Uber technologies“Business of artificial intelligence data services (AI), Uber AI solutionsAdded new solutions and now provides them with laboratories and companies in 30 countries.
Uber AI Solutions offers other companies solutions that Uber has developed in the last decade while using data and an AI in its own operations, said the company on Friday June 20) press release.
“We bring together the Uber platform, people and AI systems to help other organizations to build smarter AI more quickly”, ” Megha Yethadkasaid the director general and head of Uber AI Solutions, in the press release. “With today’s updates, we are expanding our platform worldwide to meet the growing demand for reliable and real data.”
A solution offered by Uber AI Solutions is a platform that connects companies to global talents that can provide annotation, translation and publishing for multilingual and multimodal content, according to the press release. The available talent includes experts in coding, finance, law, science and linguistics.
The company also offers data sets to train large models of AI for generative AI, mapping, voice recognition and other use cases; Tasks, annotations, simulations and multilingual support to help train AI agents; And its own internal platforms to manage large-scale annotation projects and validate AI outputs, according to the press release.
“With this progress, Uber AI Solutions is about to become the human intelligence layer for the development of AI around the world – combining software, operational expertise and its massive global scale,” the press release said.
The AI industry faced a high -quality data shortage for the training of AI models, Pymnts reported in July.
Although the Internet generates enormous amounts of data per day, the quantity does not necessarily result in the quality in terms of formation of AI models. Researchers need various data, impartials and with precision, and this combination is becoming increasingly rare.
In another separate development in this space, Sandboxaq Wednesday, June 18, said that he launched a set of data designed to help researchers advance AI models in the discovery of drugs.
Generated with the use of large quantitative model capacities of Sandboxaq and the NVIDIA development platform for the training and the end of the AI, the data set allows researchers to form AI models to predict with precision the affinities of binding proteins-ligands at least 1000 times faster than traditional methods based on physics.
