I have been reading opinion pieces in which the key message was that Retrieval Augmented Generation (RAG) is a dead end. I was not convinced. The combination of RAG, knowledge graphs and large language models (LLMs) is powerful and promising. And it is work. I think that is what scares many of my peers and colleagues: it is hard to convince management that leveraging AI means investing a lot of time and resources in building a specialist LLM that does a specific task well.
So I am more supportive of approaches that take complexity into account and address the legwork that is required to fine-tune the models and embed principled design in them.
We all know that one use case is not another, that one MT engine may work well for some language pairs and some domains but may perform poorly in other pairs or domains. Large language models (LLM) occasionally outperform MT, and there are also cases where MT is needed.
In a nutshell: it is a complex landscape, and testing is required in every situation and for every use case.
Technology, when used with discernment, helps translate more and faster. But don't take anything for granted, do the research first, test, compare, evaluate. That has a cost. Managing client expectations includes explaining what this testing entails. No one-size-fits-all solution is credible.
The IEA points to the collapse of coal in the most industrialised countries. Coal demand is estimated to have dropped back to 1900 levels, with coal generation falling to a “historic low” of 17% (3/4)
The K–12 education challenges we face today and their implications for the long-term health of the economy are just as important as they were 40 years ago ... Yet corporate leaders are largely missing in action, and the silence is deafening
In comes "content recycling", not just an upgrade of the TM paradigm: here full chunks (or at least large chunks) of similar content are stored. This approach requires no (or much less) review.
So I am more supportive of approaches that take complexity into account and address the legwork that is required to fine-tune the models and embed principled design in them.