Transformers are among the state of the art for many tasks in speech, vision, and natural language processing, among others. Self-attentions, which are crucial contributors to this performance have quadratic computational complexity, which makes training on longer input sequences challenging. Prior work has produced state-of-the-art transformer variants with linear attention, however, current models sacrifice performance to achieve efficient implementations. In this work, we develop a novel linear transformer by examining the properties of the key-query product within self-attentions. Our model outperforms state of the art approaches on speech recognition and speech summarization, resulting in 1 % absolute WER improvement on the Librispeech-100 speech recognition benchmark and a new INTERVIEW speech recognition benchmark, and 5 points on ROUGE for summarization with How2.

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.

Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA), that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems.

It is well-known that typical word embedding methods such as Word2Vec and GloVe have the property that the meaning can be composed by adding up the embeddings (additive compositionality). Several theories have been proposed to explain additive compositionality, but the following questions remain unanswered: (Q1) The assumptions of those theories do not hold for the practical word embedding. (Q2) Ordinary additive compositionality can be seen as an AND operation of word meanings, but it is not well understood how other operations, such as OR and NOT, can be computed by the embeddings. We address these issues by the idea of frequency-weighted centering at its core. This paper proposes a post-processing method for bridging the gap between practical word embedding and the assumption of theory about additive compositionality as an answer to (Q1). It also gives a method for taking OR or NOT of the meaning by linear operation of word embedding as an answer to (Q2). Moreover, we confirm experimentally that the accuracy of AND operation, i.e., the ordinary additive compositionality, can be improved by our post-processing method (3.5x improvement in top-100 accuracy) and that OR and NOT operations can be performed correctly.

The world is in a climate crisis. With average global temperatures increasing every year, the threat of seasonal forest fires is becoming increasingly worse. In places like the Pacific Northwest, wildfire season causes extensive damage to woodlands, rural communities, and townships, destroying farmlands and infrastructure and forcing hundreds of thousands of residents to flee their homes. These fires also lead to terrible air quality in cities located hundreds (or even thousands) of miles away. For instance, in September of 2022, the city of Vancouver (British Columbia) was ranked as having the worst air quality in the world - per the Air Quality Index (AQI).

Artificial intelligence (AI), often recognized as machine intelligence, is an area of computer science that emphasizes developing and managing technology that can learn to make choices and can separately carry out transactions on behalf of humans. The banking, financial services, and insurance segments experience substantial expansion during the estimated period. A substantial amount of client data or transaction records are produced owing to the rising digital revolution in banking and the augmented use of mobile payment, real-time money transfers, e-banking, and mobile banking applications. The global Artificial Intelligence Service Market size was valued at USD 93.5 billion in 2021 growing at the CAGR of 38.1% from 2022 to 2032. Evolve Business Intelligence provides an in-dept research study that contains the ability to focus on the major market dynamics in several region across the globe.

The year opened with a bang. The successful film Don't Look Up, in which a comet is found to be on a collision course with Earth, had been released just before Christmas 2021. In the bleak days of post-festive gloom, the news media were on an adrenaline high, chasing any and every story about potential asteroid collisions to cheer us all up. Five asteroids were to pass close to the Earth in January alone! Happily for the health and wellbeing of humanity, none was predicted to come within a whisker of hitting the planet.

Argentina take on France in the final of World Cup 2022. Kashef, our artificial intelligence (AI) robot, has analysed more than 200 metrics, including the number of wins, goals scored and FIFA rankings, from matches played over the past century to see who is most likely to win on Sunday. Prediction: With 63 matches completed, Kashef has a 68 percent accuracy level. Sizing up all the odds, it could not be any closer. Kashef predicts that France, captained by Hugo Lloris, will edge out Argentina on Sunday to win the 2022 World Cup.

K Raveendran A global survey of companies has revealed a serious shortage of tech talent when it comes to artificial intelligence, which is threatening to slow down the shift towards the new productivity tool. A majority of respondents in the survey, carried out by management consultancy McKinsey, have reported difficulty in hiring for each AI-related role in the past year, and most say it either wasn't any easier or was more difficult to acquire this talent than in years past. AI data scientists remain particularly scarce, with the largest share of respondents rating data scientist as a role that has been difficult to fill, out of the roles we asked about. The findings are particularly relevant to India, which boasts the world's biggest talent pool, and have lessons for the country's education system. India has been one of the biggest beneficiaries of the IT boom, triggered by the highly feared Y2K problem at the turn of the new millennium, which ultimately turned out to be a non-issue.

Croatia take on Morocco for the third-place playoff at World Cup 2022. Saturday's game will be the second encounter between the Atlas Lions and 2018 runners-up at this year's World Cup in Qatar. Their opening group match ended in a goalless draw. Kashef, our artificial intelligence (AI) robot, has analysed more than 200 metrics, including the number of wins, goals scored and FIFA rankings, from matches played over the past century to see who is most likely to win on Saturday. Prediction: Morocco's dreams of reaching the World Cup final were dashed after a 2-0 loss to France in the semifinal.