Our Analysts' Insights

While the goal for project management has long been full automation (“lights-out”), few organizations achieve it – not for translation nor interpreting services, neither at LSPs nor enterprises. Many teams have tools and processes in place to enable touch-free workflows, yet our research demonstrates a huge gap between “we have the system” and “we are using it.” While some organizations use lights-out processes for a vast amount of translation work – even most of their projects – genera...

Are you an expert project manager or interpreting scheduler? We need to talk! Project management – the shepherding of work from request to delivery, ensuring all the correct criteria are met, and in a timely manner – is not specific to the localization industry. In every area of work, from construction to international shipping, a project manager (PM) plays a leading role in satisfying customers, whether in daily personal contact or acting behind the scenes. These individuals – and the projec...

A common worry about generative AI (GenAI) is that the content that it creates may be subject to copyright claims. Our recent survey of freelance linguists reflected this concern: Copyright issues are their second most important concern with GenAI, with 74% viewing the technology negatively or strongly negatively in this regard. However, an examination of claims about copyright and how GenAI works reveals a different picture. This blog post covers details about how the technology works with its ...

During our research into multimedia localization – and all the new AI-enhanced tools that are sprouting up like toadstools after summer rain – we found many new offerings, some good or excellent, and many not-so. Startup companies with an excellent product may struggle with business processes and strategies; some are already leaders in a brand new field, unimagined a year ago; but too many others are aiming to get-rich-quick based on a worldwide appetite for tools that make everything possible...

It is incredible to think that, less than eight years after the first publicly available neural machine translation (NMT) systems appeared on the scene, some media coverage already sees NMT as so 2015. As generative AI (GenAI) really exploded into public view in 2022, it wasn’t surprising that an overactive tech press’ imagination would see it as the be-all and end-all of technology. Our recent survey with freelance linguists certainly reflects this view, with many language workers expressing ...

Multimedia, transcribed audio, and AI-generated content in all the world’s digital languages join more traditional content types in filling up data centers. Together they create challenges and opportunities across organizations, raise the alarm for more oversight of content, and further the case for aligning enterprise content strategies, investment, and operations.

Large language models have been in the news a lot in November and December and the coverage has been mixed, to put it mildly. Meta posted its Galactica model on November 15 but took it down just three days later in the face of intense criticism. By contrast, when OpenAI released ChatGPT two weeks later, on November 30, the response was much more positive. Examining why the reactions were so different provides insight into the potential and limitations of machine translation (MT) as well as cauti...

As part of an ongoing investigation into multimedia localization tools and practices, CSA Research is examining enterprises’ global use of video. A combination of professional interest while researching marketing content and personal interest because I’ve just moved, led me to view several TV ads and online videos by international energy providers, including EDF and E.ON. These marketing videos took me down the proverbial rabbit hole, trying to figure out the source and target languages. Which...

Technology developments tend to follow a typical pattern of improvement over time, known as an S-curve. Although it is a familiar pattern, it is worth unpacking its five phases and considering how they apply to language technology and forecasts about it. Examining how they have played out with successive generations of machine translation points to a future in which other advanced natural language processing technologies have tremendous potential to deliver useful and innovative capabilities.

Last week, the Washington Post published an article about Blake Lemoine’s claim that his employer Google’s LaMDA language model/chatbot system had achieved sentience and had a “soul.” Lemoine, an engineer in the company’s responsible AI group, based his assertion on a dialogue in which LaMDA expressed human-seeming sentiments and concepts. Google placed Lemoine on leave, thereby sparking renewed discussion about what machine sentience is and what it means. What can the experience of the lan...