<h3 style="font-size: 22px; font-weight: normal;">AI Began with Text</h3>Most of the AI we use today started with text. Early Natural Language Processing (NLP) had a clear goal: understanding sentences. Tasks like translation, Q&amp;A, summarization, and sentiment analysis were all grounded in text data. Large Language Models (LLMs) exploded this capability, but there is one fundamental flaw: The real world is not text-centric.The real world consists of:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Images, Video, Audio</li><li class="editor_editorListItem__FKzSL">Spatial information</li><li class="editor_editorListItem__FKzSL">Sensor data</li></ul>When a person drives a car, most of the information used is visual. Before we even read a road sign, we see and comprehend the scene. Human cognition is inherently multimodal. AI that understands only text cannot truly grasp reality. This is the most crucial reason for the emergence of Multimodal AI.<h3 style="font-size: 22px; font-weight: normal;"></h3><img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777018784008.png" width="90%" draggable="true" style="max-width: 100%; width: 100%; height: auto; cursor: pointer;"><h3 style="font-size: 22px; font-weight: normal;">Multimodality is Not Just a Feature Addition</h3>Many simplify Multimodal AI as "a model that can take text + image input." However, this oversimplifies its essence. The core of multimodality is integrating multiple representation systems into a single semantic space.Consider this situation: A user shows a photo of a screen displaying code and asks, "Why isn't this working?" The AI must simultaneously understand:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">The user's intent.</li><li class="editor_editorListItem__FKzSL">The structure of the code in the image.</li><li class="editor_editorListItem__FKzSL">The error message.</li><li class="editor_editorListItem__FKzSL">The programming context.</li></ul>This is not just image recognition; it is a system that interprets and aligns text and visual information to synthesize meaning. Multimodal AI is not just a system that processes multiple data types—it is a system that integrates multiple cognitive frameworks.<h3 style="font-size: 22px; font-weight: normal;">Human Interfaces are Already Multimodal</h3>Another reason why Multimodal AI is inevitable is that human interfaces are already multimodal. Our digital devices are equipped with:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Cameras, Microphones</li><li class="editor_editorListItem__FKzSL">Touch interfaces</li><li class="editor_editorListItem__FKzSL">Keyboards</li></ul>Users naturally provide input in various forms: asking via voice, taking photos, typing text, or sharing videos. Converting all this data into text for processing is inefficient. It is far more natural for AI to understand these modalities directly. As software evolves into a multimodal environment, AI naturally follows suit.<h3 style="font-size: 22px; font-weight: normal;">Multimodality Creates a New Interface </h3><img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777018794664.png" width="90%" draggable="true" style="max-width: 100%; width: 100%; height: auto; cursor: pointer;">With the advent of Multimodal AI, user interfaces themselves are transforming. Past interfaces were text-centric—inputting keywords, reading documents, and clicking buttons. In the multimodal era, interactions become more natural:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Taking a photo and asking a question about it.</li><li class="editor_editorListItem__FKzSL">Giving commands through voice.</li><li class="editor_editorListItem__FKzSL">Describing something while showing the screen.</li></ul>This is a shift toward a conversational interface, representing not just a UI change, but a shift in the computing paradigm.<h3 style="font-size: 22px; font-weight: normal;">Software is Moving Toward Multimodality</h3>Soon, most software will include Multimodal AI as a basic component.<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Development Tools: Developers can upload code screenshots to have the AI analyze structures and errors.</li><li class="editor_editorListItem__FKzSL">Education: Students can snap a photo of a math problem, and the AI explains the step-by-step solution.</li><li class="editor_editorListItem__FKzSL">Customer Support: Users can photograph a broken product, and the AI analyzes the situation to offer a solution.</li></ul>In this way, Multimodal AI stops being a "feature" and becomes the fundamental infrastructure of product design.<h3 style="font-size: 22px; font-weight: normal;">Multimodality Extends to Understanding Reality</h3>The next stage of Multimodal AI transcends simple input processing. It is evolving into a system that understands the real world:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Autonomous driving, Robotics</li><li class="editor_editorListItem__FKzSL">Smart glasses, AR interfaces</li></ul>These systems process vision, space, text, and voice simultaneously. Ultimately, Multimodal AI is not just a functionality—it is a computational structure that understands reality.<h3 style="font-size: 22px; font-weight: normal;">Conclusion: Multimodality Becomes the Basic AI Structure</h3>Past AI was a text model, but the real world is not text. It is an environment where images, video, sound, and spatial information are intertwined. For AI to truly interact with humans, it must understand all this data.Therefore, Multimodal AI is not a choice; it is an inevitability. In the products of the future, AI will no longer function as a text model, but as a multimodal cognitive system.

AI-Based Software Standardization: The Rise of Multimodal AI

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