Artificial intelligence is all around us and has helped many industries push forward in their progress and break new grounds. It’s a very useful tool in some contexts, and there’s no doubt that we’re going to see adoption rates for AI tech increasing even more in the future.
However, when it comes to some types of work, AI is far from perfect. Despite the fact that many are trying to sell it as a “one size fits all” solution, that’s far from the case right now. It will probably take a long time until AI is at a state where it can provide a complete replacement for some skills.
Translation is one of the most notable examples of this. While modern translation agencies use AI to improve and streamline their work with great success, there’s a reason why all the best players on this market still do the bulk of their work manually and rely on experienced human translators.
Most Systems Are a Black Box
One of the biggest problems with AI is that it’s difficult to see what exactly is going on beneath the surface of the system. This isn’t a problem when everything is going well. But as soon as something breaks down, it can be a real pain to figure out what went wrong and identify possible fixes.
This is the case not just for end users but also for people working on AI solutions. Due to the way most AI systems work, there’s not much that can be done to modify some of their parameters on the go, often requiring a full round of new training and other complicated operations.
You Lose the Human Touch of the Original Writing
AI can often translate things into another language in a meaningful way, but that usually removes a crucial component from those translations – the human touch of the original writing. AI still has trouble identifying the underlying meaning of certain phrases and structures, resulting in translation that’s either bland, or even completely wrong. The latter is often the case when things like idioms and other complex language elements are brought into the mix.
It Can Introduce Subtle Errors That Are Hard to Detect
All work produced by an artificial intelligence translator must be verified in detail. This is especially important when it comes to correctness and truthfulness. Sometimes the AI might introduce errors that are very subtle, and as a result, difficult to detect by human observers.
The final result is that humans have to invest a lot of time and effort into verifying the final result, which often defeats the whole purpose of using AI for translation work in the first place.
Your Translations Will Get Repetitive After a While
Another problem with AI is that the variety of its output is limited to the input it’s received in its training. This means that using AI-based systems for large orders results in repetitiveness after a while. It might take some articles, but the cracks will eventually start to show and people will be able to identify that the work was produced by someone without any actual human knowledge in the field.
This is especially problematic in niche areas when a customer has placed a large order that doesn’t have much in the way of learning material. A human might be able to infer more from the original text and look up additional references, but an AI can’t do that without specialized functionality.
Large-scale Use Is Associated with Significant Costs
On the topic of large orders, there’s also a cost issue associated with using AI for particularly large batches, especially when they have to be processed quickly. This limits the capacity of human verification workers and introduces other bottlenecks which can be difficult to address.
There’s also the fact that AI translation systems are often barred behind costly subscriptions. While some of the most popular models are available for free, they still require expensive resources to run locally and operate them to their full potential. This makes large-scale use of AI cost-ineffective and therefore not suitable for commercial purposes.
SEO Performance Might Be Compromised
AI translation often also lacks any SEO impact that the original work might have had. While there is currently some ongoing work on systems that could change this by combining AI-based translation with automated SEO, they’re still far from reality and not widely available.
When work is to be translated in a way that preserves its original search engine performance, this is best done with the help of human specialists. Many tend to underestimate the impact this can have on pieces that have already been extensively optimized.
Correcting Flawed Work Can Slow Down the Process
We touched on this above, but it’s an important point to reiterate. When working with AI-based translation systems, it’s important to dedicate enough resources to the verification of the translated work. This can sometimes be a very slow process if multiple revisions are required, especially ones that involve several different people who need to collaborate on the project.
Experiments have shown that working with an AI-based translation system often makes things slower rather than faster when the time required for edits and revisions is factored in. Will this improve in the future? Probably, but we’re still not there and likely won’t be for a while.
Collaboration Can Be More Difficult
When several people need to collaborate on a translation project, the presence of AI systems often makes this more complicated and time-consuming instead of doing the opposite. Work needs to go through additional verification steps, among other issues. Collaboration tools are also not in a place where they can support several people working on AI-produced translation.
Collaboration is already a problematic factor when it comes to large-scale translation projects, and these situations only make that point clearer.
Closing Thoughts
While there’s no doubt that artificial intelligence holds a lot of promise for the future – including in the area of translation work – we’re still far from the point where it can serve as a full replacement for a competent human translator. There are some viable use cases, but for the most part companies need to continue relying on human experts wherever quality and speed are of particular importance.
