# Building the Open AI Ecosystem of the Future with BagelDB and Bacalhau
Artificial Intelligence holds immense promise, but realizing its full potential requires overcoming key challenges like scarcity of data, lack of affordable compute, and difficulty collaborating. Two open source projects, [BagelDB](https://www.bageldb.ai/) and [Bacalhau](https://bacalhau.org), are tackling these problems in innovative ways. Together, they provide the building blocks for the community-driven, decentralized AI ecosystem of the future.

## The Bottlenecks in AI Development Today
Let's look at some of the major pain points in developing impactful AI applications today:

### Accessing Training Data
- Massive datasets required to train modern AI models, with billions of parameters. For example, GPT-4 was trained on 1.7 trillion text tokens.
- Most valuable datasets are proprietary and tightly controlled by Big Tech companies like Google, Facebook, OpenAI etc.
- Lack of tooling to easily build high-quality datasets and collaborate across organizations.
This data scarcity limits AI innovation to handful of large entities.

### Affording Powerful Compute
- Training complex neural network models requires specialized hardware like clusters of GPUs or TPUs.
- Compute costs for SOTA model training can easily run into millions of dollars.
- Makes running experiments accessible only to most well-funded companies and research labs.

Small teams don't stand a chance.

## Introducing BagelDB and Bacalhau
BagelDB and Bacalhau take a completely new approach to tackling these challenges in a decentralized way:

### [BagelDB](https://bageldb.ai) - The Open Home for AI Training Data
- BagelDB is building the most advanced vector database designed specifically for decentralized AI development.
- Enables publishing cleaned, vectorized data that is ready for model training.
- Flexible tools to collaborate on building Bespoke datasets tailored to specific needs.
- Incentives to contribute data to an AI dataset marketplace.

This unlocks an open ecosystem for AI data.

### [Bacalhau](https://www.bacalhau.org/) - A Shared Fabric for Affordable Distributed Compute
- Bacalhau provides a distributed compute engine to run jobs across a shared resource pool.
- Effortlessly scales model training by spreading workload across decentralized nodes.
- Runs jobs where the data is collected, saving money and time.
- Flexible job orchestration integrates with Docker, Python, JavaScript etc.

This makes large-scale model training accessible.

By combining open decentralized data and affordable compute, BagelDB and Bacalhau overcome the bottlenecks holding back AI innovation.

## Unlocking New Possibilities for AI
With BagelDB and Bacalhau, users can build models using community data on affordable distributed infrastructure. Let's see examples of what this enables:

### Advancing Healthcare with Medical Imaging AI
- Hospitals publish anonymized scans tagged with pathology on BagelDB.
- Medical researchers use Bacalhau's compute grid to train diagnosis models, without moving the data (limiting HIPAA exposure).
- Highly accurate models created with more data than any one institution could provide.

Democratizing access to data and resources advances medical imaging AI.

### Enabling Responsible Language Models
- Diverse language datasets curated openly on BagelDB.
- Models like GPT-4 trained using Bacalhau's worldwide capacity.
- Dramatically lower costs and carbon footprint compared to siloed efforts.
- Shared benefits instead of profit-driven motives.

Decentralization spurs creation of responsible language models.

These examples provide a glimpse into the transformative potential of open, community-driven AI.

### Let's Build the Open AI Future Together
BagelDB and Bacalhau demonstrate how decentralizing data and compute can accelerate innovation. By collaborating as a community, we can overcome the constraints holding back AI today. The future is open, accessible.