Data School's 8-week Master Course begins September 28. More than two-thirds of the available spots are gone! Learn more about the course and enroll: http://www.dataschool.io/learn/ This info session was recorded on September 13. View the chat history and complete Q&A: http://ccst.io/e/text-course

A series of chatterbots will attempt to fool judges - including me - into thinking they are also human. No computer has ever triumphed at the Loebner Prize - a version of the Turing Test, first proposed by the computer scientist Alan Turing, who worked at Bletchley codebreaking during World War Two. Steve Worswick is the person behind Mistuku, a bot anyone can chat with online and which was judged the best system in the 2013 contest. "It's slowly becoming more and more involved in our everyday lives," he told Sky News. "At the stage we're at at the moment, I don't think we need to be fearing about people's jobs. "The use is in call centres, frequently asked questions on websites.

How Grand Theft Auto helps train self-driving cars, emotional intelligence algorithms, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

Enter Microsoft Women Health Risk Assessment competition, develop machine learning solutions to accurately categorize young women for their particular health risk. Enter now - submissions due Sep 30. Microsoft is awarding up to 5,000 in cash prizes for the Women's Health Risk Assessment competition. They are calling for optimized machine learning solutions that allow a young woman (age 15-30 years old) to be accurately categorized for their particular health risk. Based on the category a patient falls within, healthcare providers can offer appropriate education and training programs to help reduce the patient's reproductive health risks.

Artificial intelligence (AI) has gradually become an integral part of modern life, from Siri and Spotify's personalized features on our phones to automatic fraud alerts from our banks whenever a transaction appears suspicious. Defined simply, a computer with AI is able to respond to its environment by learning on its own--without humans providing specific instructions. A new report from Stanford University in Palo Alto, California, outlines how AI could become more integrated into people's lives by 2030, and recommends how best to regulate it and make sure its benefits are shared equally. Here are five examples--some from this report--of AI technology that could become a part of our lives by 2030. Smart traffic lights using artificial intelligence technology to learn and adapt to traffic patterns in real time could make intersections safer and more efficient.

It's hard to put your finger on why, but the voices our computers use to speak just sound wrong. Even with the best voice programming, like Amazon's Alexa or Apple's Siri, computers sound--well--robotic when they talk. But that could change soon. Neural networks are now tackling the problem of making computer speech sound more natural, filling sentences with nonverbal sounds like lip smacks, breath intakes, and irregular pauses. DeepMind, an Alphabet-owned world leader in artificial intelligence research, recently published a blog post about WaveNet, a convolutional neural network (like DeepDream) that can reduce the performance gap between computer and human speech by about 50%, researchers say.

Artificial intelligence is now smart enough to be accepted into most Japanese universities. The National Institute of Informatics said Saturday that its AI program, developed with university and corporate researchers, achieved an above-average score on a college entrance exam for the first time. The test covered five subjects including math, physics and English. The institute has been seeking to develop an AI by 2021 that would be able to score high enough on Japan's standardized college entrance exam to be accepted into the University of Tokyo, the nation's top-ranked university. The AI received a score of 511 points out of 950, above the national average of 416, and did exceptionally well on math and history-related problems, the institute said.

I was a wayward kid who grew up on the literary side of life, treating math and science as if they were pustules from the plague. So it's a little strange how I've ended up now--someone who dances daily with triple integrals, Fourier transforms, and that crown jewel of mathematics, Euler's equation. It's hard to believe I've flipped from a virtually congenital math-phobe to a professor of engineering. One day, one of my students asked me how I did it--how I changed my brain. I wanted to answer Hell--with lots of difficulty! In fact, I didn't start studying remedial math until I left the Army at age 26. If there were a textbook example of the potential for adult neural plasticity, I'd be Exhibit A. Learning math and then science as an adult gave me passage into the empowering world of engineering.

For a sequence model to be both expressive and parsimonious in its parameterization, it is crucial to equip the model with memory thus allowing its prediction at time t to depend on previously seen inputs. Recurrent neural networks form an expressive class of nonlinear sequence models. Through their many variants, such as long-short-term-memory [HS97], recurrent neural networks have seen remarkable empirical success in a broad range of domains. At the core, neural networks are typically trained using some form of (stochastic) gradient descent. Even though the training objective is non-convex, it is widely observed in practice that gradient descent quickly approaches a good set of model parameters.

Just this weekend i submitted my week 3 assignment for the Stanford University's Machine Learning course that i enrolled at Coursera. It wasn't easy for me as i don't have any strong background in data science or programming -- but Andrew Ng made it very simple. Recently i wrote about How Machine Learning is used in IT Services? There are many influencing factors that made me learn Machine Learning concepts and if you continue reading this article -- i am sure you would agree. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.