Towards Transformative AI

🌈 Abstract

The article discusses the author's experience with generative AI tools and their conclusion that while these tools can generate content that looks good on the surface, their purely generative output is ultimately lacking in substance and quality. The author argues that the true power of these AI systems lies not in their ability to generate new content, but in their transformative capabilities - their ability to take existing data and information and transform it into something new and highly useful.

🙋 Q&A

[01] Generative AI vs. Transformative AI

1. What are the key differences the author highlights between generative AI and transformative AI?

• Generative AI can produce content that looks good on the surface, but is ultimately shallow and lacking in substance.
• Transformative AI excels at finding patterns in existing data and information, and using that to transform the data into new, highly useful formats.
• Examples of transformative AI use cases include:
  • Correcting errors in dictated text
  • Transforming press releases into article drafts
  • Turning handwritten notes into searchable text

2. Why does the author believe transformative AI is more powerful and impactful than generative AI?

• Transformative AI builds on human-created input and insights, rather than attempting to generate new content from scratch.
• This allows transformative AI to create useful, substantive output, rather than just a veneer of good-looking but ultimately useless content.
• Transformative AI also hallucinates less, as it is focused on amplifying the best qualities of the human-provided input rather than inventing new data.

[02] Limitations of Generative AI

1. What examples does the author provide to illustrate the limitations of generative AI?

• The author tested purely AI-written Facebook ads and found they performed worse than human-written ads, with higher costs per click.
• The author also cites research showing that while AI assistance can help writers generate ideas, the ideas themselves are often not as novel or unique as those generated by unaided human writers.

2. Why does the author believe Acemoglu is wrong in arguing that AI will have only a modest impact on productivity?

• The author believes that while generative AI may have limitations, the transformative capabilities of AI will ultimately have a massive impact, far beyond what Acemoglu predicts.