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With the advent of photography, a tiny fraction of 19th-century scientists believed they could develop methods of accurately identifying criminals by their facial features. While their hypotheses were eventually discredited, new artificial intelligence technology suggests their claims might've been valid after all. Xiaolin Wu and Xi Zhang from Shanghai Jiao Tong University in China have resurrected this facial recognition tradition and built a neural network that can supposedly pick out criminals by simply looking at their faces. To accomplish this, the researchers used an array of machine-vision algorithms to examine a series of facial juxtapositions between photos of criminals and non-criminals with the goal of finding out whether a neural network can reliably tell them apart. In the process, the scientists fed the neural network a total of 1856 ID photos of men with no facial hair between the ages of 18 and 56, only half of whom had a criminal past.

TOKYO (Reuters) - Japan plans to build the world's fastest-known supercomputer in a bid to arm the country's manufacturers with a platform for research that could help them develop and improve driverless cars, robotics and medical diagnostics. The Ministry of Economy, Trade and Industry will spend 19.5 billion yen ($173 million) on the previously unreported project, a budget breakdown shows, as part of a government policy to get back Japan's mojo in the world of technology. The country has lost its edge in many electronic fields amid intensifying competition from South Korea and China, home to the world's current best-performing machine. In a move that is expected to vault Japan to the top of the supercomputing heap, its engineers will be tasked with building a machine that can make 130 quadrillion calculations per second - or 130 petaflops in scientific parlance - as early as next year, sources involved in the project told Reuters. At that speed, Japan's computer would be ahead of China's Sunway Taihulight that is capable of 93 petaflops.

Machine Learning systems are complex. At their core, they ingest data in a certain format, to build models that are able to predict the future. A famous example in the industry is identifying fragile customers, who may stop being customers within a certain number of days (the "churn" problem). These predictions only become valuable when they are used to inform or to automate decisions (e.g. which promotional offers to give to which customers, to make them stay). In many organizations, there is often a disconnect between the people who are able to build accurate predictive models, and those who know how to best serve the organization's objectives.

Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by engineering researchers at the University of Toronto. The researchers trained their algorithm to identify people's hair in photographs--a much more challenging task for computers than it is for humans. The team designed an algorithm that learns directly from human instructions, rather than an existing set of examples, and outperformed conventional methods of training neural networks by 160 percent. More surprisingly, their algorithm also outperformed its own training by nine percent--it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap forward for artificial intelligence. "Our algorithm learned to correctly classify difficult, borderline cases--distinguishing the texture of hair versus the texture of the background," said researcher Parham Aarabi.

Banking and financial-services providers have more data than any other industry at their disposal – it's time to make the most of this, for the benefit of customers themselves. Digital transformation is changing the way banks talk to customers, but forward-looking financial institutions in the region are looking at how they can deploy artificial intelligence and automation to transform the customer experience, says Danny Drew, Avaya's Managing Director for South Africa. When was the last time you actually visited your bank branch? For most people, the answer is likely to be somewhere in between the last time you had to do a major financial transaction, such as applying for a loan or a mortgage, and… you just can't remember. The way consumers interact with banks has fundamentally changed over the past few years as trends like mobile and online banking have become increasingly important.

Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis. For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We've done the same in the past for Machine Learning and NLP terms, which you might also find interesting. In the human brain, a neuron is a cell that processes and transmits information.

To get started, let's examine 10 fascinating facts about the AI industry. These devices will likely include many connected appliances, cars, wearables, and other gadgets included in the broader Internet of Things market.

Last week, IBM released a public beta of Watson Analytics, a platform for data exploration, visualization and predictive analytics. This product follows on Microsoft's Azure Machine Learning service, which provides cloud-based machine learning solutions. Interested to see how the offerings compare, I set up accounts with both services and set out to explore several datasets. For fairness, I should note that IBM's Watson analytics is in a public beta, while Microsoft's product is a significantly more mature offering. Besides relative maturity, the more striking difference between the products is the fundamentally different use cases they address.

The company showcases HCM Suite innovations to help drive inclusion for better business results. SAP has unveiled plans to use machine learning to help eliminate bias in hiring and employee performance reviews. At the SuccessConnect event in Las Vegas, the company exhibited planned upcoming capabilities within its cloud-based SAP SuccessFactors HCM Suite to help global organisations detect and avoid unconscious bias. As part of its commitment, SAP intends to optimise existing solutions and roll out new functionality in important decision areas that have prevented organisations from using total talent. The German company is designing the new features with a customer advisory group particularly focused on diversity and inclusion issues.