How Technology Advancement Could Increase Our Understanding Of AI Limitations And Mitigate Its Risks

The term "black box AI" expresses the lack of clarity of how artificial intelligence works for its users. Examples are Large Language Models (LLMs) which perform natural language processing tasks enabling them to engage in dialogues which seem realistic to the user who, however, doesn't understand how these models generate their output.

This lack of transparency in current AI systems has opened the door to exaggerations about AI capabilities. Specifically, the use of complex machine learning algorithms which the user doesn’t understand how they work gave rise to speculations about AI having human-like capabilities such as reasoning and consciousness. These complex algorithms primarily existed to compensate for limitations of computing resources in current AI systems 

As technology continues to advance, accompanied by an increase in computing capabilities of AI systems, only simple and clear algorithms will be required by an AI system to perform well as there will be no need to achieve strict optimization goals to compensate for a lack of resources. This clearness and simplicity with which AI can work in such a case will limit exaggerations about its capabilities and will eventually end the current AI hype.

Moreover, achieving transparency in AI systems limits the risks of AI hallucination since it not only improves our understanding of how hallucination happens but also allows for verification of the credibility of information provided by AI models. Finally, transparency of AI systems would limit risks associated with data privacy breaches and make sure that these systems comply with corresponding privacy standards.