This demo paper introduces the BLAB Reporter, a robot-journalist covering the Brazilian Blue Amazon. The Reporter is based on a pipeline architecture for Natural Language Generation; it offers daily reports, news summaries and curious facts in Brazilian Portuguese. By collecting, storing and analysing structured data from publicly available sources, the robot-journalist uses domain knowledge to generate and publish texts in Twitter. Code and corpus are publicly available

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available.

Clickstream data, which come with a massive volume generated by human activities on websites, have become a prominent feature for identifying readers' characteristics by newsrooms after the digitization of news outlets. Although the nature of clickstream data has a similar logic within websites, it has inherent limitations in recognizing human behaviours when looking from a broad perspective, which brings the need to limit the problem in niche areas. This study investigates the anonymized readers' click activities on the organizations' websites to identify news consumption patterns following referrals from Twitter,who incidentally reach but propensity is mainly routed news content. Methodologies for ensemble cluster analysis with mixed-type embedding strategies are applied and compared to find similar reader groups and interests independent of time. Various internal validation perspectives are used to determine the optimality of the quality of clusters, where the Calinski Harabasz Index (CHI) is found to give a generalizable result. Our findings demonstrate that clustering a mixed-type dataset approaches the optimal internal validation scores, which we define to discriminate the clusters and algorithms considering applied strategies when embedded by Uniform Manifold Approximation and Projection (UMAP) and using a consensus function as a key to access the most applicable hyperparameter configurations in the given ensemble rather than using consensus function results directly. Evaluation of the resulting clusters highlights specific clusters repeatedly present in the separated monthly samples by Adjusted Mutual Information scores greater than 0.5, which provide insights to the news organizations and overcome the degradation of the modeling behaviours due to the change in the interest over time.

Google's DeepMind has beaten a 50-year-old record, contributing to a major development in the field of machine learning. Researchers at the lab trained a new version of its board game-playing AI, AlphaZero, to figure out a faster way to do matrix multiplication, a fundamental problem in computing that powers everything from displaying images on a screen to simulating complex physics. Speeding up the calculation could have "a big impact on thousands of everyday computer tasks," according to MIT Technology Review, and cut costs and save energy.

An artificial intelligence model developed by Facebook owner Meta can generate sounds from a text prompt. AudioGen, an AI worked on by Meta and the Hebrew University of Jerusalem, turns text prompts such as "whistling with wind blowing" into an audio file that sounds like the scenario described.

As companies struggle to find ways to take on the nationwide labor shortage, automation has become increasingly appealing. Initially seen as a job killer, automation may actually be the answer to ongoing worker shortages, particularly in the manufacturing sector, which is projected to have 2.1 million unfilled jobs by 2030, according to a May 2021 study by Deloitte and the National Association of Manufacturers (NAM). "You've got to start thinking that robots can do some of these jobs," Lauren Hein, head of advisor relations at ROBO Global, an investment firm specializing in automation investment, told Yahoo Finance Live (video above). In July 2022, there were nearly six million unemployed workers but 10 million vacant jobs, according to the U.S. Chamber of Commerce. Rather than waiting to find enough workers to fill that void, Hein suggested automation as a reasonable alternative.

This installation enables a live plant to control a machete. It has a control system that reads and utilizes the electrical noises found in a live philodendron. The system uses an open source micro-controller connected to the plant to read varying resistance signals across the plant's leaves. Using custom software, these signals are mapped in real-time to the movements of the joints of the industrial robot holding a machete. In this way, the movements of the machete are determined based on input from the plant. Essentially the plant is the brain of the robot controlling the machete determining how it swings, jabs, slices and interacts in space.

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This work describes the automatic registration of a large network (approximately 40) of fixed, ceiling-mounted environment cameras spread over a large area (approximately 800 squared meters) using a mobile calibration robot equipped with a single upward-facing fisheye camera and a backlit ArUco marker for easy detection. The fisheye camera is used to do visual odometry (VO), and the ArUco marker facilitates easy detection of the calibration robot in the environment cameras. In addition, the fisheye camera is also able to detect the environment cameras. This two-way, bidirectional detection constrains the pose of the environment cameras to solve an optimization problem. Such an approach can be used to automatically register a large-scale multi-camera system used for surveillance, automated parking, or robotic applications. This VO based multi-camera registration method has been extensively validated using real-world experiments, and also compared against a similar approach which uses a LiDAR - an expensive, heavier and power hungry sensor.

In this work, we provide a broad comparative analysis of strategies for pre-training audio understanding models for several tasks in the music domain, including labelling of genre, era, origin, mood, instrumentation, key, pitch, vocal characteristics, tempo and sonority. Specifically, we explore how the domain of pre-training datasets (music or generic audio) and the pre-training methodology (supervised or unsupervised) affects the adequacy of the resulting audio embeddings for downstream tasks. We show that models trained via supervised learning on large-scale expert-annotated music datasets achieve state-of-the-art performance in a wide range of music labelling tasks, each with novel content and vocabularies. This can be done in an efficient manner with models containing less than 100 million parameters that require no fine-tuning or reparameterization for downstream tasks, making this approach practical for industry-scale audio catalogs. Within the class of unsupervised learning strategies, we show that the domain of the training dataset can significantly impact the performance of representations learned by the model. We find that restricting the domain of the pre-training dataset to music allows for training with smaller batch sizes while achieving state-of-the-art in unsupervised learning -- and in some cases, supervised learning -- for music understanding. We also corroborate that, while achieving state-of-the-art performance on many tasks, supervised learning can cause models to specialize to the supervised information provided, somewhat compromising a model's generality.