Few technologies have had a big an impact – and promise to have more in the future – than artificial intelligence, or AI.
That’s why it was no surprise that the New York Times Magazine featured an article entitled, “Can A.I. Be Taught to Explain Itself.” For me, it was riveting. Some excerpts:
It has become commonplace to hear that machines, armed with machine learning, can outperform humans at decidedly human tasks, from playing Go to playing “Jeopardy!” We assume that is because computers simply have more data-crunching power than our soggy three-pound brains. Kosinski’s results suggested something stranger: that artificial intelligences often excel by developing whole new ways of seeing, or even thinking, that are inscrutable to us. It’s a more profound version of what’s often called the “black box” problem — the inability to discern exactly what machines are doing when they’re teaching themselves novel skills — and it has become a central concern in artificial-intelligence research. In many arenas, A.I. methods have advanced with startling speed; deep neural networks can now detect certain kinds of cancer as accurately as a human. But human doctors still have to make the decisions — and they won’t trust an A.I. unless it can explain itself.
“Artificial intelligence” is a misnomer, an airy and evocative term that can be shaded with whatever notions we might have about what “intelligence” is in the first place. Researchers today prefer the term “machine learning,” which better describes what makes such algorithms powerful.
The idea was to connect leading A.I. researchers with experts in data visualization and human-computer interaction to see what new tools they might invent to find patterns in huge sets of data. There to judge the ideas, and act as hypothetical users, were analysts for the C.I.A., the N.S.A. and sundry other American intelligence agencies.
Even if a machine made perfect decisions, a human would still have to take responsibility for them — and if the machine’s rationale was beyond reckoning, that could never happen.
Intrigued? You can read the full article here
