SAN FRANCISCO - As handy as all our voice recognition friends are, conversing with them still feels you're talking to a foreign relative. Whether its Siri (Apple) or Alexa (Amazon) or Google Assistant or Cortana (Microsoft), each requires the human to speak in slow, articulated phrases to increase the odds of comprehension. But researchers at Microsoft say they've reached a milestone that promises a future where machines can transcribe us as well as another person. In a paper published Monday called "Achieving Human Parity in Conversational Speech Recognition," engineers with Microsoft Artificial Intelligence and Research announced they'd developed a speech recognition system that makes the same or fewer errors as professional transcriptionists. The team hit a word error rate of 5.9 percent, down from the 6.3 percent WER the team reported just last month.

Photographs of nearly half of all U.S. adults--117 million people--are collected in police facial recognition databases across the country with little regulation over how the networks are searched and used, according to a new study. Along with a lack of regulation, critics question the accuracy of facial recognition algorithms. Meanwhile, state, city, and federal facial recognition databases include 48 percent of U.S. adults, said the report from the Center on Privacy & Technology at Georgetown Law. The search of facial recognition databases is largely unregulated, the report said. "A few agencies have instituted meaningful protections to prevent the misuse of the technology," its authors wrote.

When humans try to transcribe a spoken conversation all in one go, they manage to miss 5.9 percent of what they hear on average. Microsoft announced on Tuesday that, for the first time, they've managed to get a computer to perform that same transcription task just as well as a person. "We've reached human parity," Microsoft's chief speech scientist Xuedong Huang, said in a statement. To accomplish the 5.9 percent error rate, which beats a 6.3 percent record set just last month, the Microsoft team leveraged neural language models resembling associative word clouds. That is, a word like "fast" resides much closer to "fast" than it does to "slow".

Losing customers means losing revenue. Not surprisingly, then, companies strive to detect potential customer churn through predictive modeling, allowing them to implement interventions aimed at retaining customers. This might sound easy, but it can actually be very complicated: Customers leave for reasons that are as divergent as the customers themselves are, and products and services can play an important, but hidden, role in all this. What's more, merely building models to predict churn for different customer segments--and with regard to different products and services--isn't enough; we must also design interventions, then select the intervention judged most likely to prevent a particular customer from departing. Yet even doing this requires the use of analytics to evaluate the results achieved--and, eventually, to select interventions from an analytical standpoint.

Many important problems can be represented and studied using graph. If we accept graphs as a basic mean of structuring and analyzing data about the world, we shouldn't be surprised to see it being widely use in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become this resilient piece of logic transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed community); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g.

Salakhutdinov researches very large neural networks used in a technology called deep learning, which lets a computer learn to perform a difficult task by consuming copious training examples. Speaking recently, Salakhutdinov said that there are three big areas where AI is progressing: giving computers better language understanding; enabling them to learn through repetition and positive reinforcement; and developing ways for machines to learn from unlabeled data. In recent years, competitors such as Google and Facebook have hired leading figures in deep learning to lead their AI efforts. Deep learning has gained prominence in recent years, after proving spectacularly good at enabling machines to recognize objects in images and spoken words in audio.

Vehicles and transportation systems must undergo major security overhaul before connected cars can enter our daily lives. Interest in the concept of connected cars is spreading fast, as recent estimates show by 2020 we will witness 150 million connected cars roaming our streets. Considering the potential impact this development will most definitely have at consumer and corporate levels, many people across the globe are paying close attention to this new state-of-the-art technology. Governments are also demonstrating their commitment to enhancing the development of the autonomous car industry, understanding the positive impact on their economy's future. However, their lies the risk of our governments, and the profit-seeking auto industry to push the limits and in the process neglect the essential security 1-2-3s in their drive for further innovation.

Zürich, Switzerland / Bad Neustadt, Germany - 18 Oct 2016: Today, RHÖN-KLINIKUM AG (RKA), a private hospital group in Germany, has announced, that by the end of the year, it will begin piloting a Watson-powered cognitive assistance system to help support physicians at the group's Centre for Undiagnosed and Rare Diseases located at the University Hospital Marburg. Since it opened in 2013, the renowned Center has been contacted by more than 6,000 patients to visit Prof. Dr. Jürgen Schäfer, a leading expert in rare diseases, who is also known as the "German Dr. House," based on the character of the eponymous American medical television drama. Most of the patients he and his team meets with have year-long medical histories, which include a large amount of unstructured data, such as laboratory tests, clinical reports, drug prescriptions, radiology findings as well as pathology reports. "It's not uncommon for our patients to have thousands of medical documents, leaving us overwhelmed, not only by the large number of patients, but also by the huge amount of data to be reviewed," said Prof. Dr. Jürgen Schäfer, University Hospital Marburg. "This is especially challenging because our work is often like searching for the proverbial needle in the haystack -- even the smallest piece of information could lead to an accurate diagnosis."

Why is it that every time humans develop a really clever computer system in the movies, it seems intent on killing every last one of us at its first opportunity? In Stanley Kubrick's masterpiece, 2001: A Space Odyssey, HAL 9000 starts off as an attentive, if somewhat creepy, custodian of the astronauts aboard the USS Discovery One, before famously turning homicidal and trying to kill them all. In The Matrix, humanity's invention of AI promptly results in human-machine warfare, leading to humans enslaved as a biological source of energy by the machines. In Daniel H. Wilson's book Robopocalypse, computer scientists finally crack the code on the AI problem, only to have their creation develop a sudden and deep dislike for its creators. Is Siri just a few upgrades away from killing you in your sleep? And you're not an especially sentient being yourself if you haven't heard the story of Skynet (see The Terminator, T2, T3, etc.) The simple answer is that -- movies like Wall-E, Short Circuit, and Chappie, notwithstanding -- Hollywood knows that nothing guarantees box office gold quite like an existential threat to all of humanity. Whether that threat is likely in real life or not is decidedly beside the point. How else can one explain the endless march of zombie flicks, not to mention those pesky, shark-infested tornadoes? The reality of AI is nothing like the movies. Siri, Alexa, Watson, Cortana -- these are our HAL 9000s, and none seems even vaguely murderous. The technology has taken leaps and bounds in the last decade, and seems poised to finally match the vision our artists have depicted in film for decades. Is Siri just a few upgrades away from killing you in your sleep, or is Hollywood running away with a tired idea? Looking back at the last decade of AI research helps to paint a clearer picture of a sometimes frightening, sometimes enlightened future. An increasing number of prominent voices are being raised about the real dangers of humanity's continuing work on so-called artificial intelligence.

A group of Microsoft engineers have built an artificial intelligence technique called deep neural networks that will be deployed on Catapult by the end of 2016 to power Bing search results. They say that this AI supercomputer in the cloud will increase the speed and efficiency of Microsoft's data centers and that their will be a noticeable difference obvious to Bing search engine users. They say that this is the "The slow but eventual end of Moore's Law." "Utilizing the FPGA chips, Microsoft engineering (Sitaram Lanka and Derek Chiou) teams can write their algorithms directly onto the hardware they are using, instead of using potentially less efficient software as the middle man," notes Microsoft blogger Allison Linn. "What's more, an FPGA can be reprogrammed at a moment's notice to respond to new advances in artificial intelligence or meet another type of unexpected need in a datacenter."