February 14, 2020
Don't gussy up your thoughts. No surplus words or unnecessary actions.
--Marcus Aurelius, Meditations
Confused about AI? Find the jargon bewildering? Join the crowd, I feel the same way. It's not your fault.
AI confusing and seemingly esoteric for a number of reasons. AI has its roots in academic research, and the terminology is based on communication between experts. AI is also new and few people have hands-on experience with it—just as nobody understood the Internet until they used it.
But unfortunately AI is also confusing because many people talking about AI don't actually understand it. They're just bullshitting, hoping to take advantage of the AI bandwagon instead of actually understanding it. And others, I'm sad to say, will deliberately obfuscate the discussion as a means of signaling how smart they are or satisfying their ego. Like most of our clients, I'm sure you've been on the receiving end of pitches which just left you more bewildered.
This confusion is frustrating because it serves as an obstacle for wider AI adoption. It creates fear. It serves as an obstacle for your smart engineers to begin applying machine learning. It prevents your CEO from beginning to make smart bets on transforming your infrastructure.
Fortunately you have a solution - demand simplicity.
True experts like Jeff Dean and Andrew Ng are able to simplify AI for any business audience. Jeremy Howard teaches traditional software developers how to build applications with deep learning.
Don't understand the value of a solution or meaning of a term? Demand simplicity.
The people who understand AI will appreciate the feedback and the opportunity to find easier ways to communicate. A client recently asked me, "What do you mean by 'transfer learning'? What is that?"
"Oh," I replied. "Using pre-trained models. Pre-trained models lower the cost and time to build custom applications."
There is simply no good reason for me to use the term "transfer learning" outside of a technical discussion. Fortunately this client gave me the opportunity to try again.
Ask why. Ask for definitions. Ask for examples. Ask for a value proposition.
And if someone cannot simplify AI for you? Just move on. Your time is too valuable to waste with a bullshitter or an egomaniac. Someone else will surely thank you for the opportunity to do better.
Image credit: Chris
August 15, 2021
A company’s transition to AI is incredibly hard. As non-tech companies look for ways to evolve their existing teams and software infrastructure to support machine learning, they often make a common mistake: the “just a binary” machine learning (ML) antipattern. This approach to deploying models considers the ML model as an isolated binary inside the existing infrastructure. Although seemingly reasonable, the antipattern is fraught with hidden dangers.
June 6, 2021
Many data science projects die a slow, painful death because the organization isn’t motivated to make it succeed. In this post we address the three primary reasons projects fail and provide suggestions for what you can do to overcome these challenges.:
May 2, 2021
Your goal as an AI leader is to get your teams to think like pros. You want them to strategically look for ways in which AI can lift the entire business instead of just solving a narrowly defined problem. Your team should constantly seek ways to advance the bigger vision of becoming an AI-driven company. In this issue of FeedForward, I’ll describe the difference between how pros and amateurs think about AI.