It was launched by the Global Observatory for Urban AI, which is an initiative of the Cities Coalition for Digital Rights and led by CIDOB – the Barcelona Centre for International Affairs, and the cities of Barcelona, London and Amsterdam. The programmes aim to help cities deploy AI effectively and ethically through frameworks and real-world examples of projects, policies and strategies. The Atlas of Urban AI so far includes 106 initiatives in 36 cities, with municipalities invited to submit their own. Cities using AI include Los Angeles to better understand air quality, London in Canada to predict the likelihood of individuals becoming chronically homeless, and a chatbot from Buenos Aires. As well as providing information on individual projects, the mapping aims to track how cities' use of AI evolves over time.

Seedtag, the leader in contextual advertising in EMEA and LATAM, has announced that it has raised over €250M in funding from private equity investor Advent International. The company intends to use the funds to further scale its Contextual AI technology, LIZ, as well as for innovation and worldwide operations, advancing its expansion into the US, the world's largest advertising market, and providing additional firepower for further M&A activity as Seedtag embarks on its next phase of international growth. Growth in the United States is a key strategic focus, with Albert Nieto, co-CEO and co-founder of Seedtag relocating, and offices in New York, Miami, Chicago and Los Angeles now established. Over the past eight years, Seedtag has built a privacy-first advertising solution, pioneering the use of AI and machine learning to create the best contextual product in the market. Seedtag's solution is currently the leading contextual solution in Europe and Latin America, with its AI and programs such as Seedtag LAB providing advertisers with a much deeper understanding of user interest without the use of personal data.

This week NVIDIA, LXAI, and Tecnológico de Monterrey announced the upcoming launch of the AI Supercomputer Network in collaboration with Hub de IA del Tec de Monterrey. Many countries in Latin America including Mexico, Brazil, Perú, Chile, and Colombia, are developing their national AI strategies but many LATAM universities and research centers struggle to access high-performance computing due to high prices, low government investment in research, and limited international collaborations. This initiative aims to address these challenges by providing an international network of state of the art GPUs for LATAM access. The official launch will be in Guadalajara on August 9th. The objective of this initiative is to strengthen the capacities of the AI ecosystem in Latin America including developing and attracting AI experts, building technological infrastructure, boosting international collaboration, and providing easy access to public data.

Visual understanding of geometric structures with complex spatial relationships is a fundamental component of human intelligence. As children, we learn how to reason about structure not only from observation, but also by interacting with the world around us -- by taking things apart and putting them back together again. The ability to reason about structure and compositionality allows us to not only build things, but also understand and reverse-engineer complex systems. In order to advance research in interactive reasoning for part-based geometric understanding, we propose a challenging new assembly problem using LEGO bricks that we call Break and Make. In this problem an agent is given a LEGO model and attempts to understand its structure by interactively inspecting and disassembling it. After this inspection period, the agent must then prove its understanding by rebuilding the model from scratch using low-level action primitives. In order to facilitate research on this problem we have built LTRON, a fully interactive 3D simulator that allows learning agents to assemble, disassemble and manipulate LEGO models. We pair this simulator with a new dataset of fan-made LEGO creations that have been uploaded to the internet in order to provide complex scenes containing over a thousand unique brick shapes. We take a first step towards solving this problem using sequence-to-sequence models that provide guidance for how to make progress on this challenging problem. Our simulator and data are available at github.com/aaronwalsman/ltron. Additional training code and PyTorch examples are available at github.com/aaronwalsman/ltron-torch-eccv22.

To reduce carbon emissions, electrical power systems are Outside of the power systems community, novel machine increasingly incorporating renewable generation resources into learning techniques for partial differential equations (PDEs) the energy mix. These resources are often dependent on have been used to efficiently learn evolution equations for weather inputs and, as a result, they behave stochastically PDFs of system states. We refer to such equations as PDF in the short and long terms, posing planning and operational equations, and unlike the FPE [9], many are unclosed.

A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate Dengue infections via COVID-19 data in countries that lack sufficient Dengue data.

We introduce RealTime QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). RealTime QA inquires about the current world, and QA systems need to answer questions about novel events or information. It therefore challenges static, conventional assumptions in open domain QA datasets and pursues, instantaneous applications. We build strong baseline models upon large pretrained language models, including GPT-3 and T5. Our benchmark is an ongoing effort, and this preliminary report presents real-time evaluation results over the past month. Our experimental results show that GPT-3 can often properly update its generation results, based on newly-retrieved documents, highlighting the importance of up-to-date information retrieval. Nonetheless, we find that GPT-3 tends to return outdated answers when retrieved documents do not provide sufficient information to find an answer. This suggests an important avenue for future research: can an open domain QA system identify such unanswerable cases and communicate with the user or even the retrieval module to modify the retrieval results? We hope that RealTime QA will spur progress in instantaneous applications of question answering and beyond.

Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SRl achieves new state-of-the-art performance when combined with NSM, a subgraph-oriented reasoner, for embedding-based KBQA methods.

A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items. The CR is formulated as a combinatorial optimization problem with the objective of maximizing the recommendation reward of the whole list. Despite its importance, it is still a challenge to build a practical CR system, due to the efficiency, dynamics, personalization requirement in online environment. In particular, we tear the problem into two sub-problems, list generation and list evaluation. Novel and practical model architectures are designed for these sub-problems aiming at jointly optimizing effectiveness and efficiency. In order to adapt to online case, a bootstrap algorithm forming an actor-critic reinforcement framework is given to explore better recommendation mode in long-term user interaction. Offline and online experiment results demonstrate the efficacy of proposed JDRec framework. JDRec has been applied in online JD recommendation, improving click through rate by 2.6% and synthetical value for the platform by 5.03%. We will publish the large-scale dataset used in this study to contribute to the research community.

Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports betting, or patient risk prediction, rely on a classifier's predicted probabilities rather than predicted labels. In these instances, practitioners are concerned with producing a calibrated model, that is, one which outputs probabilities that reflect those of the true distribution. Model calibration is often analyzed visually, through static reliability diagrams, however, the traditional calibration visualization may suffer from a variety of drawbacks due to the strong aggregations it necessitates. Furthermore, count-based approaches are unable to sufficiently analyze model calibration. We present Calibrate, an interactive reliability diagram that addresses the aforementioned issues. Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data. We further validate Calibrate by presenting the results of a think-aloud experiment with data scientists who routinely analyze model calibration.