What is Grass (GRASS)?
Grass (GRASS) is a decentralized network that takes unused internet bandwidth and uses it to gather information from the public web. This information is then used to train large language models (LLMs), which are AI algorithms capable of understanding and generating text, just like a human would. Grass is crucial in helping AI labs access the massive amounts of data required to create these models.
Think of LLMs as the brain behind AI. They process billions of words and phrases from the internet to learn how language works. The more data they have, the smarter they get. Grass provides a continuous stream of public web data, ensuring that AI models stay up-to-date and improve over time.
Who Created Grass (GRASS)?
Grass is the product of a talented team of engineers and AI enthusiasts, but their individual names are not publicly known. Instead of focusing on the people behind it, Grass has built its reputation through its powerful technology and network. At the moment, Grass has attracted more than 2 million active nodes.
What VCs Back Grass (GRASS)?
Grass has generated a lot of excitement, and recently completed a $3.5 million seed round. This funding will help grow the network and make it even more powerful. The seed round was led by Polychain Capital and Tribe Capital, two major venture capital firms. Other notable investors include Bitscale Capital, Big Brain VC, Mozaik Capital, Advisors Anonymous, Typhon V, etc. With this impressive list of investors, Grass is set to make significant strides in the AI industry. These funds will help Grass expand its network, improve its data-gathering capabilities, and support its mission to train better AI models.
How Grass (GRASS) Works
Grass works by collecting unused internet bandwidth from users who choose to run a Grass node. A node is just a fancy term for the part of the network that handles data. People who join the Grass network allow the system to access their extra bandwidth, which helps AI labs collect data from across the web. This data is then processed and fed into AI models to help them learn.
Here’s a simple way to think about it: Imagine you’re watering your garden with a hose. While you water your plants, there’s still a lot of water flowing through the hose that doesn’t get used. Grass takes that extra water (in this case, your unused internet bandwidth) and uses it to help grow massive fields of knowledge for AI labs to harvest.
The Role of Public Data
Grass collects public web data, meaning it scrapes information that is freely available on websites like Wikipedia, Reddit, and news sites. It’s important to know that Grass does not access your personal data or private information. Everything it gathers is already public and can be accessed by anyone with an internet connection.
For example, AI models trained through Grass might analyze news articles to learn about current events, or social media posts to understand how people feel about a certain topic. The goal is to gather as much varied, real-world data as possible so that the AI can generate more accurate and relevant responses.
One of the biggest advantages of Grass is that it taps into real-time data. While some AI models rely on static datasets (like old encyclopedias or textbooks), Grass gives access to constantly updated information. This means AI models can answer questions about current events, trends, and even cultural shifts.
Large Language Models: How AI Learns from Grass
To understand how Grass fits into the AI ecosystem, let’s take a closer look at how large language models (LLMs) work. LLMs are like the brains behind AI chatbots, translators, and virtual assistants. They are trained on huge amounts of text data to learn how language works and how different words relate to each other. This allows them to generate human-like responses when asked a question.
But here’s the tricky part: Training an LLM takes enormous amounts of data. The more text the model reads, the smarter it gets. For instance, if an AI model is trained to understand everything written on Wikipedia, it can answer questions about any topic covered in those articles. However, to get even more accurate, the model needs to read from many different sources and keep up with constantly changing information. This is where Grass shines.
Grass allows AI models to access up-to-date public information by using its network of nodes. The AI labs connected to Grass can then use this data to create better, more accurate LLMs capable of answering all sorts of questions, from simple queries about everyday life to complex scientific problems.
