Machine Learning: Whom Does One Believe?
June 28, 2019
Ah, another day begins with mixed messages. Just what the relaxed, unstressed modern decider needs.
First, navigate to “Reasons Why Machine Learning can Prove Beneficial for Your Organization.” The reasons include:
- Segment customer coverage. No, I don’t know what this means either.
- Accurate business forecasts. No, machine learning systems cannot predict horse races or how a business will do. How about the impact of tariffs or a Fed interest rate change?
- Improved customer experience. No, experiences are not improving. How do I know? Ask a cashier to make change? Try to get an Amazon professional to explain how to connect a Mac laptop to an Audible account WITHOUT asking, “May I take control of your computer with our software?”
- Make decisions confidently. Yep, that’s what a decider does in the stable, positive, uplifting work environment of a electronic exchange when a bug costs millions in a two milliseconds.
- Automate your routine tasks. Absolutely. Automation works well. Ask the families of those killed by “intelligence stoked” automobiles or smart systems on a 737 Max.
But there’s a flip side to these cheery “beneficial” outcomes. Navigate to “Machine Learning Systems Are Stuck in a Rut.” We noted these statements. First a quote from a technical paper.
In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas.
Next this comment by the person who wrote the “Learning Systems” article:
The thrust of the argument is that there’s a chain of inter-linked assumptions / dependencies from the hardware all the way to the programming model, and any time you step outside of the mainstream it’s sufficiently hard to get acceptable performance that researchers are discouraged from doing so.
Which is better? Which is correct?
Be a decider either using a black box or the stuff between your ears.
Stephen E Arnold, June 28, 2019
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