Over the last few years, artificial intelligence (AI) has taken the world by storm, developing increasingly global implications as its capability grow. Large Language Models (LLMs) like Google’s Gemini or OpenAI’s ChatGPT represent one of the first and prolonged most significant catalyst for global use but at a cost.
Despite being able to compose music, generate complete essays with bibliographies and even create games from code from scratch, the huge computing power requirements add up fast. Due to this level of power needed and the growing demand, concerns have begun to arise about the accessibility and the sustainability of AI technology.
LLMs: How They Work
The rise of LLMs has enabled humans to achieve the automation and optimization of tasks previous thought impossible, bringing science fiction to reality. With seemingly unlimited capabilities, the power of the LLMs comes from computational resources that involve the use of hundreds to thousands of graphic processing units (GPUs) working in harmony for long periods.
Due to the demand of these requirements, the infrastructure needed both to operate and to train LLMs means that only organizations — usually larger and wealthier corporations — can afford to develop and utilize them. This concentration of AI utilizatiion and development not only shuts down the opportunities for innovation elsewhere but even raises fears surrounding the monopolization of AI tech.
The Hardware Hurdle
Dependence on computational power means there is an immediate hurdle faced that ultimately creates a bottleneck for more wide-scale and decentralized AI innovation. Due to the rising complexity — and in turn costs — of AI development and innovative pursuits, many opportunities in more social applications like education and environmental sustainability go unexplored.
Because social applications often lack the profitability and the commercial-oriented incentivization, these endeavours in AI research go underfunded as profit-driven firms turn away.
Distributed AI Potential
Unlike AI development and concentration in big companies, distributed AI computing could unlock a door to truly democratic use of AI for societal goals and innovation. By utilizing unused or underused computational capacities of user devices across the world, distributed computed enables complex AI tasks to be performed in a completely decentralized manner.
Lacking the centralized requirement for power to carry out AI research and development (R&D), this decentralized approach cuts costs and improves the scalability of computational power for AI operations and training.
Qubic: Scaling AI Capabilties
Qubic is one such project that is utilizing distributed computing to scale AI operations, leveraging its Useful Proof of Work (uPoW) to direct mining process toward socially beneficial AI tasks. By exceeding just transaction verification, Qubic, developed by Sergey Ivancheglo, operates on a quorum-based computing system embedded within a blockchain framework.
This innovators model offers a more inclusive AI development option that distributes the concentration of power across the globe for truly decentralized network participation. Qubic ensures that the advancements pursued in AI R&D are established and powered by a decentralized and broad base of contributors — making decision-making accessible world-wide.
AI Accessibility implications
The Qubic system highlights a key advantage of distributed computing and its capacity to establish truly decentralized computational power to take back innovation into the hands of the users. Due to the current state of AI R&D, traditional models are built and limited by centralized entities in control — but with distributed computational power, not for long.
Through distribution systems like that seen at Qubic, distributed computational power aligns with both ethical and practical goals for community-built and guided equitable AI benefits for all.The removal of the accessibility bottleneck from AI operations and training can help to mitigate biases that occur when that computing power becomes too centralized.
Leveling the Playing Field – Ethically
The expansion of this technology’s capabilities, while innovative and useful, presents a number of profound ethical considerations and technical challenges. To ensure a future never arrives where AI is predominantly benefiting the few rather than the greater population, the democratized access to AI is imperative.
By creating a level playing field globally, users impacted by social and geographical issues can work in a collaborative manner — utilizing a collective resource for societal beneficial initiatives and public good.
Crafting an Inclusive Future
On the peak overseeing the path ahead for AI use and R&D, it’s clear that the decisions made over the next decade will shape the future of AI and its implications for all societies. Although the potential this technology offers is as expansive as the imagination, the benefits of these advancements are not being evenly distributed.
Through distributed computing, the future trajectory of AI R&D can be driven by those that use it rather than a handful of large entities that concentrate that computational power for personal goals. To avert disparity between users and avoid exacerbation of the inequalities that already exist, the democratization of AI technology is vital.
To craft an inclusive future that is accessible and society-driven — rather than drive by centralized forces that be — the deployment of distributed AI models like Qubic are essential.
Distribution>Demand
To overcome the rising demand and centralization of AI technology, the prioritization of accessibility and fairness will be key in the years ahead. This approach addresses both forward-thinking considerations without shunning the immediate growing technological needs, encouraging long-term inclusivity and sustainability for AI.
By creating a paradigm in which the broad vision of this technology is considered, not for mere economical gains but for societal goals and public good, AI development can exceed the profitability-first perspective. Through the distribution of AI resources and democratic accessibility, we can surpass commercial greed to ensure that global needs remain the priority.
