IntroductionArtificial Intelligence is evolving over time. What started as a simple chatbot that could only understand text has grown into a system capable of detecting, analyzing, and processing audio, video, and static images. This means we have transitioned from a unimodal intelligent system to a multimodal AI system. This is a step toward General Artificial Intelligence, as modality provides systems with human-like sensing capabilities, excluding smell and taste. The gateway to these intelligent systems relies on what we call Encoders, which translate different inputs in a way the system can understand. In layman's terms, "AI has all been about transforming data in a way that a computer can understand and interpret," using tools such as GPTs.How Encoders Function?Encoders coupled with latent spaces are the fundamental components that allow AI to transition from being unimodal (text-only) to multimodal, enabling it to process and understand various types of data like audio, video, images, and sensory information.Their primary functions include:1. Data Transformation: An encoder takes an input, such as a text prompt or an image, and transforms it into a numerical representation2. Specialization: In a multimodal system, different encoders are specialized for different data types. For example, some are designed for text, while others are optimized for the complex geometric data found in images and videos3. Feature Extraction: Researchers use labeled data to train encoders to extract "good features," ensuring the system understands the input accurately4. Customization: Unlike rigid computer compilers, encoders can be customized and trained on specific datasets to create specialized tools for industries like banking (e.g., for fraud detection)How does Latent Space function?The latent space is the "numerical compressed data" produced by the encoder. It serves as a bridge within the AI architecture:1. Universal Language: It provides a format that the decoder can understand, allowing the AI to eventually translate the input into a helpful output or action.2. Data Unification: In multimodal AI, researchers aim to have all modes (sensory, image, audio) share data within this space. This allows the system to unify different inputs into one optimal result, mimicking how a human perceives the world through multiple senses simultaneously3. Representation Accuracy: The effectiveness of the AI depends on how "near accurate" the latent space is to the original input. Currently, creating accurate latent spaces for images and videos is more challenging than for text due to the complexity of the data (geometric data).Data Complexity for Image and Video Encoders.Images and videos have inherent data, which is called geometric data, which makes it difficult for encoders to translate visual information into an accurate numerical format. The primary reasons this data is challenging include:1. Accuracy in Latent Space: While current AI systems are highly efficient at understanding text-based structures, they struggle with the vast amount of geometric data found in visual media. This abundance of geometric information makes it difficult for the AI to produce a latent space (the numerical representation of the input) that is "nearly accurate" to the original image or video.2. Feature Extraction: Because visual data is more complex than text, encoders require specialized training to identify and "extract good features." Without this, the AI cannot effectively understand or process the nuances of the visual input.Measures to solve the data complexity challenge.1. Annotated Data: Researchers are collecting and labeling images to provide the AI with better context, helping it learn to interpret geometric data more effectively2. Specialized Encoders: There is a shift toward developing specialized encoders, such as Google's "nano banana," which are specifically optimized for image generation and editing to handle visual data better3. Custom Training: Businesses are encouraged to build and fine-tune their own custom encoders based on specific datasets to improve how the system resonates with and understands the unique data it processes.ConclusionThe shift toward multimodal AI marks a transformative era in which artificial intelligence moves beyond simple text-based tasks toward achieving General Artificial Intelligence (AGI).