Zafer Demirkol is the author of 10 books on programming. His latest book is "Coding for Kids". In recent years he has been working on artificial intelligence. He develops artificial intelligence content and programmatic tools, especially for children and beginners. He also provides coding and artificial intelligence training for children.

Bottom Line: The stronger a CHRO's and companies' AI digital dexterity skills, the stronger they are at overcoming talent management challenges they're facing today. For HR leaders to excel at improving AI digital dexterity skills across their organizations, they need to learn from the shortcomings of existing Learning & Management systems that continue to have dismal adoption. First, all employees, especially Millennials and Gen Z, need to understand "Why" they should invest the time to take certain classes, courses, and what's in it for them after they have learned those skills. Second, employees want to learn about AI in a self-service mode, where they have greater control over the pace, review, and mastery of each lesson. Third, self-service learning tailored to employees' learning preferences has proven to be more effective than them participating in classes led by HR team members who themselves may not be aware of all the available options.

Abstract: The deployment of AI is truly transformational when it impacts the core business tasks and processes of the enterprise. For this reason, most organizations should only undertake AI initiatives with strategic impact potential. Experience shows that AI transformation programs achieve better results if organized in successive iterations of projects that implement high-value or even disruptive use cases. If managed properly, each project will create momentum and elements that will accumulate until critical mass is achieved. This iterative bottom-up approach is the most effective and realistic way of facing the daunting task of achieving the required AI proficiency within a reasonable time and cost.

Drone point of view of a Tractor spraying on a cultivated field. What does the future hold for machine learning/AI within the agricultural sector? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. From the food we eat to the clothing we wear and the gasoline in millions of cars, agriculture touches our daily existence like few other industries. This year alone, we've experienced several major supply shocks--including massive flooding in the American Midwest, a trade war, and the outbreak of serious crop and animal diseases in China--all of which highlight just how unpredictable the system is. Fortunately, we've also reached the point where there are nearly infinite amounts of data available to understand and forecast the complex interplay between global agricultural markets.

This article was contributed by Vincent Brissot, Head of Digital Automation & Channel Operations at HP. When you think of Artificial Intelligence (AI), it's easy to imagine Jarvis from Iron Man, or any of the lovable droids from the Star Wars universe. Sometimes, you may even think of AI villains, like Skynet from the Terminator series, or HAL 9000 from Stanley Kubrick's 2001: A Space Odyssey. AI has long been a tool only seen in science fiction stories, but now it is a reality and its transforming the way we live. From autonomous vehicles to cashier-less shops, robot tutors to robo-advisors, it's safe to say that society is quickly embracing its automated future.

Artificial intelligence (AI) systems can be used to improve collaborative learning and enhance the learning experience for students. Many educators and other experts have found flaws in the current education system. These experts have claimed that the present curriculum fails to focus on relevant life skills that can help in the personal development of students. One of these life skills is communicating and collaborating with other people. Such skills can be taught with the help of modern technologies like AI. Educators are already using AI in education to improve the learning experience.

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 $\tilde{\Theta}(\frac{T}{\sqrt{K}})$.