TELUS International, a digital customer service pioneer that develops, creates, and produces next-generation technologies for multinational and innovative brands, has completed the previously discussed acquisition of Lionbridge AI, the company's artificial intelligence (AI) subsidiary. On December 31, 2020, the contract was concluded. TELUS International is a branch of TELUS Group, a major telecom and information technology organization headquartered in Canada. As the organizations collaborate to promote the increasingly evolving domain of artificial intelligence, Lionbridge AI will supplement TELUS International's evolving range of next-generation digital solutions. The sale of Lionbridge AI will bolster Lionbridge's determination to providing creativity for its core language customers throughout technology, life sciences, gaming, digital commerce, and more.

TELUS International announced that it would buy Lionbridge's artificial intelligence business unit in a deal worth US$935 million that is expected to close by December 31st. Let's first explain what Lionbridge AI does, review the deal, analyze what it means to the language services and technology sector, and show what it represents to investors. Here's a quick synopsis: Lionbridge's private equity investor will sell a non-core business unit for a sizeable return on its 2017 purchase price, thus allowing it to position the core language service business as a pureplay LSP - and maximize the value of its remaining asset in one of several ways. Lionbridge AI is a business unit of Lionbridge Technologies (#2 in CSA Research's 2020 global list of leading LSPs) that processes multilingual data used in training artificial intelligence algorithms – that is, human-annotated data for text (for example, semantic, sentiment, intent, named entities like places, company names, and phone numbers), images (captions, keywords), audio (transcription and time stamps), videos (outlining objects – think of the incessant reCAPTCHA challenges to prove you're not a robot). "Processing" takes a variety of forms.

With over 20 years of experience as a trusted training data source, Lionbridge AI helps businesses large and small build, test and improve machine learning models. Our community of qualified contributors is located across the globe and available 24/7, providing access to a huge volume of data across all languages and file types.

Training data is absolutely essential to the development of any machine learning model. The data you use will define your project, so a clear understanding of how it works will drastically improve your chances of success. We've briefly discussed AI training data on our website, but it's worth explaining in a little more detail. Let's dive into the world of training data and figure out why it's so important. Essentially, training data is the textbook that will teach your AI to do its assigned task, and will be used over and over again to fine-tune its predictions and improve its success rate.

AI conferences are a great place for knowledge sharing about machine learning, big data, natural language processing, chatbot development, and more. In this article, we'll share our strategies for choosing the right AI conference to attend, and then making the most out of your experience at that conference. When deciding which AI conference to attend, you should think about your main goal for attending a conference. If you think about what you want to get out of attending an AI conference, that should help to narrow down your choices. If you're interested in debating and discussing machine learning trends with other data scientists, you might consider attending an intimate conference with round-table discussions.

Data annotation is the task of labeling data, which could be in any form such as text, audio, images, or video. In this article, we'll explore the different types and uses for data annotation in machine learning. Data scientists need to use clean, annotated data to train machine learning models. Data annotation is an indispensable stage of data preprocessing in supervised learning. Machine learning models learn to recognize recurring patterns in the annotated data.

Natural language is the conversational language that we use in our daily lives. If machines can understand natural language, then the potential use for technology like chatbots would increase dramatically. Since 2016, chatbot innovation has received a lot of media attention as a new interface that was expected to surpass smartphones. Now, chatbot services are becoming popular at AI conferences, but at a global scale, chatbot technology is still in the beginning stages. That is because natural language processing technology has not advanced far enough to support chatbots.

It's logical to want to know how much training data you will need to ensure top-of-the-range performance from your algorithm. After all, no-one has ever built a functioning model without a strong foundation in richly-detailed data. However, it's not as easy as you might think to come up with the magic number of data points that will turn your model from good into great. In fact, trying to figure it out can seriously complicate things for your data scientists. The quantity of data points you need is affected by a huge range of factors, all of which have a varying degree of influence on the eventual size of your dataset.

With deepfakes receiving a lot of media coverage recently, synthetic media is a trending topic among AI forums and a growing area of machine learning. The possible threats posed by manipulated or synthetic media has caught the attention of government officials and even led to a House of Representatives hearing in June of 2019. Like every new and emerging technology, synthetic media comes with risks. However, companies like Lyrebird are proof that the positive applications of synthetic media outweigh the negative. From chatbots to virtual assistants, research in ASR and higher-quality audio training data have led to some of the most useful tech of the current generation.

Chatbots are one of the most common applications of natural language processing and machine learning. Replika AI has created a platform where anyone, including people with zero knowledge of machine learning, can create and train a chatbot of their own. After the tragic death of her best friend, Eugenia Kuyda (Founder of Luka inc.) used the text message and email history of her friend to recreate him as a chatbot. The feedback from other friends and family inspired her to expand the project and create Replika AI, a chatbot users train themselves. Numerous companies utilize chatbots for customer interactions, and thus chatbot training data is one of the most in-demand services in the AI industry today.