Update index.md

index.md

@@ -30,10 +30,12 @@ One of the historic challenges algorithmic models grappled with was ambiguity. T
 
 #### 2.2. Conceptual Processing and Learning
 
-Our framework is based on the new pattern-matching techniques that can handle ambiguity. We process raw data (like text, audio, images) into a conceptual representation. It can then trigger different actions that are associated with the concept an agent was able to find in the raw data. For example a question might trigger an action of thinking about an answer and then telling it. But the agent's learning journey doesn't end there. By employing pattern matching on its subsequent actions and the feedback it garners from the environment, the agent is able to understand its influence on its surroundings, continiously building it's world model. This is made possible as both actions and outcomes are represented as graphs, allowing for a deeper, graph-based pattern recognition.
+Our framework is based on the new pattern-matching techniques that can handle ambiguity. We process raw data (like text, audio, images) into a conceptual representation. It can then trigger different actions that are associated with the concept that an agent was able to find in the raw data. For example a question might trigger an action of thinking about an answer and then telling it. But the agent's learning journey doesn't end there. By employing pattern matching on its subsequent actions and the feedback it garners from the environment, the agent is able to understand its influence on its surroundings, continiously building it's world model. This is made possible as both actions and outcomes are represented as graphs, allowing for a deeper, graph-based pattern recognition. The use of pattern-matching on the agent's actions introduces an additional layer of non-liniarity. Now, the agent's behavior isn't solely influenced by environmental input but also by its own potential responses to such input. Consider a scenario where the agent contemplates a potentially dangerous action; the pattern-matching mechanism identifies the associated concept of danger (from my actions), that concept will trigger an action to question the appropriateness of its intended action.
 
 While this framework is ambitious and teeming with complexities, it underscores the potential pathways we could explore as we inch closer to AGI.
 
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 In sum, as we navigate the intricate maze toward AGI, it becomes crucial to introspect, innovate, and iterate. The strengths and challenges of LLMs, combined with the foundational ideas behind Controllable AI, paint a fascinating canvas for the future of AI research.
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+The interface between humans and computers has long been a limiting factor in fully harnessing computational power. Traditional methods, which rely heavily on programming languages, create a bottleneck, preventing most individuals from tapping into the vast potential that computers offer. With the advent of controllable AI, this paradigm is poised to shift. This new era promises a more intuitive interaction, bridging the gap between human intention and machine capability. By bypassing the intricacies of traditional programming, controllable AI paves the way for a future where we can readily access and leverage the dormant power of computers that we've been overlooking in our daily lives.