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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 in Medicine / AI Systems / Applied AI / Artificial Intelligence / Intelligence Amplification / Machine Learning August 13, 2021
Should companies base AI systems on knowledge or data? The answer may surprise you: the best AI systems today use both. If you’ve come to AI in recent years, you might be surprised to learn that early AI applications were almost exclusively logic-based: models were built from facts and rules, not learned from data. MYCIN […]
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AI Business / AI Companies / AI Projects / AI Systems / Applied AI / Artificial Intelligence / ROI July 30, 2021
I see it all the time. A brilliant and passionate team contacts me for advice or investment. They’ve conceptualized an amazing fully automated AI system that will (eventually) solve a customer’s problem. And it’s really hard to do and it’s going to be very expensive in terms of time and money but, the team is […]
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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, […]
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As I’ve explained in previous posts, many organizations are rushing to enter the AI fray, hiring teams of data scientists, coders, and project managers in the urgency to get into the game. But what many teams retain in terms of dedication and enthusiasm for AI projects, they often lack in understanding of the art of […]
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Applied AI / Artificial Intelligence / Book / Decision Intelligence / Introductory / Product Management June 16, 2021
Typically, AI innovation teams want to use AI to solve important problems that people or the business really need solved, which could not be solved by other traditional approaches. In practice, we tend to misjudge what the customer needs, under-estimate what alternative approaches can accomplish, and overestimate our AI abilities. This is a standard recipe […]
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The robotics team had a great idea. Why not use self-driving car technology for a task that was essentially easier, and could make money on the way to autonomous nirvana? And, as often happens, they were pitching me for investment. A solid and practical team, I liked these guys for their clever concept: to build self-driving (“robot”) industrial […]