When China's government said last summer it intends to surpass the US and lead the world in artificial intelligence by 2030, skeptics pointed to a major problem. Despite gobs of data from the world's largest online population, lightweight privacy rules, and 8 million fresh college graduates in 2017, the country doesn't have enough people skilled in AI to overtake America. This week Kai-Fu Lee, onetime head of Google's operations in China, launched a new project to help close the country's AI talent gap. His helpers include the Chinese government and some of North America's leading computer scientists. The project is an example of how US and Chinese efforts to progress in AI are entangled, despite recent rhetoric about superpower technology rivalry.

Similarity/Distance measures play a key role in many machine learning, pattern recognition, and data mining algorithms, which leads to the emergence of metric learning field. Many metric learning algorithms learn a global distance function from data that satisfy the constraints of the problem. However, in many real-world datasets that the discrimination power of features varies in the different regions of input space, a global metric is often unable to capture the complexity of the task. To address this challenge, local metric learning methods are proposed that learn multiple metrics across the different regions of input space. Some advantages of these methods are high flexibility and the ability to learn a nonlinear mapping but typically achieves at the expense of higher time requirement and overfitting problem. To overcome these challenges, this research presents an online multiple metric learning framework. Each metric in the proposed framework is composed of a global and a local component learned simultaneously. Adding a global component to a local metric efficiently reduce the problem of overfitting. The proposed framework is also scalable with both sample size and the dimension of input data. To the best of our knowledge, this is the first local online similarity/distance learning framework based on PA (Passive/Aggressive). In addition, for scalability with the dimension of input data, DRP (Dual Random Projection) is extended for local online learning in the present work. It enables our methods to be run efficiently on high-dimensional datasets, while maintains their predictive performance. The proposed framework provides a straightforward local extension to any global online similarity/distance learning algorithm based on PA.

Artificial intelligence (AI) is a field of technology that is surrounded by both hype and misconceptions. It is predicted that $60 billion will be spent by brands on AI technology by 2025, so this hype is having a direct impact on where companies allocate their budgets. A significant difficulty in defining the size of the AI market is in defining exactly where its boundaries lie. Although we tend to imagine eerily human robots that mimic our mannerisms, AI is actually a very broad field that encompasses a range of disciplines – some more relevant to search marketing than others. More often than not, it is embedded in software that can process vast amounts of data to make or inform more intelligent decisions.

As a software engineer at Microsoft, Elena Voyloshnikova's job is to make informed recommendations about how to improve the performance of software engineering tools. But too often, she spends her days manually analyzing the data she needs to make those decisions. Lately, her team has been discussing the potential of building machine learning models to automate that task – creating more time to focus on the decision-making. That's why she was intrigued when she received an email announcing an upcoming AI training session for Microsoft employees. "I asked my manager, 'Can I go to this?'" she said.

Ted Dunning (Chief Application Architect at MapR) and Ellen Friedman have written a new O'Reilly Media book on _"Practical Machine Learning – Innovations in Recommendation" _(released in January 2014). This book examines one of the most interesting, fun, and powerful data science applications in the big data universe: recommendation systems. For me, this was one of the most interesting applications of data mining that immediately captured my imagination after I embarked on the journey to data science (drifting away from my astrophysics roots) about a dozen years ago. It is also one of the most common use cases that are taught in data science MOOCs and other analytics training courses. I believe that the love affair with recommender systems can be partly attributed to two things.

Invictus Capital has pioneered the Titan AI Tool which identifies fraudulent and copycat content to address the rise of white paper plagiarism in the cryptocurrency space - and the resulting damage to the reputation of the industry. "Performing due diligence is vital for the health of the cryptocurrency community -- we need to stand together to prevent dubious and fraudulent projects from taking investor funds," says Daniel Schwartzkopff, CEO of Invictus Capital. The Titan AI Tool uses machine learning techniques to analyze and detect plagiarism in ICO white papers. It evaluates the originality and legitimacy of early-stage investment opportunities within the ICO space. Uploading white papers for comparison also helps to expand the Titan database and benefits the community.

DUBAI: With technological advances being the norm, many of the everyday tasks have slowly been replaced by computer-enabled machines, robots or automation -- from watering the plants, sweeping the floor to driving to work or checking blood sugar levels. Innovations aren't just happening on the homefront and in many companies, there has been a widespread adoption of cool new technology. Eventually, as more employers get more high-tech, a number of workers will see their jobs taken over by their non-human counterparts. In its report released on Tuesday, the Organisation for Economic Cooperation and Development (OECD) identified the specific occupations that are at risk of being replaced by automated technology or artificial intelligence. Ranked highest on the list are those at the bottom of the payscale, including labourers, cleaners and helpers, garbage collectors, assemblers and food preparation assistants.

Be careful which words you feed into that machine-learning software you're building, and how. A study of news articles and books written during the 20th and 21st century has shown that not only are gender and ethnic stereotypes woven into our language, but that algorithms commonly used to train code can end up unexpectedly baking these biases into AI models. Basically, no one wants to see tomorrow's software picking up yesterday's racism and sexism. A paper published in the Proceedings of the US National Academy of Sciences on Tuesday describes how word embeddings, a common set of techniques used by machine-leaning applications to develop associations between words, can pick up social attitudes towards men and women, and people of different ethnicities, from old articles and novels. In word-embedding models, an algorithm converts each word into a mathematical vector and maps it to a latent space.

DeepMind Technologies Inc., the machine learning company owned by Alphabet Inc., announced today that it's opening a new artificial intelligence lab in Paris. The new lab will be headed by Remi Munos (pictured), a French native and senior researcher at DeepMind who has authored 150 research papers. In an announcement video, Munos said Paris is a perfect fit for DeepMind's next lab because the city has a thriving AI and machine learning ecosystem that's still growing. "Effectively, there are a large number of research labs in universities, engineering schools and public research centers together with a large number of AI startups who have appeared, as well as large companies that are setting themselves up," said Munos. "Joining this network is a very positive move for DeepMind, to collaborate with this scientific community in order to contribute to research and also to teach students." Frédérique Vidal, France's Minister of Higher Education, Research and Innovation, said in a statement that DeepMind's Paris lab "demonstrates the excellence and attractiveness of the Artificial Intelligence Ecosystem in France," and she added that the country will soon establish partnerships with "the public actors of French research."

Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent detection techniques based on subset scanning make strong independence assumptions and suffer degraded performance in correlated data. We introduce methods for identifying anomalous patterns in non-iid data by combining Gaussian processes with novel log-likelihood ratio statistic and subset scanning techniques. Our approaches are powerful, interpretable, and can integrate information across multiple data streams. We illustrate their performance on numeric simulations and three open source spatiotemporal datasets of opioid overdose deaths, 311 calls, and storm reports.