One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as loneliness questionnaires completed by the participants themselves, which can be biased. It revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.
Oracle open-sources Tribuo to fill the gap for enterprise applications focused on machine learning in Java. Committed to deploying machine learning models to large-scale production systems, Oracle has released Tribuo under an Apache 2.0 license. What does Tribuo provide under machine learning? Tools for building and deploying classificationTools for clustering and regression models Unified interface for many popular third-party machine learning librariesA full suite of evaluations for each of the supported prediction tasksData loading pipelines, text processing pipelines, and feature level transformations for operating on dataIn addition to its implementations of Machine Learning algorithms, Tribuo also provides a common interface to popular ML tools on the JVM. Apart from the features mentioned above, Tribuo Model knows when you've given it features it has never seen before, which is particularly useful when working with natural language processing.
Finally, AI is ready for the mainstream. When your enterprise is handling transactions between 25 million sellers and 182 million buyers, supporting 1.5 billion listings, manual decision-making processes just won't cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, "in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more." As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI.
How AI can support small businesses and self-employed individuals during the pandemic? As the COVID-19 pandemic plays out around the world, consumers, small businesses, self-employed workers and accountants face unprecedented challenges, and these challenges only continue to grow. Many people are struggling to make ends meet and provide for their families. They might be facing a loss of income, a lack of adequate savings to weather the storm or poor access to health care. With shelter-in-place mandates proliferating around the world, small businesses have had to close their doors and are running out of cash to pay their employees and their bills.
Easy access to smartphones and affordable data plans has led to increased access to the internet worldwide. Therefore if companies want to find and connect with possible prospects, create their brand awareness, sell them products and services, and engage them in future marketing through online platforms is a necessity. Hence the scope for digital marketing in various companies and firms has increased exponentially. Studies by Global data show that the growth of Indian e-commerce markets is predicted to push to 7 trillion rupees by 2023 majorly due to lockdowns. Since the last couple of years, the face of the digital marketing industry has been changing and has shown considerable development.
Artificial intelligence (AI) is surpassing human performance in a growing number of domains. However, there is limited evidence of its economic effects. Using data from a digital platform, we study a key application of AI: machine translation. We find that the introduction of a new machine translation system has significantly increased international trade on this platform, increasing exports by 10.9%. Furthermore, heterogeneous treatment effects are consistent with a substantial reduction in translation costs.
Indic bert is a multilingual ALBERT model that exclusively covers 12 major Indian languages. It is pre-trained on our novel corpus of around 9 billion tokens and evaluated on a set of diverse tasks. Indic-bert has around 10x fewer parameters than other popular publicly available multilingual models while it also achieves a performance on-par or better than these models. We also introduce IGLUE - a set of standard evaluation tasks that can be used to measure the NLU performance of monolingual and multilingual models on Indian languages. Along with IGLUE, we also compile a list of additional evaluation tasks.
I am Imtiaz Adam, and this article is an introduction to AI key terminologies and methodologies on behalf of myself and DLS (www.dls.ltd). This article has been updated in September 2020 to take into account advances in the field of AI with techniques such as NeuroSymbolic AI, Neuroevolution and Federated Learning. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalise to unseen domains. This is the form of AI that we have today, for example Google Translate.
Medical billing and coding have been undergoing many changes in recent years as the healthcare industry increases in complexity while the variety of treatments and procedures grow by the minute. The healthcare industry is in urgent need of a scalable solution that can process the vast amount of patient data without compromising speed and accuracy of the billing procedure. The use of artificial intelligence in the medical billing and coding industry can help healthcare organizations facilitate their billing procedures while minimizing costly errors. AI-driven technologies, such as machine learning and natural language processing (NLP), have the ability to interpret and organize a large amount of data quickly and accurately. For instance, an AI program can arrange data from different records into a logical timeline to make sense of disparate events, diagnoses, and procedures, minimizing coding and reporting errors.
However, one of the ways professionals are keeping up their relevance in their organisations as well as in the industry is by upskilling and learning the latest tools and technologies of this evolving field. Webinars and workshops have always been an excellent way for professionals and enthusiasts to keep themselves updated with the latest trends and technologies. For attendees, these webinars and workshops are not only an easy way to know and train themselves on the latest tools and technologies but also allows them to hear from the best minds of the industry on relevant topics. In fact, for a few years now, large tech companies have been conducting free webinars and workshops, which will not only boosts the community and users at large but also acts as a great marketing tool for advertising their solutions and services. With machine learning being explored in various industries, including healthcare, eCommerce, finance and retail, the possibilities are endless.