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cover of episode AI's Unsung Hero: Data Labeling and Expert Evals

AI's Unsung Hero: Data Labeling and Expert Evals

2025/6/27
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Manu Sharma
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Matt Bornstein
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Manu Sharma: 在人工智能模型训练的早期阶段，监督学习占据主导地位，但随着GPT-3和DALL-E等模型的出现，无监督学习开始崭露头角。在ChatGPT时代，通过强化学习从人类专家那里获取偏好变得越来越重要。现在，我们正处于一个强化学习回归的时代，专家们不仅要教算法如何给出正确答案，还要教它们如何评估答案的质量。我亲身经历了数据标注从计算机视觉到推理模型，再到语音模型的演变，并带领Labelbox成功适应了这些变化。 Matt Bornstein: 我认为数据标注和评估在模型训练中起着至关重要的作用。价值已经从标注预训练数据转移到评估强化学习阶段的输出。这种转变反映了模型能力、架构和应用的变化，以及对人类专家在更复杂模式和更苛刻用户中帮助模型执行的需求增加。

Deep Dive

Chapters
This chapter explores the evolution of data labeling and evaluation in AI, from early supervised learning to today's sophisticated reinforcement learning loops. It discusses the shift from pre-training to post-training and the role of human experts in assessing the quality of AI responses.
  • Supervised learning was replaced by unsupervised learning.
  • Reinforcement learning emerged as a new technical vector.
  • Experts teach algorithms how to assess the quality of answers, not just the correctness.

Shownotes Transcript

Labelbox) CEO Manu Sharma joins a16z Infra partner Matt Bornstein to explore the evolution of data labeling and evaluation in AI — from early supervised learning to today’s sophisticated reinforcement learning loops.

Manu recounts Labelbox’s origins in computer vision, and then how the shift to foundation models and generative AI changed the game. The value moved from pre-training to post-training and, today, models are trained not just to answer questions, but to assess the quality of their own responses. Labelbox has responded by building a global network of “aligners” — top professionals from fields like  coding, healthcare, and customer service, who label and evaluate data used to fine-tune AI systems.

The conversation also touches on Meta’s acquisition of Scale AI, underscoring how critical data and talent have become in the AGI race. 

Here's a sample of Manu explaining how Labelbox was able to transition from one era of AI to another:

*It took us some time to really understand like that the world is shifting from building AI models to renting AI intelligence. A vast number of enterprises around the world are no longer building their own models; they're actually renting base intelligence and adding on top of it to make that work for their company. And that was a very big shift. *

*But then the even bigger opportunity was the hyperscalers and the AI labs that are spending billions of dollars of capital developing these models and data sets. We really ought to go and figure out and innovate for them. For us, it was a big shift from the DNA perspective because Labelbox was built with a hardcore software-tools mindset. Our go-to market, engineering, and product and design teams operated like software companies. *

But I think the hardest part for many of us, at that time, was to just make the decision that we're going just go try it and do it. And nothing is better than that: "Let's just go build an MVP and see what happens."

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