San Francisco: Google can see a future where robots help us unload the dishwasher and sweep the floor. The challenge is making sure they don't inadvertently knock over a vase--or worse--while doing so. Researchers at Alphabet Inc. unit Google, along with collaborators at Stanford University, the University of California at Berkeley, and OpenAI--an artificial intelligence development company backed by Elon Musk--have some ideas about how to design robot minds that won't lead to undesirable consequences for the people they serve. They published a technical paper on Tuesday outlining their thinking. The motivation for the research is the immense popularity of artificial intelligence, software that can learn about the world and act within it.

Google can see a future where robots help us unload the dishwasher and sweep the floor. The challenge is making sure they don't inadvertently knock over a vase -- or worse -- while doing so. Researchers at Google, along with collaborators at Stanford University, the University of California at Berkeley, and OpenAI -- an artificial intelligence development company backed by Elon Musk -- have some ideas about how to design robot minds that won't lead to undesirable consequences for the people they serve. They published a technical paper on Tuesday outlining their thinking. The motivation for the research is the immense popularity of artificial intelligence, software that can learn about the world and act within it.

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

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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."