Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training. By examining this masking, or dropout, in the linear case, we uncover a duality between such adaptive methods and regularization through the so-called "η-trick" that casts both as iteratively reweighted optimizations. We show that any dropout strategy that adapts to the weights in a monotonic way corresponds to an effective subquadratic regularization penalty, and therefore leads to sparse solutions. We obtain the effective penalties for several popular sparsification strategies, which are remarkably similar to classical penalties commonly used in sparse optimization. Considering variational dropout as a case study, we demonstrate similar empirical behavior between the adaptive dropout method and classical methods on the task of deep network sparsification, validating our theory.

Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training. By examining this masking, or dropout, in the linear case, we uncover a duality between such adaptive methods and regularization through the so-called "η-trick" that casts both as iteratively reweighted optimizations. We show that any dropout strategy that adapts to the weights in a monotonic way corresponds to an effective subquadratic regularization penalty, and therefore leads to sparse solutions. We obtain the effective penalties for several popular sparsification strategies, which are remarkably similar to classical penalties commonly used in sparse optimization. Considering variational dropout as a case study, we demonstrate similar empirical behavior between the adaptive dropout method and classical methods on the task of deep network sparsification, validating our theory.

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This story was originally published by Slate and is reproduced here as part of the Climate Desk collaboration. It also contains spoilers for the film Don't Look Up. Streaming just in time for Christmas, Adam McKay's decidedly uncheery Netflix comedy, Don't Look Up, finds Jennifer Lawrence and Leonardo DiCaprio playing a pair of intrepid astronomers as they try (and mostly fail) to warn the world about a planet-killing comet that's hurtling toward Earth. From the beginning, the scientists' efforts are marked by futility, encapsulated in an early scene in which Kate Dibiasky (Lawrence) and Randall Mindy (DiCaprio) are brought to the White House to debrief President Janie Orlean (Meryl Streep) on the impending extinction-level event. Predictably, the meeting goes disastrously.

So, let us get back to our main two core questions: How polite should a human being be with a voicebot, and how polite should a voicebot be with a human being? In my opinion, the answer to the first question is straightforward. The human being should be able to behave in any way that they wish to behave, with their only concern being to have the voicebot do what they want it to do and do it as quickly or as slowly as they want it to do it. If the human wishes to use "Please" and "Thank you" and other politeness markers, then the voicebot should accommodate such markers. If not, then the voicebot should accommodate their absence.

At first sight, these kinds of examples may seem amusing but less interesting, and irrelevant to deploying agents in the real world, where there are no simulator bugs. However, the underlying problem isn't the bug itself but a failure of abstraction that can be exploited by the agent. In the example above, the robot's task was misspecified because of incorrect assumptions about simulator physics. Analogously, a real-world traffic optimisation task might be misspecified by incorrectly assuming that the traffic routing infrastructure does not have software bugs or security vulnerabilities that a sufficiently clever agent could discover. Such assumptions need not be made explicitly – more likely, they are details that simply never occurred to the designer.

Asteroids, supervolcanoes, nuclear war, climate change, engineered viruses, artificial intelligence, and even aliens -- the end may be closer than you think. For the next two weeks, OneZero will be featuring essays drawn from editor Bryan Walsh's forthcoming book End Times: A Brief Guide to the End of the World, which hits shelves on August 27 and is available for pre-order now, as well as pieces by other experts in the burgeoning field of existential risk. It's up to us to postpone the apocalypse. There is no easy definition for artificial intelligence, or A.I. Scientists can't agree on what constitutes "true A.I." versus what might simply be a very effective and fast computer program. But here's a shot: intelligence is the ability to perceive one's environment accurately and take actions that maximize the probability of achieving given objectives.

Has AI raised the floor but not the ceiling with marketing? Have we over-indexed on having content at scale? And is there a way for marketers to understand when hyper-personalization will cross the line into creepiness? In this episode of thinkPod, we are joined by Kate Bradley Chernis (Founder & CEO of Lately) and Joey Camire (principal & founding team of Sylvain Labs). We talk to Kate and Joey about whether AI will replace human marketers, where we are currently with AI and marketing, the difficulty of getting marketers to write, and how AI can bring delight to consumers. We also get into the hot debate around a company's responsibility with user data and imagine a future where each cup of yogurt is tracked. "AI as it relates to marketing is raising the floor. It doesn't totally feel like it's currently raising the ceiling." "I'm here to tell you that when marketing, it'll never replace humans altogether because it just doesn't work. "AI is not at the place right now where it's saying like, well, based on my understanding of supply and demand economics, you should be changing your price model. What you choose to do with the understanding that the system is providing you is still going to land on someone's lap. So your ability to be creative, your ability to write, your ability to wrangle concept and insight. What do you do with the information that you're being provided from a system that is finding things that you might not otherwise be able to find." –Joey Camire "How can we consistently use that [hyper-personalization] in our messaging without compromising our brand? And so the way that we succeed in doing that is really being super emotional and human.

Andrew Yang announced he is vying for the 2020 Democratic presidential nomination back in February. But how is he going to do that? I got the chance to sit down with him at the Work Awesome conference in New York yesterday to ask him about his stances on trucking automation, AI policy, and his favorite topic, universal basic income (UBI). This article first appeared in Clocking In, our newsletter covering the impact of emerging technology on the future of work. Erin: Why focus on automation and UBI? Andrew: The reason why I'm focused on this issue is I'm convinced it's driving the social, economic, and political dysfunction we are seeing.

The first panel of the symposium began at 11:05 a.m. and reached a broad range of topics during the discussion entitled "The good, the bad, and the ugly of AI and robotics." The speakers of the panel included Jason Millar, assistant professor in the School of Electrical Engineering and Computer Science, Cindy Grimm, associate professor of mechanical engineering, Geoffrey Hollinger, assistant professor in the Collaborative Robotics and Intelligent Systems Institute at OSU and Stephanie Jenkins, assistant professor in the School of History, Philosophy and Religion at OSU. The panel then allowed each speaker to give a brief opinion of what the greatest risk and the greatest benefit of the widespread adoption of AI and robotics are. Grimm began by explaining that a large benefit of AI will be its ability to complete simple tasks, allowing people more time to tackle larger issues. Grimm went on to explain that the flip side of this is as AI becomes more common in daily, simple tasks, the public may become too trusting of these systems and allow them to make decisions that may be beyond their capability.