Large language models (LLMs) are powerful AI systems that are trained on vast amounts of text data, enabling them to generate and understand human-like language. These models excel at a wide range of tasks, including:

1. Text Generation: LLMs can generate coherent and contextually relevant text, such as articles, stories, and even code, based on a given prompt or input.

2. Text Summarization: LLMs can quickly and accurately summarize long passages of text, capturing the key points and main ideas.

3. Question Answering: LLMs can understand and respond to a wide variety of questions, drawing upon their broad knowledge base to provide informative and relevant answers.

4. Language Translation: LLMs can translate text between multiple languages, often with high accuracy and fluency.

5. Sentiment Analysis: LLMs can analyze the emotional tone and sentiment expressed in text, which can be useful for tasks like customer service, social media monitoring, and market research.

6. Text Completion: LLMs can suggest relevant and contextually appropriate words or phrases to complete a given sentence or paragraph, making them useful for tasks like writing assistance and content generation.

7. Code Generation: Some LLMs, such as Codex, have been trained on large amounts of code and can generate, explain, and debug code in various programming languages.

Despite their impressive capabilities, LLMs also have limitations. They rely on their training data and can sometimes produce biased or factually incorrect outputs. Additionally, they lack the ability to reason about the world in the same way humans do and may struggle with tasks that require deeper understanding or reasoning beyond language processing.

AI workflows are a step-up from basic large language models (LLMs) like ChatGPT. In an AI workflow, the LLM is given a predefined path to follow in order to accomplish a specific task. This path is set by a human and involves the LLM retrieving information from external tools or data sources before providing a response.

The key trait of AI workflows is that the human programs the control logic, or the sequence of steps the LLM must follow. For example, if you asked ChatGPT about your upcoming calendar event, it would fail because it doesn't have access to your personal calendar data. However, in an AI workflow, you could instruct the LLM to first check your Google Calendar, retrieve the event details, and then provide the response.

This workflow-based approach allows for more complex tasks to be automated, as the LLM can now access and integrate information from various sources. However, it also means the workflow is limited to the predefined path set by the human. If you wanted the LLM to also check the weather for the calendar event, you'd need to add that step to the workflow.

Retrieval Augmented Generation (RAG) is a type of AI workflow where the LLM is able to look up and incorporate external information before generating its response. This helps overcome the limited knowledge inherent in LLMs.

Overall, AI workflows represent a step towards more capable and useful AI assistants, but they are still constrained by the human-defined control logic. The next evolution is true AI agents, which can reason about the best approach to achieve a goal and autonomously execute the necessary steps.

AI agents represent the next evolution in AI capabilities, going beyond the limitations of large language models (LLMs) and predefined AI workflows. Unlike LLMs, which passively respond to prompts, or AI workflows, which follow predetermined paths, AI agents possess the ability to reason, act, and iterate autonomously.

The key traits that distinguish AI agents are:

1. Reasoning: An AI agent can analyze a given goal or task and determine the most efficient approach to achieve it, rather than relying on a human-programmed workflow.

2. Acting: Instead of waiting for a human to provide instructions, an AI agent can independently utilize various tools and resources to carry out the necessary actions to reach its goal.

3. Iterating: AI agents can observe the interim results of their actions, assess their effectiveness, and autonomously make adjustments to improve the final output.

This level of autonomy and decision-making capability is a significant advancement from the passive nature of LLMs and the predefined paths of AI workflows. AI agents represent a future where AI systems can operate with greater independence and adaptability, tackling complex tasks and challenges without the constant need for human intervention.

By understanding the core traits of AI agents, you can better appreciate the potential impact they will have on various industries and applications. As AI technology continues to evolve, the emergence of more sophisticated and capable AI agents will undoubtedly transform the way we interact with and leverage artificial intelligence.

One real-world example of an AI agent is the AI vision agent created by Andrew, a prominent figure in the AI field. This agent demonstrates how an AI agent works in practice.

When you search for a keyword like "skier" on the demo website, the AI vision agent in the background first reasons about what a skier looks like - a person on skis going fast in the snow. It then acts by looking through video footage, trying to identify what it thinks a skier is, and indexing the relevant clips. Finally, it returns the identified clips to the user.

This process is impressive because the AI agent, rather than a human, is responsible for the entire workflow - from reasoning about the concept of a skier to taking action by searching and indexing the video footage. The programming behind the scenes is more technical and complicated, but the end result is a simple and intuitive application for the user.

Another example of an AI agent that the presenter is building is a basic AI agent using Nan. The presenter invites the audience to suggest in the comments what type of AI agent they would like to see a tutorial on next.

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In conclusion, we have covered the three levels of AI capabilities: large language models (LLMs), AI workflows, and AI agents.

LLMs are powerful text-generation models that can respond to prompts, but they have limited knowledge and are passive in nature. AI workflows, on the other hand, involve predefined paths that LLMs can follow to retrieve information from external sources and perform tasks. However, the human is still the decision-maker in an AI workflow.

The key difference in an AI agent is that the LLM becomes the decision-maker, able to reason about the most efficient approach to achieve a goal, take action using various tools, and iterate on the interim results to produce a final output. This shift from human decision-making to AI decision-making is the defining characteristic of an AI agent.

The examples provided, from the hypothetical social media post creation to the real-world AI vision agent demo, illustrate how AI agents can automate complex tasks and decision-making processes. As AI technology continues to advance, the role of AI agents in our daily lives is likely to grow, empowering us to accomplish more with greater efficiency and autonomy.