A look at MuZero, the most advanced reinforcement algorithm ever. You are given this task- learn to play a game, but you won't be told the game's rules or have someone to play against. All you will be told is if a particular move is legal and if the game has ended. Do you think you can learn to play this game? And learn it to a superhuman extent.

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and application in multiple fields such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efficiency in real-world applications. In this work, we survey the state-of-the-art methods that target long-term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS.

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Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

While mindfulness provides a meditation-based tool for alleviating anxiety and depression levels [68], PP is a branch of psychology that aims to enhance well-being by focusing particularly on the positive experiences of people and positive individual traits [33], [84]. Such positive reflection has been shown to increase feelings of positive affect and future expectancy in individuals [76]. With the recent COVID-19 pandemic, the general population has been severely impacted, resulting in negative mental health outcomes [87]. People have found it particularly difficult to seek mental health advice and treatment due to social distancing regulations imposed [52]. As a result, digital forms of healthcare have been applied to assist individuals in need [66]. These include telehealth services via video calls, online therapies, and self-help resources through mobile and web apps.

In this work, we try to decipher the internal connection of NLP technology development in the past decades, searching for essence, which rewards us with a (potential) new learning paradigm for NLP tasks, dubbed as reStructured Pre-training (RST). In such a paradigm, the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing. Based on that, we operationalize the simple principle that a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access. We achieve this by pre-training models over restructured data that consist of a variety of valuable information instead of raw data after overcoming several engineering challenges. Experimentally, RST models not only surpass strong competitors (e.g., T0) on 52/55 popular datasets from a variety of NLP tasks (e.g., classification, information extraction, fact retrieval, text generation, etc.) without fine-tuning on downstream tasks, but also achieve superior performance in National College Entrance Examination - English (Gaokao-English), the most authoritative examination in China, which millions of students will attend every year. Specifically, the proposed system Qin () achieves 40 points higher than the average scores made by students and 15 points higher than GPT3 with 1/16 parameters. In particular, Qin gets a high score of 138.5 (the full mark is 150) in the 2018 English exam (national paper III). We have released the Gaokao Benchmark with an online submission platform that contains ten annotated English papers from 2018-2021 so far (and will be expanded annually), which allows more AI models to attend Gaokao, establishing a relatively fair test bed for human and AI competition and helping us better understand where we are. We test our model in the 2022 College Entrance Examination English that happened a few days ago (2022.06.08), and it gets a total score of 134 (v.s.

Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world."

The advancements in Artificial Intelligence in the healthcare industry are being used to diagnose, treat, and prevent illnesses. Years of technological development and innovation have prepared AI to remain a key player in the healthcare industry for years to come. Ahead of the RE•WORK - AI in Healthcare Summit Boston, we asked Kapila Monga, Head of Data Science at Bon Secours Mercy Health her thoughts on the topic. Here's what she had to say: What do you think is the most important advancement for AI in healthcare? What do you think will be AI's crowning achievement for healthcare and patient outcomes?

A few weeks ago, before the improvement on Stable Diffusion, I wrote an article on Storytelling with AI, where I pasted my Storyworth answers into Midjourney as prompts. I decided to update you with a before and after because, it has updated, and I'm significantly better at these! Where did you go on vacations as a child?

Artificial Intelligence is quickly becoming one of the key factors in advancements in the healthcare industry. Ahead of the RE•WORK – AI in Healthcare Summit Boston, we asked Nathan Wang – Deep Learning/Medical Imaging Researcher at Johns Hopkins University his thoughts on the topic. Here's what he had to say: What do you think is the most important advancement for AI in healthcare? In recent years, the field has made great strides in model interpretability. As a researcher, being able to intuitively grasp the "reasoning" behind our AI helps us build more robust and accurate models.