A team of primary care researchers and computer scientists at the University of Nottingham compared a set of standard guidelines from the American College of Cardiology (ACC) with four'machine-learning' algorithms. The algorithms analyse large amounts of data and self-learn patterns to make predictions on future events – which in this case was a patient's future risk of having heart disease or a stroke. The results showed the self-teaching'artificially intelligent' tools were significantly more accurate in predicting cardiovascular disease than the established algorithm. In computer science, the AI algorithms that were used are called'random forest', 'logistic regression', 'gradient boosting' and'neural networks'. Cardiovascular disease (CVD) is a general term for conditions affecting the heart or blood vessels.

If a recent interaction between a human journalist and his Chinese robot counterpart is anything to go by, it'll be a while before a journalist job will be lost to (a robot in) China. On Monday (April 24), Jia Jia, a Chinese-manufactured robot under development for about three years (link in Chinese), had a conversation with AI expert Kevin Kelly, the co-founder of technology magazine Wired. State-run Xinhua news agency broadcast the chat live, billing Jia Jia as a special reporter. Jia Jia looks like a young woman in her early twenties, blinks and smiles in a fairly human way, moves her lips when she speaks, and has micro-expressions. But she had a hard time responding naturally to many of the questions posed by Kelly, sometimes taking up to 10 seconds to answer.

The best performing artificial intelligence model was a combination of the AlexNet and GoogLeNet, with a net accuracy of 96 percent. "The relatively high accuracy of the deep learning models is exciting. The applicability for TB is important because it's a condition for which we have treatment options. It's a problem that can be solved," Dr. Lakhani shared. The two DCNN models had disagreement in 13 of the 150 test cases. For these 13 cases, the scientists evaluated a workflow where an expert radiologist was able to interpret the images, accurately diagnosing 100 percent of the cases.

If a recent interaction between a human journalist and his Chinese robot counterpart is anything to go by, it'll be a while before a journalist job will be lost to (a robot in) China. On Monday (April 24), Jia Jia, a Chinese-manufactured robot under development for about three years (link in Chinese), had a conversation with AI expert Kevin Kelly, the co-founder of technology magazine Wired. State-run Xinhua news agency broadcast the chat live, billing Jia Jia as a special reporter. Jia Jia looks like a young woman in her early twenties, blinks and smiles in a fairly human way, moves her lips when she speaks, and has micro-expressions. But she had a hard time responding naturally to many of the questions posed by Kelly, sometimes taking up to 10 seconds to answer.

The original goal of this post was to explore the relationship between the softmax and sigmoid functions. In truth, this relationship had always seemed just out of reach: "One has an exponent in the numerator! One has a 1 in the denominator!" And of course, the two have different names. Once derived, I quickly realized how this relationship backed out into a more general modeling framework motivated by the conditional probability axiom itself.

Recently we talked before how big data is the new frontier with just .05% of all data available today having been analyzed. This means that all kinds of gold prospectors are lining up with their freshly crafted artificial intelligence (AI) algorithms looking to extract all the value they can from this wild west of data before someone else does. Perhaps nowhere is there more excitement at the moment than the applications to be had in the healthcare industry. Here's a look at just some of the startups that are applying artificial intelligence and big data to healthcare (courtesy of the bright minds over at CB Insights): The application that we've circled above is "drug discovery" using AI or what's also known as "computational drug discovery". The reason that this is now a thing is not just because of all the big data that's available now, but also because of how cheap cloud computing has become, not to mention the emergence of deep learning algorithms.

Let's take a breath: robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven't happened. Taxing this promising field of innovation is not only reactionary and antithetical to progress, it would discourage the development of technologies and systems that can improve everyday life. Imagine where we would be today if policy makers, fearing the unknown, had feverishly taxed personal computer software to protect the typewriter industry, or slapped imposts on digital cameras to preserve jobs for darkroom technicians.

Machine learning is poised to help marketers garner phenomenal new insights and results, and to change many processes and jobs along the way. We discussed this potential in "Machine Learning is About to Turn the Marketing World Upside Down." Machine learning can't provide better results alone, of course. Marketers need to collaborate with data scientists to identify important questions to explore, accelerate tests, improve the accuracy of answers, and make better decisions. And to effectively collaborate, they need a common language.

A chat with AI instructor Chris Mohritz Christopher Mohritz is a lifelong entrepreneur and technologist with a number of successful businesses under his belt; bringing a unique blend of technology know-how coupled with creative thinking and business acumen to each of his projects. Since 2009, Chris has been building and leveraging artificial intelligence systems to cognify a wide range of business functions -- marketing, sales, customer support and decision automation to name a few. And over the past five years, he has been building and operating a business accelerator for web/mobile startups, helping other entrepreneurs launch exceptional "AI-first" businesses. Chris draws heavily from a deep background in technology -- from operating nuclear reactors in the U.S. Navy to designing datacenters at Lockheed Martin. Complemented by a broad range of business experience -- from technical sales for the Fortune 500 to project management in the public sector.

"Thou canst not touch the freedom of my mind," wrote the playwright John Milton in 1634. But, nearly 400 years later, technological advances in machines that can read our thoughts mean the privacy of our brain is under threat. Now two biomedical ethicists are calling for the creation of new human rights laws to ensure people are protected, including "the right to cognitive liberty" and "the right to mental integrity". Scientists have already developed devices capable of telling whether people are politically right-wing or left-wing. In one experiment, researchers were able to read people's minds to tell with 70 per cent accuracy whether they planned to add or subtract two numbers.