YandexGPT to Expand Capabilities with Translation from Images, Audio, and Video

Posted by By Dan Taylor 2 Min Read

Yandex is advancing the capabilities of its YandexGPT by training the AI to recognize and translate text from images, audio, and video files. The company is actively seeking translators to assist in this ambitious project, with plans to hire approximately ten specialists to train the model.

According to sources from Vedomosti, Yandex has posted vacancies for AI trainers who are proficient in English and possess professional translation skills. These experts will play a crucial role in refining the generative model by providing reference translations and evaluating the quality of the AI’s output. The translation tasks will cover a wide range of specialized fields, including jurisprudence and physics, making the role particularly challenging.

The primary challenge, as highlighted by experts, lies in the fact that training modern language models requires translations that are semantically accurate rather than literal. This is a shift from the traditional approach and is necessary because neural networks often default to literal translations, which may not always convey the intended meaning.

Yandex’s efforts to enhance its neural network are part of a broader strategy to improve its AI-driven services. In June, the company unveiled a new version of its machine translation service, powered by YandexGPT. By July, YandexGPT was already being used to correct and enhance text within Yandex Keyboard, demonstrating the company’s commitment to integrating AI advancements across its platforms.

This new initiative to train YandexGPT to handle complex translations from multimedia sources marks a significant step forward in Yandex’s AI development, promising more versatile and accurate language processing tools in the near future.

Source: Vedomosti

Dan Taylor
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Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.
Founder & Editor