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DUBAI: With technological advances being the norm, many of the everyday tasks have slowly been replaced by computer-enabled machines, robots or automation -- from watering the plants, sweeping the floor to driving to work or checking blood sugar levels.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.

Speaking verbally and performing sign language require the same parts of the brain, according to a new study. Researchers at New York University found that the neural skills needed to perform sign language are the similar to those required for speaking out loud. Their report is the first of its kind to prove the association between the two communication forms. Sign language communicators and verbal English speakers rely on the same neural skills, a new report says. The new research was published in the journal Scientific Reports.

Andrew Ng's five courser aims to give newbies and practitioners a crash course on all things deep learning – from fully connected neural networks to convolutional nets to sequence models. I've taken all five courses, and completed four. For some more online course recommendations, check out the best online courses to get started with data science. The first course in the specialization focuses on the building blocks of deep learning. It goes over logistic regression interpreted as a one-layer network, shallow networks, and finally deep networks as stacked shallow networks. Well, if you've taken Andrew Ng's precursor course Machine Learning, then the first course in Deep Learning is basically just an elaboration of the neural network part.

Deb Roy and Rupal Patel pulled into their driveway on a fine July day in 2005 with the beaming smiles and sleep-deprived glow common to all first-time parents. Roy was an AI and robotics expert at MIT, Patel an eminent speech and language specialist at nearby Northeastern University. For years, they had been planning to amass the most extensive home-video collection ever. From the ceiling in the hallway blinked two discreet black dots, each the size of a coin. Further dots were located over the open-plan living area and the dining room. There were 25 in total throughout the house – 14 microphones and 11 fish-eye cameras, part of a system primed to launch on their return from hospital, intended to record the newborn's every move. It had begun a decade earlier in Canada – but in fact Roy had built his first robots when he was just was six years old, back in Winnipeg in the 1970s, and he'd never really stopped. As his interest turned into a career, he wondered about android brains. What would it take for the machines he made to think and talk? "I thought I could just read the literature on how kids do it, and that would give me a blueprint for building my language and learning robots," Roy told me. Over dinner one night, he boasted to Patel, who was then completing her PhD in human speech pathology, that he had already created a robot that was learning the same way kids learn.