Decoding Google Gemini with Jeff Dean
Google DeepMind: The Podcast
The Evolution and Foundations of Neural Networks in AI
Neural networks represent a significant development in artificial intelligence, emerging from early AI's focus on rule-based systems in the mid-20th century. They gained traction in the late 1980s and early 1990s, largely as a means to model complex problems through parallel processing. Neural networks consist of multiple layers of artificial neurons that communicate through connections to process information. These neurons evaluate incoming signals, determining their relevance to generate outputs. The bottom layers identify simple features such as colors or edges, while higher layers synthesize this information into more complex representations, like faces. Despite their potential, early neural networks had limited capabilities, primarily handling simple, artificially generated patterns rather than complex real-world objects.
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