The ideal fusion of AI and cryptocurrency investigates the future
Overview of AI: From mimicking human cognition to real-world breakthroughs
Artificial Intelligence (AI) as a discipline aims to mimic or simulate human cognitive intelligence, performing a range of tasks such as learning, reasoning, problem solving, and understanding natural language. Although AI has long been a niche field of study, it has seen real breakthroughs with the emergence of technologies, especially those like ChatGPT.
Panorama of AI Technology
When it comes to AI, ChatGPT and generative AI prompt are likely the first things that come to mind. however, this is just the tip of the iceberg. In fact, the complexity of the AI field is far beyond our imagination, and in order to better understand the field, let's briefly explore the various technology layers and components that make up the AI technology panorama:
AI Computing Hardware
The world of AI is not just about code. It is resource-intensive and requires specific physical infrastructure. These physical devices ultimately form the basis for executing computations and algorithms that ensure the proper functioning of AI systems. It goes without saying that without this hardware, there would be no modern AI.
Cloud Platforms
Cloud service providers are large corporations with vast resources that acquire and operate this powerful hardware, allowing developers to use these resources on a pay-as-you-go or subscription basis. This eliminates the need for developers to invest in maintaining their own physical infrastructure.
The perfect blend of Crypto and AI
1. The combination of smart contracts and machine learning: smart contracts are a kind of programmed protocols that can automatically execute contract conditions. Combined with machine learning, smart contracts can be more intelligent to deal with a variety of complex situations, providing a more powerful user experience.
2. Security and transparency of cryptography: through cryptography, AI systems can handle sensitive information more securely and protect user privacy. At the same time, the transparency of blockchain technology provides a more credible data source for AI systems, enhancing their trust and reliability.
3. Unlocking new AI use cases: the development of cryptography provides new application scenarios for AI. In addition, smart contracts can be combined with AI systems to automate transactions and contract execution, thereby increasing efficiency and reducing costs.
Emerging Crypto x AI Verticals
From centralized cloud providers to DePIN:
The foundational layer of AI is the hardware and cloud providers. While crypto cannot compete in producing better hardware, it can play a role in accessing multi-node supercomputing in a more efficient, secure, and decentralized manner. This is a subfield of the crypto space known as DePINs (Decentralized Physical Infrastructure). These represent blockchain protocols that incentivize decentralized communities to build and maintain physical hardware.
Support for transparency, user management and data ownership:
As with all things, models have biases, and depending on how the model is created and the training data, the output can be very different. There's a good reason why AI models and training should be chained to decentralization and should have a higher level of transparency.
By utilizing crypto-infrastructure, we can avoid repeating the same mistakes of internet applications. We can have collective ownership, decentralized governance, and transparency at every level. That's the way forward.
Alignment Incentives and AI Monetization:
Cryptography can incentivize individuals to monetize private and public datasets as well as AI models, intelligences, and other parts of the AI stack.
On-chain AI/ML (ZKML & opML):
When we talk about ZKML, we are talking about the possibility of bringing ZK (zero-knowledge) proofs to the "reasoning" and "data" parts of machine learning models (rather than the computationally too intensive training part).
Another approach is OPML (Optimistic Machine Learning), which uses an OPTIMISTIC approach to implement AI model inference and training/fine-tuning on a blockchain system.
Authentication and Privacy:
With the growth of AI applications, we are approaching a tipping point where no one will know if online content is real or simulated. Given this reality, there is a strong case for storing decentralized identities on the blockchain. This would prevent people from interacting with AI bots without realizing it, and would allow for the distinction between real information and deeply fake information.
