ISC (RotM) Artificial intelligence is about to transform society in the same way electricity did 100 years ago, but researchers are nowhere near producing the sort of self-aware sociopathic systems beloved of sci-fi writers. At least that's what Andrew Ng, Silicon Valley-based chief scientist at Chinese Web giant Baidu, when he kicked off the International Supercomputing Conference, by sketching the progress of neural networks, or deep learning platforms over the last decade. Ng said that in 2007, researchers were working on the CPU level, and were making networks with one million connections. As technology has progressed through the use of GPUs, and onto the cloud, and into the realms of HPC technology, networks were being constructed with 100 million connections. At the same time, he said, researchers were able to use much larger data sets. Whereas academic research projects on speech recognition had worked with data sets of 2000 hours of speech, Baidu's own speech recognition project was using 40,000 hours, he said, resulting in something close to a game-changing 99 per cent accuracy.

That dream of universal expertise is what IBM says its Watson question-answering, machine-learning system makes possible. Watson can be trained to answer questions on any subject you choose. The system uses natural language processing to read huge numbers of documents, extracts and organises information about a particular topic and then refines its understanding of that subject based on human feedback. But how useful are the answers given by Watson and how difficult is it to train? One person who's well-placed to talk about using the Jeopardy!-winning

Note: This article originally appeared in our sister publication, HPCwire. Greg Diamos, senior researcher, Silicon Valley AI Lab, Baidu (the China-based web services and search engine company), is on the front lines of the reinvigorated frontier of machine learning. Before joining Baidu, Diamos was in the employ of NVIDIA, first as a research scientist and then an architect (for the GPU streaming multiprocessor and the CUDA software). Given this background, it's natural that Diamos' research is focused on advancing breakthroughs in GPU-based machine learning. He answered questions about his research and his machine learning vision.

As if the technology needed any more promotion, Twitter announced its acquisition of a London-based company that has developed machine-learning techniques for visual processing. According to reports, Twitter (NYSE: TWTR) paid about 150 million for AI startup Magic Pony Technology. "Our team has researched and developed state-of-the-art machine learning techniques for visual processing that can identify the features of imagery and use that information to process it in new ways," said Rob Bishop, Magic Pony CEO and co-founder. The startup's technology reportedly works by combining neural networks designed to learn with machine learning tools to expand the amount of data in an image.

Bayesian Statistics continues to remain incomprehensible in the ignited minds of many analysts. Being amazed by the incredible power of machine learning, a lot of us have become unfaithful to statistics. Our focus has narrowed down to exploring machine learning. We fail to understand that machine learning is only one way to solve real world problems. In several situations, it does not help us solve business problems, even though there is data involved in these problems. To say the least, knowledge of statistics will allow you to work on complex analytical problems, irrespective of the size of data. In 1770s, Thomas Bayes introduced'Bayes Theorem'.

Machine learning continues to make inroads among hyper-scalers who are increasingly using it to train rather then program algorithms. As if the technology needed any more promotion, Twitter announced its acquisition of a London-based company that has developed machine-learning techniques for visual processing. According to reports, Twitter (NYSE: TWTR) paid about 150 million for AI startup Magic Pony Technology. Other details of the deal announced Monday (June 20) were not disclosed. The social media company's stock rose on news of the transaction.

Elon Musk has built cars and rockets. OpenAI -- the artificial intelligence research nonprofit co-chaired by Tesla Motors CEO Musk and Y Combinator President Sam Altman -- wants to build a robot for your home.

While the rise of artificial intelligence has caused more and more people to believe robots may one day make their jobs obsolete, those specializing in creative careers have always felt their skills could never be replicated by a mere computer program. Unfortunately, this feeling of assurance has taken a hit from IBM and a marketing company called The Drum, who have announced that Watson -- of Jeopardy! That's right, the brainy computer program that went toe-to-toe with Ken Jennings just edited an entire magazine all on its own. In other words, we're doomed. According to a press release published via The Drum, the magazine edited by Watson consists of a variety of features that cover Watson's different analytical functions, as well as how it can assist modern-day marketers.

New research suggests that computer models could help doctors achieve greater accuracy in the diagnosis of cancer and other diseases. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed an artificial intelligence (AI) system which is able to train computers to analyse pathologic image data [PDF]. The scientists hope that the programme could one day aid in diagnosing disease. 'Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition,' explained Andrew Beck, director of bioinformatics at the Cancer Research Institute at BIDMC and associate professor at HMS. He added: 'This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs.'

The future of artificial intelligence is now. After years of steady progress in making computers "smarter," AI prototypes are being incorporated into hundreds of day-to-day actions, such as self-driving cars, intelligent smartphone assistants and several applications in academia, government and industry. As the technology improves, it will be applied in ever more high-impact economic, social, political and cultural areas. Stanford's Russ Altman, left, and Fei-Fei Li will host a June 23 panel on artificial intelligence. In the face of this revolution, Stanford and the White House Office of Science and Technology Policy will host a panel of AI visionaries from academia, government and industry to discuss how to responsibly integrate the ever-evolving technology into the real world.