Google's artificial intelligence (AI) technology, already proven capable of contending with human problem-solving, may soon be able to think creatively. Google AI continues to make strides in its development, this year being legally considered a driver of the tech giant's autonomous cars, beating people in geotagging and location recognition, and even learning to understand words and events taking place around it in the real world. Its practical applications are constantly expanding, but with its new Google Magenta project, the company hopes AI can become artistic as well. "The question Magenta asks is, 'Can machines make music and art? If not, why not?'" Google machine-learning research scientist Douglas Eck wrote in a blog post on the project.

We're proud to be supporting the new developer survey run by our friends at VisionMobile. This is the 11th developer survey and it's entitled Developer Tools Benchmarking – as you can understand, the focus is on developer tools. The survey features questions on topics like programming languages, platforms, app categories, tool categories, revenue models, IoT verticals. This year, they have included specific survey questions on Machine Learning tools such as BigML as a new area to explore. It's a survey made by developers, for developers – so the questions are very relevant. Whether you're looking to share your thoughts with the dev community, find out something new, contribute to the leading developer research – or win a great prize – this is the survey for you.

Artificial intelligence has come a long way since the term was coined in the 1950s, but computers still don't think and feel in quite the same way humans do. Yet rapid progress has the federal government, and others, thinking about new legal and policy issues. Artificial intelligence has been around for decades. Think airplanes that fly on auto-pilot. Today, that technology is developing quickly, thanks to breakthroughs in computer science.

It is not only Google, Microsoft or Facebook that can afford such a system. It pays off to have an internal platform at hand where the engineer(s) can quickly test new hypothesis, try out or implement new algorithms, run anything from simple Bayesian classifiers to the more time consuming deep learning. And as we focus on narrow domains here, Andrew Ng, the chief scientist at Baidu recently said: "Most of the value of deep learning today is in narrow domains where you can get a lot of data."

When the artificial intelligence program AlphaGo defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into the AlphaGo servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that define how humans approach the game.

Earlier this year, the Institute of Electrical and Electronics Engineering's (IEEE) published "AI's 10 to Watch" – a list of 10 people who are doing phenomenal work in the field of artificial intelligence. A Pakistani researcher Haris Aziz, who had graduated from LUMS, had his name published in this prestigious list for his work in the field related to computational social choice, an intersection between artificial intelligence and economics. Its seems that was just the beginning of the road for Haris Aziz, who is now back in the news for solving an'unsolvable' mathematical situation. Who will get the larger share of the profit from a business? Shall it be equally allocated or otherwise? Perhaps its your child's birthday and its time to cut and divide the cake in a way that none of the children gets sad by his/her share?

Sixty years ago this summer, four computer science professors established a summer project aimed at creating "artificial intelligence"--the first time this phrase was ever used. The hope was to figure out how to make machines use language and perform abstract thought. "We think that a significant advance can be made if a carefully selected group of scientists work on it together for a summer," the now famous project proposal said. In retrospect that was almost hubristically optimistic. We still don't have software that can talk or write as we do, or perform abstract reasoning.

At Pomato, we believe the future of recruiting certainly includes a place for AI. Our technology acts as a virtual screener to automatically assist recruiters to understand and properly validate technical resumes. Recently a number of articles and blog posts have been written as prognosticators weigh in on how much machine learning will affect the recruitment industry. So we decided to check them out and capture the best quotes and thoughts found in the recruiting blogosphere. However, if the'chatbot' technology lifts off, there should be a measured adoption to ensure recruitment companies protect the perception of their business in the recruitment sector.

SAP rolled out a series of partnerships and advancements at its Sapphire Now event. Google unveiled a host of initiatives at Google I/0. HP Enterprise introduced an Internet of Things (IoT) platform. AtScale, a BI for Hadoop platform provider, raised a new round of funding. We've got it covered in our Big Data Roundup for the week ended May 22, 2016.

Justin Gerrard speaks quickly, Brian Gerrard speaks slowly. If you met them separately, you would never guess they were brothers. But their oil-and-water partnership helped them create Bae, a dating app for black people. Bae works pretty much like Tinder, but tailor-made for black users. The Gerrards came up with the idea after they realized how difficult it is for black singles to find dates on existing platforms.