We often talk about feeding the database. The question quickly becomes what database?
It is important to explain what we are dealing with and the potential of LeoAI. We easily see the potential of what we are dealing with.
databases are key. This leads to the development of things such as recommend features, search engines, and extremely personalized experiences.
So, with LeoAI, what are we dealing with?
In this article we will dig into the two databases that are in operation along with what can be built.
The Important Of LeoAI
LeoAI is nothing without its databases. There are two that we are dealing with.
The first is the Hive Database. This is where LeoAI is pulling much of its information. Whatever is posted on Hive is pulled and feed into the model.
Of course, everything posted using a Hive front end is eligible. We have to be clear, we are dealing with text. That is all that resides on Hive.
We also have a second database. This is the vector database that is built as a result of the training of LeoAI. This is where the data is pulled from Hive and assimilated into another database. This is what allows LeoAI (and other AI systems) to pull the information.
Essentially, the feeding of Hive with more data means that we will see a larger vector database.
Before going any further, let's dive a big deeper.
Vector Database
What is a vector database?
Vector databases are the powerhouses designed to store, manage, and query complex data like images, text, and even abstract concepts. But their true superpower lies in their ability to perform lightning-fast similarity searches, finding needles in digital haystacks in the blink of an eye.
The key is how they operate in a rapid manner.
Another key is the enormous benefit of adding to them. This is more than just adding to a database like Hive. When feeding a vector database, the ability to learn and expand grows.
They're the powerhouse behind numerous machine learning algorithms, playing a crucial role in everything from traditional AI to cutting-edge generative models. By adding content to a vector database, you're not just storing data – you're fueling a system that learns and evolves with your business. The beauty of vector databases extends beyond machine learning, though. They unlock a world of possibilities, from supercharging search capabilities to enabling hyper-personalized customer experiences.
The algorithms are then used to find patterns. computers operate based upon a set of numbers, something that aligns with vector databases. This is why data can be pulled quickly and with growing accuracy.
It becomes a self-feeding proposition.
More data is added to the database, increasing its learning capability. This helps to create better predictions cause the computer finds even more patterns. It is a situation that keeps expanding as more information becomes available.
Building Out AI Services
The most common utility, so far, are chatbots. We see all the major players rolling them out.
All of this is based upon the capabilities of these vector databases. Here are just a few of the features that can be built:
- image recognition
- natural language processing
- recommend systems
- search
Obviously, natural language processing is what allows for chatbots.
Meta builds AI search engine to cut Google, Bing reliance. This is where we can see Big Tech starting to incorporate larger offerings. Meta is certainly going to be at the core.
Why is this important?
We can start to take the same approach to LEO. There is no reason that, over time, LeoAI can be utilized for search. The concept if very simple. Keep feeding it data and let the training handle the rest.
Essentially, whatever the big players are doing can be duplicated with Leo. The contingency is cost which is, of course, a barrier to many things.
Vector databases are very powerful.
In NLP, vectors can represent words, sentences, or even entire documents. A vector database can then be used to find text that is semantically similar, even if it doesn't contain the exact same words. This capability is foundational for applications like search engines, chatbots, and recommendation systems, where understanding the meaning behind the words is more important than just matching keywords. It is also important for effective use of generative AI in many systems.
In other words, whatever is entered is compounded as compared to simply writing to the Hive database. When it is fed into the vector database, it is actually part of the learning process.
Hence, whatever services (features) are developed, such as search, recommend, or image manipulation, this is possible due to the vector database.
In Conclusion
What Leo is doing is a lot more than just feeding the Hive database. Each post, comment, and thread add to the vector database. To my knowledge, nobody else is doing this which is going to reveal the power of LeoAi.
One final thought: it is crucial build as fast as possible.
The basic components are data, algorithms, and processing.
Obviously, we have no control over the amount of processing power is utilized. It would be great to be able to double or triple that but it is out of our hands.
The other is algorithms which is in the hands of the developers.
What is not is the data. That is something that is influenced by what is generated on the platform. Again, this is more than just feeding the Hive database. It is actually impacting the capabilities of LeoAI and what it capabilities it has in the future.
Ultimately, we are going to end up at the agentic Internet.