The convergence of AI and crypto has the potential to enable decentralized AI and long-running AIs independent of centralized companies.
AI can enhance trust and security in the crypto space by detecting suspicious transactions and addressing minor extractable value (MEV) issues.
Decentralized social networks powered by on-chain social graphs offer users control over their data, identity, and filtering algorithms, enabling a more personalized and secure online experience.
Deep dives
Decentralizing AI and Crypto
The podcast episode discusses the convergence of AI and crypto, exploring the potential benefits and challenges of these two technologies coming together. The guests, R. Dan Benet and Ali Yaya, highlight the tension between centralization and decentralization. They discuss the vision of building long-running AIs that are independent from centralized companies, using blockchain and zero knowledge proofs to maintain privacy, control, and data ownership. The conversation also touches on the intersection of AI and blockchain, such as using machine learning to write secure smart contract code, using zero knowledge proofs to verify AI computations, and the potential for decentralized social networks. Additionally, the discussion highlights the role of AI in enhancing trust and security in crypto, such as detecting suspicious transactions and helping users protect themselves from minor extractable value (MEV) exploitation. Finally, the speakers propose innovative solutions to combat deep fakes in the age of AI, including the use of blockchain timestamping and trusted hardware for data verification.
AI and Crypto: Opportunities and Challenges
The podcast delves into the opportunities and challenges that arise from the intersection of AI and crypto. The guests discuss the potential for decentralized AI, where compute tasks are distributed and verified using zero knowledge proofs. They explore how machine learning models generated by large language models (LLMs) can be used to enhance code writing and verification processes. The speakers also emphasize the importance of decentralized social networks, powered by on-chain social graphs, that give users control over their data and identity. Additionally, the conversation touches on the role of AI in improving trust and security in crypto, including the detection of suspicious transactions and addressing minor extractable value (MEV) issues. Finally, the podcast presents innovative ideas for combating deep fakes using blockchain timestamping and trusted hardware for data signing.
Decentralized Social Networks and AI-driven Security
The podcast episode explores the potential of decentralized social networks facilitated by blockchain technology and the role of AI in enhancing security. The guests highlight the advantages of social networks built on public on-chain social graphs, granting users control over their identities, data ownership, and filtering algorithms. They discuss the diverse approaches to content curation, from collaborative filtering to interest graphs, offering users the ability to choose their preferred recommendation algorithms. The conversation also addresses the need for AI-driven trust and security in the crypto space, such as the detection of suspicious transactions through machine learning models and protection against minor extractable value (MEV) exploitation. Additionally, the speakers propose innovative solutions, including the use of blockchain and trusted hardware, to address the challenges of deep fakes and ensure the authenticity and integrity of digital content.
Creating a decentralized marketplace for data in machine learning
One key idea discussed in the podcast is the potential for creating a decentralized marketplace for data in machine learning. Instead of relying on a single centralized entity to collect and train machine learning models, a marketplace could be established to incentivize individuals to contribute new and unique data to a collective dataset. The challenge would be to verify the authenticity and quality of the contributed data, preventing the inclusion of duplicate or unreliable information. Possible solutions include technological verification mechanisms or reputation metrics to build trust within the community. The decentralized marketplace could significantly enhance the coverage and diversity of training data, leading to more performant machine learning models and better performance in edge cases.
The intersection of AI and crypto in establishing decentralized governance
Another interesting area discussed in the podcast is the intersection of AI and crypto in establishing decentralized governance. With the ability to prove individual personhood through biometric or identity verification systems, governance systems for decentralized networks could be more inclusive and equitable. Currently, governance often relies on token-based voting, which can be influenced by individuals holding large amounts of tokens. By incorporating proof of humanity, where individuals can prove their singular identity, governance systems could become more democratic, allowing for one human, one vote rather than one token, one vote. This could lead to fairer decision-making processes and prevent manipulation by bots or multiple identities controlled by a single individual. The integration of AI-generated media with crypto-powered community platforms also enables the creation of unique experiences and narratives around art and media, distinguishing the human element from machine-generated content and fostering stronger connections between creators and their communities.
This week's all-new episode covers the convergence of two important, very top-of-mind trends: AI (artificial intelligence) & blockchains/ crypto. These domains together have major implications for how we all live our lives everyday; so this episode is for anyone just curious about, or already building in the space.
The conversation covers topics ranging from deep fakes, bots, and the need for proof-of-humanity in a world of AI; to big data, large language models like ChatGPT, user control, governance, privacy and security, zero knowledge and zkML; to MEV, media, art, and much more. Our expert guests (in conversation with host Sonal Chokshi) include:
Dan Boneh, Stanford Professor (and Senior Research Advisor at a16z crypto), a cryptographer who’s been working on blockchains for over a decade and who specializes in cryptography, computer security, and machine learning -- all of which intersect in this episode;
Ali Yahya, general partner at a16z crypto, who also previously worked at Google -- where he not only worked on a distributed system for a fleet of robots (a sort of "collective reinforcement learning") but also worked on Google Brain, where he was one of the core contributors to the machine learning library TensorFlow built at Google.
The first half of the hallway-style conversation between Ali & Dan (who go back together as student and professor at Stanford) is all about how AI could benefit from crypto, and the second half on how crypto could benefit from AI... the thread throughout is the tension between centralization vs. decentralization. So we also discuss where the intersection of crypto and AI can bring about things that aren't possible by either one of them alone...
pieces referenced in this episode/ related reading:
As a reminder: none of the following should be taken as investment, legal, business, or tax advice; please see a16z.com/disclosures for more important information -- including to a link to a list of our investments – especially since we are investors in companies mentioned in this episode.
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