control law, a finite-$N$ controlled interacting particle algorithm is obtained. For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter. This allows strong conclusions on convergence and error properties based on the classical filter stability theory for the Kalman filter. It is shown that, under certain technical conditions, the mean squared error (m.s.e.) converges to zero even with a finite number of particles. A detailed propagation of chaos analysis is carried out for the finite-$N$ algorithm. The analysis is used to prove weak convergence of the empirical distribution as $N\rightarrow\infty$. For a certain simplified filtering problem, analytical comparison of the m.s.e. with the importance sampling-based algorithms is described. The analysis helps explain the favorable scaling properties of the control-based algorithms reported in several numerical studies in recent literature.

Researchers at CHILI Lab (Ecole Polytechnique Fédérale de Lausanne) in Switzerland and GAIPS Lab (University of Lisbon) in Portugal have recently developed an autonomous system designed to assist children in improving their handwriting skills. The system they created, presented in a paper published in Springer's International Journal of Social Robotics, entails the use of a social robot in one-to-one learning sessions with children. For some children, handwriting can be a difficult skill to acquire, yet it is a fundamental stepping stone in their academic path. In fact, poor handwriting can negatively affect a child's academic performance, self-esteem and learning motivation. To master handwriting, a child needs to learn to coordinate cognitive, motor and perceptual abilities, thus he/she might also require a considerable amount of practice.

Artificial intelligence is having a huge impact on education, transforming the sector in many positive ways and its impact is growing. In fact, the artificial intelligence sector in the U.S. education market is expected to grow 47.5% between 2017 and 2021 according to the latest market research report by Technavio. Artificial intelligence is changing how teachers are doing their jobs and how students are learning and studying. AI makes personalized learning possible, can assist teachers with curriculum adaptationand streamline administrative tasks. Now, scientists are trying to find out if the technology can be leveraged to measure student engagement.

A Rochester Institute of Technology professor has earned a prestigious National Science Foundation award to use computers to better understand human behavior and social interaction. Ifeoma Nwogu, an assistant professor of computer science, received an NSF Faculty Early Career Development (CAREER) award and grant for her five-year project. She aims to study human behavior in a new way, by using machine learning techniques to analyze and find patterns in the many signals that individuals display during social interactions. Her work will specifically look at groups working in science, technology, engineering and math (STEM), with the aim of supporting underrepresented groups in STEM. "In a conversation, people are constantly displaying and processing different non-verbal signals, such as how fast someone is talking or the facial expressions they are making," said Nwogu.

As higher education costs continue to rise, students bear the ultimate burden of choosing the right school, major, and delivery format to maximize post-graduation success. Unlike previous generations, millennials and adult learners are searching for alternatives to full-time, on-campus programs, and universities are eager to offer non-traditional routes to a degree. Distance learning programs have existed since the 1980s, but technological innovation, content scalability, and widespread mobile adoption have enabled the online degree program to be a competitive option for aspiring students. Long gone are the days of aggressive marketing tactics and empty promises made by degree mills and unaccredited for-profit universities. Today, a learner can enroll in competitive bachelor's and master's programs at U Penn, Columbia, Johns Hopkins, NYU, and more.

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Elite independent schools are currently in talks with a technology company exploring a new artificial intelligence programme that could replace GCSEs. Yet while a digital alternative to the GCSE that some schools view as "increasingly dry" might sound promising, how feasible would it really be? Mike Buchanan, executive director of the Headmasters' and Headmistresses' Conference, has spoken of how this could create a far more "personalised" style of learning, with computer programmes constructing a digitised profile of each pupil's skills. And arguably, the historic reason for GCSEs no longer exists, given that most students do not leave school at 16. So the idea of a bespoke digital learning platform is attractive, and according to one source in the private school sector, senior figures at Ofsted are interested. Rather than studying a raft of GCSEs, pupils would learn from an online programme alongside their usual class teaching.

This course will introduce you to the core capabilities of Artificial Intelligence (AI) and empower you to contribute to this exciting and transformational new era in global technological development. Developed by the pioneering Data Science team at the University of Southampton – ranked among the top 100 universities globally – this course will equip you with the specialist knowledge and skills you need to understand and effectively apply AI technologies within your organisation. Taught by industry-leading experts in AI and data science using a practical approach to learning, this 6-week flexible online course will see you exploring a range of AI capabilities in the context of real-life business case studies – teaching you how you can effectively harness the power of AI to transform your business.