Broadly speaking educational activities can be split into two categories – "Life skills" and "Professional Skills". The Life skills that we all need to learn and the way we learn them have remained relatively consistent across the ages – how we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning, it's more true to say their role is to prepare students for life-long careers. But while that was a relatively simple task in the past, it's now much more difficult.

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants.

In an ongoing effort to extract more useful information and insights from massive volumes of structured and unstructured data, many organizations have turned to cloud based Hadoop big data analytics solutions such as Qubole. And as effective as these solutions are at capturing and analyzing large data volumes, their ability to interact with powerful Business Intelligence (BI) tools such as Machine Learning Libraries (MLL), is taking big data analytics capabilities to a whole new level. What follows is a look at 5 Machine Learning Libraries and the Big Data use case for each. MLlib features a host of common algorithms and data types, all designed to run at speed and scale. This makes MLlib a good fit for network security and other use cases such as predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis.

We'll also discuss a case study which describes the step by step process of implementing kNN in building models. This algorithm is a supervised learning algorithm, where the destination is known, but the path to the destination is not. Understanding nearest neighbors forms the quintessence of machine learning. Just like Regression, this algorithm is also easy to learn and apply. Let's assume we have several groups of labeled samples.

Google has designed its own computer chip for driving deep neural networks, an AI technology that is reinventing the way Internet services operate. This morning at Google I/O, the centerpiece of the company's year, CEO Sundar Pichai said that Google has designed an ASIC, or application-specific integrated circuit, that's specific to deep neural nets. These are networks of hardware and software that can learn specific tasks by analyzing vast amounts of data. Google uses neural nets to identify objects and faces in photos, recognize the commands you speak into Android phones, or translate text from one language to another. This technology is even transforming the Google search engine.

Back in 1994, Tony was learning to play chess under the tutelage of long-time Costa Rican National Champion Bernal Gonzalez. While the chess training didn't stick, he very clearly remembers a conversation where the teacher explained that the world's best chess playing computer wasn't quite strong enough to be among the top 100 players in the world. Technology can advance exponentially, and just 3 years later in 1997, World Champion Garry Kasparov was defeated by IBM's chess playing supercomputer Deep Blue. But chess is a game of logic where all potential moves are sharply defined and a powerful enough computer can simulate many moves ahead. Things got much more interesting in 2011, when IBM's Jeopardy playing computer Watson defeated Ken Jennings who held the record of winning 74 Jeopardy matches in a row.

Inc.'s 11th annual 30 Under 30 list features the young founders taking on some of the world's biggest challenges. In 2013, a high-frequency trader named Michael Coscia was arrested in New Jersey for an activity called "spoofing"--essentially manipulating the market by flooding trading systems with future orders he had no intention of completing. He was fined 6 million--with the possibility of jail time. It was the first such prosecution under a new set of financial regulations from the 2010 banking reform law called the Dodd-Frank Act. That was an aha! moment for David Widerhorn, 28, and it became his reason for founding Neurensic.

It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities - and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider. "But one reason is that these MOOCs do not provide any teaching assistants. So you can sign up for a course, say in mathematics, or computer science, or web design, or whatever. But you cannot ask anyone a question like'So how do I download this material?'

From Tony Stark's Jarvis to Apple's Siri, artificial intelligence (AI) is ubiquitous in fiction and real life. Albeit with different levels of skill, AI is supposedly built to maximize chances of reaching a goal, therefore supporting humans. But what would happen if robots, AI systems, and humanoids went rogue? For computer scientist Roman Yampolskiy, the possibilities are endless. Partially funded by SpaceX CEO Elon Musk, Yampolskiy and Pistono's study is conducted for the same reason that DARPA asked techies to turn household items into weapons.

Deep learning, frequently referred to as artificial intelligence, a branch of machine learning utilizing multiple layers of neurons to model high-level abstractions in data, has outperformed humans in tasks including image, text and voice recognition, autonomous driving and others, and is now being applied to drug discovery and biomarker development. In a study published in Molecular Pharmaceutics, a prestigious journal published by the American Chemical Society, scientists from Insilico Medicine in collaboration with Datalytic Solutions and Mind Research Network trained deep neural networks to predict the therapeutic use of large number of multiple drugs using gene expression data obtained from high-throughput experiments on human cell lines. Deep neural networks outperformed other machine learning techniques and did not result in significant drop in performance as the number of classes increased. When the networks got confused and guessed the therapeutic use of the drugs incorrectly, the drugs often had dual use, indicating the possibility of using DNNs for drug repurposing. This is the first known application of deep learning to drug discovery using transcriptional response data.