The automatic and efficient discovery of skills, without supervision, for long-living autonomous agents, remains a challenge of Artificial Intelligence. Intrinsically Motivated Goal Exploration Processes give learning agents a human-inspired mechanism to sequentially select goals to achieve. This approach gives a new perspective on the lifelong learning problem, with promising results on both simulated and real-world experiments. Until recently, those algorithms were restricted to domains with experimenter-knowledge, since the Goal Space used by the agents was built on engineered feature extractors. The recent advances of deep representation learning, enables new ways of designing those feature extractors, using directly the agent experience. Recent work has shown the potential of those methods on simple yet challenging simulated domains. In this paper, we present recent results showing the applicability of those principles on a real-world robotic setup, where a 6-joint robotic arm learns to manipulate a ball inside an arena, by choosing goals in a space learned from its past experience.

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The field of artificial intelligence (AI) is growing at a rapid pace, developing algorithms and automated machines that show promise in making the workplace more efficient and less biased. Many of us already interact with artificial intelligence in our daily lives, often without even realizing it--it's responsible for everything from credit score calculators to search engine results to what we see on social media.1 Likewise, organizations have introduced AI into many work processes, especially recruiting and talent-management functions. In many cases, algorithms sort through numerous factors to profile people and make predictions about them. AI hiring and talent-management systems have the potential to move the needle on gender equality in workplaces by using more objective criteria in recruiting and promoting talent.2 But what happens if the algorithm is actually relying on biased input to make predictions?

"The most exciting breakthroughs of the twenty-first century will not occur because of technology, but because of an expanding concept of what it means to be human" Before we dive into why more women should lead AI teams, I want to share a fascinating story I heard from Tania Biland, a 3rd-year student of Lucerne University of Applied Sciences and Arts. After 4 weeks of work, each team had to present their work. Group 1, composed of only women, developed a safety solution for women in the dark. As the jury was only male we decided to tell a story using a persona, music, and videos in order to make them feel what women are experiencing on a daily basis. We also put emphasis on the fact that everyone has a mother, sister or wife in their life and that they probably don't want her/them to suffer.

A new open-source, artificially intelligent prosthetic leg designed by University of Michigan and Shirley Ryan AbilityLab researchers is now available to the scientific community. Opensourceleg.com has been created to offer a united platform to combine and accelerate research efforts across the bionics field. The University of Michigan College of Engineering is one of the world's top engineering schools. Michigan Engineering is home to 12 highly-ranked departments, and its research budget is among the largest of any public university. Instead of starting from scratch, these researchers can take this common platform and, after some assembly, begin working on better solutions to help people with mobility impairments.

Dave Christopher with AMPED (Academy of Music Education, Production and Development) started his organization to advance kids in low-income areas, teaching them vital skills in music and tech. As a child of poverty whose parents couldn't afford college, Christopher said he taught himself IT. Now with almost 30 years in the technology field under his belt, he's still only worked with less than 30 African-Americans.

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to 22 times speedup has been achieved.

We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with it, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including influence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper confidence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online influence maximization algorithm. And extensive empirical evaluations on two real-world networks showed the effectiveness of our proposed solution.

Dealing with sparse rewards is a longstanding challenge in reinforcement learning. The recent use of hindsight methods have achieved success on a variety of sparse-reward tasks, but they fail on complex tasks such as stacking multiple blocks with a robot arm in simulation. Curiosity-driven exploration using the prediction error of a learned dynamics model as an intrinsic reward has been shown to be effective for exploring a number of sparse-reward environments. We present a method that combines hindsight with curiosity-driven exploration and curriculum learning in order to solve the challenging sparse-reward block stacking task. We are the first to stack more than two blocks using only sparse reward without human demonstrations.

State-of-the-art CNN based recognition models are often computationally prohibitive to deploy on low-end devices. A promising high level approach tackling this limitation is knowledge distillation, which let small student model mimic cumbersome teacher model's output to get improved generalization. However, related methods mainly focus on simple task of classification while do not consider complex tasks like object detection. We show applying the vanilla knowledge distillation to detection model gets minor gain. To address the challenge of distilling knowledge in detection model, we propose a fine-grained feature imitation method exploiting the cross-location discrepancy of feature response. Our intuition is that detectors care more about local near object regions. Thus the discrepancy of feature response on the near object anchor locations reveals important information of how teacher model tends to generalize. We design a novel mechanism to estimate those locations and let student model imitate the teacher on them to get enhanced performance. We first validate the idea on a developed lightweight toy detector which carries simplest notion of current state-of-the-art anchor based detection models on challenging KITTI dataset, our method generates up to 15% boost of mAP for the student model compared to the non-imitated counterpart. We then extensively evaluate the method with Faster R-CNN model under various scenarios with common object detection benchmark of Pascal VOC and COCO, imitation alleviates up to 74% performance drop of student model compared to teacher. Codes released at https://github.com/twangnh/Distilling-Object-Detectors