Two armed men have stolen eight engravings by French artist Matisse and at least another five by Brazilian painter Cândido Portinari from a library in São Paulo. Brazilian officials say the thieves held up a security guard and an elderly couple who were visiting the library before making off with the artworks on foot. They reportedly entered the library by the main entrance at 10:00 (13:00 GMT) on Sunday, and left by the same route, heading towards the nearest metro station. The heist comes less than two months after the art world was rocked by a brazen break-in at the Louvre museum in Paris, where thieves made off with priceless jewels. The engravings stolen from Biblioteca Mário de Andrade on Sunday formed part of a joint exhibition with the São Paulo Museum of Modern Art.

Although large language models (LLMs) demonstrate strong text generation capabilities, they struggle in scenarios requiring access to structured knowledge bases or specific documents, limiting their effectiveness in knowledge-intensive tasks. To address this limitation, retrieval-augmented generation (RAG) models have been developed, enabling generative models to incorporate relevant document fragments into their inputs. In this paper, we design and evaluate a RAG-based virtual assistant specifically tailored for the University of S\~ao Paulo. Our system architecture comprises two key modules: a retriever and a generative model. We experiment with different types of models for both components, adjusting hyperparameters such as chunk size and the number of retrieved documents. Our optimal retriever model achieves a Top-5 accuracy of 30%, while our most effective generative model scores 22.04\% against ground truth answers. Notably, when the correct document chunks are supplied to the LLMs, accuracy significantly improves to 54.02%, an increase of over 30 percentage points. Conversely, without contextual input, performance declines to 13.68%. These findings highlight the critical role of database access in enhancing LLM performance. They also reveal the limitations of current semantic search methods in accurately identifying relevant documents and underscore the ongoing challenges LLMs face in generating precise responses.

Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. To address this issue, this work presents an alternative and more efficient method for vehicle detection and counting. The proposed approach eliminates the need for a tracking step and focuses solely on detecting vehicles in key video frames, thereby increasing its efficiency. To achieve this, we developed a system that combines YOLO, for vehicle detection, with Visual Rhythm, a way to create time-spatial images that allows us to focus on frames that contain useful information. Additionally, this method can be used for counting in any application involving unidirectional moving targets to be detected and identified. Experimental analysis using real videos shows that the proposed method achieves mean counting accuracy around 99.15% over a set of videos, with a processing speed three times faster than tracking based approaches.

São Paulo, Brazil – As the city of São Paulo prepares to roll out thousands of surveillance cameras with facial recognition, experts are raising concerns on the indiscriminate use of this technology in the Brazilian megalopolis could exacerbate problems such as structural racism and inequality, while also posing risks to data privacy and cybersecurity. The Smart Sampa project is the latest among a series of initiatives involving modern surveillance techniques in various Brazilian states. It is significant due to the sheer size of the population it will impact: São Paulo, the most populous city in the Southern Hemisphere, is home to 12 million people. The project aims to roll out a single video surveillance platform that integrates and supports the operations of emergency and traffic services, the city's public transport network, and police forces. By 2024, up to 20,000 cameras will be installed, and an equal number of third-party and private cameras will be integrated into the network.

Nubank was founded in 2013 to free people from a bureaucratic, slow, and inefficient financial system. Since then, through innovative technology and outstanding customer service, the company has been redefining people's relationships with money across Latin America. With operations in Brazil, Mexico, and Colombia, Nubank is today one of the largest digital banking platforms and technology-leading companies in the world. Today, Nubank is a global company, with offices in São Paulo (Brazil), Mexico City (Mexico), Buenos Aires (Argentina), Bogotá (Colombia), Durham (United States), and Berlin (Germany). It was founded in 2013 in Sao Paulo, by Colombian David Vélez, and cofounded by Brazilian Cristina Junqueira and American Edward Wible.

A set of steps for implementing a chatbot, to support decision-making activities in the software incident management process is proposed and discussed in this article. Each step is presented independently of the platform used for the construction of chatbots and are detailed with their respective activities. The proposed steps can be carried out in a continuous and adaptable way, favoring the constant training of a chatbot and allowing the increasingly cohesive interpretatin of the intentions of the specialists who work in the Software Incident Management Process. The software incident resolution process accordingly to the ITIL framework, is considered for the experiment. The results of the work present the steps for the chatbot construction, the solution based on DialogFlow platform and some conclusions based on the experiment.

The impact of climate change on Brazil's Atlantic coastline is a research focus at the University of São Paulo's machine-intelligence centre.Credit: Antonello Veneri/AFP via Getty Artificial intelligence (AI) is increasingly becoming a tool for researchers in other science and technology fields, forging collaborations across disciplines. Stanford University in California, which produces an index that tracks AI-related data, finds in its 2021 report that the number of AI journal publications grew by 34.5% from 2019 to 2020; up from 19.6% between 2018 and 2019 (see go.nature.com/3mdt2yq). AI publications represented 3.8% of all peer-reviewed scientific publications worldwide in 2019, up from 1.3% in 2011. Five AI researchers describe the fruits of these collaborations, beyond journal publications, and talk about how they are helping to break down barriers between disciplines. At the University of São Paulo in Brazil, where I lead the Center for Artificial Intelligence (C4AI), our main goal is to produce machine-intelligence research that has a direct impact on society and industry.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence peaked in Manaus, Brazil, in May 2020 with a devastating toll on the city's inhabitants, leaving its health services shattered and cemeteries overwhelmed. Buss et al. collected data from blood donors from Manaus and São Paulo, noted when transmission began to fall, and estimated the final attack rates in October 2020 (see the Perspective by Sridhar and Gurdasani). Heterogeneities in immune protection, population structure, poverty, modes of public transport, and uneven adoption of nonpharmaceutical interventions mean that despite a high attack rate, herd immunity may not have been achieved. This unfortunate city has become a sentinel for how natural population immunity could influence future transmission. Events in Manaus reveal what tragedy and harm to society can unfold if this virus is left to run its course. Science , this issue p. [288][1]; see also p. [230][2] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly in Manaus, the capital of Amazonas state in northern Brazil. The attack rate there is an estimate of the final size of the largely unmitigated epidemic that occurred in Manaus. We use a convenience sample of blood donors to show that by June 2020, 1 month after the epidemic peak in Manaus, 44% of the population had detectable immunoglobulin G (IgG) antibodies. Correcting for cases without a detectable antibody response and for antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in São Paulo, in southeastern Brazil, where the estimated attack rate in October was 29%. These results confirm that when poorly controlled, COVID-19 can infect a large proportion of the population, causing high mortality. [1]: /lookup/doi/10.1126/science.abe9728 [2]: /lookup/doi/10.1126/science.abf7921

Fabrizio Poltronieri is an artist who explores the relationship technology and deep-rooted philosophical concepts, such as chance. His current artwork involves Artificial Intelligence, applying machine and deep learning techniques to create and design narratives, moving images and objects. He is a self-taught programmer who started to code during his childhood. His first degree was in Maths, he has a Master Degree in Education and Culture and holds a PhD in Semiotics from the Pontifical Catholic University of São Paulo (PUC/SP). Poltronieri is an Associate Professor and permanent member of the IOCT (Institute of Creative Technologies) at De Montfort University, Leicester, UK, supervising PhD students and teaching creative code in the Digital Arts MA.

Kaggle is a subsidiary of Google that functions as a community for data scientists and developers. Those interested in machine learning or other kinds of modern development can join the community. There you can participate in Machine learning competitions, you can wright notebooks with your codes in summary you can create an online portfolio because many companies want to see your profile on Kaggle. For this analysis, I am going to use Google collaboratory also called Colab, which is a Google cloud-based service that replicates Jupyter Notebook in the cloud. You don't need to install anything on your system to use it.