The Year's Best Science Fiction - Thirty-Third Annual Collection

by Gardner Dozois

  Hank said, “I wonder how primitive man ever wiped these beaver out. If that’s really what happened to them.”

  The light came on the rest of the way for Jack. “You remember that time when we were kids and made that deadfall? You read about deadfalls in some old book, so we made one, and we caught a squirrel in it.”

  “Yeah. A deadfall is one of the most primitive kinds of trap there is.”

  “That’s why I suggested it. We don’t need anything but what we find right here.”

  “How in hell would we make a deadfall big enough to kill a giant beaver?”

  “With this treetrunk here. If we can get rid of that spruce…”

  Hank studied the tree and trail. “I don’t have a better idea. If you can free that tree trunk so it will fall, I’ll make a trigger.”

  Jack found two straight, young-but-stout birch trees. With his ax he cut and trimmed them into poles, which he lashed together near the top. He raised this bipod up to brace the cottonwood trunk, then cut down the spruce that had supported it. He held his breath as the spruce fell away. The massive cottonwood groaned as its full weight came to rest on the bipod, but it stayed in place.

  By then Hank had carved out the three trigger pieces and base, also out of birch. The pieces fit together as a figure-4, with the long horizontal piece extending across the trail. When that part of the trigger was disturbed, it would pop out of a notch in the vertical piece, which in turn would kick the angled and vertical trigger pieces out of place, allowing the tree trunk to fall. In theory.

  They took the birch bipod away, allowing the full weight of the cottonwood to rest on the trigger. It held. They tested their deadfall by holding the bipod a short distance below the trunk and tripping the trigger. Sure enough, the cottonwood dropped immediately, a fraction of an inch, before catching on the bipod. They reset the trigger and took the bipod away, carrying it uphill, out of sight of the trail, in order to minimize disturbance at the site.

  “One thing bothers me,” Hank said. “We educated the beaver that’s using this trail, and now we’ve left our scent all over the place. What if he’s come to associate our scent with danger and shies away from this part of the trail?”

  Jack thought about it. “Let’s move a snare a bit up the trail from here. Make him think that’s the new danger. He should go around the snare, then be back on the trail before he hits the deadfall.”

  By the time they finished, it was getting dark and they hurried back to camp. Every sound in the forest caused them to raise their rifles.

  “Can you imagine a couple tower-dwellers trying to face that bear?” Jack said as they made their way across the dam.

  “They’d never get that far,” Hank told him. “They’re all too dependent on the web connection they’ve had since they were kids. There’s nothing to connect to here in the past. The ones that tried to go back in time were so lonely and panic-stricken that they could barely get out of their capsules.”

  Jack smiled. “So us poor backward enclavers are good for something after all.”

  * * *

  The night was very still, and in the wee hours they both awoke to the sound of the cottonwood falling on the other side of the lake. On a windy night it might have been any tree. Even now, the deadfall could have been tripped by accident or by some other animal. That was it, then. They either had a beaver or they didn’t.

  They broke camp in the dark, got what little was left of their gear ready to go. At first light they crossed the dam and went up the lake. The beaver lay in the middle of the trail with its legs folded up as if it were taking a snooze. The tree had fallen squarely and broken the animal’s back just behind the forelegs. It was an old male, even larger than the female they had seen. Its teeth were cracked and the right foreleg had been lost to some accident, predator, or beaver fight. The remnant of the missing snare stretched between the animal’s right shoulder and left foreleg.

  They used the birch poles to lever the cottonwood off the beaver. The big animal’s legs were stiff with rigor mortis and they had to work them loose before they could roll it onto its back. Its tail was proportionately longer and narrower than a regular beaver’s and its incisors were like spades, with ridges running from the roots to the tips.

  Jack breathed in the beaver’s sweet, damp scent and took out his knife. Starting at the animal’s lower lip, he began to split the tough, thick hide down the chest and abdomen all the way to where the fur ended at the base of the long scaly tail.

  “What are you doing?” Hank asked. “All they need is a tissue sample.”

  “It may not be prime this time of year, but I ain’t going to waste a fur like this. They can get all the tissue samples they want from the flesh and fat we scrape off the hide at home.”

  “I guess our contracts don’t say we can’t bring back a fur.” Hank took out his own knife. “But we don’t have much time left.”

  While Hank skinned out the forequarters and head, Jack did the hindquarters, cutting around the ankles and working the hide free of the legs with his hands and the knife. When they were done, they dragged the carcass off, folded the hide once, skin to skin, and rolled it up.

  Jack took one last look at the carcass and noticed the dark castor sacs showing through the thin wall of the animal’s lower abdomen. He was staring at them and suddenly felt his mouth form a smile. Castoreum was attractive to most mammals and had been used for centuries in perfume. It also made a great trapping lure. He took out his knife and removed the sacs, each larger than his hand and consisting of wrinkled convolutions like brain matter inside a translucent membrane.

  “Why are you taking the castors?” Hank asked.

  “We may just find a use for them.”

  They tied the beaver skin together with a couple of snares and slung it over their shoulders like a roll of carpet. Jack looked at his watch. “We’d better shake a leg.” They collected their remaining snares as they went.

  As they waded across a spillway on the top of the dam, Jack paused to catch his breath. Fat-cell suppression would sure improve his mileage. Looking down at the water, he smiled again. Hell, he’d already survived an afternoon in one of the towers. And none of those people could survive one hour here in the Pleistocene. After the trip to the doctor’s, maybe he and Carol could visit Josh and Shari, and even take Eddie along. Better to show him the outside world than let him stay curious about it. Katie was gone no matter what he did. Better to live with the living than the dead.

  They reached the sphere with twenty minutes to spare. “What do you keep grinning about?” Hank demanded.

  “Let’s get loaded up and I’ll tell you.”

  * * *

  The beaver hide and castors barely stuffed into the compartment under the seat. They had to set the remains of their gear on their laps.

  While they were waiting, Hank said, “Okay, what’s so amusing?”

  Jack felt his grin get bigger. “I’ve been thinking. Suppose a bunch of these giant beaver are turned loose in our time. What do you think they’ll do?”

  Hank blinked. “They’ll do what beaver do—cut trees, build dams, make more beaver.”

  “Lots of trees. Giant dams. And a bunch of beaver.”

  “Yeah, well, that’s what the tower-dwellers want. They couldn’t law us out of existence, so they want to drown us out.”

  “We’ll survive, we always have. But what happens when these giant beaver dams start flooding the tower and skyway foundations? The lakeshore retreats? In ten years, Ohio could be one big swamp.”

  Hank stared at him a moment, then got it. “They’ll probably need some giant-beaver-removal services.”

  “And which two guys are going to be the only ones who know anything about trapping giant beaver? The only ones with giant-beaver castor lure ready to go?”

  “If I can put up with your ornery ass, it could be a beautiful partnership.”

  Jack felt the initial sense of movement when the wormhole found the capsule.

&n
bsp; Back across your time again, Katie. But maybe I’d better say goodbye to you here.

  Machine Learning

  NANCY KRESS

  Nancy Kress began selling her elegant and incisive stories in the mid-seventies. Her books include the novel version of her Hugo- and Nebula-winning story, Beggars in Spain, and a sequel, Beggars and Choosers, as well as The Prince Of Morning Bells, The Golden Grove, The White Pipes, An Alien Light, Brain Rose, Oaths & Miracles, Stinger, Maximum Light, Nothing Human, The Floweres of Aulit Prison, Crossfire, Crucible, Dogs, and Steal Across the Sky, as well as the Space Opera trilogy Probability Moon, Probability Sun, and Probability Space. Her short work has been collected in Trinity And Other Stories, The Aliens of Earth, Beaker’s Dozen, Nano Comes to Clifford Falls and Other Stories, The Fountain of Age, Future Perfect, AI Unbound, and The Body Human. Her most recent books are the novels Flash Point, and, with Therese Pieczynski, New Under the Sun. In addition to the awards for “Beggars in Spain,” she has also won Nebula Awards for her stories “Out Of All Them Bright Stars” and “The Flowers of Aulit Prison,” the John W. Campbell Memorial Award in 2003 for her novel Probability Space, and another Hugo in 2009 for “The Erdmann Nexus.” Most recently, she won another Nebula Award in 2013 for her novella “After the Fall, Before the Fall, During the Fall,” and another Nebula Award in 2014 for her novella “Yesterday’s Kin.” She lives in Seattle, Washington, with her husband, writer Jack Skillingstead.

  Here she suggests that while we’re learning from machines, the machines may also be learning from us …

  Ethan slipped into the back of the conference room in Building 5 without being noticed. Fifty researchers and administrators, jammed into the room lab-coat-to-suit, all faced the projection stage. Today, of course, it would be set for maximum display.The CEO of the company was here, his six-foot-three frame looming over the crowd. Beside him, invisible to Ethan in the crush, would be tiny Anne Gonzalez, R&D chief. For five years a huge proportion of the Biological Division’s resources—computational, experimental, human—had been directed toward this moment.

  Anne’s clear voice said, “Run.”

  Some people leaned slightly forward. Some bit their lips or clasped their hands.Jerry Liu rose onto the balls of his feet, like a fighter. They all had so much invested in this: time, money, hope.

  The holostage brightened. The incredibly complex, three-dimensional network of structures within a nerve cell sprang into view, along with the even more complicated lines of the signaling network that connected them. Each line of those networks had taken years to identify, validate, understand. Then more time to investigate how any input to one substructure could change the whole. Then the testing of various inputs, each one a molecule aimed at the deadly thing near the center of the cell, the growing mass of Moser’s Syndrome. All this hard work, all the partnering with pharmaceutical companies, in order to arrive at Molecule 654-a, their best chance.

  So far, no one had noticed Ethan.

  The algorithm for 654-a began to run, and in a moment the interaction combinations produced the output on the right side of the screen. Only two outputs were possible: “continued cell function” or “apoptosis.” The apoptosis symbol glowed. A second later, in a burst of nonrealistic theatrics, the cell drooped and sagged like one of Dali’s clocks, and the lethal structure at its heart vanished.

  Cheering erupted in the room. People hugged each other. A lab tech stood on tiptoe and kissed the surprised CEO. They had done it, identified a possible cure for the disease that attacked the bodies of children, and only children, killing half a billion kids worldwide in the last five years. They had done it with molecular computation, with worldwide partnerships with universities and Big Pharma, and with sheer grit.

  Someone to Ethan’s left said, “Oh!” Then someone else noticed him, and someone after that. Ethan’s story was company-wide gossip. The people at the front of the room went on burbling and hugging, but a small pocket of silence grew around him, the embarrassed silence of people caught giggling at a wake. Laura Avery started toward him.

  He didn’t want to talk to Laura. He didn’t want to spoil this important celebration.Quickly he moved through the door, down the corridor, into the elevator. Laura, following, called “Ethan!” He hit the DOOR CLOSE button before she could reach him.

  In the lobby he walked rapidly out the door, heading in the rain toward his own facility. Buildings of brick and glass rose ghostly in the thick mist. MultiFuture Research was a big campus and he was soaked by the time he reached Building 18. Inside, he nodded at Security and shook himself like a dog. Droplets spun off him.What the hell had he done with his umbrella? He couldn’t remember, but it didn’t matter. The important thing was to get back to his own work.

  He didn’t belong at a celebration to defeat Moser’s Syndrome.

  Too late, too late. Way too late.

  * * *

  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 found 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 as well as reasoning. We don’t retain learning unless it’s accompanied by 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 could impair 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 at Jamie’s inner fire, and other periods where he avoided Jamie for days at a time.

  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 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.