What are the limitations of current multimodal AI models?

Current multimodal AI models face three key limitations: difficulty aligning and contextualizing cross-modal data, high computational costs, and challenges in generalizing to real-world scenarios. While these models can process multiple data types (e.g., text, images, audio), their ability to deeply understand relationships between modalities remains inconsistent. For example, models like CLIP or Flamingo might struggle to associate specific elements in an image with corresponding text descriptions, especially when context is ambiguous. In visual question answering (VQA), a model might correctly identify objects in an image but fail to answer questions requiring spatial reasoning (e.g., “Is the cup to the left of the book?”), highlighting gaps in cross-modal alignment.

Training and deploying multimodal models demands significant computational resources. Models such as GPT-4V or PaLM-E require large-scale datasets and specialized hardware like TPUs or high-end GPUs, making them inaccessible to smaller teams or researchers with limited budgets. For instance, fine-tuning a multimodal model for a custom task (e.g., combining satellite imagery and weather data for climate analysis) could cost thousands of dollars in cloud compute time. Additionally, inference latency can be high—processing video with audio and text inputs in real-time remains impractical for many applications, limiting their use in low-resource environments like mobile devices.

Finally, these models often underperform in real-world scenarios requiring nuanced reasoning or robustness to noisy inputs. A medical AI analyzing X-rays and patient notes might miss subtle correlations between image features and textual symptoms, leading to unreliable diagnoses. Similarly, video understanding tasks (e.g., tracking objects across frames while interpreting dialogue) frequently expose weaknesses in temporal reasoning. Adversarial attacks further compound these issues: adding imperceptible noise to an image can cause a model to misclassify it, even if accompanying text context is correct. These limitations underscore the gap between benchmark performance and practical usability.