Anthony Acock was swamped at work and needed to make some space on his desk. So the assistant professor of graphic design at California State Polytechnic University, Pomona unplugged his desktop phone and stuck it in a drawer.
“It’s giant and has like five million buttons,” said Mr. Acock, 40 years old. “I don’t even know how to dial out on it.”
At home and work, technology habits have changed a lot over the years—and then there’s the office desk phone. With people carrying smartphones everywhere, a segment of the workforce has a hang up with the clunky office versions. Employees find them annoying and complicated, if they use desk phones at all.
Disconnecting may not be easy, as Mr. Acock found. After a few months, he said, an office manager noticed the phone’s absence and ordered it be put back on the desk.
“I was told to make an appointment with IT to get a training on it, but I just don’t have time for that,” he said.
In Portland, Ore., Kevin Murphy “unofficially abandoned” his office desk phone in August after a cable that connects to it broke. “I don’t plan to ask IT to fix it,” said Mr. Murphy, who is 46 and directs digital strategy at creative agency CMD. “Nothing good ever comes over a call to your desk phone.”
The device still sits on his desk, quietly collecting dust. “There is a sense of freedom,” Mr. Murphy said.
Office desk telephones, like email, seem very difficult to kill. Like cockroaches.
When people come in and say “How do I actually implement this artificial-intelligence project?” we immediately start breaking the problems down in our brains into the traditional components of AI—perception, decision making, action (and this decision-making component is a critical part of it now; you can use machine learning to make decisions much more effectively)—and we map those onto different parts of the business. One of the things Google Cloud has in place is these building blocks that you can slot together.>
Solving artificial-intelligence problems involves a lot of tough engineering and math and linear algebra and all that stuff. It very much isn’t the magic-dust type of solution.
What mistakes do companies make in adopting AI?
There are a couple of mistakes I see being made over and over again. When people come and say “I’ve got this massive amount of data—surely there’s some value I can get out of it,” I sit them down and have a strong talk with them.
What you really need to be doing is working with a problem your customers have or your workers have. Just write down the solution you’d like to have; then work backwards and figure out what kind of automation might support this goal; then work back to whether there’s the data you need, and how you collect it.
A must read.