Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the attention to tasks that only require what robotics simulators can simulate: motion and physical contact. We present iGibson 2.0, an open-source simulation environment that supports the simulation of a more diverse set of household tasks through three key innovations. First, iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Additionally, given a logic state, iGibson 2.0 can sample valid physical states that satisfy it. This functionality can generate potentially infinite instances of tasks with minimal effort from the users. The sampling mechanism allows our scenes to be more densely populated with small objects in semantically meaningful locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations. As a result, we can collect demonstrations from humans on these new types of tasks, and use them for imitation learning. We evaluate the new capabilities of iGibson 2.0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI. iGibson 2.0 and its new dataset will be publicly available at http://svl.stanford.edu/igibson/.

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Gift This Course What you'll learn Description Welcome to The Complete 2021 Android Machine Learning Course. In this course, you will learn the use of Machine learning in Android without knowing any background knowledge of machine learning. In modern world app development, the use of ML in mobile app development is compulsory. We hardly see an application in which ML is not being used. So it's important to learn how we can integrate ML models inside Android applications.

In May, Twitter said that it would stop using an artificial intelligence algorithm found to favor white and female faces when auto-cropping images. Now, an unusual contest to scrutinize an AI program for misbehavior has found that the same algorithm, which identifies the most important areas of an image, also discriminates by age and weight, and favors text in English and other Western languages. The top entry, contributed by Bogdan Kulynych, a graduate student in computer security at EPFL in Switzerland, shows how Twitter's image-cropping algorithm favors thinner and younger-looking people. Kulynych used a deepfake technique to auto-generate different faces, and then tested the cropping algorithm to see how it responded. "Basically, the more thin, young, and female an image is, the more it's going to be favored," says Patrick Hall, principal scientist at BNH, a company that does AI consulting.

International Institute of Information Technology, Hyderabad (IIITH) and Agastya International Foundation are collaborating to create solutions that will bring innovative, high-quality and high-relevance learning and training to economically disadvantaged school children. The partners will jointly identify problems in the grassroots that can be addressed by solutions based on emerging technologies such as artificial intelligence (AI), machine learning (ML), language technologies and computer vision. The objective is to enable those on the wrong side of the digital divide by leveraging cutting-edge research. The partnership is set up under the aegis of the Raj Reddy Centre on Technology in Service of Society at IIITH. This centre is an initiative of IIITH to enable research and emerging technology-led solutions for grassroot education and public health, with specific emphasis on the rural population.

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.

Few-shot learning (FSL) is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data. Surprisingly, recent works have outperformed meta-learning methods tailored to FSL by casting it as standard supervised learning to jointly classify all classes shared across tasks. However, this approach violates the standard FSL setting by requiring global labels shared across tasks, which are often unavailable in practice. In this paper, we show why solving FSL via standard classification is theoretically advantageous. This motivates us to propose Meta Label Learning (MeLa), a novel algorithm that infers global labels and obtains robust few-shot models via standard classification. Empirically, we demonstrate that MeLa outperforms meta-learning competitors and is comparable to the oracle setting where ground truth labels are given. We provide extensive ablation studies to highlight the key properties of the proposed strategy.

Teamwork is cooperative, participative and power sharing. In science of science, few studies have looked at the impact of team collaboration from the perspective of team power and hierarchy. This research examines in depth the relationships between team power and team success in the field of Computer Science (CS) using the DBLP dataset. Team power and hierarchy are measured using academic age and team success is quantified by citation. By analyzing 4,106,995 CS teams, we find that high power teams with flat structure have the best performance. On the contrary, low-power teams with hierarchical structure is a facilitator of team performance. These results are consistent across different time periods and team sizes.

Learning generalizable manipulation skills is central for robots to achieve task automation in environments with endless scene and object variations. However, existing robot learning environments are limited in both scale and diversity of 3D assets (especially of articulated objects), making it difficult to train and evaluate the generalization ability of agents over novel objects. In this work, we focus on object-level generalization and propose SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill), a large-scale learning-from-demonstrations benchmark for articulated object manipulation with 3D visual input (point cloud and RGB-D image). ManiSkill supports object-level variations by utilizing a rich and diverse set of articulated objects, and each task is carefully designed for learning manipulations on a single category of objects. We equip ManiSkill with a large number of high-quality demonstrations to facilitate learning-from-demonstrations approaches and perform evaluations on baseline algorithms. We believe that ManiSkill can encourage the robot learning community to explore more on learning generalizable object manipulation skills.

Recent E-commerce applications benefit from the growth of deep learning techniques. However, we notice that many works attempt to maximize business objectives by closely matching offline labels which follow the supervised learning paradigm. This results in models obtain high offline performance in terms of Area Under Curve (AUC) and Normalized Discounted Cumulative Gain (NDCG), but cannot consistently increase the revenue metrics such as purchases amount of users. Towards the issues, we build a simulated search engine AESim that can properly give feedback by a well-trained discriminator for generated pages, as a dynamic dataset. Different from previous simulation platforms which lose connection with the real world, ours depends on the real data in AliExpress Search: we use adversarial learning to generate virtual users and use Generative Adversarial Imitation Learning (GAIL) to capture behavior patterns of users. Our experiments also show AESim can better reflect the online performance of ranking models than classic ranking metrics, implying AESim can play a surrogate of AliExpress Search and evaluate models without going online.