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RingCentral’s Curtis Peterson – Big Data LDN

How AI is already transforming business communications

RingCentral’s Curtis Peterson – Big Data LDN

Among the many standout presentations at Big Data LDN this week (13th & 14th November, Olympia, London), we’re expecting the role of AI and ML to be taking centre stage. We managed to catch up with RingCentral’s SVP of Operations, Curtis Peterson, ahead of the show, to talk about what he’s excited to be sharing with delegates.

“While I know the buzzword is AI this and AI that, there are actually some current actual usable business AI models, as well as those on the edge with great promise for the future. We’re still early in the journey of exposing the data of business communications, and using it to generate real insight”.

And as the industry still speculates about the future of sentient AI that trains itself to do everything, the current cutting edge of trainable AI is already making transformative inroads into areas such as natural language processing and analysis.

From content, to topic, to intent:

Peterson explains in terms of a continuum. We already have fast and accurate transcription and translation, but there’s a shift toward deeper analysis of content, to meaningful grouping and interpretation by topic, to ultimately determining the intent of a spoken communication:

“We’re on the doorstep of the topic level now, crossing that threshold within the next year. But while topics are great, you really need to understand and be able to analyse the real sentiment, the intent, behind a communication. Was it frustrating, or triumphant, a call to action or a call to learn…?”

“Our brains do this well, but in AI terms this is a frontier technology, that we’ll see moving into mainstream business communications within the next few years. The advances we’ve seen in the past couple of years are pretty stunning.”

It’s already being deployed and tested in specialised environments, Peterson continues. “We provide a lot of communication services to fire departments, emergency medical centres and so on. And if you can detect in a call, or in a message, or even in a video stream that the person is highly agitated and extreme duress, that can provide insight into that call pretty quickly.”

The ethics of machine learning and training

All this has urgent implications for the ways we inform and communicate about how data is being used to train AIs, now and in the future.

“If you call into a call centre, there’s often a message that says this call may be recorded for training purposes, but should we disclose that there might be an artificial intelligence analysing it as well? Do people care about that distinction? We’re getting to the point where it’s time to discuss privacy and other items in a new way, and companies need to be addressing this now, because the data they’re collecting now, they might want to analyse it in new ways in the very near future”.

Peterson certainly has a more close-up view of what we can expect from AI in business and personal communications breakthroughs, and while he’s careful not to make overly detailed predictions about what’s coming down the line, the anticipation is enthusiastic:

“The reality is a bunch of it is probably two years away, some really important stuff is five years away, and some super ‘aha!’ stuff and complicated stuff is probably 10 years away–but it’s a journey, not a one-moment item”

So take a seat because it’s going to be an exciting ride, into our AI transformed communications future.

 

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