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This Week in AI: Let us not forget the humble data annotator | TechCrunch

Mar 30, 2024 - techcrunch.com
The article discusses the importance of data labeling and annotation in AI and machine learning, highlighting the often overlooked issues faced by data annotators. It mentions companies like Scale AI, which is reportedly raising funds at a $13 billion valuation, and the brutal working conditions faced by annotators, often in third-world countries, who are paid low wages and exposed to disturbing content. The article suggests that the solution to these issues may lie in policymaking, rather than expecting platforms to self-regulate.

The article also provides a roundup of recent AI news, including OpenAI's development of a voice cloner, Amazon's investment in AI power Anthropic, Google.org's launch of an accelerator for nonprofits developing generative AI tech, and the release of new generative AI models by AI21 Labs and Databricks. Other news includes Uber Eats' fight against AI bias in the UK, EU's draft election security guidelines, Adobe's expansion of Firefly, and developments in weather prediction and underwater imagery using AI.

Key takeaways:

  • The article highlights the importance of labeling and annotation in AI and machine learning, and the often poor working conditions of data annotators, particularly in third-world countries.
  • Some annotation and labeling platforms claim to provide “fair-trade” work, but there are no regulations or industry standards for what ethical labeling work means.
  • Several AI stories are mentioned, including OpenAI's development of a voice cloner, Amazon's investment in AI power Anthropic, Google.org's launch of an accelerator for nonprofits developing generative AI tech, and Uber Eats' fight against AI bias in the UK.
  • The article also discusses advancements in AI weather prediction, the use of AI in underwater imagery, the simplicity of the recall mechanism in language learning models, and the need for caution in the intersection of AI and search due to potential bias and bad habits.
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