Have you ever stood still, contemplating about how cool would it be to build a model that can distinguish cats from dogs? Don't be shy now! Of course you did! Let's get going! We have 25,000 labeled pictures of dogs and cats. The data comes from Kaggle's Dogs vs Cats challenge. That's how a bunch of them look like: Let's focus on a specific image.

Google taught DeepMind to play Atari games all on its own, but letting humans help may be faster, according to researchers from Microsoft and Germany. They invited folks of varying skills to play five Atari 2600 titles: Ms. Pac-Man, Space Invaders, Video Pinball, Q*Bert and Montezuma's Revenge. After watching 45 hours of human gameplay, the algorithm could beat its mentors at pinball, though it struggled at Montezuma's revenge -- just as Deepmind did. Unlike with DeepMind's trial-and-error methods (below), however, the human-aided AI learned to play the games in less time than other AI systems. "Current state-of-the-art approaches require millions of training samples," the paper states.

Andrew Ng is one of the foremost thinkers on the topic of artificial intelligence. He founded and led the "Google Brain" project which developed massive-scale deep learning algorithms. In 2011, he led the development of Stanford University's main Massive Open Online Course (MOOC) platform. His course on Machine Learning would eventually reach an "enrollment" of over 100,000 students. That experience led Ng to co-found Coursera, a MOOC that partners with some of the top universities in the world to offer high quality online courses. Today, Coursera is the largest MOOC platform in the world.

A large part of the industry, with years of experience training their teams, designing their strategies and operating their business niches, either voluntarily or under obligation, are having to adapt to new market conditions. One of the most frequent shifts in this industry, including retail and investment banking, is how artificial intelligence can be used as a competitive edge to earn money old- and new-style. Methods like machine learning and deep learning are helping entities in many different operational fields. Logically, APIs specializing in machine learning and deep learning are the starting point for any transformation. They allow banks to create finalist products that create value for the entity and its customers: they allow extracting important information from Big Data, searching for patterns to tailor offers, price corrections and detecting bank fraud processes.

In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways.

It's pretty clear that the next big battleground for public cloud providers will involve artificial intelligence. Just as companies like Amazon Web Services made it possible for ten-person startups to take advantage of world-class computing infrastructure, so too will the big cloud providers compete to provide artificial intelligence expertise to companies that can't afford to duplicate the advanced machine-learning research already underway. Swami Sivasubramanian, vice president of Amazon AI, is one of the key drivers of AI research for AWS. Cloud rivals like Google and Microsoft have signaled quite clearly that they will attempt to compete for the cloud workloads of the future by pushing the envelope of AI and machine-learning research and abstracting that effort for their cloud customers, and AWS must at least match those efforts to stay on top. Sivasubramian will be talking about Amazon's work in this area at our Cloud Tech Summit this Wednesday in Bellevue, and I recently caught up with him to get a preview of his talk.

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. However, I am fully convinced if humanity prepares appropriately for the AI-age, artificial intelligence will prove to be the next successful area of cooperation between humans and machines. Concerning healthcare, artificial intelligence will redesign it completely – and for the better.

This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. At its annual Worldwide Developers Conference (WWDC) in San Jose, California, on Monday, Apple offered a first look at iOS 11, the next iteration of its OS. The upcoming updates are focused largely on technologies, such as artificial intelligence (AI) and machine learning, that make it easier for users to interact with their devices. The voice assistant, which reaches 375 million monthly active users globally, will be the overarching interface that ties together Apple's multiple operating systems: iOS, macOS, tvOS, and watchOS. Siri is being supercharged with deep-learning capabilities, meaning it can change intonations to sound more expressive and natural; it can also translate from English into several other languages.

Now, I can safely assume that we are at the same level of understanding. Ready to fire up some tensors? TensorFlow is a library for number crunching created and maintained by Google. It's used mainly for machine learning (especially deep learning) tasks. While still in beta (version 1.0 is currently in alpha), the library was open sourced more than a year ago (November 9, 2015).

From time to time, the Singularity Hub editorial team unearths a gem from the archives and wants to share it all over again. It's usually a piece that was popular back then and we think is still relevant now. This is one of those articles. It was originally published June 19, 2016. We hope you enjoy it!