Chatbots don't think on their own, they respond to human input. However, it's straightforward to expand the capabilities of an AI system to running on its own and taking actions on its own. This is commonly called an AI agent. An agent is an entity that can run and take actions on its own.

The idea of AI agents is starting to take off, with many saying it might be a path to AGI. What are the additional benefits from having multiple AI agents working together? We examine some cases of complex AI problems and emergent behavior in nature and existing systems. There are many cases where emergent behavior and emergent intelligence are much greater than the sum of the parts.

The study of multi-agent systems tells us a bit about what to expect of the future. From a game theory perspective, multiple agents acting together have to have a good stable equilibrium so that all agents will continue to participate. Importantly, we already have multi-agent systems where the agents are humans or existing software systems, so we have learned a lot about the field already.

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#ai #agent #swarm #emergent

0:00 Intro
0:21 Contents
0:27 Part 1: What are agents?
0:48 Example comparison with apps
1:00 Autonomous agents
1:47 Safety risk of autonomous agents
2:00 Agents discussed by OpenAI at DevDay
2:35 GPT to support structured output
2:59 Part 2: Current AI products
3:18 Getting ChatGPT to use tools
4:10 Industry perspective on GPT-4
4:35 Example: Opus Clip introduction
5:04 Opus Clip leverages multiple models
6:04 Is Opus Clip an agent?
6:37 Opus Clip reference
6:54 Example: smartphone app comparison
7:32 Distributed system
7:46 Part 3: Emergent intelligence
8:06 Symbiotic relationships
8:21 Ant colony emergent intelligence
9:22 Example: internet routing
9:54 Example: efficient markets in economics
10:20 Example: YouTube algorithm
11:06 Field of multi-agent systems
11:24 Consensus problem (Paxos)
12:04 Similarity to software infrastructure
12:46 We didn't talk about swarms
12:57 Conclusion
13:32 Benefits of modularization
14:11 Outro