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cover of episode How To Build The AGI Future: Bob McGrew

How To Build The AGI Future: Bob McGrew

2025/1/31
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Bob McGrew: 我认为人们对AGI的理解存在误区,普遍认为AGI一旦出现,人类就会失业,但这并不准确。目前我们正处于预训练和数据瓶颈期,但推理和测试时计算的出现为我们提供了新的机制,能够解锁更可靠、更强大的AI代理,并为扩展到AGI铺平道路。在OpenAI的早期,我们尝试通过大量研究和论文撰写来构建AGI,但后来证明这是一个错误的理论。我们早期的一些项目,例如让机器人手解决魔方,以及解决Dota 2游戏,都强化了我们对规模定律的理解,即规模是改进人工智能的关键路径。通过大规模训练,模型能够学习和泛化。OpenAI在大型语言模型上的成功,正是源于将这些经验应用于语言模型的训练。OpenAI的文化也至关重要,我们能够有效地协调研究人员对署名和荣誉的需求,避免了学术界常见的合作障碍。在模型蒸馏技术方面,我们已经取得了显著进展,能够构建更小、更快的模型。对于AI创业公司,我建议优先使用最好的模型,并在模型有效运行后考虑成本优化。未来AI应用的一个重要方向是高度个性化的AI助理,能够访问用户的各种数据并提供个性化建议。AI的实际应用速度比2018年预测的要慢,这其中存在深层次的原因。AI的应用需要更贴合用户实际需求的软件和用户界面,而不是简单地将现有工作流程自动化。即使AI能够进行编程,学习编程仍然对培养批判性思维至关重要。我认为未来的工作将主要分为两类:天才型和管理型。历史表明,自动化会创造新的就业机会,我们不必对未来的就业形势过于悲观。机器人技术领域正处于快速发展阶段,未来几年可能会出现类似于大型语言模型的突破性进展。自动化将首先影响科学家的角色,而非实验操作人员,这将极大地加速科学进步。 Garry Tan: (问题引导,未形成核心论点)

Deep Dive

Chapters
This chapter explores the concept of Artificial General Intelligence (AGI), its capabilities, and the potential impact on employment and humanity. It introduces the idea of reasoning and test-time compute as a new mechanism to unlock the potential of AGI.
  • AGI is defined as an interactive model passing the Turing test
  • Current AGI capabilities are not as disruptive as initially feared
  • Reasoning and test-time compute are key for reliable AGI agents

Shownotes Transcript

According to OpenAI's former Chief Research Officer Bob McGrew, reasoning and test-time compute will unlock more reliable and capable AI agents— and a path to scale to AGI.

In this episode of How to Build the Future, YC's @garrytan sits down with Bob to discuss the lessons learned from his time at OpenAI, scaling laws, his advice for startups, and what all of this means for the jobs of the future.