Eric Drexler at AI Prospects: Toward Global Goal Alignment:
Automating routine tasks expands possibilities. Before automatic differentiation, deep learning practitioners derived and implemented gradients by hand for each model family, a laborious and error-prone process. When Theano and its successors automated this mathematical labor, they transformed neural networks from a specialized practice into a broadly accessible discipline. This unlock, combined with massive datasets and GPU computing, catalyzed the deep learning revolution.
Today, we’re seeing a confluence of similar advances happening simultaneously across the ML stack. This isn’t the “recursive self-improvement” of AGI mythology, where a monolithic entity modifies itself toward superintelligence. It’s a systemic process in which specialized tools automate routine tasks while making new tasks tractable. Researchers increasingly orchestrate these tools to build automated workflows.
Today’s trajectory is toward orchestrating systems that integrate piecemeal-superhuman capabilities of increasing scope. Looking forward, the comprehensive automation of research tasks has become a question of timelines, not outcomes. What we’re witnessing now are the early stages, and in this domain, automation accelerates automation.
More here.
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