Meta AI translation breakthrough in interpretation
Meta translator interprets 200+ languages
The idea of automatic universal translation has long been the substance of science fiction. A recent paper from research groups in artificial intelligence at Facebook’s parent company Meta is said to be a step in that direction. The study demonstrates that machine learning, the AI-supporting technology, is capable of translating 204 languages—more than had ever been attempted—at a higher level of quality.
That seems to include over a hundred rarely spoken languages, such as the Acehnese language of Indonesia and the Chokwe language of Central and Southern Africa, which have never been easy for computers to translate because there isn’t much of them online. Mark Zuckerberg, CEO of Facebook and Meta, praised the achievement and dubbed AI translation a “superpower,” and the researchers’ enthusiasm was scarcely dampened.
It represents the most recent advancement in artificial intelligence, a contentious field of study that recently gained attention after a Google engineer was fired for asserting that a chatbot could express thoughts and emotions.
One of the 38 academics and Meta experts who worked on the project, Professor Philipp Koehn of Johns Hopkins University, said, “The paper presents impressive work to push production-level translation quality to 200 languages.”
Additionally, a ton of resources will be made available so that everyone can use this model and retrain it on their own, promoting that field’s research.
Computer scientists who weren’t involved in the project stressed that although the paper claimed to be “laying the crucial groundwork toward the realization of a universal translation system,” it was only a small step on a long, winding path with no obvious end in sight.
“An amazing engineering achievement”
According to Dr. Alexandra Birch-Mayne, Reader in Natural Language Processing at the University of Edinburgh, the paper’s main machine learning technique, a model known by the baroque term Sparsely Gated Mixture of Experts, was not inherently novel. She claimed that its biggest contribution was compiling, cleaning, and presenting fresh data on languages that were underrepresented on the internet, the primary source of data for machine translation.
“It’s a remarkable engineering achievement, though it may not represent a fundamental advance in science, “said Dr. Birch-Mayne. The paper claimed to translate languages with fewer speakers and to raise the bar for translation quality.
Data and algorithms will be made available to the public. Measuring machine learning progress is difficult, but the Meta paper significantly increased translation quality over the prior state-of-the-art by using a metric called BLEU. Dr. Diptesh Kanojia, Lecturer in Artificial Intelligence for Natural Language Processing at the University of Surrey, declared that “BLEU is an imperfect metric.” However, quoting BLEU scores is a common practice in natural language processing research. “If we only look at this statistically, a 44% improvement is quite significant.”
The language data and the algorithms used to translate it will be made publicly available, so other researchers will be able to use them for the first time. Although the work will be used to improve Facebook’s software, it will also provide authoritative datasets on languages like Eastern Yiddish, Northern Kurdish, and Cape Verdean Creole. Importantly, the Meta researchers recruited native speakers to proofread their translations, a laborious task that helps ensure the accuracy of the algorithm and the underlying linguistic information. “Engaging with the community is admirable.
“They’re not necessarily starting this trend, but they are following good practice,” said Dr. Birch-Mayne, who also pointed out the effort’s shortcomings because it relied on native speakers from the US and Europe rather than the countries where the languages originated. Some academics criticized Meta for engaging in “peer review by media” by publishing the paper without first submitting it for peer review. The method, according to Professor Koehn, was “common practice in the field… for better or worse” and aided in the hastened dissemination of research findings.
Improvements in machine learning
The paper is just one of several recent developments in machine learning, which is advancing much more quickly than anticipated. A model that Google unveiled last week performed dramatically better, accurately resolving a third of MIT undergraduate math problems. Although there is speculation about new forms of consciousness with every new development, the majority of experts in the field hold that AI systems are neither sentient nor intelligent, saying they merely mimic the data they are given. There won’t be a rebellion of the robots.
The greater risk posed by AI systems is that they will cause catastrophe by instilling humans with a false sense of confidence in their still very limited capabilities. This risk is especially real because Facebook has previously faced criticism for not having enough native-speaking moderators to detect calls for violence on its platform. Furthermore, given the sensitive nature of the tasks that could involve translation at Facebook, With these developments, our apps will be able to translate more than 25 billion words per day. Mr. Zuckerberg pledged According to Facebook, these translations could be used to stop online sexual exploitation, identify harmful content, and secure elections.
Dr. Birch-Mayne, who recently completed a three-year project with the BBC on 17 languages in Africa and India, issued a warning against the use of machine translation in situations where accuracy is crucial. These systems are not reliable, she said. “It could be right, but it also could not be.”