Blockchain Scalability Solutions: Enabling AI's Next Leap

The digital ecosystem continues to grow as a result of the growing volume of data that is created in the information society and the latest technologies that are developed as a result of an unprecedented wave of investment, as well as technology ventures and companies that are betting on innovation. Public policies try to follow the pace and speed of technological advance but achieve the necessary consensus at the government level. However, the relationship between technocracy and political decision is called into question by the vision of progressivism that sometimes understands that there is no coherence between the purpose of the academy, mainly the research of universities and think tanks of a private nature and national purposes. The laws on these issues remain rather an example of multiple projects that failed in their conception and pragmatic development. Legislative bodies sometimes do not have enough expertise to synthesize and resolve the main problems between technology and law.

Blockchain and artificial intelligence (AI) are two innovative and disruptive technologies that, although different in their applications and functionalities, can complement and benefit each other in certain contexts. Both technologies are constantly evolving, and their convergence has the potential to transform various sectors such as data management and analytics, cybersecurity, and global e-commerce supply chain traceability.

Solutions are being developed to address blockchain scalability challenges, allowing for greater performance and broader adoption. This could make it easier to manage the large amounts of data needed for training AI models.

For its part, the creation of standards and protocols that improve interoperability between different blockchain development company platforms, which would facilitate the transfer of data and assets between systems, benefiting artificial intelligence applications. Protocols and standards such as ERC-20 (Ethereum Request for Comments 20) for tokens or the Hyperledger Fabric standard are examples of standards that allow different blockchains to work together.

The mechanisms are diverse, a chain bridge is a mechanism that allows the transfer of assets or data between different blockchains. This facilitates interoperability by connecting different blockchain ecosystems and allowing assets to move from one chain to another securely. Likewise, robust and standardized application programming interfaces (API) can be provided, facilitating communication and interaction between different blockchains. Well-defined APIs allow developers to build applications that can work on multiple blockchains.

Blockchain & AI

Security and trust:

Blockchain provides a decentralized and secure system for data storage. The immutability of records in the blockchain helps prevent manipulation of information. As a consequence of the blockchain, smart contracts appear, autonomous computer programs that automatically execute agreements when certain conditions are met. Smart contract interoperability involves the ability to execute contracts on different blockchains, enabling collaboration and process automation across chains.

Some solutions seek to achieve interoperability through consensus mechanisms that span multiple blockchains. These mechanisms allow different blockchains to come to an agreement on the state of the network and the validity of transactions.

Artificial Intelligence can use secure data stored on the blockchain to train models reliably, since the data cannot be altered once it is recorded on the blockchain.

AI can contribute to the formation of consortia and alliances between different projects. Blockchain can foster interoperability by establishing shared rules and standards that facilitate collaboration between the parties involved.

1. Data management:

Blockchain offers an efficient and secure way to manage and share data between different parties without the need for an intermediary.

AI requires large amounts of data to train effective models. Blockchain can be used to guarantee the provenance and authenticity of data used in training AI models.

One of the fundamental characteristics of blockchain is immutability, meaning that once information is recorded in a block, it cannot be modified. This can be a challenge when it comes to personal data, as any errors or incorrect sensitive information initially recorded cannot be easily corrected.

While cryptocurrency addresses on a blockchain are pseudonymous, some blockchain systems may have features that allow the linking of addresses with real-world identities. Managing user privacy and anonymity is crucial to protecting personal information.

The inclusion of personal data in a blockchain must be supported by the informed consent of individuals. Users need to understand how their data will be used and what implications the immutability of on-chain information may have.

Since blockchain is transparent and accessible to all participants, it is essential to ensure that personal data is encrypted and protected. Information security must be a priority to prevent unauthorized access.

2. Tokenization of assets and smart contracts:

Blockchain enables the creation of tokens and smart contracts, making it easier to represent and automate agreements and transactions.

AI can leverage tokenization to improve efficiency in contract and transaction execution, as well as digital asset management.

3. Privacy protection:

Blockchain can provide greater control over data privacy and access through mechanisms such as encryption and decentralized key management.

AI can benefit from the enhanced security and privacy that blockchain offers when manipulating sensitive data during model training.

4. Incentives and rewards:

Blockchain allows the creation of reward systems and tokens that can motivate user participation and contribution.

AI can use these incentives to improve the collection and quality of data used in training AI models.

Together, the combination of blockchain and AI can contribute to a more secure, transparent and efficient digital ecosystem, especially in situations where trust, data integrity and automation are crucial. Both technologies are constantly evolving, and their interrelation continues to be a challenge for public policies that tend to Balkanize their study and take technologies separately to regulate them or create legal or ethical standards in this regard.