OpenAI’s fired researcher, Leopold Aschenbrenner, published his “situational awareness” manifesto this summer, which made a huge splash on social media. In the text of this manifesto, Aschenbrenner predicts that artificial intelligence will consume nearly 20% of all US electricity by 2029, telling the unspoken and shaping the destructive powers that will change the geopolitical order of the world within a few years. .
Aschenbrenner’s startling idea of how fast AI is progressing is based on one key premise: “That AI will soon become powerful enough to conduct AI research itself, leading to recursive self-improvement and the formation of AI super-intelligence.” »
The idea of ”information explosion” and the formation of self-healing artificial intelligence is not new, and for a long time this concept has been raised in discourses about the long-term future of artificial intelligence.
The last invention that man should think about
Indeed, Alan Turing’s close associate Irving John Goode eloquently expressed this possibility, “Let a superintelligent machine be defined as one which can surpass all the intellectual activities of any man, however intelligent. Since designing machines is one such intellectual activity, a superintelligent machine can design even better machines. In this case, there will undoubtedly be an “information explosion” and human intelligence will be very backward; Therefore, the first super-intelligent machine is the last invention that mankind has to do.”
Self-healing AI is an interesting intellectual concept, but, even amid today’s AI hype, it has a whiff of science fiction, or at least still feels abstract and hypothetical. However, this concept is starting to become more real day by day; Researchers have made tangible progress in building AI systems that can themselves build better AI systems.
These systems aren’t ready for prime time just yet, but they could be in production sooner than you think. Artificial intelligence is gaining the ability to automate larger parts of human activities. Before long, it can do all the human jobs itself, from customer service agent to software engineer to even taxi driver. In order for an AI to reach its level of improvement, all it needs to do is learn to do a specifically human job: “the job of an AI researcher.”
Using artificial intelligence to automate narrow parts of the AI development process has long been common. Neural architecture search and hyperparameter optimization are two examples of these cases. But automating the process of scientific discovery, without human intervention, is a different concept.
The job of an AI researcher is relatively simple: “read the ML literature and come up with new questions or ideas, run experiments to test those ideas, interpret the results, and iterate.”
This description may sound overly simplistic and reductive, but it points to the fact that automating AI research may be surprisingly feasible.
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