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.

Adopting a digital transformation strategy in the face of tight budgets and cuts could save the NHS from becoming increasingly burdened and possibly collapsing as the healthcare service we know. Health secretary Jeremy Hunt has committed 4bn to invest in technology for the NHS, but the level of digital transformation and its maturity across 239 trusts is mixed at best. Achieving a data-sharing, paper-free NHS appears a little way off. Solid projects have been undertaken recently to adopt more digital services over traditional on-premise IT systems in public healthcare. These projects are not as comprehensive as the IT overhaul undertaken by the Cambridge University NHS Hospitals Trust in partnership with HP, but they do show how the NHS is beginning to use more digital technology that yields results.

This morning I found myself looking all over my apartment for an interesting news article I wasn't able to finish yesterday. The search eventually took me to the living room, which was littered with 3-D models. Thankfully, it took me only a couple of hand gestures to tidy up. Then I found the article I was looking for, floating in midair just behind the spot where a life-size model of an astronaut had been only moments before. That narrative sounds like something out of a Harry Potter book.

Recently, the social media giant Facebook has also jumped on the bot development bandwagon. There is no exaggeration in saying that bots will be an integral part of apps interface and revolutionize the way of using both web and mobile apps in the future. At this point, it is interesting to link bots with Artificial Intelligence (AI), as bot development is the first step toward this groundbreaking technology. We can imagine new apps economy as a combination of bots, automation, and cross-platform frameworks. In a way, machine learning and connectivity concepts are going to take center stage.