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Home / Technology / Amazon AI Chief: Benchmarks Are Fake, Utility Matters

Amazon AI Chief: Benchmarks Are Fake, Utility Matters

3 Dec

•

Summary

  • Amazon's AI head dismisses benchmarks, prioritizing real-world utility.
  • Nova Forge service allows custom AI training at lower costs.
  • Reddit uses Forge for specialized moderation models, finding it effective.
Amazon AI Chief: Benchmarks Are Fake, Utility Matters

Amazon's top AI executive is urging the tech industry to shift focus away from benchmark leaderboards, emphasizing practical utility over abstract performance metrics. Rohit Prasad, Amazon's SVP of AGI, stated that current benchmarks are unreliable and do not reflect the true capabilities of AI models, advocating for a more grounded assessment of AI advancement.

This stance accompanies the launch of Nova Forge at AWS re:Invent. This new service aims to democratize the development of custom AI models, enabling companies to train specialized AIs using Amazon's Nova model checkpoints at various stages. This approach allows for the injection of proprietary data early in the training process, a significant advantage over existing methods.

Reddit, an early adopter, is utilizing Nova Forge to create custom safety models trained on extensive community moderation data. The company reports promising results, aiming to consolidate multiple specialized models into a single, expert AI capable of understanding nuanced community guidelines. This strategy allows for greater control, data ownership, and avoidance of third-party dependencies.

Disclaimer: This story has been auto-aggregated and auto-summarised by a computer program. This story has not been edited or created by the Feedzop team.
Rohit Prasad believes current benchmarks are noisy, not standardized, and don't reflect real-world AI utility.
Nova Forge allows companies to train custom AI models using Amazon's Nova checkpoints at a fraction of the usual cost.
Reddit is training custom AI models for community moderation, aiming for a single expert model to understand nuanced rules.

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