The Intel Nervana AI Academy for Students program was created to work collaboratively with students at innovative schools and universities doing great work in the Machine Learning and Artificial Intelligence space. I had the opportunity to get to know Intel Student Ambassador Karandeep Singh Dhillon and learn about how he became interested in deep learning and how he wants to make it easy for anyone to understand and apply to real-life situations. Tell us about your background and what got you started in technology. My parents bought me a computer when I was in 6th grade and by the time I was in 8th grade would make small Bash programs to help me automate tasks. I loved to create those small programs and I decided that I wanted to learn more and would take Computer Science courses for my undergraduate degree.

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

Tech startups have always had a recruiting advantage over the industry's giants: Take a chance on us and we'll give you an ownership stake that could make you rich if the company is successful. Now the tech industry's race to embrace artificial intelligence may render that advantage moot -- at least for the few prospective employees who know a lot about AI. Tech's biggest companies are placing huge bets on artificial intelligence, banking on things ranging from face-scanning smartphones and conversational coffee-table gadgets to computerized health care and autonomous vehicles. As they chase this future, they are doling out salaries that are startling even in an industry that has never been shy about lavishing a fortune on its top talent. Typical AI specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them.

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I remember his face in a look of horror, and it was agony and malice. I was trapped in this hospital bed. This isn't the start of the latest Stephen King novel, it's in fact the start of a horror story told by artificial intelligence. Shelley AI is currently working on a different short scary tale once an hour leading up to Halloween. It's powered by deep learning algorithms that have been trained on stories collected from the subreddit /r/nosleep where people share their own original eerie works.

Imagine reducing your training time for an epoch from 30 minutes to 30 seconds, and testing many different hyper-parameter weights in parallel. Available now, in public preview, Batch AI is a new service that helps you train and test deep learning and other AI or machine learning models with the same scale and flexibility used by Microsoft's data scientists. Managed clusters of GPUs enable you to design larger networks, run experiments in parallel and at scale to reduce iteration time and make development easier and more productive. Spin up a cluster when you need GPUs, then turn them off when you're done and stop the bill. Developing powerful AI involves combining large data sets for training with clusters of GPUs for experimenting with network design and optimization of hyper-parameters.

People were unnerved when Alphabet Inc.-owned Artificial Intelligence (AI) firm DeepMind's computer programme, AlphaGo, beat Go champion Lee Seedol in March 2016. In a paper published in Nature magazine on 18 October, DeepMind said AlphaGo's new version, AlphaGo Zero, is now so powerful that it does not need to train on human amateur and professional games to learn how to play the ancient Chinese game of Go. Further, the new version has not only learnt from AlphaGo, the world's strongest player of the Chinese game Go, but also defeated it. AlphaGo Zero, according to the recently published paper, uses a new form of reinforcement training to become "its own teacher". Reinforcement learning is an unsupervised training method that uses rewards and punishments. The system begins with a neural network (loosely modelled on the brain, hence the name) that knows nothing about the game of Go.

Demand for Artificial Intelligence (AI) expertise and technologies is being driven by compelling business value from across industries. Teradata (NYSE: TDC), the leading data and analytics company, is helping clients capitalize on the power of AI to deliver high value business outcomes in the areas of fraud detection, manufacturing performance optimization, risk modelling, and precision recommendation engines. To help clients accelerate their AI initiatives, Teradata leads with data science acumen and deep learning algorithms that significantly outperform most rules-based and machine learning approaches. For example: Danske Bank worked with Teradata to create and launch a state of-the-art, AI-driven fraud detection platform expected to meet 100 percent ROI in its first year of production. The engine uses deep learning to analyze tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding both true and false fraudulent activity.

The convergence of broadcast television and internet-delivered content services has become a reality -- one that soon will be cemented by the media industry's embrace of ATSC 3.0. In this cross-media environment, broadcast stations face many challenges, a mix of old and new. While ratings are a long-familiar concern for broadcasters, fierce competition for audience share today comes from a variety of providers on platforms ranging from linear channels to over-the-top (OTT) services to social and digital media. The new complexity of content distribution and consumption makes it hard for executives at a station to understand that station's place -- and the value of its media inventory -- within the larger marketplace. Even more difficult is to be proactive in ensuring that the station leverages that inventory to its best advantage.

In today's game industry, titles like "Clash Royale" and "Pokemon Go" are free for most people to enjoy because there's a small number of players who pay for extras, like special weapons or more lives. Game developers have to strike a delicate balance in this free-to-play model between drawing the masses and encouraging big spenders -- and they need both for a successful title. Silicon Studio Corp. is trying to help by providing game makers with deep-learning algorithms to create what amounts to a psychological profile of each player. The Tokyo-based company's software predicts how long people will play, what levels they might achieve, how much money they might spend and on what. Even more important, the technology lets game creators mold player behavior to keep them hooked.