Nowadays, technological advances have influenced all human activities, creating new dynamics and ways of communication. In this context, some artists have incorporated these advances in their creative process, giving rise to unique aesthetic expressions referred to in the literature as Generative Art, which is characterized by assigning part of the creative process to a system that acts with certain autonomy (Galanter, 2003). This research work introduces a computational system for creating generative art using a Brain-Computer Interface (BCI) which portrays the user's brain activity in a digital artwork. In this way, the user takes an active role in the creative process. In aims of showing that the proposed system materializes in an artistic piece the user's mental states by means of a visual and sound representation, several tests are carried out to ensure the reliability of the BCI device sent data. The generated artwork uses brain signals and concepts of geometry, color and spatial location to give complexity to the autonomous construction. As an added value, the visual and auditory production is accompanied by an olfactory and kinesthetic component which complements the art pieces providing a multimodal communication character.

Nearly 10,000 square kilometers of the Brazilian Amazon, an area the size of Lebanon, is at high risk of being cleared, according to a new tool using artificial intelligence technology to help forecast deforestation before it actually happens. Named PrevisIA (from the Portuguese previsão for "forecast" and IA for "artificial intelligence"), the tool analyzes images provided by European Space Agency satellites, and through an algorithm created by the Brazilian conservation nonprofit Imazon, finds areas prone to deforestation. Imazon studies published in scientific journals show that 95% of accumulated deforestation in the Amazon is located within a 5.5-kilometer (3.4-mile) radius of roads; 90% of annual fires occur 4 km (2.5 mi) from illegal roads built in the middle of the forest for logging, mining and land grabbing. In 2006, the non-profit started to monitor satellite images manually to find these roads before the area around them was cleared of trees, but the laborious and time-consuming work prevented it from scaling up -- a problem that the new technology aims to solve. The tool mapped so far the Brazilian Amazon, but could potentially be expanded to any forested area on Earth, the developers say.

Masayoshi Son said he would begin to make personal investments alongside SoftBank Group Corp.'s Vision Fund, a controversial step that could lead to conflicts of interest as his company backs technology startups. The Japanese billionaire made the disclosure as his company reported earnings, explaining he will begin to co-invest in Vision Fund 2, an investment vehicle where SoftBank has been the sole source of capital. Son can invest up to $2.6 billion and will own 17.25% of the equity. He will have a similar arrangement with SoftBank's Latin America fund. Business leaders tend to avoid mixing their personal financial interests with corporate responsibilities.

Modeling tap or click sequences of users on a mobile device can improve our understandings of interaction behavior and offers opportunities for UI optimization by recommending next element the user might want to click on. We analyzed a large-scale dataset of over 20 million clicks from more than 4,000 mobile users who opted in. We then designed a deep learning model that predicts the next element that the user clicks given the user's click history, the structural information of the UI screen, and the current context such as the time of the day. We thoroughly investigated the deep model by comparing it with a set of baseline methods based on the dataset. The experiments show that our model achieves 48% and 71% accuracy (top-1 and top-3) for predicting next clicks based on a held-out dataset of test users, which significantly outperformed all the baseline methods with a large margin. We discussed a few scenarios for integrating the model in mobile interaction and how users can potentially benefit from the model.

As an open and collaborative knowledge graph created by users and bots, it is possible that the knowledge in Wikidata is biased in regards to multiple factors such as gender, race, and country of citizenship. Previous work has mostly studied the representativeness of Wikidata knowledge in terms of genders of people. In this paper, we examine the race and citizenship bias in general and in regards to STEM representation for scientists, software developers, and engineers. By comparing Wikidata queries to real-world datasets, we identify the differences in representation to characterize the biases present in Wikidata. Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented. Based on these findings, we have found and linked to Wikidata additional data about STEM scientists from the minorities. This data is ready to be inserted into Wikidata with a bot. Increasing representation of minority race and country of citizenship groups can create a more accurate portrayal of individuals in STEM.

Maybe it's a cliche--I think I've used it myself--to say that scientists' and philosophers' explanations for how the brain works tend to metaphorically track the most advanced technology of their time. Greek writers thought brains worked like hydraulic water clocks. European writers in the Middle Ages suggested that thoughts operated through gear-like mechanisms. In the 19th century the brain was like a telegraph; a few decades later, it was more like a telephone network. Shortly after that, no surprise, people thought the brain worked like a digital computer, and that maybe they could build computers that work like the brain, or talk to it.

When working with their clients Accenture under Tricarico's guidance focuses on "on guiding (their) clients to more safely scale their use of AI, and build a culture of confidence within their organizations." Not all companies have an established north star of AI use. Companies and partners like Accenture are vital to these companies and their proper and ethical use of the technology.

We're looking for a talented and passionate Senior Data Architect, to join Wildlife's Data Governance team in São Paulo, Brazil. You will be part of the Tech Management structure, being responsible for processes related to Data Architecture, contributing to the fulfillment of general data protection standards, helping the company to deliver reliable data, and being responsible by aligning the Data Architecture to the corporate strategy. We know that the work we do has a high impact on our company's success and culture. The right person for this position is curious by nature, and comfortable in a "take the initiative" environment, loves solving problems, and can thrive in a fast and growing business. Wildlife is one of the leading mobile game developers and publishers in the world.

Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.

Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise. This model mimics the nature of scientific data sets, where the noises arise as realizations of some random processes whose underlying distributions are known. The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -- and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.