Beyond the Black Box: Exploring the Human Side of AI with Lachlan Phillips

Crazy Wisdom

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop welcomes Lachlan Phillips, founder of LiveMind AI, for a compelling conversation about the implications of decentralized AI. They discuss the differences between centralized and decentralized systems, the historical context of centralization, and the potential risks and benefits of distributed computing and storage. Topics also include the challenges of aligning AI with human values, the role of supervised fine-tuning, and the importance of trust and responsibility in AI systems. Tune in to hear how decentralized AI could transform technology and society. Check out LiveMind AI and follow Lachlan on Twitter at @bitcloud for more insights.

Check out this GPT we trained on the conversation!

Timestamps

00:00 Introduction of Lachlan Phillips and discussion on decentralized AI, comparing it to human brain structure and the World Wide Web.

00:05 Further elaboration on decentralization and centralization in AI and its historical context, including the impact of radio, TV, and the internet.

00:10 Discussion on the natural emergence of centralization from decentralized systems and the problems associated with centralized control.

00:15 Comparison between centralized and decentralized systems, highlighting the voluntary nature of decentralized associations.

00:20 Concerns about large companies controlling powerful AI technology and the need for decentralization to avoid issues similar to those seen with Google and Facebook.

00:25 Discussion on Google's centralization, infrastructure, and potential biases. Introduction to distributed computing and storage concepts.

00:30 Lachlan Phillips shares his views on distributed storage and mentions GunDB and IPFS as examples of decentralized systems.

00:35 Exploration of the relationship between decentralized AI and distributed storage, emphasizing the need for decentralized training of AI models.

00:40 Further discussion on decentralized AI training and the potential for local models to handle specific tasks instead of relying on centralized infrastructures.

00:45 Conversation on the challenges of aligning AI with human values, the role of supervised fine-tuning in AI training, and the involvement of humans in the training process.

00:50 Speculation on the implications of technologies like Neuralink and the importance of decentralizing such powerful tools to prevent misuse.

00:55 Discussion on network structures, democracy, and how decentralized systems can better represent collective human needs and values.

Key Insights

1. Decentralization vs. Centralization in AI: Lachlan Phillips highlighted the fundamental differences between decentralized and centralized AI systems. He compared decentralized AI to the structure of the human brain and the World Wide Web, emphasizing collaboration and distributed control. He argued that while centralized AI systems concentrate power and decision-making, decentralized AI systems mimic natural, more organic forms of intelligence, potentially leading to more robust and democratic outcomes.

2. Historical Context and Centralization: The conversation delved into the historical context of centralization, tracing its evolution from the era of radio and television to the internet. Stewart Alsop and Lachlan discussed how centralization has re-emerged in the digital age, particularly with the rise of big tech companies like Google and Facebook. They noted how these companies' control over data and algorithms mirrors past media centralization, raising concerns about power consolidation and its implications for society.

3. Emergent Centralization in Decentralized Systems: Lachlan pointed out that even in decentralized systems, centralization can naturally emerge as a result of voluntary collaboration and association. He explained that the problem lies not in centralization per se, but in the forced maintenance of these centralized structures, which can lead to the consolidation of power and the detachment of centralized entities from the needs and inputs of their users.

4. Risks of Centralized AI Control: A significant part of the discussion focused on the risks associated with a few large companies controlling powerful AI technologies. Stewart expressed concerns about the potential for misuse and bias, drawing parallels to the issues seen with Google and Facebook's control over information. Lachlan concurred, emphasizing the importance of decentralizing AI to prevent similar problems in the AI domain and to ensure broader, more equitable access to these technologies.

5. Distributed Computing and Storage: Lachlan shared his insights into distributed computing and storage, citing projects like GunDB and IPFS as promising examples. He highlighted the need for decentralized infrastructures to support AI, arguing that these models can help sidestep the centralization of control and data. He advocated for pushing as much computation and storage to the client side as possible to maintain user control and privacy.

6. Challenges of AI Alignment and Training: The conversation touched on the difficulties of aligning AI systems with human values, particularly through supervised fine-tuning and RLHF (Reinforcement Learning from Human Feedback). Lachlan criticized current alignment efforts for their top-down approach, suggesting that a more decentralized, bottom-up method that incorporates diverse human inputs and experiences would be more effective and representative.

7. Trust and Responsibility in AI Systems: Trust emerged as a central theme, with both Stewart and Lachlan questioning whether AI systems can or should be trusted more than humans. Lachlan argued that ultimately, humans are responsible for the actions of AI systems and the consequences they produce. He emphasized the need for AI systems that enable individual control and accountability, suggesting that decentralized AI could help achieve this by aligning more closely with human networks and collective decision-making processes.