Russia innovates omnipurpose AI entity for automated machines and manufacturing procedures
Transformed Article:
Ta-da! The AIRI Institute has just dropped an open-source action model named Vintix that's causing some serious buzz. Unlike its competitors like JAT and GATO, Vintix analyses an astounding three times more information, showcasing a game-changing performance. It ain't just about solving specific tasks; it's all about self-correction, self-improvement, and handling delays and incomplete data like a champ - skills crucial for industrial applications, according to the AIRI team.
Ever heard of action models? They're basically AI's brainy decision-makers deciding actions based on environmental info, handling real-life situations instead of just working from a rigid script.
So, what's the deal with Vintix? Well, it employs the ICRL (In-Context Reinforcement Learning) approach, allowing it to tweak its strategies on the fly during inference - the key stage where AI models start crunching fresh data. As the AIRI crew puts it, this approach differs fundamentally from conventional reinforcement learning, moving away from expert mimicry towards algorithmic mimicry, enabling self-correcting and self-improving properties.
Now, here comes the cool part – Vintix outperforms its competitors on manipulator robot tasks by an approximation of 32%, with the advantage ranging from 20 to 40% on specific tasks. In systems with stable movement, the edge is less apparent, but it still kicks up results by an average of 12%. Kudos and acceptance at the ICML (International Conference on Machine Learning, A*) – one of the oldest and most prestigious AI conferences out there – prove the model's legitimacy.
ICRL – a promising direction that could unlock the potential of reinforcement learning in real-world scenarios – is currently under investigation by numerous research groups. Daniil Gavrilov, head of T-Bank AI Research's research laboratory, considers Vintix as a major stride in implementing and scaling ICRL. He explains that by training the model across multiple domains simultaneously, they've bridged the gap between synthetic conditions where ICRL was previously applied.
The industrial sector is already showing a keen interest in Vintix thanks to its ability to tackle complex and dynamic conditions. With its self-correction and self-improvement skills, Vintix can significantly optimize production processes, slashing equipment reconfiguration time and minimizing human error-related mishaps, as noted by "Yandex".
The acceptance of papers at top-tier conferences like ICML underlines the caliber and significance of Russian research in machine learning, elevating Russian scientists' standing on the global stage and paving the way for knowledge and experience exchange, thereby accelerating overall AI progress. "Yandex" also shared six papers by their researchers, exploring various aspects of machine learning, from neural network algorithmic thinking to optimizing memory usage with large language models, were accepted to the conference. Go team "Yandex"! 🚀💻
The AIRI Institute's new action model, Vintix, leverages artificial-intelligence for industrial applications. In particular, it employs the In-Context Reinforcement Learning (ICRL) approach, a technology that enables self-correcting and self-improving properties, crucial for business environments. This technology is not only making waves in the AI community but also attracting interest from the industrial sector, promising to optimize production processes and minimize errors.
