SYSTEM AND METHOD FOR A LARGE CODEWORD MODEL FOR DEEP LEARNING
A Large Codeword Model (LCM) is a deep learning architecture that operates on discrete, compressed representations of data called codewords. Unlike traditional models that use raw tokens and dense embeddings, LCMs can efficiently process and generate data in various modalities, including text, images, audio, and time series. By capturing the inherent structure and patterns in the data, LCMs learn more generalizable and interpretable features, enabling transfer learning across different domains. The LCM architecture offers a scalable, flexible, and computationally efficient approach to building AI systems, with potential applications in natural language processing, speech recognition, and beyond.
1 . A system for large codeword models for deep learning, comprising one or more computers with executable instructions that, when executed, cause the system to:
receive a plurality of inputs;
tokenize the plurality of inputs into a plurality of sourceblocks;
assign each of the plurality of sourceblocks a codeword, where each sourceblock is mapped to a particular codeword through a codebook;
process a sequence of the plurality of codewords through a trained machine learning core;
generate a codeword response to the plurality of inputs using the trained machine learning core; and
translate the codeword response into a translated response which matches the modality of the inputs;
wherein the trained machine learning core was trained directly on codeword sequences to generate, given an input codeword sequence consisting solely of codewords, a plurality of probable future codewords that extend the input codeword sequence.
2 . The system of claim 1 , wherein the machine learning core has a transformer-based machine learning architecture.
3 . The system of claim 1 , wherein the machine learning core has a variational autoencoder-based machine learning architecture.
4 . The system of claim 1 , wherein the machine learning core has a recurrent neural network-based machine learning architecture.
5 . The system of claim 1 , further comprising a plurality of codebooks and a plurality of machine learning cores, wherein each codebook and machine learning core is configured to process a different language.
6 . The system of claim 5 , further comprising a codeword translator which translated codewords between any plurality of languages.
7 . The system of claim 1 , wherein the machine learning core comprises a plurality of embedding layers wherein each embedding layer is tailored to the modality of a particular input.
8 . The system of claim 1 , further comprising a codeword clustering component which clusters codewords prior to being processed by the machine learning core.
9 . A method for a large codeword model for deep learning, comprising the steps of:
receiving a plurality of inputs;
tokenizing the plurality of inputs into a plurality of sourceblocks;
assigning each of the plurality of sourceblocks to a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook;
processing the plurality of codewords through a trained machine learning core;
generating a codeword response to the plurality of inputs using the trained machine learning core; and
translating the codeword response into a translated response which matches the modality of the inputs;
wherein the trained machine learning core was trained directly on codeword sequences to generate, given an input codeword sequence consisting solely of codewords, a plurality of probable future codewords that extend the input codeword sequence.
10 . The method of claim 9 , wherein the machine learning core has a transformer-based machine learning architecture.
11 . The method of claim 9 , wherein the machine learning core has a variational autoencoder-based machine learning architecture.
12 . The method of claim 9 , wherein the machine learning core has a recurrent neural network-based machine learning architecture.
13 . The method of claim 9 , further comprising a plurality of codebooks and a plurality of machine learning cores, wherein each codebook and machine learning core is configured to process a different language.
14 . The method of claim 13 , further comprising a codeword translator which translated codewords between any plurality of languages.
15 . The method of claim 9 , wherein the machine learning core comprises a plurality of embedding layers wherein each embedding layer is tailored to the modality of a particular input.
16 . The method of claim 9 , further comprising a codeword clustering component which clusters codewords prior to being processed by the machine learning core.
17 - 20 . (canceled)