Have you heard of truly distributed AI?
Challenge of being a non-AI professional
After almost 2 years in doing personal research on artificial intelligence, especially on the new architectures like transformers and diffusion model, it isn't easy for people like me as I am not from data science background, nor am I of machine learning background. I am a software architect, with software development background, All I have is sheer curiosity and passion and I have no other way but to self-learn and pick things up from scratch.
Centralized AI
I learned that a great majority of generative AI applications, stemming from Large Language Models are extremely centralized. It has to be because how else can the creator earns from their creation if it isn't centralized?
However, there are growing concern if a centralized model becomes too powerful as what IF it displaces many jobs and render society into chaos? What are the chance of this happening? To add, did you know this is already happening with many large corporations now already started to retrench employees by over thousands. However many reasons were given for such drastic move, but it all rooted into the current hype of artificial intelligence.
Meta Inc's Yann LeCun had warned of that the real existential threat to humanity is not the AI but when one or a few large corporations controls the AI, that is the by all definition, the REAL Existential Threat to Humanity. I personally support his view because I think having just a few large corporation have grasp over such large and powerful AI, will indeed lead to 'cronyism' and potential unfair practices that benefits only a few elites.
However, with the release of Meta's Llama-3.1 models (405B, 70B, 8B, 7b) and learning of how Meta's true business model of being advertising rather than earning through the AI models, it opens up an entire new perspective for me. Meta plan to disrupt the large centralized companies and according to Meta's Mark Zuckerberg's vision of 'many, many AI agents that outnumbers human population, reveals a very clever strategy indeed.
I think, Meta hope by making all its smaller models free of charge, in near future there will be a massive adoption of Llama models. This makes sense but after reading Meta Inc's user terms with a cleverly worded custom user license, that is similar to General Public License (GPL) but with additional restrictions on top of GPL's 'already restrictive' terms and conditions. Therefore this means, Meta is still maintaining some control over its models, especially the smaller ones.
So this is purely about competition and disrupting the competition, not truly about benefiting society. This brought about a new term that I contrive to describe the type of future state artificial intelligence system that embodies a truly distributed open-source architecture that can truly benefit the entire society globally and not succumb to any undesired central control by a few elites.
SYNERGISTIC ARTIFICIAL INTELLIGENCE MODELS ECOSYSTEM (S.Ai.M.E.)
S.Ai.M.E. is a revolutionary approach to AI Model Integration. In the rapidly evolving landscape of artificial intelligence, we are constantly seeking ways to push the boundaries of what AI can achieve. While AI/LLMs Aggregator platforms like HuggingFace.co, Poe.ai, Lab.Perplexity.ai and others have democratized access to powerful AI models, a new concept is emerging that could redefine how these models work together.
S.Ai.M.E. vs Current State of AI Model Aggregators
Today, many AI enthusiasts are familiar with platforms that aggregate large language models (LLMs) and other AI tools. Websites like Hugging Face, Poe.ai, and Lab.Perplexity.ai offer users a wide range of models, each capable of impressive feats in natural language processing, computer vision, and beyond.
However, these platforms typically allow users to deploy one model at a time, with each model functioning independently of the others. This "one-at-a-time" approach is useful but limited. Each model operates in isolation, with no real-time interaction or synergy with other models. While you can choose the best model for a particular task, you're not harnessing the collective power of multiple models working together. This is where S.Ai.M.E. offers a fresh perspective.
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Introducing S.Ai.M.E.: The Future of AI Synergy
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S.Ai.M.E. stands for Synergistic Artificial Intelligence Models Ecosystem and represents a bold new vision for how AI models can collaborate. Unlike traditional AI model aggregators, S.Ai.M.E. proposes an interconnected ecosystem where multiple models communicate in real-time, working together to produce outputs that are more accurate, coherent, and contextually relevant.
In a S.Ai.M.E. system, an orchestrating AI model coordinates the activities of various connected models. These models share information, adjust to each other’s inferences, and create outputs that are greater than the sum of their parts. Imagine a network where a language model, a computer vision model, and a reinforcement learning model all contribute their expertise to solve complex problems together, dynamically adapting to new data and evolving over time.
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How S.Ai.M.E. Differs from Meta Inc.'s Llama-3.1
To appreciate the significance of S.Ai.M.E., it’s helpful to compare it with existing large AI models like Meta Inc.'s Llama-3.1 which is essentially based on the 'Agentic AI' concept. Llama-3.1 is an impressive large language model, available in sizes ranging from 7 billion to 405 billion parameters. While this range allows for flexibility in deploying the model for different tasks, Llama-3.1 remains a single, static architecture.
Each version of Llama is designed to perform independently, without real-time interaction with other models. In contrast, S.Ai.M.E. envisions a system where models of different types and sizes are constantly communicating and collaborating. Rather than relying on a single massive model like Llama-3.1, S.Ai.M.E. would leverage multiple models, each contributing to a more powerful and contextually aware output. This approach could lead to AI systems that are not only more efficient but also more adaptable to a wide range of tasks and environments.
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Beyond Federated Learning: A New Paradigm
Some might wonder how S.Ai.M.E. compares to federated learning, a decentralized approach where multiple instances of a model are trained on different datasets across various locations. While federated learning is an important technique, it usually involves training homogeneous models that ultimately converge into a single shared model. These models, while decentralized in training, do not exhibit the real-time, heterogeneous interaction that S.Ai.M.E. aims to achieve. S.Ai.M.E. goes a step further by integrating different types of AI models into a modular, dynamic ecosystem. In this ecosystem, models aren't just working in parallel; they're interacting and learning from each other in real time. This creates a more versatile and powerful system capable of addressing complex, multifaceted problems that would be challenging for a single model to solve.
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The Road Ahead for S.Ai.M.E.
While the concept of S.Ai.M.E. is still in its early stages, it represents a significant leap forward in how we think about AI model integration. As research in this area progresses, we may soon see the emergence of systems that embody this vision, revolutionizing the way we approach AI research and development. For AI enthusiasts, the promise of S.Ai.M.E. is an exciting glimpse into the future— a future where AI models don't just coexist but truly collaborate, leading to breakthroughs that are currently beyond our reach. As AI continues to evolve, S.Ai.M.E. could become the foundation for a new generation of intelligent systems, more powerful and versatile than anything we've seen before.
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Challenges
However, before something like S.Ai.M.E. to function as envisioned, requires breakthrough in a number of other enabling technologies and preliminary conditions:
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