Machine Learning

Machine Learning

by Nancy Kress (excerpt)

Building 18 was devoted to machine learning. Ethan’s research partner, Jamie Peregoy, stood in their lab, welcoming this afternoon’s test subject, Cassie McAvoy. The little girl came with her mother every Monday, Wednesday, and Friday after school. Ethan took his place at the display console.

That end of the lab was filled with desks, computers, and messy folders of printouts. The other end held child-sized equipment: a musical keyboard, a video-game console, tables and chairs, blocks, and puzzles. The back wall was painted a supposedly cheerful yellow that Ethan fund garish. In the center, like a sentry in no-man’s land, stood a table with coffee and cookies.

“The problem with machine learning isn’t intelligence,” Jamie always said to visitors.” It’s defining intelligence. Is it intelligence to play superb chess, crunch numbers, create algorithms, carry on a conversation indistinguishable from a human gabfest? No. Turing was wrong. True intelligence requires the ability to learn for oneself, tackling new tasks you haven’t done before, and that requires emotion, and we learn best when emotional arousal is high. Can our Mape do that? No, she cannot.”

If visitors tried to inject something here, they were out of luck. Jamie would go into full-lecture mode, discoursing on the role of the hippocampus in memory retention, on how frontal-lobe injuries taught us that too little emotion, no how arousal levels were a better decision making as deeply as too much emotion, on how arousal levels were a better predictor of learning retention than whether the learning was positive or negative. Once Jamie got going, he was as unstoppable as a star running back, which was what he resembled. Young, brilliant, and charismatic, he practically glittered with energy and enthusiasm. Ethan went through periods where he warmed himself as Jamie’ inner fire, and other periods where he avoided Jamie for days at a time.

Machine Learning. Photo by Elena

MAIP, the MultiFuture Research Artificial Intelligence Program based in the company’s private cloud, could not play chess, could not feel emotion, and could only learn within defined parameters. Ethan, whose field was the analysis of how machine learning algorithms performed, believed that true AI was decades off, if ever. Did Jamie believe that? Hard to tell. When he spoke their program’s name, Ethan could hear that to Jamie it was a name, not an acronym. He had given MAIP a female voice. “Someday,” Jamie said, “she’ll smarter than we are.” Ethan had not asked Jamie to define “someday.”

The immediate, more modest goal was for MAIP to learn what others felt, so that MAIP could better assist their learning.

“Hello, Cassie, Mrs. McAvoy,” Jamie said, with one of his blinding smiles. Cassie, a nine-year-old in overalls and a t-shirt printed with kittens, smiled back. She was a prim little girl, eager to please adults. Well-mannered, straight A’s, teacher’s pet. “Never any trouble at home,” her mother had said, with pride. Ethan guessed she was not popular with other kids. But she was a valuable research subject, because MAIP had to learn to distinguish between genuine human emotions and “social pretense” – feeling expressed because convention expected it. When Cassie said, “I like you,” did she mean it?

“Ready for the minuet, Cassie?” Jamie asked.

“Yes.”

“Then let’s get started! Here’s your magic bracelet, princess!” He slipped it onto her thin wrist. Mrs. McAvoy took a chair at the back of the lab. Cassie walked to the keyboard and began to play Bach’s “Minuet in G,” the left-hand part of the arrangement simplified for beginners. Jamie moved behind her, where she could not see him. Ethan studied MAIP’s displays.

Sensors in Cassie’s bracelet measured her physiological responses: heart rate, blood pressure, respiration, skin conductance, and temperature. Tiny cameras captured her facial-muscle movement and eye saccades. They keyboard was wired to register the pressure of her fingers. When she finished the minuet, MAIP said, “That was good! But let’s talk about the way you arch your hands, okay, Cassie?” Voice analyzers measured Cassie’s responses: voice quality, timing, pitch. MAIP used the data to adjust the lesson: slowing down her instruction when Cassie seemed too frustrated, increasing the difficulty of what MAIP asked for when the child showed interest.

They moved on, teacher and pupil, to Bach’s “Polonaise in D.” Cassie didn’t know this piece as well. MAIP was responsive and patient, tailoring her comments to Cassie’s emotional data.