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NVIDIA's Jensen Huang believes AI hallucinations can be resolved, anticipates artificial general intelligence within the next 5 years
According to NVIDIA's chief executive Jensen Huang, we could encounter AGI, also known as artificial general intelligence, much earlier than 2030. He predicts that the first AGIs will be observed by humans within the following 5 years.
Artificial General Intelligence, also known as AGI, is a significant topic of discussion in the AI industry and a primary goal that nearly all individuals involved in AI development are eagerly anticipating. According to Jensen Huang, the CEO of NVIDIA, it's believed that we'll be achieving AGI in no time.
AGI is anticipated to bring about a significant advancement in technological prowess. Frequently referred to as "strong AI" or "human-level AI," AGI implies the possibility of machines demonstrating intellectual capabilities that are comparable to or even better than human abilities. In contrast to conventional or narrow AI that is tailored for particular tasks, AGI is expected to shine in a broad range of cognitive areas.
During the yearly GTC developer conference held by Nvidia, their CEO Jensen Huang spoke to the media, providing his perspective on the path of AGI and dealing with the profound questions it prompts. Huang recognized the importance of AGI, but also showed tiredness towards the constant questioning about it. He believes this exhaustion comes from the media regularly misunderstanding his comments.
The rise of Artificial General Intelligence (AGI) triggers deep philosophical thoughts, challenging the dominance and position of humans in a future where machines might exceed human skills. At the core of these worries is the erratic nature of AGI's decision-making procedures and goals, which may deviate from human morals and preferences— a concept that has been a subject of science fiction for many years.
Huang, in response to media pressure for a schedule for AGI's progress, highlighted the difficulty in defining AGI and warned about exaggerated conjecture. He compared this to concrete goals like the New Year or arriving at a certain place, stressing the need for an agreement on how to gauge the achievement of AGI.
Huang presented a detailed viewpoint, proposing realistic standards for AGI and predicting it could meet certain performance criteria within five years. However, he stressed the importance of precisely outlining AGI's boundaries to make accurate forecasts.
Discussing worries over AI hallucinations – situations where AI produces believable but incorrect responses—Huang proposed a solution based on comprehensive study. He suggested a "retrieval-augmented generation" strategy, similar to fundamental media literacy, where AI cross-checks responses with trustworthy sources before replying. Huang especially suggested this for vital areas such as health advice, recommending the use of multiple sources for cross-verification to guarantee precision.
Basically, Huang's knowledge illuminates the intricate nature of AGI development and the importance of ethical AI management to lessen possible dangers. As AI progresses further, all parties involved need to tackle moral issues and implement plans to make sure AI systems coincide with human ethics and benefit the greater good of society.
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