Soon, software will know how you feel--and will use that data to sell you things. The gig economy will go global (but it's not Uber-take-all). The tech industry will finally be inclusive. AI will achieve something like common sense, and it will be open source too. But that future won't build itself. Actual people (at least for now) have to make these things happen, and they aren't the C-suite hotshots you always hear about. The 25 people in these pages are the unsung creative, technical, and social visionaries working to bring the incredible world of tomorrow to you today. Get to know them now. Welcome to our second annual Next List. Surveillance is about to get much harder for overly snoopy governments. In November 2014 the Facebook-owned messaging service WhatsApp made a big change to its Android app: It encrypted messages so that even Facebook can't descramble them, no matter how many court orders the company receives. But the crypto software wasn't written by a Facebook employee.

Poaching is one of the greatest threats in the conservation of wildlife, and even patrol rangers' extreme efforts are not enough to completely fend off poachers, especially in very large protected areas. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual," said Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California, in a statement. With these in mind, researchers, in collaboration with the National Science Foundation and the Army Research Office, have developed a new Artificial Intelligence (AI)-based application employing game theory to efficiently map out patrol routes and areas. According to the press release of National Science Foundation, game theory uses mathematical and computer models of conflict and cooperation between rational decision-makers to predict the behavior of adversaries and plan optimal approaches for containment. The application, dubbed "Protection Assistant for Wildlife Security" or PAWS, uses mathematical models to effectively analyze data from previous patrols and evidence of poaching.

Inside a large, windowless room in an electronics factory in south Shanghai, about 15 workers are eyeing a small robot arm with frustration. Near the end of the production line where optical networking equipment is being packed into boxes for shipping, the robot sits motionless. "The system is down," explains Nie Juan, a woman in her early 20s who is responsible for quality control. Her team has been testing the robot for the past week. The machine is meant to place stickers on the boxes containing new routers, and it seemed to have mastered the task quite nicely. But then it suddenly stopped working. "The robot does save labor," Nie tells me, her brow furrowed, "but it is difficult to maintain."

One of the huge drawbacks of modern technology is that it fills the air around us with radio signals. From your kitchen radio to your LTE-enabled smartphone, all of these devices use radio waves to communicate. Unfortunately, there is only a certain amount of radio frequencies that can be used. DARPA (Defense Advanced Research Projects Agency) is looking for a way around this problem, and wants teams to develop an artificially intelligent system that will control what devices use what radio waves and when. Basically, instead of forcing devices to make use of narrow frequency bands when the spectrum gets congested, DARPA wants devices to negotiate sharing frequencies when they need them.

Where exactly is all the water on Earth's surface? Stand-alone satellite images have their limitations, but using artificial intelligence to examine them can now glean precise levels of water around the world and how they are changing week by week. Palo Alto startup Orbital Insight uses freely available images taken by the U.S. Geological Survey's Landsat 7 and 8 satellites, much like the images you see on Google Maps. The startup feeds the images into a neural network, which pinpoints the exact location and area of surface water. While it's not a new feat to track the Earth's water levels, Orbital Insight is approaching the task with a specially trained neural network that labels water pixel by pixel.

We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperforming weight noise and dropout. We achieve competitive performance (18.6\% PER) on the TIMIT phoneme recognition task for RNNs evaluated without beam search or an RNN transducer. With this penalty term, IRNN can achieve similar performance to LSTM on language modeling, although adding the penalty term to the LSTM results in superior performance. Our penalty term also prevents the exponential growth of IRNN's activations outside of their training horizon, allowing them to generalize to much longer sequences.

We consider data in the form of pairwise comparisons of n items, with the goal of precisely identifying the top k items for some value of k < n, or alternatively, recovering a ranking of all the items. We analyze the Copeland counting algorithm that ranks the items in order of the number of pairwise comparisons won, and show it has three attractive features: (a) its computational efficiency leads to speed-ups of several orders of magnitude in computation time as compared to prior work; (b) it is robust in that theoretical guarantees impose no conditions on the underlying matrix of pairwise-comparison probabilities, in contrast to some prior work that applies only to the BTL parametric model; and (c) it is an optimal method up to constant factors, meaning that it achieves the information-theoretic limits for recovering the top k-subset. We extend our results to obtain sharp guarantees for approximate recovery under the Hamming distortion metric, and more generally, to any arbitrary error requirement that satisfies a simple and natural monotonicity condition.

In a small startup space just down the street from UC Berkeley's campus, robotics pioneer Homayoon Kazerooni is bragging about how no-frills his invention is. "We're trying to make the Honda," he says, "not the sportscar." Kazerooni is showing me his latest robotic exoskeleton that gives paraplegics and people with mobility problems the ability to stand up from their wheelchairs and walk again. He's been building such bionic systems for more than a decade, and back in 2005 he cofounded Ekso Bionics, the current market leader for exoskeletons. So it's no surprise that Kazerooni says the new device from his new company, SuitX, is the most advanced yet.

With Earth Day come and gone the initiative to save our planet is fresh in our minds. And that's not limited to global warming and putting a stop to pollution; poaching remains a big problem when it comes to preserving the world around us. Luckily researchers have been testing out how we can use scientists to help the fight against poaching. According to Science Daily, organizations such as the National Science Foundation (NSF) and the Army Research Office have teamed up to look into how artificial intelligence (AI) could be used to stop poaching and illegal logging. Led by scientists at the University of South Carolina (USC), researchers have found ways to use game theory, or the mathematical theory of conflict and cooperation, to protect parks in a more proactive way.

If this year's selection of electable decisionmakers feels like it leaves something to be desired, then here's a story for you. Imagine a world without politicians, or, really, politics. Policy options are instead plugged into a sophisticated program and 1,000 or 100,000 simulations are made. Likely outcomes of the policy change are analyzed and unintended consequences are made visible. Politics are reduced to an argument over what inputs should take precedence in the model, not over what decision to make, and the arguments have to contend with data, which takes precedence over all.