Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. Moreover, robotic policies learned with RL often fail when deployed beyond the carefully controlled setting in which they were learned. In this work, we study how these challenges can all be tackled by effective utilization of diverse offline datasets collected from previously seen tasks. When faced with a new task, our system adapts previously learned skills to quickly learn to both perform the new task and return the environment to an initial state, effectively performing its own environment reset. Our empirical results demonstrate that incorporating prior data into robotic reinforcement learning enables autonomous learning, substantially improves sample-efficiency of learning, and enables better generalization. Project website: https://sites.google.com/view/ariel-berkeley/

AI is transforming both our personal and professional lives. Today, building a career in AI is more exciting and accessible than ever before! Join us for a live, interactive panel discussion on Accelerating Your AI Career. Whether you're just starting out or looking to advance your AI skills this event is made for you! In this session, you will meet industry and academic leaders, and hear their stories and insights on career professional development in AI and the future of AI.

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Fine resolution spatial digital maps of soil macronutrients, which are an important factor in plant nutrition, are needed to support agricultural productivity. Digital soil maps obtained with high precision and accuracy are at the forefront of innovative technological initiatives to increase agricultural production. We had 91 topsoil observations, indices produced from satellite imagery, topographical variables produced from the DEM, and the CORINE land cover classes map which showed the effectiveness of agricultural activities for many years. Our first ultimate goal was to create digital soil maps with a spatial resolution of 30 m of various soil macronutrients (P, Ca, Mg, K). We compared three machine learning algorithms: multiple linear regression, support vector machine, and random forest algorithms.

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. Beyond Inclusion is a workforce diversity training program that aims to train people in the technical skills necessary for the metaverse. The deadline for applications is nearing on July 15, and the first program class is expected to debut on August 15. Lots of people are now dreaming about the metaverse, the universe of virtual worlds that are all interconnected, like in novels such as Snow Crash and Ready Player One. No longer the domain of science fiction, McKinsey predicts the metaverse will be worth $5 trillion by 2030.

Google AI developed a deep learning language model called Minerva which could solve mathematical quantitative problems using step-by-step reasoning. In the recently published paper related to Minerva, researchers explained the development of this deep learning Model. They achieved a state-of-the-art solution by training a deep learning model on a large training dataset that contains quantitative reasoning with symbolic expressions. The final model, Minerva, could solve quantitative mathematical problems on STEM reasoning tasks. It recalls the relevant formulas, constants, and step-by-step solutions involving numerical calculation.

Large scale databases with high-quality manual annotations are scarce in audio domain. We thus explore a self-supervised graph approach to learning audio representations from highly limited labelled data. Considering each audio sample as a graph node, we propose a subgraph-based framework with novel self-supervision tasks that can learn effective audio representations. During training, subgraphs are constructed by sampling the entire pool of available training data to exploit the relationship between the labelled and unlabeled audio samples. During inference, we use random edges to alleviate the overhead of graph construction. We evaluate our model on three benchmark audio databases, and two tasks: acoustic event detection and speech emotion recognition. Our semi-supervised model performs better or on par with fully supervised models and outperforms several competitive existing models. Our model is compact (240k parameters), and can produce generalized audio representations that are robust to different types of signal noise.

Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. Training on the data with Partial Labels in LML classification may result in more serious Catastrophic Forgetting in old classes. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN) with a built Augmented Correlation Matrix (ACM) across sequential partial-label tasks. The results of two benchmarks show that the method is effective for LML classification and reducing forgetting.

The majority of traditional text-to-video retrieval systems operate in static environments, i.e., there is no interaction between the user and the agent beyond the initial textual query provided by the user. This can be sub-optimal if the initial query has ambiguities, which would lead to many falsely retrieved videos. To overcome this limitation, we propose a novel framework for Video Retrieval using Dialog (ViReD), which enables the user to interact with an AI agent via multiple rounds of dialog, where the user refines retrieved results by answering questions generated by an AI agent. Our novel multimodal question generator learns to ask questions that maximize the subsequent video retrieval performance using (i) the video candidates retrieved during the last round of interaction with the user and (ii) the text-based dialog history documenting all previous interactions, to generate questions that incorporate both visual and linguistic cues relevant to video retrieval. Furthermore, to generate maximally informative questions, we propose an Information-Guided Supervision (IGS), which guides the question generator to ask questions that would boost subsequent video retrieval accuracy. We validate the effectiveness of our interactive ViReD framework on the AVSD dataset, showing that our interactive method performs significantly better than traditional non-interactive video retrieval systems. We also demonstrate that our proposed approach generalizes to the real-world settings that involve interactions with real humans, thus, demonstrating the robustness and generality of our framework

Optimization plays a costly and crucial role in developing machine learning systems. In learned optimizers, the few hyperparameters of commonly used hand-designed optimizers, e.g. Adam or SGD, are replaced with flexible parametric functions. The parameters of these functions are then optimized so that the resulting learned optimizer minimizes a target loss on a chosen class of models. Learned optimizers can both reduce the number of required training steps and improve the final test loss. However, they can be expensive to train, and once trained can be expensive to use due to computational and memory overhead for the optimizer itself. In this work, we identify and quantify the design features governing the memory, compute, and performance trade-offs for many learned and hand-designed optimizers. We further leverage our analysis to construct a learned optimizer that is both faster and more memory efficient than previous work. Despite the huge computational costs associated with training large neural models, the set of optimization algorithms used to train them has largely been restricted to simple update functions mapping from gradients to parameter updates (e.g. These algorithms typically depend on a small number of hand-designed features and parameters. However, the last decade in machine learning research has repeatedly seen small, hand-designed models outperformed by parameterized models (such as neural networks) trained to purpose on large amounts of data (LeCun et al., 2015). Thus, a promising direction to improve training performance and reduce costs is to replace hand-designed optimizers with more expressive learned optimizers, trained on problems similar to those encountered in practice. Learned optimizers specify parameter update rules using a flexible parametric form and learn the parameters of this function from a "dataset" of optimization tasks--a procedure typically referred to as meta-training or meta-learning (Andrychowicz et al., 2016; Finn et al., 2017; Hochreiter et al., 2001). Learned optimizers represent a path towards improved optimizer performance, and possess the ability to target different objectives (e.g. Despite being an active area of research (Andrychowicz et al., 2016; Wichrowska et al., 2017; Chen et al., 2020; Metz et al., 2020b; 2021; Almeida et al., 2021; Zheng et al., 2022), they are not yet commonly used in practice. Several challenges have limited the widespread application of learned optimizers: they are typically difficult to meta-train on a task family of interest, they can require significant memory and compute overhead when applied, and they often generalize less well to novel tasks than hand-designed optimizers.