The Decentralized Intelligence Age and the Rise of Small Players
By: Alan Burger, CEO, Infoslips
Throughout history, humanity has progressed through various ages, each marked by transformative advancements and shifts in the way society functions. From the Agricultural Revolution to the Industrial Revolution and the Information Age, we have witnessed the power of innovation to redefine the human experience. Today, we find ourselves at the dawn of a new era, the Decentralized Intelligence Age, where the AI genie is out of the lamp, and the lightning it brings can never be put back into the bottle.
The Power of Decentralized AI
In the Decentralized Intelligence Age, the power of AI will no longer be monopolized by a select few tech giants like Google and Microsoft. Instead, it will be decentralized and democratized, allowing individuals, small businesses, and communities worldwide to harness the benefits of AI-driven technologies. This shift towards decentralization is a significant departure from the concentrated control of AI we have seen so far, presenting both new opportunities and challenges for society.
Recent studies have shown that the quality of training data, rather than sheer volume, is the key to superior AI performance. This revelation has leveled the playing field, making AI more affordable and accessible to smaller competitors. As a result, we can expect to see a surge of innovation from small players in the AI landscape, as they can now tap into the power of machine learning without requiring the vast resources traditionally associated with AI development.
The Decentralized Intelligence Age
The Decentralized Intelligence Age marks a new era in human history, driven by the democratization and decentralization of information, driven mainly by artificial intelligence (AI). The open sourcing of powerful AI models, such as LLaMA and GPT-3, has made AI technology accessible to a broader range of developers, researchers, and businesses. This democratization has spurred innovation and collaboration by
literally thousands of new start-ups at an unprecedented pace, with numerous advancements being made within days, not months or years!
In a series of rapid developments that have shaken the AI community, the open-sourcing of Meta’s LLaMA, the Large Language Model Architecture, has led to groundbreaking innovations and impressive applications. While LLaMA was initially launched without conversation or instruction tuning, the release of its code fueled a surge of community-driven progress, making sophisticated AI models more accessible than ever before.
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The Timeline of LLaMA-Driven Innovations:
- February 24, 2023 – Meta launches LLaMA, open-sourcing the code but not the weights. The model is available in 7B, 13B, 33B, and 65B parameters.
- March 3, 2023 – LLaMA is leaked to the public. Despite existing licenses, the AI community eagerly experiments with it, sparking a flurry of innovations.
- March 12, 2023 – Artem Andreenko gets LLaMA working on a Raspberry Pi, paving the way for minification efforts.
- March 13, 2023 – Stanford releases Alpaca, which adds instruction tuning to LLaMA. Eric Wang’s alpaca-Lora repo uses low-rank fine-tuning to achieve this on a single RTX 4090, making fine-tuning accessible to a broader audience.
- March 18, 2023 – Georgi Gerganov employs 4-bit quantization to run LLaMA on a MacBook CPU, achieving the first practical “no GPU” solution.
- March 19, 2023 – A cross-university collaboration releases Vicuna, a 13B model that achieves “parity” with Bard. Training cost: $300.
- March 25, 2023 – Nomic creates GPT4All, an ecosystem that brings together models like Vicuna in one place. Training cost: $100.
- March 28, 2023 – Cerebras trains the GPT-3 architecture using optimal compute schedules and scaling, outperforming existing clones and freeing the community from LLaMA dependency.
- March 28, 2023 – LLaMA-Adapter introduces instruction tuning and multimodality in one hour of training using a novel Parameter Efficient Fine Tuning (PEFT) technique. The model achieves a new SOTA on multimodal ScienceQA.
- April 3, 2023 – Berkeley launches Koala, a dialogue model that rivals ChatGPT in human preference. Training cost: $100.
- April 15, 2023 – Open Assistant launches a model and dataset for Alignment via RLHF, achieving near parity with ChatGPT in terms of human preference. This development allows small experimenters to access and apply RLHF easily.
The rapid advancements in AI following the LLaMA launch have democratized access to powerful language models, making them cheaper, more efficient, and easier to use. As the AI community continues to experiment and innovate, the future of open AI technology looks brighter than ever.
In this new age, decentralized AI ecosystems are emerging, which aggregate various AI models and foster cooperation and competition within the AI landscape. These ecosystems facilitate sharing of knowledge and resources, leading to rapid progress in AI research and applications.
The Decentralized Intelligence Age is also characterized by the development of affordable AI solutions, with models like Alpaca and Vicuna being trained on low budgets. This affordability allows smaller organizations and individuals to experiment with AI, driving innovation and creativity across various industries and sectors.
Challenges and Opportunities in the Decentralized Intelligence Age
While the Decentralized Intelligence Age presents numerous opportunities for innovation and progress, it also brings with it new challenges and concerns. These include:
- Data privacy and security: As AI becomes more decentralized and accessible, ensuring data privacy and security becomes increasingly important. Developers and researchers must prioritize the protection of user data and develop AI systems that respect privacy rights.
- Ethical considerations: The democratization of AI raises ethical questions about the use of AI in various applications, such as surveillance or autonomous weapons. Establishing guidelines and regulations that promote the responsible use of AI technology is crucial.
- AI alignment and safety: Ensuring that AI systems are aligned with human values and are safe to use is of paramount importance in the Decentralized Intelligence Age. Collaborative efforts to create datasets, like those by Open Assistant, help to facilitate AI alignment and safety research, enabling smaller experimenters to contribute to this crucial area.
Conclusion
The Decentralized Intelligence Age promises to be a transformative era in human history, with the potential to reshape industries and change the way we live and work. By addressing the challenges and embracing the opportunities it presents, we can harness the power of decentralized AI to drive innovation and progress, shaping a brighter future for all.