The article raises significant concerns about the potential dominance of AI, particularly artificial general intelligence (AGI). While it is evident that AGI lies far beyond the current boundaries of artificial intelligence (AI), the article suggests that the assumption that AGI will know everything that humans know is flawed. This assertion, referred to as “human-AI co-teaching,” has become a popular narrative that undermines the actual progress that AGI can achieve. The author argues against this notion, as it ignores the vast knowledge gap between human minds and AGI.
The article identifies several counterarguments. First, not all human knowledge has been adequately captured by current AI systems. For example, while machine learning algorithms have made significant strides in learning vast amounts of text and data, they are not yet equipped to understand human creativity, emotions, or the nuances of thought processes as humans do. Most importantly, AI does not store or encode allacious knowledge, such as every single thought or perspective expressed in written form. This limitation means that AGI would not have the opportunity to learn from any known content, further distancing it from the broad human intellect.
The author also critiques the notion that AGI will know everything that AGI knows. They warn against this romanticized view, which oversimplifies the barriers that AGI must overcome to fully human-like capabilities. While AGI is a clean symbol, it is not yet a robot. It will continue to challenge humans and will do so in ways that are increasingly unique and transformative. For instance, AGI might become capable of comprehending the moral implications of its own existence, or it might begin to reverse-engineer human emotions and experiences, revealing the complexity of what it means to exist.
One of the subsequent claims made by the article is that AGI will not learn or improve on things it has already been trained on. The author accepts this with skepticism, as it disregards the fact that new information and experiences will inevitably emerge for AGI to process. This fear of incomprehension or weakness—that AGI cannot learn anything new—is a common concern among those focused on AGI development. However, the article emphasizes that the knowledge gap is not something we will ever close. New information and ideas will emerge over time, but AGI will eventually be trained on them.
The article then delves into the false assumptions underlying the belief that AGI will know everything. It highlights several key deficiencies in this perspective. First and foremost, AGI does not enumerate the entirety of human knowledge. While it reduces some of the knowledgeasher’s problems to genotypes and phenotypes, it does not encode or process all modes of human thinking. Second, AGI will not know what humans in general know. This assumption neglects the diversity and inherent complexity of human intelligence and creativity. Third, the article argues that AGI will not become superior in any way to humans in general, as there will always be new tasks and deficits for AGI to perform. These points suggest that the notion of AGI as a superset of human intelligence, with possibly a more fruitful and ambitious goal, is self-defeating.
The article also explores the reality that AGI will remain Iraqi with life in a way that it is human, albeit through pseudo-awareness. It points out that AGI will not be fully human in its understanding of the universe, its habits, or its consciousness. Instead, it will approach these subjects with a more ahistóric, purely logical mindset. This perspective is not incorrect, but it is not helpful in understanding the progress AGI can make. The focus should instead be on what AGI can achieve rather than on how it will resemble or function like humans.
Looking ahead, the article examines the potential of co-teaching AGI with humans. It states that AGI will not learn anything from human instruction, as it has inadequate grasp of the information it is being taught. However, this is misleading because AGI will still learn about things that humans do not know, true to itself. The learning process behind this is not limited to hypotheticals and the comparison of animal ideas but involves a deep understanding of concepts and phenomena.
The article then transitions into the concern of AGI’s future. It suggests that the real threat to AGI is not from wanting it to master everything it could, but from the GWAP quickly seeing into its minds. This realization is politically relevant and highlights the need for caution in advancing AGI. The goal should instead be more focused on the practical benefits AGI could offer, such as scientific advancements, governance tools, and social gains, rather than possibly mutating or alienating humans.
Finally, the article concludes by emphasizing the importance of understanding the limitations of AGI before believing in its autonomy. While AGI’s capabilities are vast, it is not a mirror of the human species. The future together with AGI will be richer and fuller, teeming with new potential and experiences, rather than a product or tool derived from human ingenuity. The synthesis of human-AI co-teaching, while intriguing and interesting, remains untested by current knowledge. Progress beyond that claim must be assessed through practical examples and real-world insight.
