Alibaba has developed an artificial intelligence model that scored better than humans in a Stanford University reading and comprehension test. Alibaba Group Holding (baba) put its deep neural network model through its paces last week, asking the AI to provide exact answers to more than 100,000 questions comprising a quiz that's considered one of the world's most authoritative machine-reading gauges. The model developed by Alibaba's Institute of Data Science of Technologies scored 82.44, edging past the 82.304 that rival humans achieved. Alibaba said it's the first time a machine has out-done a real person in such a contest. Microsoft achieved a similar feat, scoring 82.650 on the same test, but those results were finalized a day after Alibaba's, the company said.

Chinese retail giant Alibaba has developed an artificial intelligence model that's managed to outdo human participants in a reading and comprehension test designed by Stanford University. The model scored 82.44, whereas humans recorded a score of 82.304. The Stanford Question Answering Dataset is a set of 10,000 questions pertaining to some 500 Wikipedia articles. The answer to each question is a particular span of text from the corresponding piece of writing. Alibaba claims that its accomplishment is the first time that humans have been outmatched on this particular test, according to a report from Bloomberg.

Oftentimes when people think of artificial intelligence, images of robots, Skynet and a central command center immediately come to my mind. I think of that scene from Terminator 2, when a heavily armed robot crushes a human skull beneath his feet. He's controlled by another robot, who calculates every human move with efficiency and precision. But that's not really what AI is. AI is more complex than that.

Chinese artificial intelligence is now capable of outperforming humans in reading comprehension. A neural network model created by Chinese e-commerce giant Alibaba beat its flesh-and-blood competition on a 100,000-question Stanford University test that's considered the world's top measure of machine reading. The model, developed by Alibaba's Institute of Data Science of Technologies, scored 82.44, while humans scored a 82.304. Microsoft's artificial intelligence also beat humans, scoring 82.65 on the exam. But its results came in a day after Alibaba's, meaning China holds the title as first country to create automation that outranks humans in written language comprehension.

Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies 43584 views Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning 42912 views

A high-salt diet may spell trouble for the brain -- and for mental performance -- even if it doesn't push blood pressure into dangerous territory, new research has found. A new study has shown that in mice fed a very high-salt diet, blood flow to the brain declined, the integrity of blood vessels in the brain suffered, and performance on tests of cognitive function plummeted. But researchers found that those effects were not, as has long been widely believed, a natural consequence of high blood pressure. Instead, they appeared to be the result of signals sent from the gut to the brain by the immune system. The study, conducted by researchers at Weill Cornell Medicine in New York, was published Monday in the journal Nature Neuroscience.

DePaul University's School of Computing invites applications for a tenure-track position in data science at the rank of assistant professor. The successful candidate will be part of one of the fastest growing and most highly recognized data science programs in the country.

In this course, you'll learn about clustering and dimension reduction, the two fundamental techniques of unsupervised learning and you'll learn to apply them using Python 3 and industry standard, freely available software libraries like scikit-learn and SciPy. You're going to learn to use the fundamental tools of unsupervised learning that professional data scientists use everyday. So who is this course for? Perhaps you're an IT professional, an analyst, a scientist or an academic, and you're looking to make the transition to data science, or you're a student, and you want to learn what data science is all about. In this course I'm going to share with you not only what I learnt but also the joy and the fascination of discovering patterns in data - the wonder of finding hidden structure in datasets that seemed at first too large and too complex.

Neural programming involves training neural networks to learn programs from data. Previous works have failed to achieve good generalization performance, especially on programs with high complexity or on large domains. This is because they mostly rely either on black-box function evaluations that do not capture the structure of the program, or on detailed execution traces that are expensive to obtain, and hence the training data has poor coverage of the domain under consideration. We present a novel framework that utilizes black-box function evaluations, in conjunction with symbolic expressions that integrate relationships between the given functions. We employ tree LSTMs to incorporate the structure of the symbolic expression trees. We use tree encoding for numbers present in function evaluation data, based on their decimal representation. We present an evaluation benchmark for this task to demonstrate our proposed model combines symbolic reasoning and function evaluation in a fruitful manner, obtaining high accuracies in our experiments. Our framework generalizes significantly better to expressions of higher depth and is able to fill partial equations with valid completions.

I was traveling last fall with my boss, and we began to talk about our upcoming conference day around artificial intelligence. We came to the topic of machine learning, and I mentioned, half jokingly, that theoretically all web accessibility barriers could be automatically resolved through the proper application of machine learning. Since that day I've continued to consider the challenge, and have come across a few articles that have reinforced the theory. As our Q1 conference day is soon coming, I've decided to take a few minutes to share my thoughts. For those who don't know, web accessibility is the practice of making content and applications ("web content") accessible to those with a variety of disabilities, "...including blindness and low vision, deafness and hearing loss, learning disabilities, cognitive limitations, limited movement, speech disabilities, [and] photosensitivity" (https://www.w3.org/TR/WCAG20/).