Many real-world bandit problems involve non-stationary reward distributions, where the optimal decision may shift due to evolving environments. However, the performance of some typical Multi-Armed Bandit (MAB) models such as Upper Confidence Bound (UCB) algorithms degrades significantly in non-stationary environments where reward distributions change over time. To address this limitation, this paper introduces and evaluates FDSW-UCB, a novel dual-view algorithm that integrates a discount-based long-term perspective with a sliding-window-based short-term view. A data-driven semi-synthetic simulation platform, built upon the MovieLens-1M and Open Bandit datasets, is developed to test algorithm adaptability under abrupt and gradual drift scenarios. Experimental results demonstrate that a well-configured sliding-window mechanism (SW-UCB) is robust, while the widely used discounting method (D-UCB) suffers from a fundamental learning failure, leading to linear regret. Crucially, the proposed FDSW-UCB, when employing an optimistic aggregation strategy, achieves superior performance in dynamic settings, highlighting that the ensemble strategy itself is a decisive factor for success.

Microsoft's Think about Cup is one thing I sit up for yearly. The scholars and younger entrepreneurs who submit their extraordinarily early stage initiatives to this world competitors are just like the seeds of future startups and doubtlessly world-changing initiatives. This yr's winner, V Bionic, created a robotic glove to assist sufferers with neurological harm recuperate quicker at a fraction of the value of different choices. The crew, from Saudi Arabia, was led by Zain Samdani, who though he's a pupil has been researching and inventing issues within the robotics class for years. The remainder of the crew are equally on the begins of fascinating careers within the trade.

Imagine a world where your every sensation is augmented with music. When you pick up speed while out running, the music responds; as you enter a forest, instruments of deeper bass are seamlessly layered into the piece; and as your car creeps beyond the speed limit, the music alters rhythm to keep you in check. It may not be as far away as you think. "When we say we want to soundtrack your life, it's a big ambition," acknowledges Philip Sheppard, CEO of LifeScore. "But take a movie: when a soundtrack is considered, it is getting under your skin and leading your emotional response. "We're not telling people what to feel, but hopefully forcing serendipity – which is the point when the movie director of your brain goes'I'm here and present for this'." Virtuoso cellist Sheppard knows quite a bit about soundtracks – a renowned composer who has written with the likes of David Bowie and Queens of the Stone Age, he has more than 65 film scores to his credit and has worked on global music projects including Olympic Games ceremonies, the Rugby World Cup and the Tour de France. Accentuating the small moments which together make up a lifetime is the goal of a London start-up which uses AI – the company considers this to mean'Augmented Intelligence' – to help music respond to these changes in environment. "Massively generalising, the music falls into five camps: calming and sleep; flow and focus for work; daydreaming and wonder; narrative fantasy; or energy and uplift," says Sheppard. "We've really got into sensory ontology, which is how you link colour, texture, flavour and taste to sound.

Horizon asked a selection of scientists featured in the magazine last year for their opinion on priorities for 2020. READ: Prosperity is about more than money. But what else should count? '2020 is a super-year for international policy action,' said Sandrine Dixson-Declève, co-president of global think tank the Club of Rome and chair of an expert EU group on the economic and societal impact of research (ESIR). An oceans treaty will be agreed, biodiversity targets announced, it's the first opportunity for nations to increase their climate goals, and the start of the decade to scale action for the 2030 Sustainable Development Goals, she says.

A person's skill with the tool does.

AI systems have huge potential for good, but they're only as good as the data they're trained with. As machine learning expands into all areas of our lives, finding uses in healthcare, autonomous driving and law enforcement (to name just a few), any bias could be not just inconvenient, but could mean it does more harm than good. The problem of AI bias isn't just theoretical; after personal experience with systems recognizing her lighter –skinned colleagues' faces more readily than her own, MIT researcher Joy Buolamwini began a project to find out whether the software had trouble with her particular features, or if there was a wider issue. Buolamwini tested systems from IBM, Microsoft and Chinese company Face, showing them 1,000 faces and asking them to identify the subjects as either male or female. She found that all the systems were significantly better at identifying male faces than female ones, and perform better on lighter faces than darker faces.