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AI in Medicine / Applied AI / Robotics / Uncanny Valley March 24, 2022
Most of us think that improving quality or capability of an AI system makes it incrementally (or at least monotonically) better for the users. However, just like computer graphics and robotics, there can be an uncanny valley for AI systems. As artificial faces get more realistic, people are happier, but there is an uncanny valley where faces get to a point that is close to realistic but not…quite. Faces look “eerie” or […]
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AI Business / AI in Medicine / AI Projects / Applied AI / Artificial Intelligence / Machine Learning / Product Management / ROI January 6, 2022
If you’ve been reading my posts so far, you’ve probably learned solidly that AI models become useful when they leave the lab and become part of a deployed system, where they are integrated into business software and processes. So, say you’ve done that. You built your model. You improved your accuracy and demonstrated that it’s high […]
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AI Capabilities / AI in Medicine / AI Model / AI Systems January 6, 2022
“As a rule of thumb, you’ll spend a few months getting to 80% and something between a few years and eternity getting the last 20%.” Chris Dixon in “The idea maze for AI startups” It may seem obvious to some—but bears repeating for others—that our AI product needs to satisfy our customers’ needs to be […]
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AI Capabilities / AI Systems / Artificial Intelligence / Big Data / Machine Learning August 12, 2021
Getting the right data—and getting the data right—is essential for good AI accuracy, whether that data is collected before or during system deployment. Obtaining Data Obtaining data to train your AI system may seem like a straightforward task, but there are some hidden gotchas. Consider a company I’ll call WebLeads. It set out to create the […]
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AI Business / AI in Medicine / Applied AI / Artificial Intelligence July 19, 2021
In my previous post I talked about how to identify the opportunities for AI to solve key operational challenges and deliver on organization goals. As I described there, a good place to start is by understanding the categories of AI and ML use cases. The first half-dozen categories were classification, prediction, optimization, robotics and control, […]