Author: DWF Ventures Source: medium Translation: Shan Oppa, Golden Finance

< p style="text-align: left;">This article will delve into one of the hottest topics this year - artificial intelligence (AI). Over the past year, artificial intelligence has been at the center of discussion thanks to the launch of OpenAI’s ChatGPT 3.5. The launch demonstrates the huge economic potential of artificial intelligence. This has sparked global discussions about its future, impacts and associated risks.

As optimism grows, so does skepticism. Potential problems are starting to sound alarm bells to regulators. It echoes the early days of the cryptocurrency space, with the rapid rise of artificial intelligence and a vague regulatory framework. There are similarities between the two industries that highlight the decentralized nature of web3, which seems to complement the potential centralizing power of artificial intelligence. Soon, almost all web3 VC discussions in Q1 were focused on the transformative potential of AI. (At one point, I wondered if I was attending a web3 or AI event.) During the year, we also saw a number of VC firms pivot to AI or incorporate it into their investment mandates.

Now that the hype has died down over time,DWF Ventures plans to take a fresh look at the field of artificial intelligence with an unbiased look at it. This article provides a brief overview of the evolution of artificial intelligence and how it reached its current level of popularity. However, there is a clear shift in the narrative of the article, from the traditional focus on how artificial intelligence affects web3 to exploring the opposite direction - how web3 affects artificial intelligence. In this exploration, we take a deep dive into how decentralization and web3 can act as catalysts to solve the current challenges facing artificial intelligence.

AI Overview and Breakthrough of ChatGPT 3.5

Contrary to the recent hype surrounding artificial intelligence, its The history goes back to the 1930s. Turing's work in 1950, including the Turing Test, helped formalize the foundations of artificial intelligence. Despite early optimism, enthusiasm waned in the 1970s due to computational difficulties and the inability to meet real-time demands, ushering in the "AI winter." In the 1980s, expert systems reinvigorated artificial intelligence by using knowledge databases to simulate human expertise. This era also saw the revival of connectionism and the rise of recurrent neural networks.

However, expert systems faced challenges in knowledge acquisition and real-time analysis, leading to their decline in the 1990s. The performance of personal computers has led to their waning relevance. The field of artificial intelligence has made great progress over the years and has expanded into different technical fields such as machine learning, natural language processing, computer vision, and speech recognition. These developments have allowed artificial intelligence to develop from simple problem solving to deep learning in complex application areas.

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In its development, artificial intelligence has witnessed the convergence of its various subfields. Among these areas, significant progress has been made in vertical transformation in the fields of machine learning and LL.M. The paper "Attention is All You Need" by Ashish Vaswani et al. Notable is the inspiration from the GPT (Generative Pretrained Transformer) model. Since then, a large number of GPTs have appeared in this field, such as the two-way “BERT” GPT and the OpenAI team’s GPT. After ChatGPT, open source alternatives such as Falcon and LLaMA2 emerged, intensifying competition for the next iteration of GPT, which may be closer to AGI (Artificial General Intelligence).

GPT’s hype has helped bring artificial intelligence from academia into the consciousness of billions of people. Within 2 months of launch, OpenAI set a record for the fastest user base to reach 100 million weekly active users. A recent study by McKinsey revealed that approximately 51% of professionals in the technology industry currently utilize AI to some extent in their work.

Artificial Intelligence Reality: Coping with Social Perceptions and Practical Limitations of Centralized AI

A recent poll conducted by Vitalik in his article shows that there is a widespread sentiment among many people to delay progress in artificial intelligence, fearing the emergence of a monopolistic version.

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The recent surge in attention can be traced to ChatGPT's meteoric rise to fame, driven by its human-like responses. However, what most people don’t realize is that while GPT mimics human interaction, it is not AGI.

GPT statistically changes every time it generates output, lacking consistency and factual accuracy guarantees. GPT also faces other limitations, but its most prominent shortcoming is its inability to perform logical reasoning, especially in mathematics.

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Given the myriad concerns surrounding AI and the existing challenges of effectively managing large AI models, exploring the integration of Web3 emerges as a potential way to mitigate challenges facing AI. Leveraging the decentralized and distributed computing principles inherent in Web3 can help solve the problems currently faced by artificial intelligence systems.

The road to decentralized artificial intelligence: overview, potential and challenges

Artificial intelligence capabilities Concentration in centralized systems raises concerns about data access, model relevance, and the overall sustainability of AI applications. Centralized AI systems face significant obstacles. Especially for large, proprietary data sets that are often proprietary.

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This results in monetization on a per-query basis and a daily limit on post views on X.com. Soon, Grok, the X.com GPT was released, allowing users to access X.com data in real time. This model creates economic barriers and raises questions about accessibility and inclusivity of AI’s benefits.

Author: DWF Ventures Source: medium Translation: Shan Oppa, Golden Finance

In addition , without continuous data updates, published models quickly become outdated, posing a huge challenge to maintaining relevance and accuracy. Currently, ChatGPT 3.5 training data constitutes information as of January 2022. Llama 2 was also trained on data from January 2023 to July 2023.

To address these challenges, DAI emerges as a promising paradigm that provides potential solutions to the limitations of centralization.

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Decentralized AI offers an alternative trajectory to address the challenges inherent in centralized models. A recent meta-analysis paper by Janbi et al. As a comprehensive guide, DAI is divided into five main areas.

DAI’s Challenges

DAI has brought about artificial intelligence development An exciting transformation that offers numerous advantages. However, it is crucial to acknowledge the challenges these advances bring.

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Conclusion

All in all, the journey to decentralized artificial intelligence is unfolding with huge potential. Realizing the full power of decentralized AI relies on reaching critical mass, driven by the existing pool of AI users. While open source alternatives face barriers due to limited vendors and users, the ChatGPT API provides a practical and affordable option for the mass market, offering ease of use and reliability.

However, given the potential consequences of monopolistic AGI, individuals should reconsider the trade-off between convenience and decentralization in their choices and actions . On a broader scale, innovators in the web3 and AI communities can address challenges by redefining AI workflows, reimagining infrastructure, adopting innovative paradigms, managing efficiently, and developing applications that adhere to decentralized principles. As we continue down this path, collaboration, inclusivity, and ethical considerations will be key to shaping the landscape of decentralized AI that truly benefits humanity.