Experts are warning that skilled jobs will soon start disappearing because of the rise of artificial intelligence. So far, robots have mainly been replacing manual labor, performing routine and intensive tasks. But smarter machines are putting more skilled professions at risk. Robots are likely to be performing 45% of manufacturing tasks by 2025, versus just 10% today, according to a study by Bank of America. And the rise of artificial intelligence will only accelerate that process as the number of devices connected to the Internet doubles to 50 billion by 2020.

The internet, and more importantly how we consume data from the web, has evolved at an incredible pace in recent years. One thing that has been steadily growing, and is only now really starting to make the headlines is Machine Learning. "Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. However, we can get incrementally closer to that, and that is basically what we work on."

This week Stephen Wolfram, founder and chief executive of Wolfram Research, announced a new component of the Wolfram Language for programming called ImageIdentify. Wolfram also introduced a new website, dubbed The Wolfram Language Image Identification Project, that demonstrates the language's new capabilities. The new site lets you upload images and get inferences and definitions in response. You can provide feedback, which should help it become more accurate. You can hit buttons like "Great!," "Could be better," "Missed the point," and "What the heck?!" After you choose one, the service offers a few more guesses, and a text box where you can type in a tag.

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.