Category: Machine Learning
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IBM THINK 2022 Champions Roundtable
At this year’s IBM THINK, I had the pleasure of joining fellow IBM Champions Jason Juliano, Steven Perva, and Craig Mullins along with IBMs Luke Schantz, JJ Asghar, Elizabeth Joseph, and the legendary Gabriela de Quieroz for a wide-ranging discussion on AI, data, bias, quantum computing, genomics, and more. Give a watch/listen! Can’t see anything?…
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Fireside Chat: Interview with Manxing Du of Talkwalker
I had a chance to sit down with Manxing Du, Senior Machine Learning Researcher at Talkwalker. We talk about pressing issues in AI and machine learning, natural language processing, bias in datasets, and much more. Can’t see anything? Watch it on YouTube here. Listen to the audio here: Download the MP3 audio here. Got a…
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You Ask, I Answer: Machine Learning vs. AI?
Maureen asks, “Why do people use machine learning and AI interchangeably?” Can’t see anything? Watch it on YouTube here. Listen to the audio here: Download the MP3 audio here. Got a question for You Ask, I’ll Answer? Submit it here! Subscribe to my weekly newsletter for more useful marketing tips. Find older episodes of You…
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How Much Data Do You Need For Data Science and AI?
How much data do you need to effectively do data science and machine learning? The answer to this question depends on what it is you’re trying to do. Are you doing a simple analysis, some exploration to see what you might learn? Are you trying to build a model – a piece of software written…
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Stop Hating Vanity Metrics in Marketing Analytics
Without fail at nearly every marketing conference, someone rails against vanity metrics. Stop measuring them. They don’t matter. They’re the devil. Variations on those themes. So let’s clear the air a bit, because just as some people put too much importance and faith in vanity metrics, other people discount them too much. What Are Vanity…
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Why AI Will Not Create Great Content Any Time Soon
I am bullish on AI creating content at scale. I am bearish on AI creating GREAT content at scale – or at all. Why? It comes down to limits of training data, fundamentally. All machine learning models, from the most basic to the most sophisticated, need something to learn from. In the case of language…
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How Often Should We Change Attribution Models?
Andrea asks, “How often are you changing your attribution modeling vs the change in organizational strategy?” An attribution model is something that’s mapped fundamentally to your sales and marketing strategy. It should change as you change strategy, or as your audience changes. First, let’s establish a baseline. Why do we need attribution models? Fundamentally, attribution…
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Why You Need to Understand Marketing Machine Learning Models
One of the technical marketing hurdles I hear marketers struggling with on a regular basis is the idea of an algorithm. Marketers talk about Google’s algorithm, Facebook’s algorithm, Instagram’s algorithm, and this bit of language matters a great deal in our understanding of what’s going on behind the scenes with big tech and marketing. To…
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One Step Closer to the Marketing Singularity
We’re one small step closer to the marketing singularity, the event where machines become our first choice for doing marketing work. Ever since OpenAI’s announcement of GPT-3 (and the relatively heavy restrictions on it), a number of other organizations have been working to make alternative models and software available that have similar performance. As background,…
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How to Think About Conversion Efficiency in Content Marketing
One of the more interesting content marketing metrics that I rarely see in the field is conversion efficiency. There’s some content that simply outperforms other content, but one of the things we forget to include in our normal analysis of content is how much effort, in terms of time and resources, went into the promotion…
