Around 1990, AI trends included which major shift?

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Multiple Choice

Around 1990, AI trends included which major shift?

Explanation:
Around 1990, AI research shifted toward stronger mathematical and empirical methods while also embracing the idea of cognition grounded in real bodies and senses. This era brought a move away from purely hand-crafted, symbolic reasoning toward learning from data using probabilistic and statistical tools, such as probabilistic models and neural networks, which rely on mathematics and real-world performance to improve systems. At the same time, the concept of embodied cognition gained traction, highlighting how perception, action, and interaction with the real world shape intelligent behavior in robots and agents. That combination—data-driven learning and the integration of perception and action through embodiment—best captures what was changing in AI at the time. The other options don’t fit: treating AI as magical misreads how the field actually progressed toward verifiable, data-backed methods; focusing on hardware alone ignores the rapid advances in algorithms and learning; and abandoning psychology runs counter to the growing interest in cognitive science and embodiment, which sought to ground intelligence in how minds interact with bodies and environments.

Around 1990, AI research shifted toward stronger mathematical and empirical methods while also embracing the idea of cognition grounded in real bodies and senses. This era brought a move away from purely hand-crafted, symbolic reasoning toward learning from data using probabilistic and statistical tools, such as probabilistic models and neural networks, which rely on mathematics and real-world performance to improve systems. At the same time, the concept of embodied cognition gained traction, highlighting how perception, action, and interaction with the real world shape intelligent behavior in robots and agents.

That combination—data-driven learning and the integration of perception and action through embodiment—best captures what was changing in AI at the time. The other options don’t fit: treating AI as magical misreads how the field actually progressed toward verifiable, data-backed methods; focusing on hardware alone ignores the rapid advances in algorithms and learning; and abandoning psychology runs counter to the growing interest in cognitive science and embodiment, which sought to ground intelligence in how minds interact with bodies and environments.

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