At the 20th annual MozCon, Britney Mullerfounder of Data Sci 101, gave an eye-opening presentation on AI and its impact on digital marketing.
His session, “The Hidden Side of AI: What Marketers Need to Know,” provided a comprehensive overview of the current and future potential of AI.
Muller discussed the ethical considerations, practical applications and limitations of AI, providing valuable guidance for marketers.
The emergence of generative AI
Muller began by discussing the rise of generative AI, which sits at the intersection of AI, machine learning, deep learning, and natural language processing (NLP).
She explained:
“Generative AI, in particular, came out of this interesting overlap of fields.
We have AI that hosts machine learning. Within machine learning, there is deep learning. And then human language comes into play with NLP or natural language processing.”
Photo taken by the author at MozCon, June 2024.
An important part of Muller’s presentation focused on the crucial role of training data in AI models.
She emphasized:
“I used to say that the AI reflects its training data, and I’m going to double down on that. It augments its training data.”
Muller highlighted the lack of diversity in datasets like Wikipedia, where contributors are mostly male, and how this can perpetuate biases in AI results.
Photo taken by the author at MozCon, June 2024.
Practical applications and limitations of AI in marketing
What Gen AI is good at
Muller presented a wide range of practical applications for AI in marketing, as shown in one of his slides.
Photo taken by the author at MozCon, June 2024.
She explained:
“LLMs are generally good at all of these things, and I’m of the unpopular opinion that generating content is one of their worst abilities. They’re much better at sentiment analysis, tagging things into categories, offering code support”.
He also shared a slide highlighting specific SEO/marketing applications of GenAI, including:
Automatic Titles and Meta Descriptions Aata Cleanup Code Help Accelerate Creativity and Ideation Custom Broadcast Sentiment Analysis Content Refresh Chatbots Transcribing Meeting Notes
Photo taken by the author at MozCon, June 2024.
What is GenAI bad at?
Photo taken by the author at MozCon, June 2024.
Muller discussed the limitations of LLMs, who struggle with tasks that require:
Factual Accuracy Common Sense Reasoning Understanding Context Handling Unusual Scenarios Emotional Intelligence Math/Counting
Marketers must recognize these strengths and weaknesses when incorporating AI into their strategies.
Quick engineering tips
To help marketers use generative AI, Muller provided helpful tips for rapid engineering.
Photo taken by the author at MozCon, June 2024.
His three proposals were:
Explain the task as you would to a person. Use examples to illustrate what you mean. Give the model a “role” and tell her about the audience
She advised:
“Explain the task or problem as you would to a person. A lot of research has been done on rapid engineering and, oh, these things work, but these things don’t. The most important thing in all this research is the examples. It just shows the model, hey, this is good or bad, and we want the output to look like that.”
Muller shared a slideshow of generative AI tools and resources such as Colab, Kaggle, GPT for Sheets, Ollama, WordCrafter.ai, and his own DataSci101.com.
Photo taken by the author at MozCon, June 2024.
Key takeaways and the future of AI in marketing
Muller concluded his presentation with several key points captured in his closing slide.
Photo taken by the author at MozCon, June 2024.
He emphasized the need for a people-centric approach to AI, recognizing its potential as an assistive technology rather than a full replacement for human experience.
Key takeaways include:
GenAI is a predictive technology A model is only as good as its training data. Marketers have the power to imagine the next brilliant GenAI app Show online where conversations about your product/service are happening
She stated:
“We need to talk more about people-centric AI, right? What will be the best model to support the people we work with? And this is predictive technology. A model is only as good as its training data and it’s assistive technology. This is not a complete replacement for you, and it won’t be.”
To sum up
Muller’s insights serve as a valuable guide to navigating the complex world of AI.
Throughout his presentation, Muller reiterated that artificial intelligence should be seen as an assistive technology rather than a complete replacement for human experience.
He encouraged marketers to identify tasks that AI can help speed up or automate while maintaining a human touch.
Muller’s key message to marketers is to maintain ethical practices, prioritize human needs, and capitalize on AI’s strengths while acknowledging weaknesses.
Featured image: abnalladin/Shutterstock
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