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cover of episode 841: Andrew Ng on AI Vision, Agents and Business Value

841: Andrew Ng on AI Vision, Agents and Business Value

2024/12/3
logo of podcast Super Data Science: ML & AI Podcast with Jon Krohn

Super Data Science: ML & AI Podcast with Jon Krohn

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Andrew Ng
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Jon Krohn
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Jon Krohn 询问企业应该如何在追求更强大的模型和利用更有效的智能体架构之间平衡投资。Andrew Ng 认为,除了少数大型 AI 公司外,几乎所有公司都应该专注于构建使用智能体工作流程的应用程序。他指出,大型语言模型的使用成本正在迅速下降,过去一年下降了约 80%。他建议企业优先构建有价值的应用程序,只有在应用成功且成本过高时才考虑优化成本。他认为,大多数企业的生成式 AI 账单非常低,无需过度关注成本优化。他建议使用最好的模型,构建有效的应用,只有在应用成功且成本过高时才考虑优化成本。他认为,在构建有价值的应用之前,过早地优化成本是不明智的。 Andrew Ng 进一步解释说,大型语言模型强大的能力部分源于其所处理数据的丰富性,而非算法的复杂性。他认为,现代 AI 正在结合两种历史方法:Marvin Minsky 的多智能体系统理论和推动深度学习发展的单一算法理论。他认为,智能体工作流程可以使 AI 模型能够针对不同的任务进行专门化。

Deep Dive

Chapters
Andrew Ng discusses the balance between investing in powerful AI models versus leveraging effective agent architectures, emphasizing that most companies should focus on building applications using agents.
  • Most companies should focus on building applications using agents.
  • The cost of using AI models is falling rapidly.
  • Companies should build something valuable first and then optimize costs if necessary.

Shownotes Transcript

In this special episode recorded live at ScaleUp:AI in New York, Jon Krohn speaks to Andrew Ng in response to his conference talk on smart agentic AI workflows. Jon follows up with Andrew about smart agentic workflows and when to use them, how businesses should direct their efforts in investing in AI, and the new ways that AI tools can process visual and unstructured data.

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode you will learn:

  • (06:13) How to weigh up cost and effectiveness in new AI workflows

  • (12:08) The crucial elements for building effective vision AI applications

  • (15:34) How large vision models might transform global industries

  • (18:40) How to mitigate risk in people not verifying accuracy in answers generated by agents

Additional materials: www.superdatascience.com/841)