WASHINGTON — Generative AI stays at the center of conversations in the worlds of technology and promoting, with advancements continuing to roll out from tech giants, agency holding firms and marketing consultancies. While causing some uncertainty about job futures, the technology can also be seen by marketers as a balm for burnout and a option to boost investment in creator content.
Amid all of these applications and experiments, generative AI still faces a raft of legal issues and practical pitfalls that marketers must navigate while integrating the tech into their operations. Those concerns were the topic of a panel at the IAB Public Policy and Legal Summit on Tuesday (April 2).
Panelists also clarified some definitional distinctions that marketers must understand, especially as agencies, ad-tech providers and other platforms rush to adopt generative AI while rebranding and highlighting AI functionality that has been part of the ad industry for greater than a decade.
“You’ve probably been using machine learning and deep learning to segment your audience, to develop ad budgets, to position ads, to grasp what type of viewers could also be more conscious of particular types of advertisements,” said Dera Nevin, managing director at FTI Consulting. “Machine learning and deep learning has been utilized in the promoting industry for a very long time … and now we’re beginning to see the use of generative AI to generate content.”
Cooking with AI
To understand AI, Nevin suggested a controlling metaphor around cooking wherein algorithms are recipes, data inputs are ingredients and generated outputs are prepared food. While machine learning is an easy recipe, the deep learning that drives large-language models and generative AI is a far more complicated one. As in the kitchen, the final product is simply pretty much as good as the ingredients, and the data which AI is trained on limits the efficacy and accuracy of the output.
“In order to essentially understand what kind of food you are going to get when the recipes interact with the ingredients, you really need to know what’s in the kitchen and who’s preparing it,” Nevin said. “But there’s often little transparency behind what the [recipe] is or what the ingredients are. Without knowing that, you simply do not know what kind of food … goes to return out.”
Agencies and types needs to be concerned about what data they’re inputting as generative AI prompts in addition to the output that’s generated. When using public-facing generative AI tools like ChatGPT, that data becomes part of the algorithm’s data set — whether it’s confidential, personal or otherwise private.
When considering output, marketers needs to be wary of acontextual content that’s generated when AI doesn’t understand context, leading to output that can be embarrassing to brands. Plus, in its attempts to simulate human behavior, AI can click or “behave” as human beings, driving incorrect metrics or understandings of engagement.
“Hallucination” has turn out to be a preferred option to describe the unexpected output generated by AI, but Nevin pushed back on the term because it attributes human characteristics to technology. So-called hallucinations are happening because of underlying math and probabilities; the technology is doing what it’s designed to do, but doesn’t have the human ability to create original ideas.
“A human being combined two concepts to provide you with ‘Sharknado.’ I do not know that an AI could try this,” she said. “But an AI could provide you with very credible ‘Sharknado 2’, ‘3’, ‘4’ and ‘5.’”
Generating opportunities
Much of the last yr has been focused on the threat vectors of generative AI, including concerns around publisher and ad traffic, compensation for the inclusion of copyrighted material in large-language models and signaling protocols for determining what should and shouldn’t be allowed to coach AI models, explained IAB CEO David Cohen in an interview at the summit. As the industry begins to resolve for those threats, brands and agencies can deal with the opportunity vectors.
“How will we use all of this for creative efficiency, workflow efficiency, making our business more agile, adaptable and efficient? There’s tons and tons of work that is occurring there on the creative side,” Cohen said. “The opportunity [piece] is what the next 12 to 24 months will appear to be.”
For marketers, some of those opportunities could be higher served by trying to tech company offerings that protect brand safety more effectively than public-facing tools. For example, Adobe and its Firefly platform can be trained on a brand’s assets — quite than publicly scraped data — giving the outputs higher resonance with the brand, explained Matt Savare, a partner at Lowenstein Sandler, LLP, during the panel. Plus, firms like Adobe, Google and Shutterstock have announced plans to indemnify users against third-party mental property claims, protecting smaller brands and agencies from legal peril.
Still, those opportunities might need to attend as brands and agencies triage more pressing concerns, like the deprecation of third-party cookies, first-party data strategies and Google’s Privacy Sandbox proposals, Cohen explained.
Whether generative AI will eventually deliver on the loftiest guarantees of its biggest boosters and have the ability to create high-level, creative campaigns stays to be seen. Other technology has revolutionized so many parts of the promoting ecosystem in previously unbelievable ways, and generative AI may very well be the next miracle tool — sooner or later.
“The generation that is coming up goes to be doing what we do in extremely novel ways, and I would not be surprised if we get to precision promoting [with AI] sooner or later,” Nevin said. “I just don’t understand how quickly that is going to return.”
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