While it may sound a bit too much like science fiction, Technological Singularity is a term used to describe the change that would occur when humans, technology, and artificial intelligence would intersect to such an extent that we are incapable of comprehending or predicting what the new race would be like, and humans after the change would no longer be able to fully relate to the previous race. Author Ray Kurzweil, a leading inventor and futurist who has made accurate predictions about technology in the past, describes The Singularity as "an era in which our intelligence will become increasingly nonbiological and trillions of times more powerful than it is today – the dawning of a new civilization that will enable us to transcend our biological limitations and amplify our creativity." The future of technology and artificial intelligence is hard for anyone to predict, as technology is enhancing at such rapid rates today. Will we have the ability to create superhuman intelligence to the point that human era will end? Some mathematicians, technology experts, and computer science experts think it is a possibility.

Microsoft has bounced back from its "Tay" bot disaster, and released a new artificial-intelligence experiment to the world. CaptionBot is a bot that will automatically create captions for any photos you upload, and is the latest in a series of periodic releases from Microsoft's AI division to show off its technical prowess in novel ways. You can upload photos to it, and it will tell you what it thinks is in them using natural language. "I think it's a baseball player holding a bat on a field," it says in response to one example photo. We decided to have some fun with CaptionBot, and asked it to interpret some famous works of art.

Imagine this: your hospital administrator asks you to help reduce the length of inpatient stays and they need a plan within a week. Chances are, most of you couldn't. But, the technology to mine and analyze your data does exist. Much like your daily Google searches, it's possible to input your search criteria, click Enter, and have answers at your fingertips in seconds. Doing so is part of radiology's push toward using big data, said Woojin Kim, MD, director of innovation at Montage Healthcare Solutions, Inc. "Radiology doesn't yet have big data like other industries, but that's changing rapidly. People want access to data to be able to turn insight into action," he said.

The source image on the left was used to generate the one on the right. A British startup is using the unique abilities of convolutional neural networks to do a sort of scaled-up version of Adobe's content-aware fill -- but instead of filling in the gaps in a picture, it's imagining a whole new picture, larger and more detailed than the original. Kind of hard to believe without seeing it, right? That's why they call their company Magic Pony. Just emerging from semi-stealth mode (and even then only barely), Magic Pony Technology's researchers have trained their system by exposing it to high- and low-resolution versions of images and video, letting it learn the differences between the two.

Artificial intelligence is currently making a resurgence since the 1990s. Today, the focus is on machine learning and statistical algorithms. This shift has served AI well. Since machine learning and statistics provide effective algorithm solutions to certain kinds of problems, such as board games, spam detection, voice and image recognition, etc. How is AI different today from 20 years ago?

The Journalism Department at Emerson College and the Emerson Engagement Lab recently invited William Li to give a talk to introduce machine learning to journalism and communications students. This is a live blog account of the talk by Catherine D'Ignazio. William Li is a 2015–2016 Fellow at the Harvard University Berkman Center for Internet and Society and a 2016 PhD computer science graduate from MIT. He develops and applies machine learning methods to answer social science questions computationally and to promote public understanding of law, politics, and public policy. His projects include predicting the authors of unsigned Supreme Court opinions, visualizing the complexity of our laws, and discovering ideas from large collections of public comments on proposed regulations. William has also worked on recommender systems, speech recognition, and user activity prediction at Apple and Mitsubishi Electric. He did his master's degrees at MIT in computer science and the Technology and Policy Program, founded the MIT Assistive Technology Club, and has taught classes that involve civic collaborations with organizations such as the Massachusetts Committee for Public Counsel Services, Greater Boston Legal Services, and the Cambridge Commission for People with Disabilities. William Li introduces the topic and that he wants to make the session very interactive.

To serve the customer better – and boost profits in the process – businesses need to make smart decisions on the fly. Direct interchanges between consumers and call centres are golden opportunities to make this happen. According to John Magliocca, chief consultant for contact centre services outsourcing company, ISG, "There have been efforts underway to put contact data to work to best understand the current mood of the customer and other information that can immediately mould client strategy and direction [for some time]." With volumes of customer and transaction data available, machine learning platforms can inform contact centre staff on ideal product suggestions based on past purchases or upgrade a subscription service to premium if a customer's financial situation has changed. Data-driven solutions will continue to inform customer insights while simultaneously helping business raise bottom lines.

After reading Pete Warden's excellent TensorFlow for Poets, I was impressed at how easy it seemed to build a working deep learning classifier. It was so simple that I had to try it myself. I have a lot of photos around, mostly of birds and butterflies. So, I decided to build a simple butterfly classifier. I chose butterflies because I didn't have as many photos to work with, and because they were already fairly well sorted.

Azure Machine Learning (Azure ML) is a fully managed cloud service that enables you to easily build, deploy and share predictive analytics solutions. Azure ML allows you to create a predictive analytic experiment and then directly publish that as a web service. The web service API can be used in two modes: "Request Response" and "Batch Execution". A Request-Response Service (RRS) is a low-latency, highly scalable web service used to provide an interface to stateless models that have been created and deployed from an Azure Machine Learning Studio experiment. It enables scenarios where the consuming application expects a response in real-time.

Smart objects: an object that enhances the interaction with not only people but also with other Smart Objects.