AIMMO, a global AI data solution company, participated in “AUTO.AI USA 2023″, a three-day conference on AI and machine learning for the advancement of autonomous driving services, held in San Francisco, USA, from June 28.
“AUTO.AI USA 2023” attracted a large number of tech experts, including Shubham Shrivastava, Ford’s 3D technology expert, Vahid Hashemi, Audi ADAS/AD AI safety assurance team leader, and Christopher Pinke, a team leader at Continental Robot Taxi.
At this conference, AIMMO introduced ‘AD-DaaS,’ a data solution specialized for autonomous driving, and shared application cases through meetings with major autonomous driving-related companies. It also discussed plans for the improvement of autonomous driving models.
During the presentation session on June 30 (Friday), senior researcher Lee Hae-bom of the AIMMO autonomous driving R&D team talked about the importance of data-centric AI models and AIMMO’s data under the theme of “Generative Models in Data-centric AI.”
Lee said, “AIMMO’s AD-DaaS curates optimized data from a large dataset, from a data-centric AI perspective, and uses synthetic data generated by generative models and simulations to solve edge cases and data imbalance problems. With this technology, we can enable the AI model to train itself with high-quality data.”
Oh Seung-taek, CEO of AIMMO, said, “As we are planning to establish a US corporation this year, this conference has provided us with a foothold for entering the US market. AIMMO has already been recognized for its competitiveness in the overseas market. We will become a leading company in the data market beyond the Korean market.”
AIMMO processes the learning data required for machine learning modeling by distinguishing the purpose and data characteristics of the AI model. It also designs projects and provides Ground Truth as a Service (a service that guarantees the accuracy of learning data through processing and inspection). AIMMO has technologies for smart labeling, automatic AI labeling, and AI SQIP, an AI statistics-based inspection.
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