Judul : AI Collapse Sparks Academic Transformation, Research Indicates
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AI Collapse Sparks Academic Transformation, Research Indicates
A claim that, once the AI bubble bursts, AI experts will exit the industry and join the scientific community to start new innovative research has been featured in the international academic journal *Nature*. Although the collapse of the AI bubble may lead to the loss of many jobs within the industry, it is proposed that these professionals might eventually play a role in generating high-quality research results in academia.
◇ Ongoing Conversations About the AI Bubble
Economists consider the present surge in AI to be a historic milestone. The current funding flowing into the AI industry equals 17 times the investments made in internet firms right before the dot-com bubble collapsed in the early 2000s. Nvidia, an artificial intelligence company valued at 4.6 trillion dollars, has a market size bigger than most countries, excluding only the United States, China, and Germany.
However, numerous companies feel that AI has not yet led to measurable increases in profits. A McKinsey study revealed that about 80% of firms that have implemented AI reported that it has had "no meaningful effect on profits." Furthermore, Sam Altman, CEO of OpenAI, recently mentioned that certain areas of the industry are "quite overhyped." This is why some experts predict that the AI sector might soon face a downturn.
◇ Investigation Persists Following the Collapse of the Bubble
On the other side, there are predictions that new scientific advancements might only start once the AI hype subsides. John Turner, an economist from Queen’s University Belfast, stated, “Even following the collapse of the dot-com bubble, the number of papers in electronic engineering, computer engineering, and science disciplines actually rose, and the growth of internet and mobile technologies did not stop.” In essence, research persisted despite industrial disruptions.
Experts think a comparable scenario might arise even if the AI bubble bursts. Brent Goldfarb, an economist from the University of Maryland, remarked, “Should the AI bubble burst, widespread job cuts are expected in AI startups or emerging firms, with only big companies such as Google, Nvidia, and OpenAI likely to remain.” Nevertheless, he mentioned that over time, AI expertise could shift to different areas and drive fresh technological advancements.
Professor Turner also mentioned, "Following the collapse of the British bicycle industry in 1896, former bicycle mechanics contributed to the development of the motorcycle, car, and aviation sectors. In a similar fashion, after the dot-com crash, the internet expanded more rapidly."
◇ Experts from Companies to Showcase 'More Advantageous Technologies'
David Kirsch, a technology historian from the University of Maryland, expressed a comparable viewpoint. He stated, "If individuals who lose their positions in the industry because of an AI downturn go back to academic settings, they might develop technologies that offer significant advantages to society." For instance, this could result in innovations such as DeepMind's AI program 'AlphaFold,' which assisted in resolving the 50-year-old challenge of predicting protein structures.
*Nature* also noted, “These kinds of initiatives have already started.” For example, this year, leading AI researchers from OpenAI, Meta, and Google departed their respective companies to establish the materials science venture 'Periodic Labs.' The company's motto is, "Our objective is to build an AI scientist." It seeks to create AI systems for conducting experiments and simulations that can be promptly employed by university laboratories, national research institutions, and hospitals.
Yann LeCun, who previously served as Meta's lead AI scientist, recently stated, "I will depart the company later this year to launch a startup dedicated to exploring a novel type of advanced AI research." LeCun is said to have made this choice following Meta CEO Mark Zuckerberg's emphasis on creating "AI superintelligence." He is now concentrating on developing "world models" that serve scientific progress, robotics, and society at large, rather than focusing on commercial large language models (LLMs). The objective is to build an AI brain capable of thinking and acting like a human, assisting in long-term decision-making.
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