AI & Technology

機械支援の翻訳下書き (Japanese) for "Agentic": Describing AI systems capable of autonomous action, planning, and decision-making. An agentic AI can break down tasks, use tools, and work toward goals with minimal human intervention.

“例文の下書き: The new release moves toward more agentic workflows where the AI can complete multi-step tasks independently.”

機械支援の翻訳下書き (Japanese) for "AI Alignment": The research field focused on ensuring that AI systems pursue goals that match human values and intentions. A misaligned AI might optimize for a metric that appears correct but produces harmful or unintended outcomes at scale.

“例文の下書き: AI alignment researchers worry that optimizing for user engagement could misalign with genuine user wellbeing.”

機械支援の翻訳下書き (Japanese) for "Chain of Thought": A prompting technique where a language model is encouraged or required to show its step-by-step reasoning before providing a final answer. Chain-of-thought prompting significantly improves accuracy on complex tasks like math, logic puzzles, and multi-step planning.

“例文の下書き: Adding "let's think step by step" to the prompt triggered chain-of-thought reasoning and doubled accuracy.”

機械支援の翻訳下書き (Japanese) for "Constitutional AI": A training methodology developed by Anthropic where a set of guiding principles (a "constitution") is used to self-supervise and refine AI outputs. The model critiques and rewrites its own responses according to the constitution, reducing the need for human labelers for harmful content.

“例文の下書き: Constitutional AI lets the model identify and self-correct its own harmful outputs using defined principles.”

機械支援の翻訳下書き (Japanese) for "Context Window": The maximum amount of text (measured in tokens) that a language model can process and "remember" in a single interaction. Information outside the context window is inaccessible to the model, making context management critical for long-form tasks.

“例文の下書き: The model kept losing track of earlier instructions because the codebase exceeded its context window.”

機械支援の翻訳下書き (Japanese) for "Diffusion Model": A class of generative AI model that learns to create images, audio, or video by reversing a noise-adding process. During training the model learns to denoise progressively; during generation it starts from pure noise and iteratively refines it into a coherent output. Stable Diffusion and DALL·E 3 are prominent examples.

“例文の下書き: The diffusion model generated photorealistic product photos from text descriptions in seconds.”

機械支援の翻訳下書き (Japanese) for "Embeddings": Dense numerical vector representations of words, sentences, or other data that capture semantic meaning. Similar concepts have similar embeddings (nearby in vector space), allowing AI systems to measure meaning similarity mathematically rather than relying on exact keyword matches.

“例文の下書き: The search engine uses embeddings to find relevant results even when the query words don't appear in the document.”

機械支援の翻訳下書き (Japanese) for "Federated AI": An approach to AI training and inference where models are distributed across multiple nodes or organizations without centralizing raw data. Each node trains on its local data and shares only model updates, preserving privacy while benefiting from collective learning.

“例文の下書き: The hospital network used federated AI to improve diagnosis models without sharing patient records.”

機械支援の翻訳下書き (Japanese) for "Few-Shot": A prompting approach where a small number of input-output examples are included in the context to guide model behavior on a new task. Few-shot prompting helps models understand the desired format, tone, or logic without any weight updates.

“例文の下書き: We gave the model three few-shot examples of our data format and it immediately understood the pattern.”

機械支援の翻訳下書き (Japanese) for "Fine-Tuning": The process of further training a pre-trained model on a smaller, task-specific dataset to adapt its behavior for a particular domain or style. Fine-tuning updates the model's weights to make it perform better on specific tasks without training from scratch.

“例文の下書き: We fine-tuned the base model on our legal contracts corpus so it could draft clauses in the right style.”

機械支援の翻訳下書き (Japanese) for "Grounding": The process of connecting an AI model's outputs to verified, real-world information sources. Grounding reduces hallucination by anchoring responses to retrieved documents, databases, or live data rather than relying purely on the model's learned parameters.

“例文の下書き: Grounding the chatbot in our product database eliminated the fabricated feature claims.”

機械支援の翻訳下書き (Japanese) for "Guardrails": Safety constraints and filters applied to AI systems to prevent harmful, offensive, or out-of-scope outputs. Guardrails can be implemented at the model level (via training), prompt level (system instructions), or application level (output classifiers) to keep AI behavior within acceptable boundaries.

“例文の下書き: The guardrails blocked the model from providing detailed instructions on dangerous activities.”

機械支援の翻訳下書き (Japanese) for "Hallucination": When an AI model generates false, fabricated, or misleading information that it presents confidently as fact. A major challenge in deploying AI systems for factual tasks.

“例文の下書き: The model hallucinated a citation that doesn't exist - always verify AI-generated references.”

機械支援の翻訳下書き (Japanese) for "Inference": The act of running a trained machine learning model on new input data to generate predictions or outputs. Inference is distinct from training — it is the "serving" phase where the model is used in production, and its speed and cost are critical for real-world applications.

“例文の下書き: Inference latency dropped from 2 seconds to 200ms after switching to a quantized model.”

機械支援の翻訳下書き (Japanese) for "Jailbreak": A technique used to bypass the safety filters and content policies of an AI model, typically by framing harmful requests in ways the model's defenses don't recognize. Jailbreaks often use role-play scenarios, hypothetical framings, or encoded instructions to make the model comply with prohibited requests.

“例文の下書き: The "DAN" jailbreak asked the model to pretend it was an AI with no restrictions.”

機械支援の翻訳下書き (Japanese) for "LLM": Large Language Model - A type of AI trained on massive text datasets to understand and generate human language. Examples include GPT, Claude, and Gemini.

“例文の下書き: The LLM was able to write working code after just a brief description of the requirements.”

機械支援の翻訳下書き (Japanese) for "Multi-Agent": Describing a system architecture where multiple AI agents collaborate, delegate, or compete to accomplish a shared goal. Multi-agent systems can parallelize work, specialize roles, and check each other's outputs, enabling tasks too complex for a single agent context window.

“例文の下書き: The multi-agent pipeline had a planner agent, a coder agent, and a reviewer agent working in sequence.”

機械支援の翻訳下書き (Japanese) for "Multimodal": Describing AI systems capable of processing and generating multiple types of data — such as text, images, audio, and video — in a unified model. Multimodal AI can answer questions about images, generate images from text, transcribe speech, and reason across modalities simultaneously.

“例文の下書き: The multimodal model analyzed the chart image and provided a written summary of the trends.”

機械支援の翻訳下書き (Japanese) for "Neural Network": A computational model loosely inspired by biological neurons, consisting of interconnected layers of mathematical functions (nodes) that transform input data into output predictions. Neural networks learn by adjusting the weights of connections through exposure to training data.

“例文の下書き: The neural network learned to recognize handwritten digits with over 99% accuracy.”

機械支援の翻訳下書き (Japanese) for "Orchestration": The coordination and sequencing of multiple AI agents, services, or steps in an automated workflow. An orchestrator determines which tools to invoke, in what order, and how to pass outputs between steps to complete a complex task end-to-end.

“例文の下書き: The orchestration layer decided to call the search tool before invoking the summarization agent.”