Some organizations are winning in a pandemic by adapting to changing customer and market demands. We’ve provided adaptive workforces for a decade. Here’s what they look like.
Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?
Achieving a high level of accuracy in data labeling is vital. This concept can be understood if we think about a mural of Rubik’s Cubes®.
Any problem (like a Rubik’s cube®) is solvable with a documented process.
How solving a Rubik’s cube® is like labeling your unstructured data.
Melody Ayeli, who reviews AI projects for Toyota’s CIO, shared insights on common AI failure points in a session at AI Summit in San Francisco.
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Our latest SlideShare provides details on your workforce options and shares best practices for choosing your AI workforce.