The case where AI reasoning capabilities become secondary to its knowledge
Posted: Sat Aug 16, 2025 1:26 am
In the hypothetical case where an abundance of data and computing resources is available to an AI system, machine learning algorithms become obsolete as the AI system will be able to achieve its target adequately through following simple traditional instructions in a timely manner without the need for training.
Since the lack of transparency and the complexity of algorithms in current AI systems have led to speculations about machine’s ability to reason, the clearness and simplicity with which AI works in this hypothetical case would lead to an end of the current AI hype.
Consequently, the distinction of an AI system would be based on its knowledge, as determined by the accuracy, unbiasedness and the amount of information included in its input data, rather than its reasoning capabilities, as observed from the fluency and coherence of its responses.
This hypothetical case could be realized through technological advancement resulting in a significant increase in computing resources.
Since the lack of transparency and the complexity of algorithms in current AI systems have led to speculations about machine’s ability to reason, the clearness and simplicity with which AI works in this hypothetical case would lead to an end of the current AI hype.
Consequently, the distinction of an AI system would be based on its knowledge, as determined by the accuracy, unbiasedness and the amount of information included in its input data, rather than its reasoning capabilities, as observed from the fluency and coherence of its responses.
This hypothetical case could be realized through technological advancement resulting in a significant increase in computing resources.