Elizabeth Bear - Future Visions - Original Science Fiction Inspired by Microsoft

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 it were possible at all. 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 be 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”—feelings 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. The 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.

It looked so effortless. But years of work had gone into this piano lesson between a machine and a not-very-talented child. They had begun with a supervised classification problem, inputting observational data to obtain an output of what a test subject was feeling. Ethan had used a full range of pattern recognition and learning algorithms. But Jamie, the specialist in affective computing, had gone far beyond that. He had built, “by hand,” one complicated concept at a time, approaches to learning that did not depend on simpler, more general principles like logic. Then he’d made considerable progress in the difficult problem of integrating generative and discriminative models of machine learning. Thanks to Jamie, MAIP was a hybrid, multi-agent system, incorporating symbolic and logical components with sub-symbolic neural networks, plus some new soft-computing approaches he had invented. These borrowed methods from probability theory to maximize the use of incomplete or uncertain information.

MAIP learned from each individual user. When Cassie’s data showed her specific frustration level rising to a point where it interfered with her learning, MAIP slowed down her instruction. When Cassie showed interest in a direction, MAIP took the lesson there. It all looked so smooth, Ethan’s and Jamie’s work invisible to anyone but them.

At the end of the hour, MAIP said, “Well done, Cassie!”

“Thank you.”

“I hope you enjoyed the lesson.”

“Yes.”

“See you on Monday, then.”

“Okay.”

Mrs. McAvoy took Cassie’s hand, exchanged a few pleasantries with Jamie, and led Cassie out the door. It closed. In the corridor, the motion-activated surveillance system turned on.

Jamie beamed at Ethan. “That went really well! Maip—”

“I don’t want to come here anymore,” said the image of Cassie on the surveillance screen.

“Why not?” Mrs. McAvoy said.

“It’s no fun. Please, Mommy, can we never come here again?”

Silence in the lab. Finally Ethan said, “I guess we need to work more on the ontology of social pretense.”

Jamie looked crushed. “Damn! I thought Cassie liked coming here! She fooled me completely!”

“More to the point, she fooled MAIP.”

“All the subagents worked so well on yesterday’s test kid!”

“There’s no free lunch.”

Jamie had a rare flash of anger. “Ethan—do you always have to be so negative? And so fucking calm about it?”

“Yes,” Ethan said, and they parted in mutual snits. Ethan knew that Jamie’s wouldn’t last; it wasn’t in his nature. There they were, yoked together, the Apollo and Cassandra of machine learning.

Or maybe just Roo and Eeyore.

The first time Ethan had heard about Moser’s Syndrome, he’d been chopping wood in the backyard and listening to the news on his tablet. Chopping wood was an anachronism he enjoyed: the warming of his muscles, the satisfying clunk of axe on birch logs, the smell of fresh wood chips on the warm August air. In a corner of the tiny yard, against the whitewashed fence, chrysanthemums bloomed scarlet and gold.

“—coup in Mali that—”

Also, if he was honest with himself, he liked being out of the house while Tina was in it. His year-old marriage was not going well. The vivacity that had originally attracted Ethan, so different from his own habitual constraint, was wearing thin. For Tina, every difference of opinion was a betrayal, every divergent action a crisis. But she was pregnant, and Ethan was determined to stick it out.

“—tropical storm off the coast of North Carolina, and FEMA is urging—”

Thunk! Another fall of the axe on wood, not a clean stroke. Ethan pulled the axe out of the log. Tina came out of the house, carrying a tray of iced tea. Although her belly was still flat, she proudly wore a maternity top. The tea tray held a plate of his favorite chocolate macaroons. They were both trying.

“Hey, babes,” Tina said. Ethan forced a smile. He’d told her at least three times that he hated being called “babes.”

He said, “The cookies look good.”

She said, “I hope they are.”

The radio said, “Repeat: This just in. The CDC has identified the virus causing Moser’s Syndrome, even as the disease has spread to two more cities in the Northwest. Contrary to earlier reports, the disease is transmitted by air and poses a significant threat to fetuses in the first and early second trimester of pregnancy. All pregnant women in Washington and Oregon are urged to avoid public gatherings whenever possible until more is known. The—”

Ethan’s axe slipped from his hand, landing on his foot and partially severing his little toe in its leather sandal.

Tina shrieked. In his first stunned moment, he thought she’d screamed at the blood flowing from his foot. But she threw the tray at him, crying, “You took me to that soccer game last week! How could you! If anything happens to this baby, I’ll never forgive you!” She burst into tears and ran into the house, leaving Ethan staring at the end of his foot. A chunk of toe lay disjointed from the rest, bloody pulp surrounded by chocolate macaroons. Vertigo swept over him. It passed. The newscaster began to interview a doctor about embryonic damage, nerve malformation, visible symptoms in newborns.