System and method for a large codeword model for deep learning
Modality agnostic Large Codeword Model (“LCM”) is an advanced deep learning architecture that processes discrete, compressed data representations called codewords across multiple modalities. Unlike traditional models using raw tokens and dense embeddings, LCMs efficiently handle diverse input types including text, images, audio, and video. The system employs a modality agnostic encoder, unified codebook, and multimodal machine learning core to capture inherent data structures and patterns. This approach enables more generalizable and interpretable feature learning, facilitating transfer learning across domains. The LCM's scalable and flexible architecture includes components for modality-specific processing, cross-modal attention, and joint representation learning. With its computational efficiency and versatility, the Modality Agnostic LCM offers significant potential for various AI applications, including natural language processing, computer vision, and multimodal reasoning.
1 . A system for a generic compound large codeword model for natively multimodal deep learning, comprising one or more computers with executable instructions that, when executed, cause the system to:
receive a plurality of inputs of different modalities;
convert the plurality of inputs to a unified representation using a modality-agnostic encoder prior to tokenization, wherein the unified representation is a fixed-size tensor in which different sections encode information from different modalities in a common semantic space;
tokenize the unified representation into a plurality of sourceblocks;
assign the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a unified codebook that is prefix-free and entropy-coded using Huffman or arithmetic coding, wherein the unified codebook is stored in a codebook library;
cluster the plurality of codewords based on semantic similarity and learn a single embedding vector for each codeword cluster rather than learning embeddings for individual codewords, thereby reducing the number of embedding vectors processed by the multimodal machine learning core;
process the plurality of codewords through a multimodal machine learning core comprising an embedding layer and a cross-modal attention mechanism configured to operate on the cluster-level embedding vectors in the shared semantic space;
generate a codeword response to the plurality of inputs using the multimodal machine learning core;
translate the codeword response into a translated response which matches one or more modalities of the inputs; and
train the multimodal machine learning core simultaneously on the multiple modalities using a joint loss function that applies uncertainty weighting to automatically balance training contributions from the different modalities within the multimodal machine learning core.
2 . The system of claim 1 , wherein the multimodal machine learning core has a transformer-based machine learning architecture.
3 . The system of claim 1 , wherein the multimodal machine learning core has a variational autoencoder-based machine learning architecture.
4 . The system of claim 1 , wherein the multimodal machine learning core has a recurrent neural network-based machine learning architecture.
5 . The system of claim 1 , further comprising a plurality of unified codebooks and a plurality of multimodal machine learning cores, wherein each unified codebook and multimodal machine learning core is configured to process inputs of different modalities.
6 . The system of claim 5 , further comprising a codeword translator which translates codewords between any plurality of modalities.
7 . The system of claim 1 , wherein the embedding layer is a unified embedding layer that maps codewords from all modalities using a single set of learned embedding parameters.
8 . The system of claim 1 , wherein the plurality of inputs comprises at least two of: text data, image data, audio data, and video data.
9 . The system of claim 1 , wherein the unified codebook is capable of mapping sourceblocks from multiple modalities to codewords within a shared codeword space.
10 . The system of claim 1 , wherein the modality-agnostic encoder comprises modality-specific processing channels that feed into a fusion layer, the fusion layer combining outputs from the modality specific processing channels into the unified representation.
11 . The system of claim 1 , wherein the system is configured to perform transfer learning between different modalities using the unified codeword representation.
12 . The system of claim 1 , wherein the system is configured to generate outputs in multiple modalities based on a single codeword response.
13 . A method for a generic compound large codeword model for natively multimodal deep learning, comprising the steps of:
receiving a plurality of inputs of different modalities;
converting the plurality of inputs to a unified representation using a modality-agnostic encoder prior to tokenization, wherein the unified representation is a fixed-size tensor in which different sections encode information from different modalities in a common semantic space;
tokenizing the unified representation into a plurality of sourceblocks;
assigning the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a unified codebook that is prefix-free and entropy-coded using Huffman or arithmetic coding, wherein the unified codebook is stored in a codebook library;
clustering the plurality of codewords based on semantic similarity and learn a single embedding vector for each codeword cluster rather than learning embeddings for individual codewords, thereby reducing the number of embedding vectors processed by the multimodal machine learning core;
processing the plurality of codewords through a multimodal machine learning core comprising an embedding layer and a cross-modal attention mechanism;
generating a codeword response to the plurality of inputs using the multimodal machine learning core;
translating the codeword response into a translated response which matches one or more modalities of the inputs; and
training the multimodal machine learning core simultaneously on the multiple modalities using a joint loss function that applies uncertainty weighting to automatically balance training contributions from the different modalities within the multimodal machine learning core.
14 . The method of claim 13 , wherein the multimodal machine learning core has a transformer-based machine learning architecture.
15 . The method of claim 13 , wherein the multimodal machine learning core has a variational autoencoder-based machine learning architecture.
16 . The method of claim 13 , wherein the multimodal machine learning core has a recurrent neural network-based machine learning architecture.
17 . The method of claim 13 , further comprising using a plurality of unified codebooks and a plurality of multimodal machine learning cores, wherein each unified codebook and multimodal machine learning core is configured to process inputs of different modalities.
18 . The method of claim 17 , further comprising translating codewords between any plurality of modalities using a codeword translator.
19 . The method of claim 13 , wherein the embedding layer is a unified embedding layer that maps codewords from all modalities using a single set of learned embedding parameters.
20 . The method of claim 13 , wherein the plurality of inputs comprises at least two of:
text data, image data, audio data, and video data.
21 . The method of claim 13 , wherein the multimodal machine learning core comprises a cross-modal attention mechanism capable of attending to information across different modalities.
22 . The method of claim 13 , further comprising using modality-specific processing channels that feed into a fusion layer, the fusion layer combining outputs from the modality specific processing channels into the unified representation.
23 . The method of claim 13 , further comprising performing transfer learning between different modalities using the unified codeword representation.
24 . The method of claim 13 , further comprising generating outputs in multiple modalities based on a single codeword response.