AI and Crypto: Current Trends and Updates

Table of Contents

The digital landscape witnessed a remarkable shift in 2023, marked by a surge in public interest in Artificial Intelligence (AI). Concurrently, the crypto market maintained its stability, offering a fascinating backdrop for the evolving narrative at the crossroads of AI and crypto. In this exploration, we dive into the state of the market and the transformative impact of AI on various facets of the crypto space.

I. Market Overview (2023):

1. Surging Interest in AI:

The year 2023 was marked by a substantial surge in global interest in Artificial Intelligence (AI), capturing the attention of the public on a scale never seen before. This surge was not merely a statistical anomaly; rather, it signified a profound shift in the collective consciousness towards embracing and understanding AI-related concepts. One of the most compelling indicators of this heightened interest was the discernible increase in Google searches for "AI" across the world.

Several factors contributed to this surge. The widespread adoption and integration of AI chatbots into various aspects of our digital lives played a pivotal role. These AI-driven conversational agents became ubiquitous, enhancing user experiences and fueling curiosity about the underlying technology. Additionally, the launch of new and innovative AI tools further propelled public interest, as people sought to comprehend the transformative potential of these advancements.

Media coverage played a crucial role in amplifying the AI narrative. News outlets, both mainstream and specialized, dedicated significant attention to AI-related developments, fostering a climate of curiosity and a desire to stay informed. Beyond this, a general thirst for knowledge about AI and its implications for diverse fields, ranging from healthcare to finance, contributed to the upswing in interest.

The pinnacle of this surge was evident in the Google Trends data, where the search interest in AI significantly surpassed that of "Crypto" and "Bitcoin." The graph painted a vivid picture of the growing fascination with AI, as it became a central topic of exploration and discussion globally.

source: google trend

2. Crypto Stability:

While the AI landscape experienced dynamic growth and heightened interest, the realm of cryptocurrency maintained a contrasting stability throughout the year. Searches related to "Crypto" exhibited a pattern of relative steadiness, with a slight downward trend in the early months of the year. This stability persisted through a substantial part of the year, interrupted only by a minor uptick towards the end.

The stability observed in crypto-related searches indicated a resilient and consistent interest in the broader cryptocurrency domain. Despite the volatility historically associated with cryptocurrencies, this steadiness suggested a maturing understanding and acceptance of crypto as a legitimate and enduring part of the digital financial landscape.

In contrast to the stable trajectory of "Crypto," the search trends for "Bitcoin" mirrored the overall crypto trends but with more noticeable fluctuations. This volatility could be attributed to various factors, including discussions around Ordinals/BRC-20s, potential spot ETFs, and the scheduled halving event in 2024. These events injected excitement into the Bitcoin narrative, resulting in price surges and, consequently, a renewed public interest.

II. AI Impact on Infrastructure:

1. GPU Demand and Shortage:

The surge in interest and advancements in Artificial Intelligence (AI) has not only captivated the public's imagination but has also generated a tangible impact on the underlying technological infrastructure. One of the most pronounced effects has been the unprecedented demand for Graphics Processing Units (GPUs), which serve as the workhorses powering the computations essential for AI processes.

As AI applications, especially large language models and deep learning algorithms, gained prominence, the demand for GPUs soared. Researchers, businesses, and startups engaged in AI-related research found themselves heavily reliant on these processing units for the computational power required to train and deploy sophisticated AI models. However, this heightened demand quickly outpaced the available supply, leading to a shortage of GPU chips in the market.

This shortage, while reflecting the remarkable pace of AI adoption, posed challenges for researchers and smaller enterprises. The high cost associated with acquiring GPUs became a bottleneck, hindering accessibility to the computing power necessary for AI-related projects. As a result, innovators in the AI space began to explore alternative solutions to address this growing issue and ensure a more inclusive environment for AI development.

2. Decentralized Compute Networks:

In response to the challenges posed by the GPU shortage, a transformative solution emerged in the form of Decentralized Compute Networks. These networks, often referred to as "DePIN" (Decentralized Physical Infrastructure Networks), presented a paradigm shift from traditional centralized cloud computing models dominated by major providers and hardware manufacturers.

Within the realm of DePIN, a notable subset emerged in the form of decentralized compute networks. Platforms like Akash, Render, Gensyn, and io.net pioneered this space, providing an innovative solution to the scarcity of GPU resources. Unlike centralized cloud solutions, these networks tapped into the latent computing power available across a decentralized network of users.

The principle driving decentralized compute networks involves establishing a two-sided marketplace. Those with idle computing power can connect with those in need of such resources, creating a mutually beneficial exchange. Suppliers of computing power receive incentives from buyers, creating a competitive and economically efficient environment. This decentralized approach not only addresses the GPU shortage but also promotes accessibility and affordability for researchers and startups engaged in AI-related projects.

- Examples: Akash, Render, Gensyn, io.net:

Akash, known for its decentralized cloud infrastructure, witnessed a surge in active leases during Q4 2023. The platform's two-sided marketplace model proved successful in connecting suppliers and buyers of computing power.

Render Network, specializing in decentralized compute services, experienced an elevation in the number of rendered scenes throughout 2023. This showcased the platform's ability to meet the demands of users seeking affordable and accessible GPU resources.

Gensyn and io.net, among others, further exemplified the diverse landscape of decentralized compute networks, contributing to the overall growth of this sector.

- Competitive Pricing and Activity Growth:

The pricing dynamics within decentralized compute networks demonstrated competitiveness. Platforms offered computing power at competitive rates, eliminating significant additional costs for suppliers.
Render Network's growth, as indicated by the elevated number of rendered scenes, highlighted the practical solutions offered by decentralized compute networks. Users found not only affordability but also increased activity on these platforms, signaling a growing recognition of their effectiveness.

The rise of decentralized compute networks not only mitigated the challenges posed by the GPU shortage but also fostered a collaborative and accessible environment for AI development. These networks, driven by the principles of decentralization, stand as a testament to the industry's resilience and adaptability in the face of technological challenges.

III. Zero-Knowledge Machine Learning (ZKML):

As the landscape of Artificial Intelligence (AI) continued to evolve in 2023, an innovative approach emerged at the intersection of AI and blockchain technology: Zero-Knowledge Machine Learning (ZKML). This paradigm-shifting concept sought to address the challenges inherent in integrating machine learning (ML) with smart contracts on blockchain networks, offering a novel solution to enhance adaptability, security, and transparency.

1. Integration with Smart Contracts:

Smart contracts, while efficient due to their code-based automation capabilities, often face limitations in adaptability, particularly in complex and unforeseen situations. This is where machine learning, a subfield of AI, stepped in to bridge the gap. ML models, trained on extensive datasets, possess the unique ability to learn, adapt, and make highly accurate predictions. Integrating these dynamic models into smart contracts presented an opportunity to introduce adaptability and flexibility, unlocking a new realm of possibilities.

However, a significant challenge arose in the form of the computational overhead associated with on-chain ML computations. This challenge prompted the conceptualization of Zero-Knowledge Machine Learning (ZKML), a groundbreaking approach that combines zero-knowledge proofs with machine learning. In this innovative setup, ML computations are processed off-chain, ensuring computational efficiency, while zero-knowledge proofs are employed to verify the integrity of these computations on-chain without revealing the actual data.

2. ZK Predictor by Upshot and Modulus Labs:

One noteworthy development in the realm of ZKML is the ZK Predictor, a collaborative effort between Upshot and Modulus Labs. This cutting-edge tool exemplifies the practical applications of ZKML by enabling secure verification of asset valuation without disclosing proprietary intellectual property.

The ZK Predictor, a product of combining Modulus Labs' zero-knowledge circuits with Upshot's expertise, holds the potential to revolutionize various aspects of the blockchain ecosystem. Its application spans from developing Automated Market Makers (AMMs) optimized for pricing long-tail assets to AI-driven on-chain index funds, each equipped with cryptographic proofs of their operation. Moreover, the ZK Predictor proves invaluable in the creation of prediction markets focused on specialized topics, enhancing the accuracy and verifiability of crowd-powered pricing signals.

3.Future Potential and Emerging Applications:

The ZK Predictor by Upshot and Modulus Labs is just the beginning of the myriad applications that Zero-Knowledge Machine Learning can enable. As the technology is still in its infancy, its potential to reshape and revolutionize diverse sectors is immense. More mature and widespread applications of ZKML can be expected in the coming years, ushering in a new era of secure, transparent, and adaptable AI integrations with blockchain technology.

The concept of ZKML is not confined to a single use case. Its versatility allows for applications beyond asset valuation, with potential implementations in areas such as price oracles, data analytics, and more. The collaborative efforts of companies like Upshot and Modulus Labs serve as a beacon, guiding the industry towards a future where AI and blockchain seamlessly coexist, maximizing the benefits of both technologies.

Conclusion

In conclusion, the integration of AI into consumer-facing applications not only transforms the way users engage with platforms but also opens up avenues for novel and personalized experiences. From content creation to interactive gaming, AI is fostering a new era of user participation and engagement in the digital realm. As these applications continue to evolve, users can expect even more dynamic, immersive, and personalized experiences facilitated by the seamless integration of AI technologies.

About Orochi Network

Orochi Network is a cutting-edge zkOS (An operating system based on zero-knowledge proof) designed to tackle the challenges of computation limitation, data correctness, and data availability in the Web3 industry. With the well-rounded solutions for Web3 Applications, Orochi Network omits the current performance-related barriers and makes ways for more comprehensive dApps hence, becoming the backbone of Web3's infrastructure landscape.

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AI and Crypto: Current Trends and Updates

I. Market Overview (2023):

II. AI Impact on Infrastructure:

III. Zero-Knowledge Machine Learning (ZKML):

Conclusion

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