Artificial intelligence is a term that still conjures images of sci-fi thrillers for the average user. While the stigma may not have fully dissipated from dystopian cinema, AI is most commonly… Continue reading
Artificial intelligence is a unique part of the emerging tech landscape, with years of science fiction shaping our expectations of AI capabilities. Today, the reality is that AI is much more of an… Continue reading
A lack of skilled people in machine learning technology continues to stymie the AI revolution. That’s why smart companies invest as much in cultural change as technology adoption.
We’re awash… Continue reading
First, there was mobile device management, the mobile application management, and enterprise mobility software management. Now EMM suites are evolving into unified endpoint management platforms.
Get ready for some significant changes in the way enterprises manage… Continue reading
Boomerang, Otter, and Voicera are three new-breed voice and AI enterprise productivity apps to help you get more done.
Boomerang (free) is an alternative email client that adds… Continue reading
We look at the role of artificial intelligence and chatbots in IT service management. Artificial intelligence (AI) technology is serving an important role in IT service management (ITSM) as organizations seek to… Continue reading
Of the three words that comprise customer relationship management, one word binds the other two. As necessity and competition dictate that CRM upgrade itself with artificial intelligence and flights… Continue reading
Machine learning isn’t only in the cloud. Microsoft is bringing it to PCs in the next Windows 10 release. Here’s how to get started now.
We’re not far away from a new release of Windows 10, and with it plenty of new APIs for your applications. One big change is support for running trained machine learning models as part of Windows applications, taking advantage of local GPUs to accelerate machine learning applications.
Building a machine learning application can be a complex process. Training a model can require a lot of data, and a considerable amount of processing power. That’s fine if you’ve got access to a cloud platform and lots of bandwidth, but what if you want to take an existing model from GitHub and run it on a PC?
Trained machine learning models are an ideal tool for bringing the benefits of neural networks and deep learning to your applications. All you should need to do is hook up the appropriate interfaces, and they should run as part of your code. But with many machine learning frameworks and platforms, there’s a need for a common runtime that can use any of the models out there. That’s where the new Windows machine learning tools come into play, offering Windows developers a platform to run existing machine learning models in their applications, taking advantage of a developing open standard for exchanging machine learning models. Continue reading
IT vendors have leaped on the artificial intelligence/machine learning bandwagon, spreading a level of confusion that threatens potential technology benefits with AI washing.
As many in the enterprise IT community will remember, technology suppliers succeeded in roundly confusing buyers in the early part of the millennium by “greenwashing” their products and services – or in other words, exaggerating the true extent of their environmentally-friendly credentials – thereby shooting themselves in the foot and, arguably, putting the brakes on the market.
But it seems that many have learned little from the experience. According to Gartner, the IT industry is now pursuing an equally self-destructive strategy of “AI (artificial intelligence) washing” – by applying the AI label too indiscriminately, suppliers are once again bamboozling potential customers, who are putting off making buying decisions as a result.
So just how true is this contention and, if it is valid, what impact is it having on the market to date? Nick Patience, research vice-president at 451 Research, believes that AI in the enterprise software space is certainly overhyped, and adoption has lagged behind uptake in the consumer market.
“A lot of startups are claiming to do AI when they’re using rules-based automation,” he says. “Suppliers also say they have AI systems, but it’s actually much more narrowly defined machine learning software that does image recognition or leads scoring.
There’s nothing wrong with that, but it’s never going to be a robot that can do many of the things humans can do, so you have to cut through the hype to know what you’re getting.”
Emma Kendrew, AI lead for Accenture Technology, agrees that the hype cycle is reaching a peak, driven by busy corporate marketing machines hoping to take advantage of the possibilities opened up by big data and the cloud, as well as burgeoning customer interest. Continue reading