Scott Alexander examines how recent AI progress in neural networks might challenge the Bostromian paradigm of AI risk, exploring potential implications for AI goal alignment and motivation systems.
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This post discusses how recent advances in AI, particularly in neural networks and deep learning, might affect the Bostromian paradigm of AI risk. Scott Alexander explores two perspectives: the engineer's view that categorization abilities are just tools and not the core of AGI, and the biologist's view that brain-like neural networks might be adaptable to create motivation systems. He suggests that categorization and abstraction might play a crucial role in developing AI moral sense and motivation, potentially leading to AIs that are less likely to be extreme goal-maximizers. The post ends by acknowledging MIRI's work on logical AI safety while suggesting the need for research in other directions as well.
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