On the other hand, the Beijing model for AI governance is a hybrid approach treating science and technology as embedded in national laws to propel the growth of the national economy. The idea is to aggregate data for AI by encouraging the population to engage in the digital transformation. Besides initiatives like Made in China 2025, the Internet Plus Initiative, and The New Generation AI Development Plan that set AI commercialisation and marketplace goals, China has established a centralised but multistakeholder-oriented body. Like in other countries, China also regulates AI by data protection legislation.

University of New Brunswick (UNB) alumnus Dick Carpenter (BA '72) and the McKenna Institute are pleased to announce a gift of $2.5 million to advance the development of artificial intelligence (AI) and data science at UNB. AI and data science have become essential elements in the creation of effective digital products and services. AI depends on large data sets for developing reliable predictive models and data science relies on AI algorithms to extract meaningful features from data sets. This interdependence has resulted in AI and data science becoming increasingly intertwined and dependent upon advances in math, computer science and software engineering. This gift will support the development of interdisciplinary AI and data science research across UNB's faculties and campuses. It was secured through the ambassadorship of UNB alumnus and former New Brunswick premier The Hon. "We tend to think of AI in terms of social media algorithms," said Dr. Paul J. Mazerolle, UNB's president and vice-chancellor.

Artificial intelligence, popularly known as AI, is a branch of computer sciences relating to building smart machines capable of performing tasks that would, otherwise, require human intelligence. Technology has been on an upward trajectory with the potential of India's technology services industry achieving $300-350 billion in annual revenue by 2025, if it can exploit the fast-emerging business potential in cloud, AI, cybersecurity and other emerging technologies, according to a report by McKinsey. AI is not limited to the world of science fiction. As a technology, AI-driven chatbots are revolutionizing business processes in multiple industries, while also impacting several aspects of people's lives. Various markets are embracing AI to be smart in today's always-on world.

According to information published by the US DoD on November 23, 2022, U.S. 5th Fleet began a three-week unmanned and artificial intelligence integration event in Bahrain that will involve employing new platforms in the region for the first time. Various unmanned systems sit on display in Manama, Bahrain. The event, called Digital Horizon, will advance the command's efforts to integrate new unmanned technologies while establishing the world's first unmanned surface vessel fleet by end of next summer. U.S. 5th Fleet's efforts are focused on improving what U.S. and regional navies are able to see above, on and below the water. Digital Horizon will include 17 industry partners bringing 15 different types of systems, 10 of which will operate with U.S. 5th Fleet for the first time.

The EU should move on from'outdated' debates over front-of-pack labelling and instead focus on tailor-made technological solutions for the future of nutrition, experts told a recent panel – but critics say this privileges only the richest in society. The European Commission is expected to put forward its proposal for an EU-wide nutritional labelling system in early 2023. The current front runner is the Nutriscore, a controversial colour-coded system championed by France which ranks food from A to E. The score has proven divisive, especially among stakeholders in Europe's South, who argue the score penalises the Mediterranean diet. But, for Pietro Paganini, co-founder of the EU think-tank Competere, the debate is redundant. Instead, efforts should focus on technological developments and personalised diets rather than'old outdated system[s]' such as front-of-pack nutritional labelling.

The threat of cyber attacks has recently increased dramatically and traditional measures now appear to be insufficiently competent. Because of this, deep learning in cyber security is rapidly gaining ground and may hold the key to solving all your cybersecurity issues. With the advent of technology, there is also an increase in threats to data security and the need to protect an organization's operations using cybersecurity tools. However, companies are struggling due to most cybersecurity tools being dependent. They rely on signatures or evidence of compromise for the threat detection capabilities of the technologies they use to safeguard their business.

It's been six years since Tesla, SpaceX (and now Twitter) CEO Elon Musk co-founded brain-control interfaces (BCI) startup, Neuralink. It's been three years since the company first demonstrated its "sewing machine-like" implantation robot, two years since the company stuck its technology into the heads of pigs -- and just over 19 months since they did the same to primates, an effort that allegedly killed 15 out of 23 test subjects. After a month-long delay in October, Neuralink held its third "show and tell" event on Wednesday where CEO Elon Musk announced, "we think probably in about six months, we should be able to have a Neuralink installed in a human." Neuralink has seen tumultuous times in the previous April 2021 status update: The company's co-founder, Max Hodak, quietly quit just after that event, though he said was still a "huge cheerleader" for Neuralink's success. That show of confidence was subsequently shattered this past August after Musk reportedly approached Neuralink's main rival, Synchron, as an investment opportunity.

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems.

With the growing size of modern data, clustering techniques play an important role in reducing the dimensionality of the features used in modern Machine Learning pipelines. Indeed, in many tasks of interest ranging from DNA sequence analysis to image classification, the relevant features are known to live in a lower-dimensional space (intrinsic dimension) than their raw acquisition format (extrinsic dimension) [1]. In these cases, identifying these features can help saving computational resources while significantly improving on learning performance. But given a corrupted embedding of low-dimensional features in a high-dimensional space, is it always statistically possible to retrieve them? And if yes - how can reconstruction be achieved efficiently in practice? In this manuscript we address these two fundamental questions in a simple model for subspace clustering: a k-cluster Gaussian mixture model with sparse centroids. In this model, the low-dimensional hidden features are given by the sparse centroids, which are embedded in a higher dimensional space and corrupted by additive Gaussian noise. We assume that the number of non-zero components of the centroids as well as the number of samples scales linearly with the dimension of the embedding space. Given a finite sample from the mixture, the goal of the statistician is to cluster the data, i.e. estimate the centroids (or features) as well as possible.

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M-and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and Machine Learning approaches to the prediction of the AR's Flare Index (FI) that quantifies the flare productivity of an AR by taking into account the numbers of different class flares within a certain time interval. Specifically, our sample includes 563 ARs appeared on solar disk from May 2010 to Dec 2017. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise, short by SMOGN). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1-day period. Compared with other 4 popular machine learning algorithms, our methods improve the accuracy of FI prediction, especially for large FI. In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.