<ul><li>Mamba and Hyena are two novel models that are transforming the way Artificial Intelligence(AI) learns and remembers.</li><li>Selective State Space Models(SSM) is the mechanism that Mamba and Hyena employ to compress SSMs in a selective manner.</li><li>Efficient implementation of Selective SSMs has made these models efficient and fast.</li><li>Both synthetic and real datasets have been used to test these models on a variety of tasks.</li><li>Better performance has been shown by these models on many tasks where traditional AI methods fall short.</li><li>Mamba and Hyena perform significantly better than existing AI models in Audio modeling and generation, Language modeling and DNA modeling tasks.</li><li>The memory requirements of Mamba's and Transformer models are comparable and we can expect to further improve Mamba's memory footprint in the future.</li><li>Efficiency benchmarks show that Mamba's core operation of Selective SSMs is faster and efficient than convolution and attention.</li><li>The paper is available on arxiv under CC BY 4.0 DEED license.</li></ul>

How Mamba and Hyena Are Changing the Way AI Learns and Remembers

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