I've been meaning to learn Theano for a while and I've also wanted to build a chess AI at some point. So why not combine the two? That's what I thought, and I ended up spending way too much time on it. Chess is a game with a finite number of states, meaning if you had infinite computing capacity, you could actually solve chess. Every position in chess is either a win for white, a win for black, or a forced draw for both players.

Ask the Google search app "What is the fastest bird on Earth?," and it will tell you. "Peregrine falcon," the phone says. "According to YouTube, the peregrine falcon has a maximum recorded airspeed of 389 kilometers per hour." That's the right answer, but it doesn't come from some master database inside Google. When you ask the question, Google's search engine pinpoints a YouTube video describing the five fastest birds on the planet and then extracts just the information you're looking for.

Some people may have been confused by IBM's decision to support TensorFlow 0.12 with its PowerAI distribution. That wouldn't be surprising since many continue to judge the company chiefly by its enterprise business roots. However, while the TensorFlow decision helps to clarify where IBM is today and where it aims to be in the future, it also details the practical steps the company is taking to get from here to there. The aggressive promotion of artificial intelligence (AI) is rife with buzzwords but AI-related core technologies are fairly straightforward. In order to support AI-related functions and tasks, like speech and pattern recognition, computer vision and text analytics, underlying systems are trained with machine learning and deep learning frameworks.

Students code software at desktops, while others assemble odd machines with wires and multi-colored boxes. Earning a spot at this elite university isn't easy; UC-Berkeley accepted a mere 14.8 percent of applicants for the class of 2020. So this young crew will likely be tomorrow's tech leaders and pioneers. Despite all the promise, it appears that BRETT is struggling. BRETT is a robot, and he – or she, or it – is attempting to place a small wooden block into a small hole. Again and again, BRETT swings his arm over the opening, attempts to place the block, but fumbles. Just can't make it fit. However, as robots go, BRETT has a huge advantage: he can learn. Every time BRETT swings his arm and fails, he calculates what went wrong. In essence he's doing what we humans do: he's failing, and in response he's deciding how to improve the next effort. I stand watching for about 15 minutes, and finally BRETT succeeds – a lengthy period given the simple task. But the astounding point is that the robot really did learn.

The field of medical AI is buzzing. More and more companies set the purpose to disrupt healthcare with the help of artificial intelligence. Here, I collected the biggest names currently on the market ranging from start-ups to tech giants to keep an eye on in the future. No one doubts that artificial intelligence has unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. Although many have their fears and doubts about AI taking over the world, Stephen Hawking even said that the development of full artificial intelligence could spell the end of the human race.

Someone sent a Starbucks Pumpkin Spice Latte to my desk at work. Which was weird because I didn't ask for it. And even weirder because it came from some of the most sophisticated technology in the world: M, the artificial intelligence–driven virtual assistant Facebook is building into its Messenger app. AI, I thought, was supposed to outsmart and kill us, not send autumnal coffee beverages. So forgive me for being a little suspicious of the grande red cup at my desk. I've had M on my phone for about a month now. Before it becomes pervasive, I wanted to understand it, and ask questions of it. I've pushed it incredibly hard.

It is an early morning of the just-arrived winter. People I can see on the street from my window wear heavy coats, but it's unclear how cold it is. I can open the window and let my natural skin sensors grab an approximate measurement, but I realize a much more accurate value can be obtained by pressing a button. I ask, with a Brazilian accent that most Americans think is Russian. It is 32 degrees outside," answers the piece of rectangular glass I hold.

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The first approach had its origins in Theano (another deep learning library Lab41 has previously used). In Theano (and Tensorflow) the user is responsible for everything. You define a graph of computation that is merely a thin layer on top of a matrix math library. In order to represent our regression in Tensorflow (or Theano) it's best to first mathematically represent our calculation:

The effective capacity of neural networks is large enough for a brute-force memorization of the entire data set.