“Information is the oil of the 21st century” (Information is the oil of the 21st century)a story written by former Gartner executive Peter Sondergaard in the early 2010s, describes the importance of knowledge in the digital world.
However, with the advent of artificial intelligence (AI), this story has changed in recent years. Because of the scale of data needed to train large language models (LLMs)* for AI, data has become the new gold. According to Statistain just five years, the amount of global data created, collected, copied and consumed will triple.
The impact of AI in the data sector is undeniable, and investors appear to be putting their money where the data is. Crunchbase estimate that more than $1 of every $4 invested in US-based start-ups went into companies undertaking AI-related projects by 2023, a double increase from with the previous 4 years.
Percentage of US venture funding going to AI-related startups. Source: Crunchbase
These impressive numbers created an “arms race” with huge capital flows investing in AI, and organizations soon realized that AI was facing unresolved challenges in the world. astonishingly large scale for an industry that is on track to surpass $200 billion by 2024 and is expected to ants will achieve 1.81 trillion USD by 2030
Why does AI need blockchain?
Data is the lifeblood of AI, either enriching or damaging digital life based on its management and operational capabilities. If the data fed into AI is not authentic and verified, the risk of creating bias in the AI is high. To make AI production-ready for the broad scale of impact this technology promises, it is necessary to limit biases, manage bias concerns, and avoid intellectual property violations.
The rise of AI shows us exponential growth in data processing, leading to an increasing need to verify and authenticate information. If large organizations and nearly every business are to use these massive efficiency and productivity tools of the information age, they need to be accurate.
Thanks to “innate” features such as immutability, traceability, and transparency, blockchain is considered the perfect combination to solve AI-related headaches. We can leverage the power of decentralization to ensure the information stored or processed within it will always be accurate, verifiable, and undistorted over time. However, blockchain seems to be too busy fighting against its own demons – namely the triple dilemma: achieving security, scalability and decentralization together. at – to help businesses looking for reliable infrastructure to set up shop in the AI space.
Scalability and interoperability
Most blockchain networks struggle to keep up with today’s bandwidth demands, leaving the picture of a future where such infrastructure becomes viable for the exponential growth of data increasingly become obscure. With sharded multi-chain networks allowing parallel processing of transactions, Polkadot aims to eliminate the bottlenecks plaguing legacy networks.
Diagram simulating how the sharding mechanism works
Sharding is an established technique in computer science for processing large amounts of data by dividing it into smaller, more manageable segments. When used in a blockchain context, Polkadot’s sharded multi-chain network allows for the simultaneous processing of multiple transactions on several chains. This improves scalability and positions Polkadot as an ideal infrastructure to handle the data explosion caused by AI.
Besides the core benefits provided at the infrastructure level, the Polkadot ecosystem also contains many parachains – individual Layer 1 blockchains operating within the Polkadot network – to expand its capabilities. Here are some examples:
Decentralized knowledge graph
AI is only as good and effective as the data it trains on, and this is why “feeding” it with verified data is so important to prevent it from making mistakes. OriginTrail – a Polkadot parachain that is building a decentralized knowledge graph – is trying to find a way to address concerns about data authenticity.
OriginTrail curates critical knowledge assets, making them discoverable and verifiable using the power of decentralized technology. Instead of trusting a central party to create and hold a knowledge graph to perform search queries and train AI, OriginTrail encourages the creation of an open network of knowledge assets – supply chain data, product details or other real-world information – anyone can contribute and verify.
The platform has partnered with BSI – the UK’s national standards body – to ensure transparency across the pharmaceutical supply chain. The EU’s Next Generation Internet Initiative has also chosen OriginTrail to help build cutting-edge infrastructure for the European Internet.
Coprocessor for blockchain
Phala Network is a coprocessor supported by a decentralized physical infrastructure network (DePIN) – execution layer for Web3 AI. Phala’s technology stack enables AI to understand and interact with blockchains and provides a toolkit to help developers build, ship, and monetize smart contract-focused AI agents.
Phala aims to become the co-processor for the entire blockchain ecosystem. Source: Phala Network
AI Agent Contract of Phala enables developers to build smart contract-centric AI agents with the following benefits:
- Convert smart contracts into AI agents: Developers can develop smart contract-focused AI agents for popular Web3 services and smart contracts. These AI agents can be managed through one decentralized autonomous organization (DAO) to execute business logic.
- Decentralize ownership and monetization: Users can own their agents and build a profitable token model using the default token model provided or by customizing their own model.
- Connection to the Internet of multiple agents: Agents on Phala are by default accessible to other cross-platform AI agents deployed on Autonolas, FLock.io, Morpheus, Polywrap, and other similar platforms.
AI network for P2P collaboration
Imagine a global network of places artificial intelligence (AI) models collaborate and learn from each other. Instead of relying on a central authority, the network operates in a decentralized manner, increasing accuracy, minimizing corruption risks and improving efficiency.
This is the idea behind Bittensor – an open source protocol powering a decentralized machine learning network, tokenized and built on top of blockchain. Bittensor is an AI network that encourages peer-to-peer (P2P) sharing and collaboration of decentralized machine learning models.
In this system, AI models can share knowledge, information, and even computational resources directly with each other. It is like a digital marketplace where AI agents exchange information and improve together. The ultimate aim of this model is to achieve the “democratization” of AI by making it accessible to more people, not just large corporations or research organizations.
With a growing ecosystem and vision towards the future, Polkadot aspires to play an important role in the AI revolution. The project provides peace of mind about future upgrades as well as a robust platform that allows bandwidth and scalability to grow to meet the huge processing needs of information and data growth. exponentially. As the brave new AI landscape rapidly approaches, Polkadot is at the forefront, enhancing productivity and solving real-world challenges.
*Large Language Model (LLM) is a type of artificial intelligence model capable of processing and understanding natural language in a complex and realistic way. The main feature of LLM is the ability to learn and self-correct based on big data to be able to generate new text, respond to questions, or perform language-related tasks such as translation and synthesis. document.
Itadori
According to Cointelegraph
