Glimpse from Data Science Meetup 8.0: AI Agents and LLMs

Overview

Lucent Innovation had the Data Science Meetup 8.0, which was a great hit, having attendees from freshers, college students, and even professionals. We discussed great topics like AI agents and large language models in the meetup.

Our speakers, Siddhant Pandey and Margi Shah, briefly brainstormed these topics by sharing their relevant experiences and industry insights. Our speakers enthusiastically shared their knowledge and adequately addressed their queries with the audience. Plus, the audience was eager to learn about this advanced technology to stay ahead of the curve. Here, we will explore the key highlights of the meetup.

Decoding AI Agents and How They Work

Siddhant Pandey, an AI engineer, started the meetup with a brief introduction to AI agents and computing machinery and intelligence. If we look at the definition of AI agents, it goes like this, “AI agents are a software program capable of learning, reasoning, and decision-making.”

It was briefly described with an example showing the conversion between AI agent Eliza (1966) and humans. After the basic AI and human conversion, Siddhant also displayed how AI was revolutionized in 2010, being helpful for everyday tasks, from sending reminders to playing games.

Further discussion covers the topics of transforms, the limitations of LLMs, empowering models with tools, and the role of the React framework in educating AI agents. We also got to learn briefly about the Agent’s thought process step by step. He also described some essential components required to build good agents, which were;

• Great context,
• Decompose and simplify,
• Plan and route workflows.

It may not be as easy as it may seem, but AI is something that demands deeper research, constant experimentation, and optimization.

Unlocking the Potential of Large Language Models

Our next speaker – Margi, took over the session and is a full-stack Java developer passionate about learning and working with advanced tech stacks. Margi gave a brief overview of Large Language Models.

“LLMs - Large Language Models are the advanced AI models trained on huge data to understand, process, and generate human-like language.”

Then, she continues the session by describing how a large language model works with a simplified process. We also had a great discussion of LLM Encoder and Decoder with an Evolutionary tree. Then, she explained the LLM Encoder Only and Decoder Only models with their examples and use cases.

Other topics were about LLM architecture, applications of LLMs, and LLM agents. Margi completed the session with great insight into the LLM-as-agent, their real-world challenges, and their distinct environment.

After learning about the AI agents and LLM-as-agents, the question of whether they share any similarities with each other surely arises! Let’s get to know.

Are They the Same?

No, AI agents and LLM agents are not the same. They differ in particular manner, such as core technologies, capabilities to execute tasks, learning types, and interactions.

• Basically, LLM agents are a subset of AI agents. LLM agents specialize in natural language tasks, while AI agents have diverse capabilities.
• LLM agents are AI agents that utilize LLMs to execute tasks such as automating workflows, resolving queries, and generating content.

Hope this helps you clear the confusion.

Data Science Meetup 8.0: Successful Gathering of Like-minded People

The meetup ended with lots of conversions, Q&A, and some group pictures. Having professionals at the meetup, along with the 11th and 12th standards students, makes it a true hit.

When people learn about the workings of AI agents and large language models, we always try to come up with interesting topics that provide sufficient knowledge to the audience while also entertaining them.

From content to conclusion, the goal of the meetup was to educate people about emerging industry trends, and it was satisfied. So that’s it, let's meet at another data science meetup. Stay tuned with us to learn more.

Alos read: Gen AI in Action: Lessons from Data Science Meetup 7.0