みんなAIはどれ使ってる？
The buzz around "Which AI are people using?" is growing, with a wide range of services from free to paid.
Users seem to be choosing chat, image, or video generation AI based on their specific needs.
However, many still ask, "Which one is truly best?" or "I'm confused, please recommend one!"
Related Keywords
Generative AI
Generative AI refers to a broad category of artificial intelligence that can "generate" various forms of content, such as text, images, audio, and video. What makes it groundbreaking is its ability to create entirely new information based on learned data, rather than merely searching and analyzing existing information. Its existence rapidly gained global recognition after OpenAI released ChatGPT in late 2022. The "AI" in the title "Which AI are you using?" often refers to this type of generative AI. Its applications are vast and ever-expanding, including drafting business reports and proposals, generating programming code, brainstorming ideas for social media posts, assisting with personal creative projects like illustrations, and helping write blog articles. Key text-generating AIs include ChatGPT, Google Gemini, Anthropic Claude, and Microsoft Copilot, while DALL-E, Midjourney, and Stable Diffusion are prominent image generators. These tools are increasingly integrating into our work and daily lives, with expectations for even more sophisticated content generation and specialized AIs in the future.
Large Language Model (LLM)
Large Language Model (LLM) is the foundational technology underpinning generative AI, particularly text-based AI services. By learning from vast amounts of text data on the internet (hundreds of billions to trillions of words), LLMs acquire the ability to understand and generate natural human language. The "large" in LLM refers not only to the volume of training data but also to the extremely high number of parameters (tens of billions to trillions) that constitute the model. This scale enables LLMs to capture complex linguistic nuances, context, and perform logical reasoning. The GPT series (GPT-3.5, GPT-4, etc.) from ChatGPT, Google's PaLM and Gemini, and Anthropic's Claude are all built upon high-performance LLMs. These LLMs can handle a wide range of tasks beyond merely answering questions, including summarization, translation, text correction, brainstorming ideas, and writing programming code. When choosing "which AI to use," the performance and characteristics of the underlying LLM (e.g., factual accuracy, creativity, ethical safety, length of information it can handle) significantly impact the user experience, making them crucial criteria for decision-making.
Multimodal AI
Multimodal AI refers to AI systems that can simultaneously understand and generate multiple different forms of information (modalities), such as text, images, audio, and video. While early generative AI often specialized in handling only text or only images, human communication combines diverse information. Similarly, by integrating multiple modalities, AI can achieve more advanced and natural interactions and content generation. For example, a user might show an image and instruct, "Describe this image," and the AI would analyze it and respond in text. Or, in the future, complex tasks like "Create a caption and background music that fits this photo" could be possible. Google Gemini and OpenAI's GPT-4V (Vision) have already put some multimodal capabilities for handling text and images simultaneously into practical use. This allows for question-answering based on visual information, analysis of image content, and even responding to image generation instructions. The evolution of this technology significantly expands AI's use cases and is key to dramatically improving the interface through which we interact with AI and the quality of AI-generated content in the future. When choosing an AI, attention is also being drawn to the trends of AI that integrate multiple modalities, not just single functions.