The Importance Of Decentralized Computer In Running Unbiased, Uncensored AI (Chatgpt)
ChatGPT has illuminated AI’s potential, but as governments recognize LLMs’ impact, they’ll influence outputs. LLMs on decentralized networks are crucial for unbiased platforms, free from corporate and governmental pressure.
ChatGPT achieved record-breaking user growth, signaling interest in AI tools for rapid, thoughtful answers. Future LLM iterations will be more potent, inviting increased scrutiny.
As AI matures, it becomes a truth source for users, yet interpretations of ‘truth’ vary among entities. Balancing perspectives on certain topics poses challenges for AI engines.
AI Answers to Sensitive Queries Are Already Attracting Notice
ChatGPT can articulate green energy benefits, but users struggle for answers on ‘the importance of carbon-based energy.’ Responses on masking in 2020 vary, reflecting changing recommendations. Sensitivities heighten in nations like India, Pakistan, and totalitarian regimes, aligning with specific narratives. The US government’s intervention in controlling topics on Twitter indicates concerns. A single truth source, like a popular LLM, threatens governments, politicians, and communities.
Managing diverse demands for specific answers poses challenges for organizations. AI engines may face bans in some countries and regulatory constraints, impacting credibility and topic coverage.
Addressing AI Biases Through Open Source Engines and Decentralized Computing Networks
The initial step to mitigate external influence involves constructing open-source AI engines, enabling transparent community model training. Projects such as Bloom and EleutherAI are actively progressing toward this objective. Yet, training is just the outset; regardless of inputs, attention focuses on outputs, subjecting any organization utilizing a model to external pressures. The ultimate safeguard against such influence is to decentralize the actual operation of the AI engine by deploying it on an open, globally distributed, decentralized network devoid of owners or central control points. When any storage provider can host an AI model and any compute provider can process auditable queries, users can trust that responses are devoid of concealed biases.
The encouraging news is that the technology already exists to actualize this vision. However, questions persist about the implementation and the responsible party. Additionally, some may deem responses from an uncensored version to be ethically contentious.
Promoting Competition Among Large Language Models (LLMs)
The future is poised to witness numerous AI engines vying to establish themselves as the definitive source of truth. Encouraging and fostering this competition is not only healthy but essential. History has shown instances of open-source software surpassing major corporations; Linux, a leading operating system, outcompeted proprietary systems like Microsoft’s Windows, showcasing the potential of a global community of developers.
Similar success can be envisioned for the open-source community in constructing AI LLM models. However, it’s not just about building and training AI; the operation of AI is equally crucial.
At Fluence, we contend that without open-source and decentralized AI, there’s a risk of succumbing to control and influence from a handful of colossal entities, be they companies or governments. Envisioning a future with decentralized computing, we advocate for an affordable, high-performance, auditable, and fully decentralized cloud. Such a decentralized approach liberates applications from centralized and closed cloud ecosystems, mitigating risks associated with oligopoly censorship and fostering empowerment for all.
Tom Trowbridge, the Co-Founder & CEO of Fluence Labs, is an accomplished business builder and a dedicated entrepreneur in the web3 space. With a background in financing telecom and technology companies at Bear, Stearns & Co., and later contributing to early-stage technology investments at a Boston-based VC firm, Tom brings a wealth of experience. As a founding member and President of Hedera Hashgraph (HBAR), he played a pivotal role before venturing into his current position at Fluence Labs. Tom holds a BA from Yale University and an MBA from Columbia University.
Fluence Labs, led by Tom Trowbridge, has pioneered an institutional-grade decentralized serverless computing network to liberate computation from centralized cloud providers. Offering cost-effective and verifiable compute services through a natively decentralized protocol, Fluence aims to reduce the web’s reliance on centralized clouds. With support from investors like 1KX and a Series A round led by Multicoin, Fluence Labs is well-positioned to enhance application development by making it faster, more accessible, and secure, empowering developers to prioritize user experience.
