Research Background
For decades, Computer Vision (CV) and Natural Language Processing (NLP) were separate fields with different architectures (CNNs vs RNNs).
The breakthrough came with CLIP (Contrastive Language-Image Pre-training) by OpenAI in 2021. CLIP taught models to map images and text descriptions to the same mathematical vector space. If the vector for "dog" is close to the vector for an image of a dog, the model "understands" the image.
This laid the groundwork for today's models which ingest pixels directly alongside text.
Core Technical Explanation
Modern Multimodal AI relies on Tokenization of Everything.
Just as text is broken into tokens (pieces of words), images are broken into "patches" (e.g., 16x16 pixel squares). To the Transformer, a patch of an image is just another "word" in a sequence.
Joint Embedding Space
The key technical achievement is aligning these different modalities.
1. Visual Encoder (e.g., ViT): Compresses an image into a series of vectors.
2. Projection Layer: Translates these visual vectors into the language model's "native language" dimensionality.
3. LLM Backbone: The model processes the sequence `[Image Patches] + "Describe this image"` and outputs text tokens.
What the Data Shows
Unified models are beginning to outperform specialized models.
| Benchmark | Task | Previous SOTA (Specialized) | Gemini Ultra (Multimodal) |
|---|---|---|---|
| MMLU | General Knowledge | 86.4% | 90.0% |
| MMMU | Multimodal Reasoning | 56.8% | 59.4% |
| Math | Visual Math Problems | 70% | 73% |
Limitations & Open Problems
1. Hallucination in Vision: Models can still "see" things that aren't there, especially text in images (OCR errors) or spatial relationships (counting objects).
2. Modality Gap: Audio and Video are far more token-heavy than text. Processing 1 minute of video requires massive compression, often losing fine-grained details.
Why This Matters
True Artificial General Intelligence (AGI) must perceive the world as humans do. A text-only model can read about gravity, but a multimodal model can watch an apple fall and derive the physics. This is critical for robotics, where AI must interact with physical reality.
---
Verified by Global AI News Editorial Board. Sources: OpenAI (CLIP), Google DeepMind (Gemini Technical Report)