Bengaluru New Delhi: A month ago, Tanmay Saksena, chief operating officer of online pharmacy 1mg, became a student again. He signed up on ed-tech platform Coursera to take the'AI For Everyone' course. With a lot of information thrown around about artificial intelligence and its increasing importance in business, he felt he needed to educate himself on its applications and limitations. As AI and other emerging technologies like machine learning (ML), blockchain, and data analytics are increasingly being seen as game-changers to drive new business models and transform workplaces, the focus has subtly shifted from early and mid-career professionals to senior leaders – those with 12-15 years of experience and more –- who are looking to upskill. The main question on CXOs' minds is how to align their longterm business strategy with today's AI capabilities, say experts.

Meta-World: A Benchmark and Evaluation for Multi-T ask and Meta Reinforcement Learning Tianhe Y u 1, Deirdre Quillen 2, Zhanpeng He 3, Ryan Julian 4, Karol Hausman 5, Chelsea Finn 1, Sergey Levine 2 Stanford University 1, UC Berkeley 2, Columbia University 3, University of Southern California 4, Robotics at Google 5 Abstract: Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on meta-reinforcement learning focuses on task distributions that are very narrow. For example, a commonly used meta-reinforcement learning benchmark uses different running velocities for a simulated robot as different tasks. When policies are meta-trained on such narrow task distributions, they cannot possibly generalize to more quickly acquire entirely new tasks. Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors. In this paper, we propose an open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks. Our aim is to make it possible to develop algorithms that generalize to accelerate the acquisition of entirely new, held-out tasks. We evaluate 6 state-of-the-art meta-reinforcement learning and multi-task learning algorithms on these tasks. Surprisingly, while each task and its variations (e.g., with different object positions) can be learned with reasonable success, these algorithms struggle to learn with multiple tasks at the same time, even with as few as ten distinct training tasks. Our analysis and open-source environments pave the way for future research in multi-task learning and meta-learning that can enable meaningful generalization, thereby unlocking the full potential of these methods. 1 . Keywords: meta-learning, multi-task reinforcement learning, benchmarks 1 Introduction While reinforcement learning (RL) has achieved some success in domains such as assembly [1], ping pong [2], in-hand manipulation [3], and hockey [4], state-of-the-art methods require substantially more experience than humans to acquire only one narrowly-defined skill. If we want robots to be broadly useful in realistic environments, we instead need algorithms that can learn a wide variety of skills reliably and efficiently.

K. Cheung, Senior Member, IEEE, and George A. Constantinides, Senior Member, IEEE Abstract--Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quantised down to binary values. Network binarisation on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K -LUT, is capable of implementing far more than an XNOR: it can perform any K -input Boolean operation. Inspired by this observation, we propose LUTNet, an end-to-end hardware-software framework for the construction of area-efficient FPGA-based neural network accelerators using the native LUTs as inference operators. We describe the realisation of both unrolled and tiled LUTNet architectures, with the latter facilitating smaller, less power-hungry deployment over the former while sacrificing area and energy efficiency along with throughput. For both varieties, we demonstrate that the exploitation of LUT flexibility allows for far heavier pruning than possible in prior works, resulting in significant area savings while achieving comparable accuracy . Against the state-of-the-art binarised neural network implementation, we achieve up to twice the area efficiency for several standard network models when inferencing popular datasets. We also demonstrate that even greater energy efficiency improvements are obtainable. Index Terms --Deep neural network, hardware architecture, field-programmable gate array, lookup table.null 1 I NTRODUCTION AND M OTIVATION D URING inference, the most common--and expensive-- computational node in a deep neural network (DNN) performs a function of the form in (1), calculating a channel output y . Each weight w n is a constant determined during training, x a vector of N channel inputs and f an activation function such as the widely used rectified linear unit. In the extreme case where w { 1, 1} N --so-called binarised neural networks (BNNs)--the multiplications become cheap or free to implement. With weight inputs left variable, multipliers become XNOR gates. When networks are unrolled, weights are fixed, and so the XNOR gates can be further simplified into buffers and inverters, all of which are usually subsumed into the downstream adder logic. Also beneficial for BNNs is the ability to use a population count (popcount) for summation: an operation that consumes half the LUT s of the otherwise-throughput-optimal balanced adder tree [1]. In modern networks, N commonly reaches numbers in the thousands [2], [3]. T o tackle this, we propose the replacement of (1) with the specifically FPGA-inspired function (2), wherein the activation function is unchanged but each product is replaced with an arbitrary term-specific Boolean function g n: { 1, 1} K { 1, 1 } .

We study the problem of switching-constrained online convex optimization (OCO), where the player has a limited number of opportunities to change her action. While the discrete analog of this online learning task has been studied extensively, previous work in the continuous setting has neither established the minimax rate nor algorithmically achieved it. We here show that $ T $-round switching-constrained OCO with fewer than $ K $ switches has a minimax regret of $ \Theta(\frac{T}{\sqrt{K}}) $. In particular, it is at least $ \frac{T}{\sqrt{2K}} $ for one dimension and at least $ \frac{T}{\sqrt{K}} $ for higher dimensions. The lower bound in higher dimensions is attained by an orthogonal subspace argument. The minimax analysis in one dimension is more involved. To establish the one-dimensional result, we introduce the fugal game relaxation, whose minimax regret lower bounds that of switching-constrained OCO. We show that the minimax regret of the fugal game is at least $ \frac{T}{\sqrt{2K}} $ and thereby establish the minimax lower bound in one dimension. We next show that a mini-batching algorithm provides an $ O(\frac{T}{\sqrt{K}}) $ upper bound, and therefore we conclude that the minimax regret of switching-constrained OCO is $ \Theta(\frac{T}{\sqrt{K}}) $ for any $K$. This is in sharp contrast to its discrete counterpart, the switching-constrained prediction-from-experts problem, which exhibits a phase transition in minimax regret between the low-switching and high-switching regimes. In the case of bandit feedback, we first determine a novel linear (in $T$) minimax regret for bandit linear optimization against the strongly adaptive adversary of OCO, implying that a slightly weaker adversary is appropriate. We also establish the minimax regret of switching-constrained bandit convex optimization in dimension $n>2$ to be $\tilde{\Theta}(\frac{T}{\sqrt{K}})$.

Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation. Code: http://github.com/HobbitLong/RepDistiller.

Consequential decision-making incentivizes individuals to adapt their behavior to the specifics of the decision rule. A long line of work has therefore sought to understand and anticipate adaptation, both to prevent strategic individuals from "gaming" the decision rule and to explicitly motivate individuals to improve. In this work, we frame the problem of adaptation as performing interventions in a causal graph. With this causal perspective, we make several contributions. First, we articulate a formal distinction between gaming and improvement. Second, we formalize strategic classification in a new way that recognizes that the individual may improve, rather than only game. In this setting, we show that it is beneficial for the decision-maker to incentivize improvement. Third, we give a reduction from causal inference to designing incentivizes for improvement. This shows that designing good incentives, while desirable, is at least as hard as causal inference.

Andromeda is at the Berkman Klein Center; in the past she has written code for the MIT Libraries, the Wikimedia Foundation, bespoke knitting patterns (http://customfit.makewearlove.com) and library space usage analytics (http://measurethefuture.net/), among other things. Previously, she was a jack of all trades at the open-licensed-ebook startup Unglue.it; She has a BS in Mathematics from Harvey Mudd College, an MA in Classics from Tufts, and an MLS from Simmons. Andromeda is a 2010 LITA/Ex Libris Student Writing awardee, a 2011 ALA Emerging Leader, and a 2013 Library Journal Mover & Shaker. She is a former president of the Library & Information Technology Association, and a past listener contestant on Wait, Wait... Don't Tell Me!

Participants engage with academics and professionals in the field to learn about cutting-edge ideas, such as AI applied to biology and healthcare. Applications for the 2019 summer program are now closed! Artificial Intelligence (AI) is a branch of computer science that allows computers to make predictions and decisions, solve problems, and perform tasks. It's a technology that's rapidly changing the world and has impact across all disciplines and industries. AI has amazingly broad applications, and we need people with comparably broad set of experiences, worldviews, and identities working on AI.

Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for.