Doesn’t assume new advances

These concerns are not as pressing if AGI is impossible, requires a paradigm shift away from deep learning, or some fundamental breakthrough in our understanding of intelligence. However, we may not be so lucky. There is a growing opinion that we may be able to achieve AGI with techniques similar to today’s deep learning systems with ever more compute. This scenario is called “prosaic AGI,” where we achieve AGI through prosaic (mundane) techniques. This possibility has begun to be taken seriously due to recent advances in large NLP language models pioneered by the transformers architecture. Computer scientists have observed that the training loss for models can decrease linearly with respect to additional compute [8]. As compute becomes cheaper as we follow Moore’s Law, we may observe a trend where existing models become more powerful without significant technological advances. Progress may accelerate by coupling prosaic advances with genuine computer science breakthroughs.

The key takeaway is that if this prosaic approach provides a plausible path to AGI, we may have less time to address many problems with deep learning that can pose risks to humans. Contemporary problems around discriminatory algorithms may become even more damaging as these models become more powerful and effective. A virtuous tool for good actors that can quickly become a weapon in the hands of bad actors. Furthermore, as we’ll explore in the next section, there are many open questions about how we’ll align AGI systems with human values.