A Theoretical Solution to Machine Reasoning
Some problems are instinctively solved, some require deliberate reasoning and processing, some are solved using imagination, and others are solved through collaboration. I’ve noticed that no man is an island; no single person could part the Red Sea, and no individual can be a band.
Let’s approach this from a machine learning perspective. Imagine we have one neural network, trained extensively on corpora of data, whether synthetic or authentic. We rely on it to solve all our problems: we use it as a chatbot, an image generation tool, and for other use cases. But what if we’re looking at it the wrong way? As Bezos said, “Neural networks are not an invention but a discovery.” We don’t fully understand their capabilities, nor do we know if we’re using them the right way. This remains an enigma, even for the brightest minds in the field.
Consider a complex, groundbreaking problem. No single person can solve it alone, which is why we hear about companies, not sole entrepreneurs, tackling such challenges. Take the Manhattan Project, for example. Oppenheimer, Teller, von Neumann, Bethe, and Feynman all worked together to create the atomic bomb. Had they worked individually, it’s doubtful they could have achieved the same outcome. So why do we rely on just one neural network? Managing the scale of even a single large neural network is cumbersome, but does it have to be so massive? Why not use multiple reasoning agents working together to solve a problem?
I believe the future of AI lies in agentic systems with advanced reasoning skills that don’t need to be continuously prompted to provide information. These systems would think independently and present information after they have completed their reasoning and reached conclusions.
When we face a system design problem or an advanced math problem, people collaborate by drawing on whiteboards, sharing ideas, and merging logical reasoning while discarding weak arguments until the problem is solved. This process can last for days or even months, depending on the complexity of the problem. This is where the brilliance and creativity of the human spirit truly shine. This is the approach needed to solve deep technical problems. You cannot simply search a neural network’s weights to solve a Clay Institute problem and generate proof for an unsolved issue. More is needed; we need collaboration, the clash of ideas, and enhanced thinking and creativity.
I propose the concept of reasoning agents rather than just neural networks. The mathematical representation of this idea is yet to be uncovered. The agent could be an abstracted version of the neural network because I still believe an abstracted version of the brain is necessary to solve any problem, as it is the only representation of intelligence and consciousness we have in the universe. The idea is that these reasoning agents would be represented in a tensor space. Through interactions with others, transformations would occur in this space, with a prompt as a question or direction influencing the manipulation of the agent.
We could use rules like the cosine rule to gauge the logic of each agent and understand the similarity between their components. Attention mechanisms could help identify valid reasoning, and Euclidean distance could help us understand the differences in reasoning between agents. These insights would guide transformations in the tensor space. After validating the transformations with these mechanisms, we could implement a last-man-standing approach, merging similar reasoning to provide a more robust rationale. If a reasoning agent’s result is significantly different from most validated reasoning, we could eliminate that vector from the environment.
There are many directions this idea could take, but I believe that while extensively training a large neural network on data may yield a better chatbot with a representation of the data in its weights, increasing the scale of these networks only brings us closer to an illusion of intelligence.
This approach mimics human collaboration in problem-solving, where diverse perspectives come together to create innovative solutions. By leveraging multiple reasoning agents, we can overcome the limitations of single, large neural networks and create more dynamic, adaptable AI systems. These agents could work together to provide insights into problems we currently can’t solve due to their dynamic collaboration. We could implement a master-worker model, similar to the Manhattan Project, with Oppenheimer as a master agent and the others as worker agents.
The concept of reasoning agents in tensor space opens up exciting possibilities for AI development. It allows for a more nuanced and flexible approach to problem-solving, where different ‘agents’ can specialize in various aspects of reasoning using solid mathematical mechanisms to validate their thought processes and eliminate absurd ideas. However, this approach must be managed effectively, as the most unconventional thoughts could lead to the most significant insights and solutions. While it’s important to control the creativity of the interaction to some extent, it’s equally important to remember that groundbreaking ideas often emerge where creativity isn’t constrained.
Moreover, this model could lead to more explainable AI. By tracking the interactions and transformations of these reasoning agents, we might gain deeper insights into how the AI system arrives at its conclusions. This transparency could be crucial for building trust in AI systems, especially in critical applications like healthcare and other realms.
However, implementing such a system would come with its own set of challenges. Effective and efficient algorithms are necessary, as these interactions would require significant computing power. Coordinating multiple agents, ensuring efficient communication between them, and developing mechanisms to resolve conflicts in reasoning would be complex tasks.
In conclusion, while large neural networks have brought remarkable advancements in AI, collaborative, multi-agent systems might be the key to solving even more complex problems. By abstracting the brain’s capabilities into reasoning agents, we could create AI systems that are more powerful, intuitive, and aligned with human problem-solving processes. This philosophical approach to machine learning opens the door to a new paradigm in AI development — one that could revolutionize how we tackle the world’s most challenging problems and push the boundaries of artificial intelligence beyond what we currently imagine possible.
rustian ⚡
