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Google’s top AI scientist says ‘learning how to learn’ will be next generation’s most needed skill
Demis Hassabis highlighted the need for meta-skills like learning how to learn amid fast AI advancements, predicting artificial general intelligence within a decade.
- Demis Hassabis, CEO of Google's DeepMind, spoke at an ancient Roman theater in Athens on rapid technological change and its impact on learning.
- He emphasized the urgent need for meta-skills, including learning how to learn, to keep pace with AI transforming education and work.
- Hassabis warned that AI is evolving week by week and that continual learning throughout one's career will be essential amid dramatic advances and risks.
- At the event, Greece's Prime Minister Kyriakos Mitsotakis expressed concern that the accumulation of vast wealth by major tech firms could lead to increased global financial inequality and potential social instability.
- The discussion suggests a future where AI brings radical abundance but also demands broad skill development and policies to ensure shared benefits.
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34 Articles
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"Learning to learn" will adapt to changes, as AI transforms education and the world of work, according to DeepMind's CEO.
·Montreal, Canada
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Google's top AI scientist says ‘learning how to learn' will be next generation's most needed skill
A top Google scientist and 2024 Nobel laureate said Friday that the most important skill for the next generation will be ''learning how to learn'' to keep pace with change as Artificial Intelligence transforms education and the workplace.
·Minneapolis, United States
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Associated Press News+23 Reposted by 23 other sources
Google's top AI scientist says ‘learning how to learn’ will be next generation's most needed skill
Google's DeepMind CEO Demis Hassabis says the most valuable human skill for the future will be “learning how to learn” as artificial intelligence transforms education, careers and daily life.
·United States
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Total News Sources34
Leaning Left11Leaning Right4Center11Last UpdatedBias Distribution42% Left, 42% Center
Bias Distribution
- 42% of the sources lean Left, 42% of the sources are Center
42% Center
L 42%
C 42%
15%
Factuality
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