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Machine Learning

Machine learning technology: How to go from theory to reality

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

How to write machine learning apps for Windows 10

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.Machine learning

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

Is artificial intelligence hype sowing damaging confusion?

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

Google cloud machine learning services

Looking to infuse machine learning into your cloud apps? Use this list of terms to explore which Google cloud services offer features for speech-to-text, image analysis and more.Google Cloud

Artificial intelligence isn’t science fiction anymore. For some enterprises, the technology already provides many benefits. With machine learning algorithms, for example, applications can “learn” from and predict possible outcomes from ever-growing data sets. Top cloud vendors, including Google, now offer various services that bring AI and machine learning to the enterprise.

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