There's more proof that machine learning can greatly aid in the diagnosis of Alzheimer's disease. The latest study, conducted by researchers at UC Davis and UC San Francisco, used artificial intelligence to detect amyloid plaques in the brains of deceased patients, automating the work typically done by pathologists. The findings concluded that machine learning was extremely accurate in analyzing the type of amyloid plaque found in the brain. Beta-amyloid plaque are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve connections. The tool developed by the University of California scientists allows them to analyze thousands of times more data than even the most experienced pathologist would have access to but doesn't replace their job completely.

The HKBU team, entitled AI Phoenix, included team leader and mathematics alumnus Xu Zhouming; computer science students Lyu Jiayou, Zeng Xuan, Wang Shihao and Xu Chen, as well as mathematics alumna Xu Fangfei. The HKBU team proposed the application of artificial intelligence to customer relationship management in the banking industry. The team enhanced the architecture of AI engines, making financial market data analysis more effective, on top to the current image processing and speech recognition applications. The enhancements are intended to raise the retail banks' competitiveness through more effective analysis of customer attributes. Team leader Xu Zhouming said a bank on the Mainland will explore a more in-depth collaboration with AI Phoenix Technology Company Limited, the start-up established by the team.

The future impacts of artificial intelligence (AI) on society and the labour force have been studied and reported extensively. In a recent book, AI Superpowers, Kai-Fu Lee, former president of Google China, wrote that 40 to 50 per cent of current jobs will be technically and economically viable with AI and automation over the next 15 years. Artificial intelligence refers to computer systems that collect, interpret and learn from external data to achieve specific goals and tasks. Unlike natural intelligence displayed by humans and animals, it is an artificial form of intelligence demonstrated by machines. This has raised questions about the ethics of AI decision-making and impacts of AI in the workplace.

The news last month that IBM would stop selling Watson for Drug Discovery due to lackluster financial returns did not come as a surprise to some executives in the field of artificial intelligence-led drug discovery and development. But, they said, that doesn't mean the field as a whole is in trouble. STAT reported April 18 that IBM would continue servicing existing customers, but would otherwise stop development and sales of the product, which the tech giant launched under a partnership with drugmaker Pfizer in 2016. The launch of IBM's Watson for Drug Discovery occurred as the idea of applying artificial intelligence and machine learning to the biopharma industry – particularly to aid discovery of new compounds – was beginning to take off in earnest. Since then, numerous companies have launched efforts of their own.

The Dali Museum in St Petersburg, Florida, got an AI to study archive footage of the great artist and recreate him as a deepfake. Microsoft will help you mind your Ps and LGBTQs with a version of Office that checks documents for inclusive language, such as changing "housewives" to "homemakers". People are coming for robot jobs. Japanese start-up Mira Robotics will soon sell a robot butler – the catch is it is controlled remotely by a human. The Orbital Reflector, a piece of "space art" in the form of a shimmering balloon, has failed in orbit.

Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output. We adopt a model selection algorithm from statistics and machine learning known as Least Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS to select DMD modes. We refer to the resulting algorithm as Least Angle Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting Dynamic Mode Decomposition (DMDSP), a popular mode-selection algorithm, serves as a benchmark for comparison. Numerical results from a Poiseuille flow test problem show that LARS4DMD yields reduced-order models that have comparable performance to DMDSP. LARS4DMD has the added benefit that the regularization weighting parameter required for DMDSP is not needed.

For graphs generated from stochastic blockmodels, adjacency spectral embedding is asymptotically consistent. Further, adjacency spectral embedding composed with universally consistent classifiers is universally consistent to achieve the Bayes error. However when the graph contains private or sensitive information, treating the data as non-private can potentially leak privacy and incur disclosure risks. In this paper, we propose a differentially private adjacency spectral embedding algorithm for stochastic blockmodels. We demonstrate that our proposed methodology can estimate the latent positions close to, in Frobenius norm, the latent positions by adjacency spectral embedding and achieve comparable accuracy at desired privacy parameters in simulated and real world networks.

Model compression is eminently suited for deploying deep learning on IoT-devices. However, existing model compression techniques rely on access to the original or some alternate dataset. In this paper, we address the model compression problem when no real data is available, e.g., when data is private. To this end, we propose Dream Distillation, a data-independent model compression framework. Our experiments show that Dream Distillation can achieve 88.5% accuracy on the CIFAR-10 test set without actually training on the original data!

Recent work in reinforcement learning demonstrated that learning solely through self-play is not only possible, but could also result in novel strategies that humans never would have thought of. However, optimization methods cast as a game between two players require careful tuning to prevent suboptimal results. Hence, we look at random play as an alternative method. In this paper, we train a DQN agent to play Sungka, a two-player turn-based board game wherein the players compete to obtain more stones than the other. We show that even with purely random play, our training algorithm converges very fast and is stable. Moreover, we test our trained agent against several baselines and show its ability to consistently win against these.

The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.