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To solve AI alignment by tackling the real bottleneck: data quality.
It has become increasingly clear that the next generation of AI applications will be driven by data-centric approaches concentrated around training data creation, management, and evaluation. Crucial within this process is the annotation or 'labelling' of data. Traditionally, such annotation has been left to human workers, which has historically made this part of the process of AI development expensive, slow, and difficult to scale.
Encord offers a computer vision annotation platform that automates a number of manual labelling tasks. Its suite of tools is designed for collaboration across roles and teams, from domain-expert annotators to project managers and machine learning engineers. This allows companies to iterate 10x faster, and develop and deploy high performing models in less time whilst saving on annotation costs.
Demand is clearly there: since its launch in 2020, Encord has facilitated the annotation of more than 100 million frames and images and served customers in verticals such as medical imaging, smart cities, sports analytics and satellite imaging. Furthermore, usage is scaling rapidly, with the number of labels generated on the platform growing ~70% month-over-month.
Steph
Company Specialist at Welcome to the Jungle
Eric Landau
(CEO)With MScs from Harvard and Stanford, Eric spent 8+ years as a senior quant at DRW deploying ML at scale—before co-founding Encord to make data the core of AI development.
Ulrik Stig Hansen
(President)Ulrik spent 3+ years in Sales & Trading at J.P. Morgan and holds science master’s degrees from UCL and Imperial—before co-founding Encord to transform how AI handles data.
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