In recent years, low-resource Machine Reading Comprehension (MRC) has made significant progress, with models getting remarkable performance on various language datasets. However, none of these models have been customized for the Urdu language. This work explores the semi-automated creation of the Urdu Question Answering Dataset (UQuAD1.0) by combining machine-translated SQuAD with human-generated samples derived from Wikipedia articles and Urdu RC worksheets from Cambridge O-level books. UQuAD1.0 is a large-scale Urdu dataset intended for extractive machine reading comprehension tasks consisting of 49k question Answers pairs in question, passage, and answer format. In UQuAD1.0, 45000 pairs of QA were generated by machine translation of the original SQuAD1.0 and approximately 4000 pairs via crowdsourcing. In this study, we used two types of MRC models: rule-based baseline and advanced Transformer-based models. However, we have discovered that the latter outperforms the others; thus, we have decided to concentrate solely on Transformer-based architectures. Using XLMRoBERTa and multi-lingual BERT, we acquire an F1 score of 0.66 and 0.63, respectively.

What do you think of artificial intelligence when it comes to your mind? All of us probably think of robots walking around us, resembling us and taking care of our desires. Are you feeling vaguely uneasy with this approach? It is estimated in a survey held in 2021 that 10.9 billion dollars will be spent on intelligent process automation (IPA). Doesn't it sound too interesting to you? Everything will be covered in this article.

As a collective and sometimes rather omniscient term, Artificial Intelligence (AI) includes the capabilities of learning systems that are perceived as …

An innocent viewer of the new Apple TV series "Foundation"--a lavish production complete with clone emperors, a haunted starship, and a killer android who tears off her own face--might be surprised to learn that the novels it's based on inspired Paul Krugman to become an economist. Isaac Asimov's classic saga revolves around the dismal science of "psychohistory," a hybrid of math and psychology that can predict the future. Its inventor, Hari Seldon, lives in a twelve-thousand-year-old galactic empire, which, his equations reveal, is about to collapse. "Interstellar wars will be endless," he warns. His followers establish a Foundation on the frontier world of Terminus--a colony tasked with conserving all human knowledge--where they spend the next millennium fulfilling "Seldon's plan" to reunite the galaxy.

October 27, 2021 — Carestream Health will highlight the imaging and workflow value of artificial intelligence (AI) in medical imaging at the …

… Drug Administration (“FDA” or the “Agency”) held a virtual workshop entitled, Transparency of Artificial Intelligence (“AI”)/Machine Learning.

In the sixty-five years since John McCarthy first coined the term "artificial intelligence," one of the most surprising discoveries in the field …

Behind every type of artificial intelligence is an algorithm, programmed code meant to simulate human intelligence by machines.

Machine Learning is the branch of science that studies how computers can learn without being explicitly programmed. As the name implies, it provides the computer with a feature that makes it more human-like: the ability to learn. Machine learning is being used actively today, possibly in many more places than one would expect. The same factors that have fueled the resurgence of interest in machine learning have also made data mining and Bayesian analysis more popular than ever before. All of this means that you can create models quickly and automatically that can analyze larger, more complex data and provide faster, more accurate results - even on a very large scale.

Recent years have witnessed a growing interest in research related to the detection of piano pedals from audio signals in the music information retrieval community. However, to our best knowledge, recent generative models for symbolic music have rarely taken piano pedals into account. In this work, we employ the transcription model proposed by Kong et al. to get pedal information from the audio recordings of piano performance in the AILabs1k7 dataset, and then modify the Compound Word Transformer proposed by Hsiao et al. to build a Transformer decoder that generates pedal-related tokens along with other musical tokens. While the work is done by using inferred sustain pedal information as training data, the result shows hope for further improvement and the importance of the involvement of sustain pedal in tasks of piano performance generations.