Brendan Dixon

Of 83 studies of the performance of the Deep Learning algorithm on diagnostic images, only two had been randomized, as is recommended, to prevent bias in interpretation.

I understand the panic many business leaders experience as they try to stay solvent while customers evaporate. Panic, however, is a poor teacher: AI-based automation will not only not solve all their problems, it may very well add to them. AI is not a magic box into which we can stuff them and make them disappear.

Starsky Robotics was not just another startup overwhelmed by business realities. In 2019, it was named one of the world’s 100 most promising start-ups (CNBC) and one to watch by FreightWaves, a key trucking industry publication. But the AI breakthroughs did not appear.

Big social media companies have long wanted to replace human content moderators with AI. COVID-19 quarantines have only intensified that discussion. But AI is far, far from ready to successfully moderate content in an age of where virtual monopolies make single point failure a frequent risk.

Building an AI entails moving parts of our intelligence into a machine. We can do that with rules, (simplified) virtual worlds, statistical learning… We’ll likely create other means as well. But, as long as “no one is home”—that is, the machines lack minds—gaps will remain and those gaps, without human oversight, can put us at risk.

Here’s the dilemma: The Moral Machine (the Trolley Problem, updated) feels necessary because the rules by which we order our lives are useless with automated vehicles. Laws embody principles that we apply. Machines have no mind by which to apply the rules. Instead researchers must train them with millions of examples and hope the machine extracts the correct message… 

We might avoid worrying that our artificial intelligence machines are trying to deceive us if we called it “Automated Intelligence rather than “Artificial Intelligence.”

Have you heard of the Law of the Instrument? It just means, to quote one formulation, “He that is good with a hammer tends to think everything is a nail.” All any given problem needs is a good pounding. This is a risk with AI, as with amateur carpentry. But with AI, it can get you into more serious trouble. Take hiring, for instance.

Over time, machine vision will become harder to fool than the one that was recently tricked into rapid acceleration by a defaced sign. But it will still be true that a fooled human makes a better decision than a fooled machine because the fooled human has common sense, awareness, and a mind that reasons.

A free course at Seattle University addresses the “meaning of ethics in AI.” I’ve signed up for it. One concern that I hope will be addressed is: We must not abdicate to machines the very thing that only we can do: Treat other people fairly.

Researchers at the Allen Institute for Artificial Intelligence (AI2) published a paper recently, deflating claims of rapid progress toward giving computers common sense.

As a lawyer with medical training reminded us recently, any one image is a snapshot in time, a brief part of the patient’s whole story. And it’s the whole story that matters, not a single image, perhaps taken out of context.

Faced with a claim that an AI language tool had given an interview, I took the advice I gave readers yesterday, and followed the links. What a revelation. The Economist story was more dishonest than the examples that Siegel discussed in Scientific American.

Media often co-operate with researchers’ inflated claims about machine learning’s powers of discovery. An ingenious “creative” approach to accuracy enables the misrepresentation, says data analyst Eric Siegel.