Early OpenAI investor bets on alternative Sam Altman’s approach to AI

Key Points:

  • Major breakthroughs in AI involve reducing human involvement in the process
  • Transformer architecture was a significant advancement in deep learning
  • Category theory could be key to developing AI models with reliable reasoning capabilities


The latest advancements in AI have revolutionized the field by eliminating the need for human involvement in certain processes. Deep learning has ushered in a new era where machines can teach themselves tasks without human labeling of data. This development has significantly boosted the capabilities of neural networks, particularly with the introduction of architectures like the transformer, leading to the creation of large language models such as ChatGPT.


Despite the progress made, the transformer architecture faces limitations, particularly in understanding the context and deeper meaning of words, resulting in what experts refer to as “hallucinations.” To address this, researchers are exploring the application of category theory, a mathematical concept that could enable AI models to grasp complex concepts such as truth and reasoning autonomously.


Symbolica, a company at the forefront of this innovative approach, aims to develop bespoke AI architectures that imbue models with precision and reliability from the outset. By leveraging category theory, they hope to create AI models with inherent guardrails and rules, circumventing the need to retrofit such features as an afterthought. This strategy aligns with the industry trend towards improving interpretability in AI systems, allowing users to understand the rationale behind model decisions.


While Symbolica’s models may be more task-specific compared to broader models like GPT-4, the focus on tailored architectures tailored to specific needs is seen as a significant advantage. This strategic shift offers a cost-effective and efficient alternative to designing one-size-fits-all models, potentially outperforming generalized models in specific tasks.


Symbolica’s commitment to developing interpretable and task-focused AI models marks a departure from the traditional approach in the industry, emphasizing the importance of customized solutions for enhanced performance. As the quest for AI that truly grasps concepts like truth and reasoning continues, innovative companies like Symbolica pave the way for a new era of machine learning.



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