GenAI — Act 3

🌈 Abstract

The article discusses the current and future state of Generative AI, focusing on the orchestration of AI agents to solve large-scale problems. It highlights the rapid advancements in language models, robotics, and AI-powered automation, and how these technologies are transforming various industries.

🙋 Q&A

[01] Productivity, Automation & Personalization

1. What is the current focus of Generative AI?

• The current focus of Generative AI is on productivity, automation, and personalization. Enterprises have already implemented AI agents for tasks like code review, workplace search, and boosting employee productivity.

2. How is the cost of intelligence evolving?

• The cost of intelligence is trending towards zero, as hardware is getting faster and more efficient. This suggests that intelligence will soon be too cheap to meter.

3. What are the recent advancements in language models?

• Language models are getting faster, cheaper, and more intelligent. For example, the cost of making a call to GPT-4 is now 12 times cheaper and 6 times faster compared to the original GPT-4 model.

4. How are language models being improved?

• Researchers are making progress in understanding how language models work and finding ways to steer them with a high degree of control.

[02] Orchestrating AI Agents to Solve Billion $ Problems

1. What is the key focus for the future of Generative AI?

• The key focus for the future of Generative AI is orchestrating AI agents to solve large-scale, billion-dollar problems. Frameworks like DSPy and LangGraph are gaining popularity for building AI workflows and agents.

2. How are AI models being applied beyond knowledge work?

• AI is being applied in various industries beyond just knowledge work, such as chip design, software development, and robotics. AI is improving AI, and every organization is being encouraged to use AI aggressively.

3. What is the challenge of technical debt, and how can AI help?

• Technical debt is a trillion-dollar problem that has plagued the software industry. Orchestrating AI agents can help address this challenge and the long-standing dream of having a single interface to rule them all.