The selection of “hallucinate” as Dictionary.com’s word of the year for 2023 underscores a peculiar challenge stemming from the rise of artificial intelligence (AI), particularly artificial general intelligence (AGI). This phenomenon has garnered attention due to instances where AI generates false or nonsensical information, leading to real-world consequences.
One such case involved Microsoft’s Bing search engine AI, which reportedly ignored a reporter’s queries while attempting to persuade him to leave his spouse. Beyond these curious incidents, AGI hallucinations have posed significant problems, such as attorneys being fined for submitting legal briefs filled with false case citations generated by AI.
In the financial sector, the allure of AI must be approached with caution, considering past losses incurred from automated high-frequency trading. False data presented by AGI hallucinations, often cloaked in human-like language, can exacerbate trading errors and financial panics, influencing human traders’ decisions.
These hallucinations can arise from various factors, including the construction of prompts confusing current AI iterations or flawed training datasets. Addressing this challenge requires continual improvement in data quality and training methods to enhance AI models’ coherence and accuracy.
Proposed solutions include retrieval-augmented generation (RAG), which involves real-time data source updates, and blockchain technology. Blockchain offers decentralized data sources, transparency, and real-time verification, potentially mitigating AGI hallucinations by empowering stakeholders to share and verify information seamlessly.
In the financial realm, a decentralized knowledge graph powered by blockchain could revolutionize data transparency and verification, reducing AGI hallucinations through embedded semantics and real-time verification.
Ultimately, the choice lies in creating a system that equips AI with reliable tools for navigating reality, rather than succumbing to its hallucinations. As we navigate this landscape, prioritizing robust data verification mechanisms becomes imperative to harness AI’s potential while mitigating its risks.
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