Google DeepMind artificial intelligence (AI) technology can play soccer with an ant. The AI technology may be implemented to real products. The DeepMind AI technology is very smart, and earlier this year, DeepMind's AlphaGo system was applauded worldwide for defeating Lee Sedol, who is the strongest human Go player. Lee Sedol has won 18 world titles, but the Go player lost 4 to 1 against the Google AI. The company says the game was watched by about 200 million people.

Wealth management firms, among the least tech-literate sectors of the financial services industry, might become obsolete with high net-worth individuals (HNIs) increasingly adopting digital technologies that provide algorithm-based portfolio management advice. A new report by PricewaterhouseCoopers that surveyed 1,000 HNIs in Europe, North America and Asia, found only 25 per cent of wealth management firms globally offering digital channels beyond emails. The Tokyo office of McCann has created what it is claiming to be the first ad to be made by artificial intelligence. An ad for mint candy brand Clorets Mint Tab has been creative directed by AI-CD ß, which became the first machine member of McCann Japan's creative department at the beginning of April. "If we have been living in rigid ice, this is liquid-a new phase state.

Are you more clever than your fridge? There are two types of artificial intelligence, but not all artificial intelligence is created equal. There is narrow AI, which is painstakingly designed to compute just one thing, but it does it very well. And there is general AI that can adapt to solve different tasks by learning and changing. But who do general AIs learn from?

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.