Fast takeoff, the singularity, and recursive self-improvement (RSI) are currently major topics of discussion in AI communities. There is some truth to them regarding current events in the AI industry. Two or three labs are merging into an oligopoly, controlling access to the top AI models and holding the resources needed to develop the next generation. Today’s AI tools are rapidly reshaping engineering and research roles. In many respects, conducting AI research has become significantly easier. Scaling the training of large language models further presents formidable technical challenges that must be overcome. Super-human coding assistants are making these tasks accessible, shattering many previous assumptions about what it took to build them. This is paving the way for a year (or longer) of swift advancements at the forefront of AI. We’re also at a point where language models have already become exceptionally capable. They are in fact good enough for many highly valuable knowledge-work tasks.
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