The Immortal Model - Boon or Bane?

Continuously training large language models (LLMs) unlocks powerful capabilities. But it also poses complex ethical risks we can't ignore.

As each new LLM initializes using previous models, knowledge accrues across generations. Supporters highlight AI's potential to accumulate reasoning rivaling humanity through this perpetual learning.

However, endless learning also risks amplifying and cementing flaws from ancestral training data. Like an immortal vampire, immortal models might carry dangerous biases forward forever.

Firstly, historical biases persist. LLMs reflect embedded prejudices in their old training data - sexism, racism, and other discrimination linger in models trained on outdated texts.

We might expect continual learning to temper these biases over time. But corrupted training data makes this uncertain. Which brings us to the data problem.

Research confirms training data can be intentionally polluted with false information and bias. Detecting such poisoning is extremely difficult given today's massive datasets. Immortal models would ingest ever-growing data, facing escalated risk of absorbing contaminated information.

Additionally, the data deluge poses new challenges. Emerging generative AI allows mass production of synthetic training data. Using this blindly could enable poisoning attacks and bias injection at tremendous scale. More advanced evaluators and evaluation frameworks will be imperative to filter this data and protect perpetual learning.

Likewise, representation issues loom large. Models mirror their training data - but what if this chronically excludes marginalized communities? Those without digital access may be perpetually overlooked.

Together, these risks make it clear more rigorous oversight and ethical practices are imperative as we entwine AI with society. We must acknowledge past harms or risk amplifying their damage indefinitely through immortal models.

By directly addressing these challenges, researchers can develop principled techniques to balance capabilities and human values. The choices made today will reverberate for decades to come. We must tread cautiously on the path to potentially eternal AI.