Exploring the Limits of Language and Its Impact on AGI
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Chapter 1: The Misconception of Language as the Key to AGI
Is mastering plain language enough to claim that Artificial General Intelligence (AGI) has been achieved? This notion is increasingly being challenged. The widely held belief that language was a technological leap that propelled human intelligence throughout history may not be entirely accurate.
The Mathematical Perspective
To delve deeper, let’s examine language through the lens of Information Theory, which serves as a framework for understanding how information is communicated. A crucial concept within this theory is Entropy, which quantifies the expected amount of information contained in a message across all potential messages.
In formal terms, the entropy of a message can be defined as follows:
H(X) = -∑ P(x_i) log P(x_i)
In this equation, P represents the probability of each state x occurring. Shannon's 1950 estimation placed the entropy of written English between 0.6 and 1.3 bits per character, while Arabic was found to have a higher entropy of 1.93 bits per character. This indicates that each language has a maximum capacity for conveying information, inherently limiting its effectiveness as a communication tool. Eric Schmidt, co-founder of Google, succinctly stated, “Language is limited and inaccurate.”
Where to Next?
While language assists in communication and social interaction, it fails to encapsulate certain fundamental ideas essential to intelligence. This reality suggests that achieving a comprehensive understanding of intelligence will not be found merely in mastering language. Paradoxically, this insight may lead us toward a more viable solution for AGI. Information theory could guide us to a better understanding of intelligence by using entropy as a critical reference point.
To investigate this further, we must contemplate: What is the entropy of mathematics itself? While mathematics is inherently incomplete, does it possess infinite entropy? Although I lack the qualifications to provide a definitive answer, I believe that advancing our understanding of intelligence will necessitate the development of model-free, mathematically driven systems with a higher entropy threshold than that of language. These systems would rely on fundamental concepts, which I discuss in greater detail elsewhere.
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References
Chapter 2: Bridging the Gap Between Language and Intelligence
In this video, "AI Won't Be AGI, Until It Can At Least Do This (plus 6 key ways LLMs are being upgraded)," we explore essential advancements in AI that may contribute to achieving AGI.
The second video, "Impediments to Creating Artificial General Intelligence (AGI)," examines various obstacles that hinder the development of AGI.