7 Tips For Scaling Your AI Strategy
CIO, December 31st, 2018
The Most Amazing Artificial Intelligence Milestones So Far
"Now that your enterprise has experimented in AI it's time to consider how to expand the efforts. Here's how, according AI visionary Andrew Ng, as well as experts from PwC and Deloitte...
Pilot projects of artificial intelligence (AI) technologies proliferated in 2018, as many enterprises tested machine learning (ML) algorithms and an array of automation tools to cement relationships with customers, improve network operations or augment their cybersecurity postures..."
Forbes, December 31st, 2018
Ten Ways AI Will Impact Enterprise Communications
"Artificial Intelligence (AI) is the hot topic of the moment in technology, and the driving force behind most of the big technological breakthroughs of recent years.
In fact, with all of the breathless hype we hear about it today, it's easy to forget that AI isn't anything all that new. Throughout the last century, it has moved out of the domain of science fiction and into the real world. The theory and the fundamental computer science which makes it possible has been around for decades..."
ITProPortal, January 2nd, 2019
Reality Check: Avoiding An AI Train Wreck
"Here are ten scenarios to consider, all supported or enabled by the intersection of AI with team communications and collaboration...
The thought of software robots and artificial intelligence (AI) in the work place may make some knowledge workers nervous. Depending on what report you read, the UK can expect anywhere between a net gain in jobs from AI or over 30 percent of jobs lost to automation by 2030..."
insideBIGDATA, January 4th, 2019
"According to the Harvard Business Review, most current AI projects will fail. And yet, some companies mostly succeed in their AI projects.
They succeed because they are not AI newbies. They have already valiantly tried and sadly failed in early forays into machine learning and AI software applications. They each defined a reasonable target problem, gathered all kinds of data, hired numerous data scientists, secured executive commitment and ample budget, and engaged in lengthy projects only to produce little or no business value. And these are all huge companies with all the resources needed to succeed on their first try, and yet, they didn't..."
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