Machine learning is not at all like human learning. For example, machine learning frequently requires millions of examples. Humans learn from a few examples.
About a year ago, I wrote that mounting AI hype would likely give way to yet another AI winter. Now, according to the panelists at “the world’s leading academic AI conference” the temperature is already falling.
Despite the hype and a few bad actors, here at the Walter Bradley Institute, we believe in AI. The deployment of any technology—dams, bridges, buildings—requires care and, at times, oversight. Not to slow down progress but to protect us from ourselves. As the great scientist Richard Feynman put it, the easiest person to fool is oneself.
It’s enough to make you want to run out and buy a smart car today. But just a minute. There are other statistics out there. Let’s look at some of them.
Pardon me but, while I know that a good truck needs to be tough, I never thought it needed to be a Mad Max-styled warrior vehicle. Apparently, Musk does. Why?
I have nothing against robots. (I am against bad pizza.) I do, however, get very tired of the science fiction-fantasy of humanity-squashing robots. And that’s all it is: A fantasy.
By all means, let’s build machines that enhance our abilities. But let’s not forget that the really amazing thing is not the tool, but the tool builder.
Automation can help some aspects of writing. But media outlets get tech “google”-eyes and too often fail to ask the hard questions about what they are automating, how, and why.
The problem with machine learning-based AI in police work is not so much its inherent bias (none of us is bias-free) but the delegation to a machine of what should be a human decision.
Predictive text analyzes what was written and guesses what comes next. Whole sentences, paragraphs, and essays. Seabrook tested it on his own work…
Why do some PR agencies think it is so important that we forget the fellow human beings who help us, using robotic devices?
