What is an AI agent? How does this differ from AI models?
These are important questions regarding Leo. We are seeing more added to the platform, so it is helpful to understand the difference.
LeoAI is something that is discussed a great deal. It is something that is in the process of being developed. We are the ones who feed it the data to train on so this is a community-wide initiative.
In fact, the AI agents can help with this process.
Here is where Leo stands now. In this article, we will go through it to explain how things are shaping up.
AI Models Versus Agents
An AI model is something that is trained. Over time, it learns, incorporating the new data in.
This contrasts with agents, which are designed for a specific purpose. One way to look at it, from the software world, is as middleware.
Basically, the agent is there to perform a specific task. One example I use is to book a hotel room. If the agent is tied to your calendar, and you have to be in Dallas on the 27th-29th, the agent will book a room based upon programmed criteria.
The agent doesn't change. It is the leopard doesnt change its spots. This is what it was designed for.
An AI model is actively learning. It is tied to a vector database, incorporating all new data that is entered. It is embedded and indexed, helping to develop meaning from the data already present.
This is what ChatGPT, Grok, Llama, and other LLMs do.
LeoAI and Agents
LeoAI is a small language model. It is taken from Llama, and then integrates the data from the Hive blockchain. Each time a new post (or thread) is made, it feeds into the vector database. This is used to train the model, similar to how Big Tech does it.
We are in the process of seeing LeoAI developed. The problem, thus far, is we lack the required data. People simply are not producing enough on Hive to properly train the model. This means we could roll out something that is insufficient in its capabilities.
An agent is something suck as Rafiki. This is designed to do certain tasks. Here, it takes input (a thread) and submits that as a prompt to VeniceAI. That model provides output, which Rafiki then posts as a reply thread.
This means that Rafiki is not going to learn. It is dependent upon the output of VeniceAI. Improvement comes from the expansion of that model.
What Rafiki does is to enlarge the vector database. This is what is feeding LeoAI, being used to train the model. Hence, Rafiki is a component of LeoAI, albeit not a model.
Posted Using INLEO
So how do you propose we change that, it is a good model but decentralized training solutions are still evolving case in point bittensor...
The LeoAI model is in need of data. So the first step is adding a ton of data each day.
Where do we get data from...
We have to provide it be engaging with InLeo. One easy way to do it is to use Rafiki, the AI agent.
Simply use the prompt #askleo on threads.
This is a very clear and helpful explanation of the difference between AI models and agents, and how they fit into the development of LeoAI. It's great to see the community-driven approach to training Leo, and understanding the role of agents like Rafiki in that process is key.
We simply need a lot more data. That is why the push is on.
We just need more people that understand why its important. If we look at it now I have a feeling that more people are against ai agents and ai on Hive.
Yep. They are in the stone age.
Why do you want to train it? What is the goal? What is LeoAI looking for?
You broke this down real well. From what I got, AI models are the brains that learn and adapt while AI agents are more like tools designed for specific tasks. LeoAI is still developing and involvement is key to its growth.
Thank you formaking this very clear and simple to understand. Glad to see the several AI agents popping up here. It'll all make leoAI bigger.
#hive