NVIDIA invented the graphics processing unit (GPU) in 1999. To some, it seems counterintuitive that a chip originally designed to play 3D games has become the engine of today's AI revolution. But in fact the problem of computer graphics has features in common with many other applications, from computational fluid dynamics and medical imaging to computer vision and natural language processing. At a high level, the unifying factor is that these problems can be parallelised. Our chip might be called a'graphics' processor, but in fact it's an incredibly versatile parallel processing engine which is playing a pivotal role in democratising AI.

As new technologies and trends emerge in the marketplace, small and medium-sized companies must look out for ways they too can benefit from improvements and advancements. Startups have demonstrated time and time again in recent years that the company willing to put new technology to use to solve an old problem (Netflix, Uber, Airbnb, etc.) are the companies that will succeed -- no matter how big or small they are when they begin. These technologies mean that work that once might have been too time consuming or expensive for a small company to do becomes quick and relatively inexpensive. Companies that can't afford a dedicated customer service representative can outsource much of that work to a chatbot that can answer simple customer service questions. While a very small business might not be able to employ the latest in robotics in-house, the advent of more automated manufacturing will make manufacturing more affordable and small runs of products more achievable. This will open up production possibilities for many small businesses.

Google's DeepMind AI business unit is hoping to teach computers to diagnose eye disease, using patient data from a U.K. hospital. Using deep learning techniques, DeepMind hopes to improve diagnosis of two eye conditions: age-related macular degeneration and diabetic retinopathy, both of which can lead to sight loss. If these conditions are detected early enough, patients' sight can be saved. One way doctors look for signs of these diseases is by examining the interior of the eye, opposite the lens, an area called the fundus. They can do this either directly, with an ophthalmoscope, or by taking a digital fundus scan.

It aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. This README serves as a guide for what mlpack is, how to install it, how to run it, and where to find more documentation. Citations are beneficial for the growth and improvement of mlpack. All of those should be available in your distribution's package manager. If not, you will have to compile each of them by hand.

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

Ed Rex believes we're heading towards a world where artificial intelligence will master creativity. Professor Lynda Gratton explains how people should prepare for it. "The secret to creativity is knowing how to hide your sources." This quote is often attributed to Albert Einstein but also to philosopher C.E.M. Joad, among others: a well-hidden source indeed. But the idea behind the phrase raises questions: if creativity is copied, is it original?

Putting aside contested definitions of what artificial intelligence actually is (Digital Journal has discussed this recently), the new Google projects are orientated towards pushing forward the concept of the thinking machine or, as the head of research Emmanuel Mogenet puts it, "teaching machines common sense." The projects form the core part of Google's significantly expanded European research center. Mogenet is head of Google's Zürich Search Team. He is credited with developing the application Shake (software), before being headhunted by the U.S. tech firm. The Google projects focus on three research areas: machine learning, natural language understanding and computer perception.

A serious problem in the Turing test for computer intelligence is exposed in a study published in the Journal of Experimental and Theoretical Artificial Intelligence. If a machine were to'take the Fifth Amendment' – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought. The test, devised in 1950 by pioneering computer scientist Alan Turing, assesses a machine's ability to exhibit intelligent behaviour indistinguishable from that of a human. Also known as the'imitation game', it requires a human judge to converse with two hidden entities, a human and a machine, and then determine which is which.

First they buy up a load of robotics companies for a series of undisclosed fees, and now they've gone splurged a reported 400million on buying an artificial intelligence firm. For a company whose motto is "Don't be evil", Google appears to doing its utmost to place itself in the Skynet role in our increasingly inevitable dystopian future. Earlier this month we told you how robots are going to be one of 2014's tech trends after Google made Boston Dynamics the eighth robotics company its bought in the last 12 months. And now its just made UK artificial intelligence start-up Deep Mind its largest European acquisition to date. DeepMind specialises in algorithms and machines learning for e-commerce and games and is the brainchild of Shane Legg, Mustafa Suleyman and Demis Hassabis, a neuroscientist and former teenage chess prodigy.