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There continues to be conflicting opinions about what we can expect from enterprise AI. On one side, there’s a lot of hype surrounding how far the technology could take us (think self-driving cars, robot assistants, etc.). On the other, there is skepticism about how much of this is just ‘hot air’, given that AI is still a considerable distance away from meeting these expectations. Both sides of the argument have their merits, which is why the conversation has been a popular topic in the Twitter-verse last month, alongside the usual talk of enterprise apps and cloud computing. There’s certainly been a lot to get our teeth into.

Think we’ve missed an important story? Let us know in the comments or on Twitter (@teamharmonie). 

 “The information age is over, welcome to the machine learning age” – how far has our reliance on #AI already gone? >

When did the information age begin? Some say it began when technology’s capacity to store information grew considerably; others say it was when the traditional library system expanded in the United States.

John Brandon believes the information age was established when everyone gained access to personal computers. More importantly, however, he also believes that we should prepare for another shift—the shift toward the ‘machine learning’ age.

Read the full Venturebeat article for more on the progression of artificial intelligence and machine learning’s reliance on crowdsourcing. For all of AI’s potential and expectation, it currently remains ‘human’-powered. 

 @KristenHRachels debunks the five most common myths associated with #EnterpriseMobile apps @EnterpriseCIO

According to the International Telecommunications Union (ITU), there are more mobile phone subscriptions than people on the planet. If this surprises you, it might surprise you even more that this was the case in 2013. It’s no wonder, then, that spending on mobility and mobile apps has been one of the top five priorities for most CIOs in 2017.

However, for many organizations, the development of mobile applications has been stunted by several myths regarding creation and deployment issues. 

In this article from Enterprise CIO, Kristen Rachels gets to the bottom of five common myths associated with enterprise mobile apps, from forcing existing desktop applications onto mobile devices to the belief that mobility isn’t all that critical to the business. 

 “You can’t build enterprise #AI if you suck at data & analytics” via @thinkmariya

In another article from CIO magazine, Mariya Yao talks enterprise AI, analytics and data with a blunt yet realistic perspective: not all companies are ready for artificial intelligence. But the promise of AI is so great, why wouldn’t you want to implement it into your business and start realizing its potential? As appealing as AI may be in theory, in practice the task of transforming to implement the technology is not a small operation, Mariya explains:

“Unlike flash drives and mobile apps, enterprise-scale AI is not standalone plug-and-play technology. The quality of your data and analytics infrastructure, as well as your organization’s engineering and business culture, are critical foundations for any AI initiative.”

That’s not to say you shouldn’t give up on artificial intelligence. As we both know, the potential and promise is indeed great. The solution, as Mariya explains, is to keep a number of implementation principles and practices in mind. Check out the full article to see how you can adhere to these practices.

 By merging with #MachineLearning, #CloudComputing is becoming more interconnected & intelligent via @infomgmt

With self-driving cars expected to reach the mass market by the year 2021 and a new partnership between Wimbledon and IBM Watson creating a virtual assistant to help fans get more out of the world-famous tennis tournament, we continue to take strides forward in the era of cloud computing and machine learning. But those are just two consumer-focused examples; the enterprise is a different story. In this information management article, Marty Puranik summarizes five ways machine learning is impacting cloud computing, and what that means for businesses everywhere.

To keep up with more stories like those above during the coming months, make sure to following on Twitter @teamharmonie!


David Lavenda
Chief Product Officer