AI and Crypto: Unveiling the Power to Authenticate and Combat Fake Content

Table of Contents

1. Introduction
2. The Fusion of AI and Crypto
   • 2.1 Generating AI and Faking Everything Online
   • 2.2 Verifying and Authenticating Content with Crypto
3. Technological Overlap between AI and Crypto
   • 3.1 Example: AI-Generated Arrest Photo
   • 3.2 Cryptographic Verification of Content
4. Web 3.0 of Trust
   • 4.1 Putting Trust in Onchain Data
   • 4.2 Decentralized Web and Crypto Verification
5. Fighting Fakes with Crypto
   • 5.1 Onchain Citations and Perplexity
   • 5.2 Decentralized Web of Trust Explorers
6. Addressing Spam with Crypto
   • 6.1 Busting Captchas with Crypto
   • 6.2 Charging Scarcity for Authenticity
7. Decentralizing AI Training and Evaluation
   • 7.1 Centralized vs. Decentralized AI Training
   • 7.2 Decentralized AI Evaluation
8. Embracing Polytheistic AGI
   • 8.1 Shifting the Concept of AGI
   • 8.2 Community-specific AGI and Onchain Citations
9. Conclusion
10. Resources

AI Plus Crypto: Beyond the Cliches

In today's digital age, two buzzwords have captured the attention of tech enthusiasts and industry experts alike: AI and crypto. While often discussed in vague and handwavy terms, the fusion of artificial intelligence (AI) and cryptocurrency (crypto) holds great potential for reshaping the way we perceive and authenticate content online. In this article, we dive beyond the cliches and explore the specific areas of technological overlap between AI and crypto, highlighting their combined power in making everything fake, then real again.

The Fusion of AI and Crypto

2.1 Generating AI and Faking Everything Online

With the rise of Generative AI, the ability to create realistic and convincing fake content has become remarkably easy. From AI-generated images to manipulated videos, the line between what is real and what is fake becomes increasingly blurred. This poses a significant challenge in ensuring the authenticity and trustworthiness of online information.

2.2 Verifying and Authenticating Content with Crypto

This is where cryptocurrency, specifically the cryptographic capabilities it offers, comes into play. Crypto provides a framework for verifying and authenticating digital content. By leveraging cryptographic algorithms, it becomes possible to digitally sign and authenticate content, ensuring that it originates from a trusted source. This verification process relies on associating content with unique cryptographic addresses or equivalents, such as Ethereum Name Service (ENS) addresses. By doing so, we can establish a link between the content and the entity that generated it, whether human or AI.

Technological Overlap between AI and Crypto

3.1 Example: AI-Generated Arrest Photo

To better understand the intersection of AI and crypto, let's consider a concrete example. Imagine an AI-generated arrest photo of a prominent figure, such as Donald Trump. In the era of deepfakes, it is becoming increasingly difficult to discern between real and fake images. However, by employing cryptographic verification, we can authenticate the source of the image. By ensuring that the image is digitally signed by a specific ENS address or associated with a public key within the Ethereum ecosystem, we can have confidence in its authenticity.

3.2 Cryptographic Verification of Content

Building upon the concept of cryptographic verification, we can extend its application beyond images to various forms of digital content. By digitally signing content and linking it to specific addresses or public keys, we establish a cryptographic trail that traces back to its origin. This process enables us to differentiate between content generated by humans and that generated by AI. While it is possible for humans to sign AI-generated content, the cryptographic verification provides a level of accountability and transparency, allowing us to identify the true source.

Web 3.0 of Trust

4.1 Putting Trust in Onchain Data

To combat the proliferation of fake content, the concept of Web 3.0 of Trust emerges. Unlike the current web, where trust is heavily reliant on centralized entities like search engines, Web 3.0 aims to decentralize trust by leveraging onchain data. Platforms like Interface.social serve as examples of Web 3.0, where interactions and data are stored and verified on the Blockchain. By having onchain data, it becomes possible to perform cryptographic verification on a wide array of actions, providing a comprehensive view of an entity's activities.

4.2 Decentralized Web and Crypto Verification

Creating a decentralized web of trust allows anyone to access and index onchain data, facilitating cryptographic verification and authentication. By visualizing onchain data through next-generation block explorers like Interface.social, the trustworthiness of content and entities can be evaluated more reliably. This decentralized approach prevents the over-reliance on centralized entities and offers a more transparent and accountable web experience.

Fighting Fakes with Crypto

5.1 Onchain Citations and Perplexity

To verify the authenticity and credibility of information, onchain citations play a crucial role. Services like Perplexity.doai provide citations for information requests, providing a layer of credibility. However, the ideal Scenario is to have onchain citations, meaning that the citations themselves are stored and verifiable on the blockchain. This would enable users to Trace back the origins of information with certainty and ensure its accuracy.

5.2 Decentralized Web of Trust Explorers

To achieve a decentralized web of trust, various block explorers, such as Interface.social, have emerged. These explorers allow users to Visualize onchain data, enabling them to verify the authenticity of content and interactions. By leveraging cryptographic verification, these explorers provide a reliable source of truth, empowering users to distinguish between genuine and fake information.

Addressing Spam with Crypto

6.1 Busting Captchas with Crypto

Captchas have long been used to distinguish humans from bots. However, AI-powered bots are becoming increasingly Adept at bypassing captchas, rendering them less effective. Crypto offers a solution by introducing a payment requirement or a history of payments to access certain services. By adding a cost element, it becomes economically infeasible for bots to spam, effectively mitigating the spam problem.

6.2 Charging Scarcity for Authenticity

Scarcity plays a crucial role in establishing authenticity. By associating a cost or requiring staking of funds, crypto introduces a level of scarcity that discourages fake actors. This scarcity can manifest in the form of requiring token payments or staking a certain amount of funds to access specific services. By making spamming costly, the authenticity and credibility of content are preserved.

Decentralizing AI Training and Evaluation

7.1 Centralized vs. Decentralized AI Training

Currently, AI training processes are predominantly centralized. However, by leveraging crypto, we can decentralize the funding aspect of AI training. Crowdfunding projects in the crypto space showcase the potential for harnessing significant sums of money to support AI training. By decentralizing funding, we open up possibilities for decentralized training, where multiple contributors participate in the training process.

7.2 Decentralized AI Evaluation

In addition to training, crypto also has the potential to decentralize AI evaluation. With the advent of powerful hardware and distributed networks, individuals can run AI models for evaluation. By decentralizing the evaluation process, we can democratize the assessment of AI models. Furthermore, by introducing token-based payments for model evaluation, those who funded the model can receive rewards, creating a self-sustaining ecosystem.

Embracing Polytheistic AGI

8.1 Shifting the Concept of AGI

Traditionally, the concept of Artificial General Intelligence (AGI) revolves around the idea of a singular superintelligence. However, by embracing decentralized funding and evaluation, we can envision a polytheistic AGI landscape. Each community or society could have its own AGI Oracle, representing their specific values and ethics. By querying these community-specific AGIs and incorporating onchain citations, we can foster a more diverse and inclusive AI ecosystem.

8.2 Community-specific AGI and Onchain Citations

Community-specific AGIs offer a unique approach to AGI development. By querying AGIs that Align with different societies' values, diverse perspectives can be incorporated. Additionally, these AGIs can provide onchain citations, ensuring transparency and accountability. This integration of decentralized funding, evaluation, and community-specific AGIs paves the way for a new era of AI that reflects the values and aspirations of various communities.

Conclusion

The convergence of AI and crypto presents an exciting frontier in the digital world. By understanding the specific areas of overlap and leveraging the power of cryptographic verification, we can combat the rise of fake content while empowering users with a decentralized web of trust. From combating fakes and spam to decentralizing AI training and evaluation, the fusion of AI and crypto offers a path towards a more trustworthy and inclusive digital ecosystem.

Resources

• Perplexity.doai
• Interface.social