In the course of our work with Intel's data science and artificial intelligence initiatives, we often encounter people who are excited about the potential of artificial intelligence, and eager to learn more about the things Intel is doing to drive the industry forward. In many cases, these people have read about Intel's focus on AI, and now they are asking how they can get more involved in this forward-looking field. They often ask how they can advance their data science careers in the direction of AI. In Part 1 of this blog series, we talked about steps organizations can take to cultivate in-house expertise in advanced analytics and data science. In this second part of the post, we will take things down to a more personal level, and talk about steps individuals can take to chart a future that involves creating AI solutions.

Artificial intelligence is the technology of the future, and public schools are indoctrinating students to prepare. STEM School Academy in Highland Ranch, Colorado allowed students to use a 3D printer and artificial intelligence to create historical figures for their history class. "I wanted to bring history alive. I wanted the students to experience the process of talking to an artificial intelligence, talking to a person long deceased," history teacher Owen Cegielski told 9News. So who did the students decide to reanimate?

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. We're inviting 250 to exhibit at TNW Conference and pitch on stage!

"I was determined to do it precisely because I was told it was impossible." So says Yasuo Kuniyoshi, professor at the University of Tokyo's Graduate School of Information Science and Technology, in a quiet tone. However, the sharp glint in his eye betrays his grand ambition of developing a truly clever artificial intelligence to benefit humankind. Some current forms of artificial intelligence (AI), such as speech recognition and automated driving, are just as competent as humans--if not better--at carrying out their given tasks.However, just as AI developed for speech recognition cannot play chess, and chess-playing AI cannot drive a car, existing forms of AI are incapable of any actions beyond those intended by their creators. Because AI does not "think" the same way humans do, it cannot adapt to conditions besides the preconceived context it was programmed for in advance.For AI to be truly intelligent and highly adaptable, it must be able to think in the same way as humans.

Five actors gathered in a room on Lafayette Street, in downtown Manhattan, to start rehearsing a new work for the Public Theatre, "Joan of Arc: Into the Fire." Written by David Byrne, formerly of the Talking Heads, the show recast the enduring, improbable story of Joan--a teen-age girl in medieval France who experienced divine visions, led an army to defeat an occupying power, and was burned at the stake for heresy--as a rock musical that spoke to the current political moment. It was early January, and, that morning, U.S. intelligence officials had arrived at Trump Tower to brief the President-elect, Donald Trump, on the findings of an investigation into the recent election, in which they had concluded that President Vladimir Putin, of Russia, had acted to insure the defeat of Hillary Clinton. Inauguration Day was looming, and the rehearsal room had a troubled mood that reflected more than the ordinary anxieties of creating a show. The actors arranged four tables into a rectangle and sat down with Alex Timbers, the director of "Joan of Arc." Timbers, who is thirty-eight, is tall and fine-featured. He wore a denim shirt and black jeans that hung off his lanky, slightly hunched frame. His hair is dark and thick, and he frequently runs a hand through it, like a Romantic poet on deadline. Despite the air of disquiet, Timbers, who talks like a cool high-school teacher--lots of vocal fry, the repeated use of "awesome"--addressed the cast with rousing enthusiasm. He explained that, though the show had been in development for two years, it remained a work in progress. "I don't think anything is sacred--we are going to be building this together," Timbers said to the actors, all of whom were men except for Jo Lampert, a thirty-one-year-old newcomer, who was to play Joan. Timbers presented a scale model of the stage design, which had been conceived by Chris Barreca. When built, the set would be black and austere, and filled with enormous L.E.D. screens. A staircase extended from wing to wing, and at center stage there was a vertiginous platform. The set was on a turntable, and as it revolved it represented everything from a cathedral to a prison tower.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. TNW Conference won best European Event 2016 for our festival vibe.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

Most training and learning systems today follow the same basic model developed a century and a half ago when the British pioneered industrial-scale education to produce a literate working class. With one teacher and a large group of students, instructors must focus on the ones in the middle. While the weakest in the class may drop out, the most talented are slowed down and forced to be average. Machine learning could change all that. Training systems today can capture individual performance characteristics and, with the help of data analytics, identify strengths and weaknesses so training can be tailored on the fly to match the specific needs and requirements of each individual student.

Too often, engineers are brainwashed into thinking they can create an impeccable artificial intelligence (AI) model -- a blank slate they release into the wild for independent learning. They think: "If I create flawless math on top of the right infrastructure, I'll have the perfect model." Train the algorithm, let it run free, and that's the end of the story, right? Just like human intelligence, artificial intelligence requires continuous learning to advance its expertise. Training a commercially applied AI is not a one-and-done exercise.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.