Key challenges when building a Multi-Model Generative AI are:
- Data volume: It requires large amount of diverse data and specialized training techniques. For example: training a model to generate speech and text together requires a dataset that includes both speech and text inputs, as well as specialized training algorithms that can handle both modalities.
- Complexity in handling: Analyzing complexity of diverse data poses challenges like high demand in advance algorithms and powerful hardware.
- Costly to maintain and manage: As a result of complexity, maintain and manage these systems require specialist and skilled professionals.
- Data Alignment: Inconsistencies in structure , timings and interpretation due to integration of data from diverse sources.
- Biasness: Multi-model systems can inherit biasness since the training data is integrated form diverse sources.
Now for the second part of the question:
You can handle the complexity of learning from diverse and heterogeneous data sources by referring to following:
Solutions:
1.Feature Integration
Early Fusion: Integrate attributes from greater than one varieties at early stages-for example by integrating them.
Late Fusion: The Join operation later, after the features have been extracted after they passed through separate networks.
Hybrid Fusion: Use both early as well as late fusion.
2.Multimodal Transformers:
Utilize transformer architectures designed for multimodal data specifically, for instance, Vision Transformers (ViTs) or Audio Transformers.
These models can learn to focus on the important parts of each type.
a. Graph Neural Networks (GNNs):
Show multimodal data as a graph, with nodes standing for different types and edges showing how they are related.
GNNs can learn to think about these graphs and derive useful information from them.
b. Multimodal Auto-Encoders:
To learn to combine different kinds of data by compressing them into the hidden space.
This can combine data from all types.
c. Data normalization and standardization:
Make different types of data appear the same so that one can compare data from various sources.
d. Transfer Learning: Leverage pre-trained models on a large-scale multimodal dataset to initialize your model and enhance performance. You can combine and manage data from various types in your Gen AI model by thoroughly thinking through these challenges and using the right techniques.
