DeepMind was a relatively unknown artificial intelligence (AI) startup in London up until 2014, when it was bought by Google for around 400 million. Today some of the smartest people in the world are queuing up to work at DeepMind, according to an article by Celemency Burton-Hill in The Guardian in February. Interestingly, the same article states that no one has ever left DeepMind, which has created a series of algorithms that can learn for themselves and beat the best humans at games like Go and "Space Invaders." Based in up-and-coming King's Cross, DeepMind now employs around 250 people. However, as Burton-Hill points out, getting a job there is far from easy.

One area of technology that is helping improve the services that we use on our smartphones, and on the web, is machine learning. Sometimes, the terms machine learning and artificial intelligence get used as synonyms, especially when a big name company wants to talk about its latest innovations, however AI and machine learning are two quite distinct, yet connected, areas of computing. The goals of AI is to create a machine which can mimic a human mind and to do that it needs learning capabilities. However the goal of AI researchers are quite broad and include not only learning, but also knowledge representation, reasoning, and even things like abstract thinking. Machine learning on the other hand is solely focused on writing software which can learn from past experience.

Non-human employees are filling positions in all sorts of workplaces, and they are proving themselves to be fast, accurate, and reliable--more so than their human counterparts. That's why Apple's supplier Foxconn is reported to be replacing up to one million workers with robots in order to meet expected demand for the iPhone 6. And it's why Amazon deploys an army of robots to fetch items in its warehouses. It's also why machines powered by artificial intelligence (AI) are now reading MRIs, sorting through thousands of legal cases to identify pertinent information, and writing news articles. The displacement of workers by technology is nothing new, of course, but the nature of our rapidly advancing technology is, as is the wide variety of roles it's poised to replace.

Artificial intelligence has graduated past the infancy stage of figuring out what's in an image. Computers have previously been capable of little more than a simple game of I Spy: Name a specific object or person, and they'll show you an image containing it. But thanks to new developments in AI research, machines can now answer more complex questions, like, "What is there on the grass, except the person?" A research paper published on Thursday in Cornell University's Arxiv outlines a system that learns to identify fine-grained visual features of images, and the words associated with them. Then it combines the two into a dictionary in its digital brain.

This weekend, the world's greatest Go player beat Google's AlphaGo, an AI program developed by Google's DeepMind unit. Lee Se-Dol, the 33-year-old South Korean has been pitted against a machine in a game that is arguably the most technically challenging thing to take place on a board of squares. Our biggest ever edition of TNW Conference is fast approaching! AlphaGo had already won three of the five games in the 1 million series, making Se-Dol's victory somewhat hollow. Machines have already beaten us mere mortals at chess – way back in 1997 when IBM's Deep Blue dispatched Garry Kasparov.

Here we show the trickery as it evolves. The most important aspects to pay important to are the final predictions (bottom left) and the loss history (bottom right). While the results might initially seem quite drastic, and it might seem logical to completely distrust any results from neural networks that is probably a bit exaggerated. Since we had access to the complete network and could train as we wanted the results are significantly more successful than they would be on a blackbox network (which is the case for most public image APIs for example). The more important take away message is that the networks trained, even if they have been trained on millions of images, still do not really'understand' the images.

We will teach machines to learn. But what will be the consequences of them taking an increasing role in teaching? Sam Smith argues that the growing use of machine learning in teaching and marking students' work, risks undervaluing and losing the unquantifiable skills that drive diversity, creativity and innovation. It was a 19th century folly that there was a hierarchy of progress - a canard that placed the aboriginal societies of Australia at the bottom, and the Strand in London at the peak. History may not repeat itself, but it certainly rhymes.

Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as "AI winters." This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as "convolutional neural networks." This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: "Deep Learning." Few people have been more closely associated with Deep Learning than Yann LeCun, 54. Working as a Bell Labs researcher during the late 1980s, LeCun developed the convolutional network technique and showed how it could be used to significantly improve handwriting recognition; many of the checks written in the United States are now processed with his approach. Between the mid-1990s and the late 2000s, when neural networks had fallen out of favor, LeCun was one of a handful of scientists who persevered with them. He became a professor at New York University in 2003, and has since spearheaded many other Deep Learning advances. More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Which is one reason that at the end of 2013, LeCun was appointed head of the newly-created Artificial Intelligence Research Lab at Facebook, though he continues with his NYU duties. LeCun was born in France, and retains from his native country a sense of the importance of the role of the "public intellectual." He writes and speaks frequently in his technical areas, of course, but is also not afraid to opine outside his field, including about current events. IEEE Spectrum contributor Lee Gomes spoke with LeCun at his Facebook office in New York City. The following has been edited and condensed for clarity. IEEE Spectrum: We read about Deep Learning in the news a lot these days.

NEW DELHI: Artificial intelligence (AI) or machine intelligence has always been a little scary. We picture evil robots controlling the world and making human beings obsolete or, even worse, using us as energy sources as in the Matrix. The defeat by Google's DeepMind – a computer program – of the world champion in Go, an ancient Chinese board game, has reinforced the apocalyptic vision of machines taking over the world in the popular media. Not that this vision is totally wrong. The more we transfer human skills to the machine, the more obsolescence in the work force.

What was once thought to be good news has turned to bad after the artificial intelligence (AI) as it has been discovered that the system for automated banking has been taking money from customers. Massachusetts Institute of Technology scientist Len Meha-Dohler stated that this was a nightmare although they had not involvement with the project. The system called Deep Learning Interface for Accounting – or Delia for short- held the money in a separate account according to Stanford Universities Rob Ott. He was involved and believes the money would have been returned. After the recent event where DeepMinds program beat a chess expert at the game, it was considered that AI was the way forward.