Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Microsoft last week released the preview of the PowerShell Module for its Azure Machine Learning (ML) service. The Azure ML PowerShell cmdlets library, available on GitHub, lets users interact with Azure ML workspaces, experiments, Web services and endpoints. The .NET-based PowerShell DLL module will let users fully manage their Azure ML workspaces, according to a blog post by Microsoft Principal Program Manager Hai Ning. The module includes the entire source code, which Ning said has a cleanly separated C# API layer. "This means you can also reference this DLL from your own .NET project and operate Azure ML through .NET code," Ning stated.

Google has released technical paper on AI safety produced in collaboration with researchers from Stanford University, University of California, Berkeley and OpenAI.

Specialized electronic circuits called graphic processing units, or GPUs, are at the heart of modern mobile phones, personal computers and gaming consoles. By combining multiple GPUs in concert, researchers can solve previously elusive image processing problems. For example, Google and Facebook have both developed extremely accurate facial recognition software using these new techniques. GPUs are also crucial to radiologists, because they can rapidly process large medical imaging datasets from CT, MRI, ultrasound and even conventional x-rays. Now some radiology groups and technology companies are combining multiple GPUs with artificial intelligence (AI) algorithms to help improve radiology care.

When we see two people meet, we can often predict what happens next: a handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

Investors backed more AI companies in the first quarter of 2016 (Q1'16) than in any other quarter, according to research from venture capital analysis firm CB Insights, which supports the idea that AI is the next major revolution in computing. In Q1'16, there were over 140 deals to startups focused on AI, CB Insights data wrote on its blog on Tuesday. Data startup Trifacta, DNA testing startup Pathway Genomics, and cognitive computing business Digital Reasoning Systems were among the AI-powered companies that raised equity funding rounds in Q1 from investors including Goldman Sachs, Accel Partners, Greylock Partners, and the IBM Watson Group. So far in 2016, more than 200 AI-focused companies have collectively raised nearly 1.5 billion ( 1 billion). The pick up in AI funding activity comes as businesses look to make their platforms and systems more human-like.

In the next ten years, we'll see diagnostics as well as medicine change. And that will come from both medicine and devices, and the information systems that support them. "I'm convinced that if it's not already the world's best diagnostician, it will be soon."

According to global consulting firm Accenture, intelligent automation powered by machine learning processes is 2016's biggest tech trend. For tech giants like Amazon and Google, machine learning has long been central to their operations - the most famous example being Amazon's recommendation engine, which many see as having been the key to its success. Such technology has huge implications for organizations across all industries though, and the wealth of data that companies now hold, along with the rise in affordable products like Microsoft Azure ML and IBM Watson, mean that they are rushing to adopt it.

The global cognitive systems market will generate revenues worth 50 billion between 2016 and 2020, aided by development of self-learning IT systems, new research from Technavio reports. The report, Global Cognitive Systems Market 2016-2020, shows that cognitive applications are being increasingly integrated with machine learning, which helps real-time communications between people and IT systems. The applications also help draw independent conclusions as well as data processing capabilities. These are developed to mimic the human thinking process or learning, understanding and evaluating different situations. Predictive analytics are driving the growth, as well as the potential for modelling techniques and the potential data from the Internet of Things (IoT), says Amit Sharma, lead analyst, ICT at Technavio Research.

Twitter Inc. agreed to acquire a London-based artificial intelligence startup to make tweeted live videos look more professional. In a blog post Monday, Twitter Chief Executive Officer Jack Dorsey said he was buying Magic Pony Technology "so Twitter can continue to be the best place to see what's happening and why it matters, first." Seeking to shore up slowing growth, the social media company has in recent months begun emphasizing video on its site. Magic Pony uses machine learning, a way of teaching software to perform tasks without explicit programming instructions based on pattern recognition, a technology that's "increasingly at the core of everything we build at Twitter," Dorsey said. Twitter paid about 150 million for Magic Pony, according to a person familiar with the matter.