Potential Replacement Technologies For Generative Pretrained Transformers
While the Transformer architecture remains the current gold standard in AI, several emerging technologies and new architectural approaches are being explored as potential replacements, primarily to address the computational cost and memory limitations of the original models. Potential replacement technologies include: State Space Models (SSMs): Architectures like Mamba and Google DeepMind's Hawk and Griffin are gaining traction as serious challengers. They combine recurrent neural network (RNN) and local attention ideas, aiming for better scalability and efficiency than the quadratic self-attention in Transformers. Mixture of Recursions (MoR): This new architecture from Google DeepMind is presented as a leaner, faster alternative. MoR allows the model to decide in real time how much computation (how many layers) each part of the input needs, leading to more efficient use of resources, faster inference, and a smaller memory footprint. Linear Attention Models: Researchers at ...
