Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to plan safe trajectories. Current methods rely heavily on historical time series to accurately predict the future. However, relying entirely on the observed history is problematic since it could be corrupted by noise, have outliers, or not completely represent all possible outcomes. To solve this problem, we propose a novel framework for generating robust forecasts for robotic control. In order to model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost. Specifically, we model this interaction as a zero-sum two-player game between a robot's forecaster and this hypothetical adversary. We show that our proposed game may be solved to a local Nash equilibrium using gradient-based optimization techniques. Furthermore, we show that a forecaster trained with our method performs 30.14% better on out-of-distribution real-world lane change data than baselines.

Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing public datasets are still insufficient to meet the need for these potential solutions due to their ignorance of complete exercising contexts, fine-grained concepts, and cognitive labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records. Specifically, we propose a framework to guarantee a high-quality and comprehensive annotation of fine-grained concepts and cognitive labels. The statistical and experimental results indicate that our dataset provides the basis for the future improvements of existing methods. Moreover, to support the convenient usage for researchers, we release a set of tools for data querying, model adaption, and even the extension of our repository, which are now available at https://github.com/THU-KEG/MOOC-Radar.

With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health.

Semantic Shift Detection (SSD) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, SSD has been addressed by linguists and social scientists through manual and time-consuming activities. In the recent years, computational approaches based on Natural Language Processing and word embeddings gained increasing attention to automate SSD as much as possible. In particular, over the past three years, significant advancements have been made almost exclusively based on word contextualised embedding models, which can handle the multiple usages/meanings of the words and better capture the related semantic shifts. In this paper, we survey the approaches based on contextualised embeddings for SSD (i.e., CSSDetection) and we propose a classification framework characterised by meaning representation, time-awareness, and learning modality dimensions. The framework is exploited i) to review the measures for shift assessment, ii) to compare the approaches on performance, and iii) to discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about CSSDetection are finally outlined.

Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the model generating answers only by associating with the questions while ignoring the visual content, resulting in biased results. We tackle the language bias problem by proposing a self-supervised counterfactual metric learning (SC-ML) method to focus the image features better. SC-ML can adaptively select the question-relevant visual features to answer the question, reducing the negative influence of question-irrelevant visual features on inferring answers. In addition, question-irrelevant visual features can be seamlessly incorporated into counterfactual training schemes to further boost robustness. Extensive experiments have proved the effectiveness of our method with improved results on the VQA-CP dataset. Our code will be made publicly available.

Brazil, India, China, and South Africa are the only countries where more than half of the population expressed strong trust and acceptance of artificial intelligence technologies, according to a study from global accounting firm KPMG. The country with the highest trust in A.I. is India, with a 75% overall acceptance rate. Moreover, the study revealed that emerging countries --specifically the BRICS bloc-- also have the highest engagement with A.I. China is the nation with the most people using A.I. in their workplace (75%), followed by India with 66% and Brazil with 50%. On the other hand, citizens of developed countries appeared to be more skeptical.

Wild elephants are one of the few known species to show signs of self-domestication. The phenomenon has only previously been documented in humans and bonobos, a closely related primate. Humans have bred animals to maximise traits such as friendliness, sociability and a docile temperament in a process called domestication. Some researchers believe humans and bonobos have gone through a similar process but that they have naturally done it to themselves. Limor Raviv at the Max Planck Institute for Psycholinguistics in the Netherlands wondered if other species have self-domesticated too.

LONDON, April 03, 2023--(BUSINESS WIRE)--C3 AI (NYSE: AI), the Enterprise AI application software company, has today announced the relocation of their European, Middle East and Africa (EMEA) headquarters from Paris, France to London, United Kingdom. "Our new EMEA headquarters represents an important next step for C3 AI," said C3 AI CEO Thomas M. Siebel. "As a global company, we are proud to call London our EMEA base and we look forward to the opportunity for even closer collaboration with the UK's world-class technology and artificial intelligence experts, businesses, and institutions." C3 AI has had a presence in Europe since the company's inception in 2009, with offices currently located in London, Paris, Munich, Rome, and Amsterdam. The decision to relocate the central EMEA operation to London ties in with the business's ambitious 2023 targets.

The star of Levi's new campaign looks like any other model . Her tousled hair hangs over her shouldersas she gazes into the camera with that far-off high-fashion stare. But look closer, and something starts to seem a little off. The shadow between her chin and neck looks muddled, like a bad attempt at using FaceTune's eraser effect to hide a double chin. Her French manicured fingernails appear scrubbed clean and uniform in a creepy real doll kind of way.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. Bridget is an expert in how AI is impacting Africa and the major challenges in implementing AI use across the continent. She tells us about what good technology means in the contexts in which she works and the benefits and drawbacks of Google and other Big Tech companies operating in Africa. Bridget Boakye is a Ghanaian entrepreneur, data scientist and writer. She is.the Artificial Intelligence Lead in the Internet Policy Unit of the Tony Blair Institute.