No canal da Inteligência Artificial – Nova temporada de desgrenhados e empertigados

The study of Artificial Intelligence (AI) has been pursued from the very beginning in two different styles, jokingly referred to as scruffy and neat. These styles actually reflect distinct viewpoints of the discipline and its objectives. In this paper, we review the tension between scruffies and neats over the history of AI. We analyze the impact of current deep learning methods in this debate, suggesting that the development of broad computational architectures is a particularly promising path for AI.O estudo de Inteligência Artificial (IA) tem sido perseguido, desde seu início, segundo dois estilos diferentes, jocosamente referidos como scruffy (desgrenhado) e neat (empertigado). Esses estilos na verdade refletem distintas visões sobre a disciplina e seus objetivos. Neste artigo revisamos a tensão entre desgrenhados e empertigados ao longo da história da IA. Analisamos o impacto do atual desempenho de métodos de aprendizado profundo nesse debate, sugerindo que o desenvolvimento de arquiteturas computacionais amplas é um caminho particularmente promissor para a IA

Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms

Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X)f(X) and g(Y)g(Y), E[f(X)g(Y)]=E[f(X)]⊠E[g(Y)]E[f(X)g(Y)]=E[f(X)]⊠E[g(Y)], where E[⋅]E[⋅] denotes interval-valued expectation and ⊠ denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties.ThefirstauthorhasbeenpartiallysupportedbyCNPq,andthisworkhasbeensupportedbyFAPESPthroughgrant04/09568-0.ThesecondauthorhasbeenpartiallysupportedbytheHaslerFoundationgrantno.10030

Comparing Computational Architectures for Automated Journalism

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.Comment: Accepted at the 19th National Meeting on Artificial and Computational Intelligence (ENIAC 2022

Benchmarks for Pir\'a 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change

Pir\'a is a reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change, built from a collection of scientific abstracts and reports on these topics. This dataset represents a versatile language resource, particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge. Despite its potential, a detailed set of baselines has not yet been developed for Pir\'a. By creating these baselines, researchers can more easily utilize Pir\'a as a resource for testing machine learning models across a wide range of question answering tasks. In this paper, we define six benchmarks over the Pir\'a dataset, covering closed generative question answering, machine reading comprehension, information retrieval, open question answering, answer triggering, and multiple choice question answering. As part of this effort, we have also produced a curated version of the original dataset, where we fixed a number of grammar issues, repetitions, and other shortcomings. Furthermore, the dataset has been extended in several new directions, so as to face the aforementioned benchmarks: translation of supporting texts from English into Portuguese, classification labels for answerability, automatic paraphrases of questions and answers, and multiple choice candidates. The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pir\'a dataset.Comment: Accepted at Data Intelligence. Online ISSN 2641-435