Omniscien Blog
Hype Cycle for AI Technologies in Business
Advances in Artificial Intelligence (AI) have become more visible than ever before in the last few years as AI has achieved new heights. Systems such as ChatGPT are showcasing what is possible with state-of-the-art AI. While these advances are truly great steps forward, many organizations are left wondering what it means for them and how they can best leverage such technologies to reduce costs, automate workflows/processes, and enhance functionality.
The Omniscien team have prepared a Hype Cycle based on what we see in the industry and where we see many of the language related AI technologies going in the coming years. We will explore many of these technologies and how they can be used in business now in the webinar.
Localization Glossary: Terminology that you should know
As the localization industry has grown, so too has the number of terms and industry jargon used by professionals. We’re so enthusiastic about the localization industry that we often get carried away and forget that words can be intimidating to beginners.
We’ve created a brief glossary to help you get up to speed with some of the more common localization terminology – and avoid looking like a novice at meetings.
The Omniscien Advantage – We wrote the leading academic machine translation textbooks!!
Translation and language processing technologies have evolved substantially over the last decade. The Omniscien team has been at the forefront of research and development, leading the way with a comprehensive set of integrated tools, features, and technologies that are powered by and drive artificial intelligence and machine learning.
Behind many of the tools design is Omniscien’s Chief Scientist, Professor Philipp Koehn who leads our team of researchers and developers. Philipp is a pioneer in the machine translation space, his books on Statistical Machine Translation and Neural Machine Translation are the leading academic textbooks globally on machine translation. Both books are available now from Amazon.com or leading book stores.
How many languages are there in the world?
How many languages are there in the world? We have collected a list of languages with nearly 9,000 entries.
The State of Neural Machine Translation (NMT) by Philipp Koehn
Neural Machine Translation (NMT) is an exciting and promising new approach to Machine Translation. However, while the technology is promising we still…
When, Why and How to Migrate from Statistical to Neural Machine
There is a running joke in the translation industry that machine translation will be a solved problem in 5 years. This has been updated every 5 years …
Achieving an ROI from Machine Translation while Keeping Translators “Happy”
It would seem a paradox that LSP’s can achieve significant ROI from Machine Translation (MT) and keep localization staff happy. While seeking efficiencies in business right from the early days of the industrial revolution has always resulted in shifts of where human efforts are applied and these changes don’t come easy, most staff members presented with machine translation output for post-editing are frustrated mainly at the poor output they are presented with. Poor output can even go as far as for example wrong domain-specific output which will likely force an editor to rewrite significant sections of a document and negatively affect both ROI as well as staff motivation.
Riding the Machine Translation Hype Cycle – From SMT to NMT to Deep NMT by Dion Wiggins
There is a running joke in the translation industry that machine translation will be a solved problem in 5 years. This has been updated every 5 years …
Deep Neural Machine Translation
Deep Neural Machine Translation is a new technology based on Machine Learning and Artificial Intelligence (AI). It is an extension of Neural Machine Translation (NMT). Both use a large neural network with the difference that Deep NMT processes multiple neural network layers instead of just one. The result: The best machine translation quality ever experienced – and customized to your unique needs.