Simultaneous speech translation (SimulST) systems aim at generating their output with the lowest possible latency, which is normally computed in terms of Average Lagging (AL). In this paper we highlight that, despite its widespread adoption, AL provides underestimated scores for systems that generate longer predictions compared to the corresponding references. We also show that this problem has practical relevance, as recent SimulST systems have indeed a tendency to over-generate. As a solution, we propose LAAL (Length-Adaptive Average Lagging), a modified version of the metric that takes into account the over-generation phenomenon and allows for unbiased evaluation of both under-/over-generating systems.

So instead, AV companies developed sophisticated simulation engines to synthetically generate the requisite volume of data and efficiently expose their AI systems to the "long tail" of driving scenarios. These simulated worlds make it possible to automatically produce thousands or millions of permutations of any imaginable driving scenario--e.g., changing the locations of other cars, adding or removing pedestrians, increasing or decreasing vehicle speeds, adjusting the weather, and so on. But it didn't take long for AI entrepreneurs to recognize that the synthetic data capabilities that had been developed for the autonomous vehicle industry could be generalized and applied to a host of other computer vision applications. Founded by AI luminary Raquel Urtasun, who previously ran Uber's AV research efforts, Waabi came out of stealth last year with a star-studded team and over $80 million in funding. Dramatic recent advances in natural language processing (NLP) are opening up virtually unbounded opportunities for value creation across the economy, as previously explored in this column.

As babies, we babble and imitate our way to learning languages. We don't start off reading raw text, which requires fundamental knowledge and understanding about the world, as well as the advanced ability to interpret and infer descriptions and relationships. Rather, humans begin our language journey slowly, by pointing and interacting with our environment, basing our words and perceiving their meaning through the context of the physical and social world. Eventually, we can craft full sentences to communicate complex ideas. Similarly, when humans begin learning and translating into another language, the incorporation of other sensory information, like multimedia, paired with the new and unfamiliar words, like flashcards with images, improves language acquisition and retention. Then, with enough practice, humans can accurately translate new, unseen sentences in context without the accompanying media; however, imagining a picture based on the original text helps.

Petuum, the creator of the world's first composable platform for MLOps, and the Inception Institute for Artificial Intelligence (IIAI), have agreed to partner on the development of revolutionary AI applications. Petuum has recently announced a limited release of the composable platform, which includes the AI OS, Universal Pipelines, Deployment Manager, and Experiment Manager, for select private beta partners. Through the partnership with Petuum, IIAI's enterprise AI/ML teams will operationalize and scale their applications into production. Founded in 2018, IIAI's mission is to build full-stack AI solutions and operating systems for enterprise businesses and developers. Besides being the research arm for G42, IIAI is also empowering stakeholders with AI applications and incubating new technology at the cutting edge of ML innovation.

Abstract-Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMSs are causing major resource wastage by unnecessarily flooding the network links. Although most spam mail originate with advertisers looking to push their products, some are much more malicious in their intent like phishing emails that aims to trick victims into giving up sensitive information like website logins or credit card information this type of cybercrime is known as phishing. To countermeasure spams, many researches and efforts are done to build spam detectors that are able to filter out messages and emails as spam or ham. In this research we build a spam detector using BERT pre-trained model that classifies emails and messages by understanding to their context, and we trained our spam detector model using multiple corpuses like SMS collection corpus, Enron corpus, SpamAssassin corpus, Ling-Spam corpus and SMS spam collection corpus, our spam detector performance was 98.62%, 97.83%, 99.13% and 99.28% respectively.

As babies, we babble and imitate our way to learning languages. We don't start off reading raw text, which requires fundamental knowledge and understanding about the world, as well as the advanced ability to interpret and infer descriptions and relationships. Rather, humans begin our language journey slowly, by pointing and interacting with our environment, basing our words and perceiving their meaning through the context of the physical and social world. Eventually, we can craft full sentences to communicate complex ideas. Similarly, when humans begin learning and translating into another language, the incorporation of other sensory information, like multimedia, paired with the new and unfamiliar words, like flashcards with images, improves language acquisition and retention. Then, with enough practice, humans can accurately translate new, unseen sentences in context without the accompanying media; however, imagining a picture based on the original text helps.

Mozilla has added an official translation tool to Firefox that doesn't rely on cloud processing to do its work, instead performing the machine learning-based process right on your own computer. It's a huge step forward for a popular service tied strongly to giants like Google and Microsoft. The translation tool, called Firefox Translations, can be added to your browser here. It will need to download some resources the first time it translates a language, and presumably it may download improved models if needed, but the actual translation work is done by your computer, not in a datacenter a couple hundred miles away. This is important not because a lot of people need to translate in their browsers while offline -- like screen door for a submarine, it's not really a use case that makes sense.

Back translation is one of the most widely used methods for improving the performance of neural machine translation systems. Recent research has sought to enhance the effectiveness of this method by increasing the 'diversity' of the generated translations. We argue that the definitions and metrics used to quantify 'diversity' in previous work have been insufficient. This work puts forward a more nuanced framework for understanding diversity in training data, splitting it into lexical diversity and syntactic diversity. We present novel metrics for measuring these different aspects of diversity and carry out empirical analysis into the effect of these types of diversity on final neural machine translation model performance for low-resource English$\leftrightarrow$Turkish and mid-resource English$\leftrightarrow$Icelandic. Our findings show that generating back translation using nucleus sampling results in higher final model performance, and that this method of generation has high levels of both lexical and syntactic diversity. We also find evidence that lexical diversity is more important than syntactic for back translation performance.

Its blockchain-based AI network combines the efficiency of artificial intelligence with the resolution of human experts, to create data sets that …

Finding similarities between two inter-language news articles is a challenging problem of Natural Language Processing (NLP). It is difficult to find similar news articles in a different language other than the native language of user, there is a need for a Machine Learning based automatic system to find the similarity between two inter-language news articles. In this article, we propose a Machine Learning model with the combination of English Urdu word transliteration which will show whether the English news article is similar to the Urdu news article or not. The existing approaches to find similarities has a major drawback when the archives contain articles of low-resourced languages like Urdu along with English news article. The existing approaches to find similarities has drawback when the archives contain low-resourced languages like Urdu along with English news articles. We used lexicon to link Urdu and English news articles. As Urdu language processing applications like machine translation, text to speech, etc are unable to handle English text at the same time so this research proposed technique to find similarities in English and Urdu news articles based on transliteration.