A recent Venture Beat article titled “AI will turn PR people into superheroes within one year” predicts that artificial intelligence and machine learning will explode within the public relations industry over the next three decades.
Data found through machine learning, combined with professional hunches and experience, can help PR experts with real-life applications like steering companies clear of future communications catastrophes. We’ll explore more about AI and media intelligence in this Q&A exchange with PublicRelay’s Bill Mitchell, Chief Technology Officer.
Q: Can artificial intelligence sometimes be too artificial?
A: I agree with the position made in the Venture Beat article that sometimes people are too quick to jump on the bandwagon of artificial intelligence. With any highly nuanced, context-based industry like media intelligence, it’s important to consistently apply machine learning and validate our results – paving the way for more increases in accuracies and efficiencies. I’m hesitant to start wheeling out the term artificial intelligence when we’re not trying to use machines to replace human thought. I prefer to call it human-assisted AI because we’re really using it to give us superpowers and visibility into a much broader range than ever before.
Supervised machine learning does need to be supervised, however. You’re often limited by the training data that you send in, and a system will miss new emerging topics or trends that were not in the original training data. It’s not a “set it and forget it” type of solution, especially when used to analyze data from highly variable inputs like traditional and social media.
Q: When can too much AI be a bad thing?
A: When you use GPS in a car, you still need to remain aware of your surroundings. Relying on a purely automated solution is like driving a car while looking solely at a GPS and not the road. There will be unexpected turns in the road and situations that the GPS (or the automated media intelligence solution) has not been trained for. When it comes to using an unsupervised system for media intelligence and analysis, you’re “driving without windows” yet it’s up to you to spot the dangers. That’s why human-assisted AI solutions deliver the best of both worlds – GPS and windows!
Q: How will human-assisted AI give superpowers to the PR industry?
A: Imagine the power of Superman or Wonder Woman if they could simultaneously read newspapers from coast to coast, listen to TV broadcasts in all local markets at once, read all tweets, and give you only what’s relevant to your brand. Human-assisted AI supercharges a company’s media monitoring capabilities by delivering the intelligence previously hidden in the context of what you are collecting.
Our systems at PublicRelay take in tens of millions of articles per week. It would be simply impossible for a human to review content at that sort of scale. Using supervised machine learning, we’re able to make intelligent routing decisions for relevant content. The result is that our analysts don’t miss important stories, yet our customers don’t miss out on the business insights that individuals uniquely attuned to their business are able to make.
From there they are able to dig deep to extract insights on the topics that you have requested. As an added bonus, they also uncover topics that you might care about (something machines cannot do). For example, one recent issue that we blogged about was the ability to extract contextual meaning from a population of articles that were summarily categorized as “negative tone” by completely automated solutions. Most news articles strive to be neutral by the way. Just because the article tone is neutral doesn’t mean that the tone for your company, product, service, or competitors was neutral. Wouldn’t that information be more useful to you?
Using machine learning to isolate a population of articles and shortlist them for more detailed inspection and rich content labeling by analysts is a great example of a complementary AI-human hybrid approach.
Q: Can human-assisted AI predict crises before they happen?
A: The ability to move very quickly around social media content can bring a real competitive advantage to media pros. Imagine if you had an extra two hours to prepare a well-researched, thought-out response to a particularly nasty allegation by a competitor before it went viral. That would be huge!
When measuring media coverage impact the first 48 hours are critical. In fact, within the first 24 hours, 99% of the social media coverage (be it through Facebook, LinkedIn, Twitter, etc.) is going to come in. The ability to receive a “heads up” notification just a couple of hours in advance of what’s to come would definitely feel like a superpower. Without accurately identifying content, you could be making predictions of virality for content that isn’t even relevant – yet another reason why having a supervised machine learning system with trusted results is the bedrock for this approach.
Q: Why is supervised machine learning more relevant for media intelligence?
A: A simple answer is because of the supervised learning component. At their core, both traditional and social media are driven to avoid the type of “sameness” that machines find easy to learn from. While brands and communicators strive to “get their message picked up” and repeated — outlets, authors and influencers will rarely repeat that message verbatim and in a predictable pattern. The same happens with spokespeople who are told to “stay on message”.
Supervised machine learning on the other hand actually gets better when it gets something wrong. It delivers the best results when its allowed to be expansive enough to find some terms that are at the decision boundary and require human interpretation. Having a system that is too tightly constrained to prior content also leaves you vulnerable to missing important content.
Human-assisted AI also provides a mechanism for fast and accurate discovery of new concepts. The human analysts that are doing the final quality assurance will catch anomalies that AI would need to see repeated multiple times before it appeared as a pattern. For example, a new influencer/expert is getting covered in outlets or by authors that you care about. If an analyst sees the name or company appear in a short time span or in multiple outlets, they alert the client immediately. Or perhaps an author (or authors) are mentioning you when they actually meant to attribute the activity to a competitor. If it’s negative, you might want to get that corrected as soon as possible.
Q: What are your personal predictions for PR and machine learning in the next few years?
A: I think that we’re going to see more companies revise their initial thinking that artificial intelligence is going to transform their business. Instead, I think they’ll focus on where machine learning can help them be more intelligent about deploying their resources. Customers are going to start demanding more of their vendors – with media mentions and initial keyword/phrase tracking becoming “table stakes.” Teasing out conceptual tags and aligning the data analysis with KPIs and real business goals will be the norm. In the future of AI, solutions that give leaders the answers they need are greater than tools.
