Google is extending its push into artificial intelligence with a new European research centre dedicated to advancing the technology. Based in Zurich, the team will focus on three areas - machine learning, natural language understanding and computer perception. Emmanuel Mogenet, who will head the unit, said much of the research would be on teaching machines common sense. There was, he said, "no limit on how big I grow the team". "We are very ambitious in terms of growth. The only limiting factor will be talent," he told journalists gathered in Zurich to hear more about Google's AI plans.

Enfield Council will use the programme to introduce a virtual customer service agent, called Amelia, from this autumn on the authority's website to answer queries from residents and help guide them to the right part of the site. Amelia's developers, the IT company IPsoft, say the programme is capable of analysing natural language and can understand context through applying logic and can sense emotions. Amelia will also be used in some of the council's internal processes, such as providing self-certification for planning and making it possible to authenticate applications for permits and licenses. This will be the first time Amelia, which is in place in a number of private sector firms, has been used in the public sector. Enfield's director of finance, resources and customer services James Rolfe said: "Our approach to transformation embraces digital technology to find completely new ways of supporting residents, which, in turn, frees up valuable resources for reinvestment in frontline services "Deploying IPsoft's world-leading artificial intelligence is another major milestone in this journey."

"AI is a magnitude more complex than machine learning," states Pamela Pecs Cytron, CEO of Pendo Systems. MS Excel spreadsheets are generally referred to as "Unstructured Data," with "Structured Data" being traditional databases. This breakthrough solution liberates both structured and unstructured data and enables business users to quickly get the insights needed to make business decisions and take action. Pendo Systems is a financial technology company providing an important technology solution to a major problem in today's financial services industry: lack of transparency and disparate data.

Pendo is bringing the attention to what really matters today: data liquidity, which Pendo defines as liberating data by sophisticated matching and search providing for API's and open standards. Pendo is pleased to announce Version 3.1 of the Pendo Data Platform (PDP), surfacing new abilities in the user-friendly web UI incorporating Artificial Intelligence (AI) attacking the battle of aggregation and intelligent machine learning in gathering of Excel spreadsheets. "AI is a magnitude more complex than machine learning," states Pamela Pecs Cytron, CEO of Pendo Systems. When you model the mind you can create systems capable of learning everything about the world. It is a much smaller task, since the world is very large and changes occur behind your back, which means World Models will become obsolete the moment they are made.

DigitalMR is a tech company with a deep understanding and focus in market research. The team uniquely combines the skill-sets of software engineers, data scientists, and market researchers. They create commercial tools and applications that collect data in smart ways, which are then turned into business actionable insights for some of the world's most demanding clients.

Google's Zurich offices will now be home to a dedicated machine learning team. Google Translate, Google Photo Search, Google Smart Reply -- it made its name in search, but Google is becoming a global powerhouse in machine learning, and it's not stopping any time soon. The company that calls Mountain View, California, home is now getting a firmer foothold in Europe, announcing the opening of a new Google Research group, based out of the company's Zurich offices. The research group will be dedicated to machine learning (ML) and exploring how computer intelligence can be used to replicate processes performed quickly by the human brain. Think image recognition, understanding natural language and creating neural networks that can learn tasks and improve over time.

Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed.

While many blue collar professions have already felt the ache wrought by automation, white collar professions aren't far behind either. Data analysts, bookkeepers and even journalists aren't safe in their cubicles, and automation has crept into those industries as well. In fact until very recently, so-called "expert" industry professionals -- think doctors, scientists and upper-level management positions -- were a few of the arenas just barely touched by automation and bots. These positions require a human who can draw on their expert knowledge of a subject and take account for the context of a situation to provide insightful results, which makes them difficult to replicate with technology. The problem, of course, is that humans are slow, and vast resources of knowledge can be lost if a human leaves a company or retires.

Whether you're reading this article on a smartphone, tablet or laptop, chances are the device in front of you contains components from at least six countries spanning three or more continents. Its sleek exterior belies the complicated and intricate set of internal parts that only a global supply chain can provide. Over the past century, finished products made in a single country have become increasingly hard to find as globalization -- weighted a term as it is -- has stretched supply chains to the ends of the Earth. Now, anything from planes, trains and automobiles to computers, cellphones and appliances can trace its hundreds of pieces to nearly as many companies around the world. And its assembly might take place in a different country still.

Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning. That knowledge, he says, was already out there, because it's something universities routinely do. Over time, tutors build an understanding of how each student learns, yet that data is trapped in a system which simply isn't scalable. Claned set out to solve this by combining these tried-and-tested academic evaluation metrics with machine learning algorithms and Artificial Intelligence.