OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages. To get started, you'll need to have Python 2.7 or Python 3.5. You can later run pip install -e .[all] to do a full install (this requires cmake and a recent pip version).

Google's artificial intelligence has already taken on the form of a human nerd, but now it's time for its next act. Can AI be an artist? Douglas Eck, a researcher working on Google Brain, recently revealed that the team will soon launch a new project called Magenta. Though it took some inspiration from DeepDream, another Google Brain scheme that yielded trippy-as-hell images (amongst other things), Magenta has one key difference: It will try to figure out if computers can actually create art, instead of reproducing or distorting it. Magenta is set to launch in a more official capacity in early June, but Eck provided some early insights during a recent discussion at Moogfest, a music, art and technology festival.

"Artificial intelligence is the art and science of making machines intelligent," Corrado explained. According to Corrado, the brain's billions of neurons all make tiny decisions based on small amounts of information, but working together they can perform advanced thinking tasks. Moving back to the image recognition example, Corrado explained that these artificial neurons will individually scan tiny patches of pixels in an image and make some judgment about them. Asked how machine learning works for things like booking a movie ticket -- a task Google's AI-powered Google Assistant performed during CEO Sundar Pichai's keynote -- Corrado explained that parts of that task were not done by AI.

The tech world is awash with talk of artificial intelligence. The seemingly out-of-nowhere magical force is now powering everything from image recognition to virtual assistant chatbots; it's on the lips of every tech executive within 10 feet of a microphone. Not surprisingly, AI was front and center last week at Google's I/O conference, a massive gathering of some 7,000 developers and media all looking to Google for a peek at the future. Google CEO Sundar Pichai did little to temper that blue-sky enthusiasm, ending the closing AI portion of his keynote with a line that felt cribbed straight from a Star Trek script: "Things previously thought to be impossible may in fact be possible." AI is becoming an increasingly more important feature in our daily lives, yet one of the more fascinating aspects of its rise is how poorly we understand what it actually is.

As technology gets smarter and takes over more and more of the work we typically deem "skilled", are professionals like accountants at risk of being replaced? The short answer is "No". The long answer is "It all depends on the professional accountant's attitude". Let's begin by taking a step back to understand the nature of this trend. The author Martin Ford has written extensively on this subject, and his recent book, The Rise of the Robots, contains a lot of food for thought. The main point that should concern professional accountants is that the ongoing drive towards automation is no longer just a threat to low-level jobs, particularly those that already rely heavily on machinery--think driverless cars, automated mining and agriculture and so on.

As we have noted over the last year in particular, GPUs are set for another tsunami of use cases for server workloads in high performance computing and most recently, machine learning. As GPU maker Nvidia's CEO stressed at this year's GPU Technology Conference, deep learning is a target market, fed in part by a new range of their GPUs for training and executing deep neural networks, including the Tesla M40, M4, the existing supercomputing-focused K80, and now, the P100 (Nvidia's latest Pascal processor, which is at the heart of a new appliance specifically designed for deep learning workloads). While we have heard a great deal over the last year from companies like Baidu, Flickr, and others, on-premises GPU-laden systems are the key to training deep neural nets, but according to IBM, there will be a new wave of users who want to circumvent the on-site boxes and take advantage of GPUs on IBM's cloud. While cloud rival Amazon Web Services, among others, are sporting GPU cards for high performance computing (HPC) and deep learning users, the partnership between Nvidia and IBM is giving Big Blue a leg up in terms of making a wider array of GPUs available to suit different workloads. Currently, IBM's cloud boasts the K80, as well as the lower power and less beefy K10.

Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for the greater Dublin area as an example data set. This work is based on a talk given to the Dublin R Users group in January 2014. SOMs were first described by Teuvo Kohonen in Finland in 1982, and Kohonen's work in this space has made him the most cited Finnish scientist in the world.

Training a machine learning algorithm to accurately solve complex problems requires large amounts of data. The previous article discussed how scalable distributed parallel computing using a high-performance communications fabric like Intel Omni-Path Architecture (Intel OPA) is an essential part of what makes the training of deep learning on large complex datasets tractable in both the data center and within the cloud. Preparing large unstructured data sets for machine learning can be as intensive a task as the training process – especially for the file-system and storage subsystem(s). Starting (and restarting) big data training jobs using tens of thousands of clients also make severe demands on the file-system. The Lustre* file-system, which is part of the Intel Scalable System Framework (Intel SSF), is the current de facto high-performance, parallel/distributed file-system.

An article explores how Nvidia Corp. microchips, the kind that are used in video games and by social media networks for photo tagging, are being applied to medicine. They are being used to add machine learning to medical imaging. The idea is to use the technology to spot conditions such as cancer and Alzheimer's disease earlier and faster. But one potential consequence of advancements in this area is reduced dependence on radiologists. Luminex has raised its offer for Nanosphere to obtain its molecular diagnostics technology from 83 million to more than 100 million.

Will artificial intelligence help banks navigate the complexities of compliance more effectively? Against a backdrop where regulations have grown by leaps and bounds in the wake of the financial crisis, The Wall Street Journal reported that banks have taken on tens of thousands of new staffers tied exclusively to compliance. But a little technology muscle may not hurt either. WSJ noted that artificial intelligence is being deployed across a number of initiatives, which include anti-money laundering programs, sanctions lists and billing functions. The movement toward automation, of course, means that flesh-and-blood workers are free to take on other tasks.