Okay, maybe it is not entirely ugly (we needed a clever blog title), but it is the truth. AI applied to media analytics needs to be guided to be successful. There are three specific areas where AI needs a boost to be successful analyzing media:
1. Changing Conversations: As seen in the Cal Berkeley research, for an AI system analyzing media content to remain accurate and relevant, it needs to be constantly trained as conversations and popular phraseology shift over time. You need enough consistently superior-quality analysis to feed back to the computer and train it.
2. Perspective: You need to tune AI specifically to understand your perspective. A solution tuned to someone else or all companies blended together just won’t work. This is because the phrase that one person (or company) determines is relevant and positive might be viewed differently by another person with different priorities or messaging goals.
3. Context: The conversation ecosystem needs to be taken into account. Often coverage is bookended by events, public discourse, and related coverage outside the sample set of coverage. In his article just a few weeks ago for MIT Sloan Management Review, Sam Ransbotham writes, “While the pace of business may be ever-accelerating, many business decisions still have time for a second opinion where human general knowledge of context can add value.”
It doesn’t mean you have to analyze everything to train an AI system, but you need to analyze enough data so that your computer can learn robustly from them. AI alone can’t teach itself about changing social conversations, perspective, or context.
[click_to_tweet tweet=”AI alone can’t teach itself about changing social conversations, perspective, or context. https://www.publicrelay.com/blog/when-reliance-on-ai-can-hurt-you/ #MediaIntelligence via @PublicRelay” quote=”AI alone can’t teach itself about changing social conversations, perspective, or context.” theme=”style4″]
On the bright side, humans can work with AI by defining, training, and maintaining a dynamic, accurate, and reliable human feedback loop. This means persistent training, unique for each individual company, with human attention to help AI bridge the gap between what it’s trained on, and what the customer is trying to know. Supervised machine learning is almost universally considered to be the leading approach to solving the human content analytics AI problem for the foreseeable future.
So how will you use AI? Smartly, I hope.
PublicRelay delivers a world-class media intelligence solution to big brands worldwide by leveraging both technology and highly-trained analysts. It is a leader on the path to superior AI analytics through supervised machine learning. Contact PublicRelay to learn more.
This article is part of a three part series, AI for Media Intelligence: The Good, the Bad, and the Ugly.