Beyond Autoregressive Models: The Next Frontier in AI
2025-03-07
Most generative AI models today are autoregressive, meaning they predict the next token, with the transformer architecture being the dominant implementation due to its computational efficiency. However, autoregressive models have inherent limitations, such as a lack of planning and reasoning capabilities, limited long-term memory, and a tendency to "hallucinate." The author argues that human thought isn't purely autoregressive, encompassing non-sequential thinking and planning. To achieve AI closer to human cognition, researchers are exploring alternative paradigms like JEPA and diffusion models, which generate content through iterative refinement or denoising from noise, mirroring human thought processes more closely.
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