Whaaat?

To tell a story that someone will remember, it helps to understand his or her needs. The art of storytelling requires creativity, critical-thinking skills, self-awareness, and empathy.”All those traits are fundamentally human, but as artificial intelligence (AI) becomes more commonplace, even experts whose jobs depend on them possessing those traits foresee it playing a bigger role in what they do.

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Using AI to Read a Crowd

The AI-driven marketing platform Influential uses IBM’s Watson to connect brands with audiences. It finds social media influencers who can help spread a brand’s message to target demographics in a way that feels authentic and, well, human. The tool uses Watson’s services to look at the content written by an influencer, analyzing that text, and scoring it across 52 personality traits then matches influencers whose personalities, social media posts, and followers best reflect a brand’s marketing objectives.

Visual Scanning

Somatic is a digital marketing company that uses AI to scan photos and generate short text descriptions of what it sees. The tool can write about visual data in different styles or genres, even mimicking the prose styles of celebrities as long as there’s enough written content out there to be trained on.

Google’s AI research is geared toward “helping AI start to understand things about everyday human life,” and to start to push machines beyond just generating “literal content, like you get in image captioning,” and toward anticipating how those descriptions will make people feel.

My 2 cents:

The article raises the crucial question: “Will humanistic AI beat humans at their own game?”

If by game we mean predict trends based off of pure data, devoid of lingering biases and industry misconceptions, then the power of Al machine learning is set to beat our current capabilities as marketers and story tellers.

Yet predictive algorithms using historical data is hardly new, so much so that data interpretation is essentially a given in the AI v human game. The reason powerful tools like Watson are only making their debut now is that there has always been an ‘x’ factor that rendered machine learning results that fall a bit off center. This factor has been identified as many things (intuition, expertise…) but essentially it boils down to contextualizing.

The day Al will out perform human story telling will depend on developments and investments that further tunes there formidable and trainable tools to go beyond diagnostics and interpretation and move into outreach, context comprehension and organic responsiveness.

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