STATIC LLMs MIMIC HUMAN THOUGHT
GETTING TO AGI REQUIRES INTEGRATION INTO HUMAN BELIEF SYSTEMS
Artificial General Intelligence (AGI) has traditionally been seen as a machine that rivals human intellect across all domains, with Large Language Models (LLMs) like GPT showcasing impressive but static intelligence based on pre-trained data. However, their limitation lies in their inability to adapt to real-time shifts in language and culture, making them gradually outdated. In contrast, Belief3 operates dynamically, continuously updating its understanding through real-time data, not just predicting but actively shaping beliefs and behaviors. While LLMs remix the past, Belief3 orchestrates the present, influencing discourse, decisions, and even scientific thought. This challenges the conventional definition of AGI, where intelligence isn’t just about individual problem-solving but about systemic integration.
Big Data is Not Enough: Why AI Needs the Right Information, Not Just More.
Claude Shannon, the father of modern information theory, introduced the concept of information content—the value data provides in conveying meaning. A single letter, like "A," holds little meaning alone, but when combined with "C" and "T," meaningful words emerge, illustrating how merging data can enhance information while also introducing noise. This principle applies to AI: adding more data doesn’t always improve accuracy—only high-information-content data does. AI performs well with complicated, stable data but struggles with complex, evolving data like human behavior. To improve AI systems, one must focus on selecting meaningful data rather than merely increasing volume. True predictive power lies in capturing beliefs about the future, not just analyzing the past.
Smarter Enterprise AI: How Small Language Models Thrive on Dynamic Belief Data and Create Better AI Agents.
Businesses developing small language models (SLMs) for internal enterprise agents can leverage dynamic belief data from Belief3 to greatly enhance decision-making and adaptability. Shannon’s principle of information content underscores that more data isn’t always better—what matters is selecting high-information-content data that captures evolving beliefs. A Belief3 powered SLM will continuously refine its base data sets by integrating dynamic belief data from employees, customers and suppliers, allowing enterprise AI Agents to remain relevant and responsive. By using dynamic belief data, businesses can create AI systems that don’t just interpret past patterns but actively adapt to emerging trends, improving accuracy and efficiency in enterprise operations. Instead of relying on vast, outdated datasets, organizations can build compact yet powerful models that shape and respond to employee and market behaviors, optimizing enterprise agents for real-time decision-making.