Artificial intelligence (AI) has become an indispensable tool across various industries, from healthcare and finance to manufacturing and entertainment.
Due to this massive AI adoption, every organization wants to integrate with AI including big tech companies and also the emerging startups. But building an AI system is a very rigorous and power consuming task. There are many requirements that need to be fulfilled to have a responsive and robust AI model. AI models are fed with lots of data, and need continuous training which in turn requires heavy power.
To sum it up, there are majorly 3 AI needs that should be catered
Compute
Power
Data
Compute
GPUs provide the compute for training and sustaining AI models. But how does this system work?
To explain briefly- Models can be trained on our PCs as well but it would take a lot of time. GPUs are designed for massive parallel processing. They have thousands of cores compared to a CPU's few cores.
Imagine training involves many calculations happening simultaneously. A CPU tackles them one by one, while a GPU can handle numerous calculations at once, significantly speeding up training.
There are a few big players in the industry that sell GPUs to other companies who want to train their own model using their resources.
But we know that when you provide power to a few people they tend to misuse it. These big players sell compute at very large prices. Even though now we have better cost efficient services but still concerns about data privacy, security, and accessibility hovers around.
Data
Data is the most valuable asset of today’s world. Companies are selling user’s data and making money. Here, Privacy is the biggest concern!
AI models need lots of quality data to be trained upon so that they can generate best results. But again, would you trust someone for your valuable ventures?
There have been cases in the past where the big AI companies have copied the ideas from the data they had and the other companies faced a huge loss.
This calls for Trustless and Permissionless systems that can power innovation in the field of Artificial Intelligence. Hence, we have Decentralised AI to solve our problems
Decentralized AI
Decentralized AI offers a new paradigm by distributing AI models and data across a network of computers.
In decentralized AI (DAI), several aspects can be decentralized, not just one :
- Compute: This is the most prominent aspect. As mentioned already, traditional AI training relies on powerful centralized servers. DAI distributes this training workload across a network of devices like PCs, smartphones, or even specialized hardware. Each device contributes its processing power for training, creating a collaborative and scalable system.
Working - A complex computational task (like training an AI model) is broken down into smaller, more manageable pieces.These smaller tasks are then distributed to a network of devices like personal computers, smartphones, or even specialized hardware. These devices contribute their unused processing power to complete the tasks. Imagine your computer working on a complex model while you're browsing the web! In some cases, users might receive rewards (like cryptocurrency) for contributing processing power. Once individual devices finish their assigned tasks, the results are aggregated back together to form the final output. Techniques like secure coding and encryption can be used to ensure data privacy and security throughout the process.
Akash is a decentralized GPU provider
AI Model Hosting: Instead of a single server storing the trained model, Decentralized AI can explore decentralized storage solutions using blockchain technology. This allows secure access to the model from various locations without relying on a central server.
Training Data: A core principle of DAI is potentially keeping the training data decentralized. This means the data might not physically reside in one location but can be distributed across the network.
Decentralized AI systems can have many aspects of decentralization. Some might focus on decentralized compute, while others might aim for a more comprehensive approach encompassing multiple aspects. The choice depends on the specific goals and functionalities of that system
Centralized AI v/s Decentralized AI
Centralized AI:
Models are stored and trained on singular servers. Hence, it relies on a single server which creates vulnerabilities for centralized AI
Data used for training might be housed in a central location which causes data privacy issues
A single entity controls access and decision-making based on the AI model which is entitled to too much power in one’s hand
Decentralized AI:
It distributes training workloads across a network of devices like personal computers or smartphones. (Decentralized compute)
It explores decentralized storage solutions using blockchain for models.
It may involve keeping training data distributed across the network. (Decentralized data)
Advantages of decentralized AI over centralized AI
Scalability: Since decentralized systems use unused resources from numerous networks, Decentralized AI is accessible to everyone for training their models. This way even complex models can be trained.
Cost-effectiveness: Since not a single entity provides all the power, hence the cost can be cut down effectively.
Privacy and Security: Data may not need to be centralized for training, potentially enhancing privacy.
Transparency and Trust: Blockchain can provide a secure and transparent way to track training processes and model ownership.
Till now you must have got an idea about why Decentralized AI is the need of the hour!
But how to integrate Decentralized AI models? Many AI industry Giants have laid down their well versed infrastructure to help others integrate AI models in their systems.
Resources for Building an AI Model
OPEN-AI playground
Open-AI Playground acts as a bridge for users to interact with pre-trained AI models, providing valuable insights for future AI development and training.
Major feature -
Experimentation -Experiment with various models for tasks like text generation, translation, or code writing.
Analyzing - Identify strengths, weaknesses, and biases in models to guide further development.
Data response - See how models respond to different data, potentially informing data collection and curation for future training.
Feedback- Users can test the models and provide feedback automatically
Hugging Face
Hugging Face is a valuable resource for anyone interested in exploring or working with NLP and machine learning. Their open-source approach and community focus make AI development more accessible and collaborative.
Major feature-
Pre trained model- A vast collection of pre-trained models for performing various tasks like Open-AI are available.
Provides Dataset- They offer a repository of datasets for training and evaluating NLP models. This allows users to find datasets relevant to their specific needs and facilitates sharing data for research purposes.
Open-source Resources- Hugging Face provides open-source libraries like Transformers, which simplify working with pre-trained models and building new NLP applications. They also offer tools and pipelines to streamline tasks like data preprocessing and model evaluation.
Replicate
Replicate is a platform focused on running and deploying machine learning models.
Major feature
- Deploying Custom Models: If you've trained your own machine learning model, Replicate offers tools to package it and deploy it on their cloud platform. This allows you to share your model with others or integrate it into your applications without worrying about server management.
Kaggle:
Kaggle is a platform designed for data science and machine learning that offers a rich environment for various activities.
Major feature
- Datasets: They have a vast public repository of datasets on diverse topics. You can find data for your own projects or practice your data wrangling skills.
But all these AI are centralized and traditional AI models as told above. So, if one wants to build/integrate decentralized AI in their workflow, what are the alternatives of the above resources, where can one get datasets from? Where to experiment your AI models?
Decentralized AI Models resources
To make the developer experience easier and better we already have great decentralized alternatives.
Bittensor
Bittensor is a project aiming to revolutionize AI development by creating a decentralized marketplace for machine learning models.
Major Features:
Contribute Models: It trains its own models and contributes those models for the network.
Sell or Share Models: Monetize their models by selling them to others or sharing them for a fee.
Access Models: Purchase or access pre-trained models for various tasks within the marketplace.
Nillion
Nillion is a company working on a decentralized, non-blockchain network for secure computation and private data storage. Here's a breakdown of what Nillion offers:
Major features:
Focus on Secure Computation: Nillion tackles the challenge of securely processing and storing sensitive data on blockchains. Traditional blockchains, while secure for value transfer, often struggle with privacy concerns when dealing with high-value data.
Decentralized Network, Not Blockchain: Unlike some competitors focusing on decentralized AI built on blockchains, Nillion builds its own decentralized network. This network utilizes concepts from secure multi-party computation (MPC) to keep data encrypted even during processing.
Cluster Protocol
This platform is a go to place for Everything AI. It caters all kinds of AI needs for developers and also end users of the protocol.
Major Features:
Proof of Compute for Transactions - It has defined mechanisms for proving the completion of computation. It utilizes this approach to harness computing resources for AI tasks efficiently and securely.
Model Deployment and Monetization- It provides easy to use services for developers to deploy their models on the marketplace and then further get monetized.
Decentralized Datasets - Instead of relying on one central location, Cluster Protocol lets people access public datasets from various sources and securely store their own private data in personal vaults.
Computational Resources- It rents out decentralized GPU services. Service providers can list down their GPUs on the platform and then Cluster Protocol provides it to the AI model in decentralized fashion.
We have many Decentralized AI platforms to seamlessly integrate our AI models in our projects/products.
Conclusion
Decentralized AI has the potential to revolutionize AI development, but for widespread adoption it needs some key improvements. Reliable networks and efficient resource management are essential for smooth operation. Additionally, robust privacy measures are crucial to ensure user trust. Standardized tools and user-friendly infrastructure have already lowered the barrier to entry for developers. Furthermore, the focus on explaining how models arrive at decisions can foster trust and transparency.
With the help of a decentralized network it makes the ecosystem more safe and accessible for both developers and users. It fosters innovation by enabling anyone to contribute to the training of powerful AI models in a collaborative manner. This technology is all set for mass adoption.