In this week’s In-Ear Insights, Katie and Chris discuss whether AI and machine learning will imperil the career of the data scientist. What is data science, and how much of it can be automated and handled by machines? Tune in to find out. Watch the video here: Can’t see anything? Watch it on YouTube here. […]
Read More… from {PODCAST} In-Ear Insights: Will AI Wipe Out Data Science Jobs?
In this episode, Katie and Chris discuss the different levels of complexity when it comes to AI and machine learning in marketing. What’s the difference between AI and data science, and when should you focus on one or the other? What cautionary tales should marketers understand before positioning a product as powered by AI? Tune […]
Read More… from {PODCAST} In-Ear Insights: Simple and Complex AI in Marketing
In this week’s In-Ear Insights, Katie and Chris talk about the newest advances in natural language generation and walk through an example of what’s available now for creating content with the assistance of AI. Watch the demonstration, listen to the implications for marketers, and start formulating your AI-based content marketing strategy. Tune in to find […]
In this episode of In-Ear Insights, Katie and Chris dig into the announcements around Google’s newly announced AI model for advanced search, MUM, or the multitask unified model. Learn what MUM is, why it should matter to content producers, and how to think about adapting your content strategy to deal with what could be a […]
Read More… from {PODCAST} In-Ear Insights: Google MUM, SEO, and Content Strategy
In this week’s In-Ear Insights, Katie and Chris discuss shiny object syndrome, blind spots in your marketing technology (especially around AI and machine learning) and how arrogance can lead to substantial technical problems in your tech stack and company culture. How can you avoid pitfalls and blind spots? How do you manage AI and machine […]
Read More… from {PODCAST} In-Ear Insights: Shiny Object Syndrome and Tech Arrogance
In this episode of In-Ear Insights, Chris and special guest John Wall discuss the state of consumer recommendation engines. Why are recommendations so narrow and ineffective many times? What could we do to improve them beyond what we get now? Listen in as we discuss limitations of computational power, algorithm choice, and more. Watch the […]
Read More… from {PODCAST} In-Ear Insights: Why Consumer Recommendation Engines Fail
In this episode of In-Ear Insights, Katie and Chris tackle the sorry state of digital advertising, and advertising in general. Why is advertising so terrible? Are companies and marketers focused on the wrong metrics? What are we doing with the data we collect, and could we be doing something different and better with it? Find […]
Read More… from {PODCAST} In-Ear Insights: The Sorry State of Advertising
In this week’s In-Ear Insights, Katie and Chris discuss the current state of AI deployment. Companies are rushing ahead to put models and algorithms into action with little to no due diligence, and the consequences can be disastrous. Should AI adopt a practice similar to clinical trials, where a model must prove that it causes […]
Read More… from {PODCAST} In-Ear Insights: Should AI Adopt a Clinical Trials Process?
Introduction The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this […]
Read More… from 5 Ways Your AI Projects Fail, After Action Reviews and Post-Mortems
This past week, our CEO Katie Robbert said, “can we be done with the “something about me and tag five people” on twitter thing? it’s really annoying now“. If you’re unfamiliar with the reference, there’s a Twitter meme where you share 5 jobs you’ve held, and then tag 5 people: These memes at first seem […]
Read More… from The Importance of Tag Memes for Understanding Social Networks
Newsletter
Podcast
