Building toward a bright post-AGI future with Eric Steinberger from Magic.dev
No Priors: Artificial Intelligence | Technology | Startups
Harness the Power of Long Contexts
Choosing a unique architectural approach over traditional transformer models can greatly enhance a model's capability, especially in handling long context windows. The early emphasis on accommodating millions of tokens enables the model to learn from extensive histories and respond effectively to fast-changing data. This capability is crucial for scaling the model to numerous users and adapting to individual data inputs, ensuring that each model can be fine-tuned more accurately. In-context learning, a standout feature of transformers, acts like an online optimizer, prioritizing the learning process over mere data compression.
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