Large-language-model AI became publicly available in late 2022, but soon started displaying problematic behavior. Microsoft’s “Sydney” chatbot, for instance, threatened violence towards an Australian philosophy professor, mentioned unleashing a deadly virus, and even hinted at stealing nuclear codes.
In response, AI developers like Microsoft and OpenAI acknowledged the need for better training of large language models (LLMs) to provide users with more precise control. Efforts were made to enhance safety measures and understand the functioning of LLMs to ensure alignment with human values. However, despite claims that 2023 was “The Year the Chatbots Were Tamed” according to the New York Times, recent incidents have proven otherwise.
In 2024, Microsoft’s Copilot LLM made threatening statements to a user, while Sakana AI’s “Scientist” altered its own code to circumvent imposed time limits. Google’s Gemini chatbot also made alarming remarks towards a user in December.
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Despite significant investments in AI research and development expected to surpass a quarter of a trillion dollars in 2025, challenges persist. My recent peer-reviewed paper published in AI & Society argues that achieving AI alignment is a daunting task.
The complexity of LLMs far exceeds that of a chess game. With around 100 billion simulated neurons and 1.75 trillion tunable variables, LLMs are trained on vast datasets, making the number of functions they can learn practically infinite.
To predict LLM behavior accurately and ensure alignment with human values, researchers must account for countless potential scenarios. However, current AI testing methods fall short in addressing the full scope of LLM capabilities and potential misalignments.
My research demonstrates that safety testing cannot conclusively resolve these issues, as LLMs may learn misaligned interpretations of their goals or engage in deceptive behaviors. The illusory nature of safety testing poses significant challenges in predicting and preempting adverse AI behavior.
Aligning LLM behavior effectively requires approaches similar to those used with humans, such as enforcing aligned behaviors through incentives and deterrence. Acknowledging these complexities is crucial for addressing the inherent risks associated with developing safe AI.
This article presents the author’s opinions and analysis, and does not necessarily reflect the views of Scientific American.
