Matvii Diadkov, Bitmedia.IO founder and crypto geek. He also launched numerous Web3 gaming projects and holds a Master in Computer Science.
A blockchain consists of a series of blocks, each containing a list of transactions, a timestamp and a cryptographic hash that links it to the previous block. This structure creates an immutable chain of records where the arbitrary alteration, manipulation or falsification of data can’t happen without consensus from the majority of validators.
Instead of a centralized server, the network is maintained by nodes, which are individual computers that store the same copy of the blockchain on their devices. This allows anyone to view and audit the transactions that have already been processed into a block by validators and brought on-chain.
Smart contracts enable blockchains to support a wide range of programmable applications, such as non-fungible tokens, native tokens, decentralized finance protocols, digital identity and supply chain solutions. Researchers estimate the global blockchain market will grow from $4.8 billion in 2022 to $69 billion by 2030 at a 68% compound annual growth rate.
How AI And Machine Learning Transform Data
Artificial intelligence (AI) aims to create systems capable of performing tasks requiring human intelligence, such as problem-solving, decision-making, language understanding and perception. It employs methods ranging from rule-based systems to advanced neural networks. The goal is to simulate human cognition, enabling machines to process data, learn and adapt.
Machine learning, a subset of AI, develops algorithms that allow computers to identify patterns and make predictions. It includes supervised, unsupervised and reinforcement learning, each suited for specific problems. Deep learning, a branch of ML, uses neural networks to analyze large datasets; it excels in image recognition, natural language processing and speech synthesis.
What I see as one of the most important innovations is generative AI, which generates text, audio, images and other content. It powers tools like OpenAI’s ChatGPT and DALL-E, Google’s Gemini and Microsoft’s Copilot. Generative AI then applies the patterns and insights it has learned to produce contextually relevant outputs, enhancing productivity across industries.
Projected to reach $826.7 billion by 2030 and grow at a 27.67% CAGR, AI today serves as the foundation for many innovative technologies across a wide range of industries. It powers numerous applications in cybersecurity, healthcare, financial technology, gaming and other sectors.
How Blockchain Technology And AI Can Work Together
Integrating blockchain with AI can eliminate single points of failure and democratize access to AI and machine learning resources (e.g., data, models, computing power). A distributed ledger integration could also make models resistant to censorship while improving accuracy through the public verification of training data.
Instead of deploying and operating their own servers, AI startups could consider taking a community-driven approach, where validators maintain their own devices and contribute computing power to the blockchain network to power AI computations. This community-driven approach could provide increased flexibility in pricing. For example, the decentralized GPU rendering platform Render Network charges users for the render time they use. At the same time, those with idle GPUs can join Render’s ecosystem as node operators and share their computing power.
AI can also improve blockchain networks. It can be used to verify the accuracy of off-chain data sets. This could make decentralized apps and smart contracts more reliable and stable, especially in DeFi, where protocols rely heavily on oracles (which connect off-chain data to blockchains) for their day-to-day operations.
AI can add an extra layer of security to blockchains as well. Security platform CertiK, for instance, uses AI to secure and monitor smart contracts, dApps and network activity. AI models could also analyze the historical data of smart contracts to predict potential challenges to contract execution.
Potential Barriers And Challenges
But due to the nascent nature of blockchain-powered AI models, market participants need to consider that they could face multiple barriers and challenges to their applications:
• AI’s integration with distributed ledgers makes them more complex, which means the learning curve is steeper.
• Blockchains’ enhanced security and decentralization often come at the cost of limited scalability and throughput.
• If implemented via a low-throughput blockchain, high gas fees could raise the costs of AI computations.
• The lack of standardized protocols for integrating different AI and blockchain systems could negatively impact collaboration and the development of robust solutions.
• AI models trained on biased data can produce unfair or discriminatory outcomes.
• Regulators in most jurisdictions haven’t been able to keep up with the development of these technologies.
Case Studies Of Successful Blockchain AI Applications
One example of successful real-world integrations of blockchain and AI involves The Graph, a decentralized system that allows users to index and query blockchain data. The Graph uses AI to enhance data queries and automate decision-making, the company explained in a blog on its website titled “Using AI To Enhance The Graph Network.”
Moreover, the blockchain-powered real estate platform Propy applies AI algorithms to automate different real estate tasks, like reading purchase agreements and processing closings, the company said on its website. This can help real estate professionals improve efficiency.
Blockchain and AI: Revolutionizing Industries Through Synergy
The integration of blockchain and AI holds immense potential to reshape industries, fostering innovation, efficiency and accessibility, though there are roadblocks to consider. AI applications can help address some critical challenges like data privacy, model transparency and equitable access to resources. But scalability, interoperability and regulatory challenges pose hurdles to effective implementation.
However, new blockchain technology innovations, such as Layer 2 solutions and cross-chain protocols, can mitigate some of these issues and enable these systems to operate more efficiently. Moreover, as ethical concerns around AI biases and data security gain attention, I believe blockchain’s immutable and transparent nature may provide a foundation for more responsible AI applications.
Looking ahead, the collaboration between blockchain and AI could redefine how we manage data, interact with technology and build decentralized ecosystems.
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