SKYNET HYBRID MODAL AI
WHAT is HYBRID MODAL Ai?
Multimodal AI refers to artificial intelligence technology that mimics various human senses (e.g., vision, hearing, touch) to simultaneously process and understand different types of data. Here, “modal” refers to the form or type of data, and multimodal AI has the capability to integrate and analyze multiple modalities such as text, images, audio, and video to provide comprehensive responses or analyses.
On the other hand, Crossmodal AI refers to artificial intelligence technology that enables interaction and transformation between different data modalities (such as text, images, and audio). While similar to multimodal AI, crossmodal AI focuses on converting information from one modality to another or connecting them seamlessly.
“Multimodal AI + Crossmodal AI = Hybrid Modal AI” is a concept that combines the strengths of both technologies to envision a more advanced artificial intelligence system.
SKYNET HYBRID MODAL AI
Feature
Key Features
Integrated Understanding + Transformation Capability:
Simultaneously processes multiple modalities (multimodal) while seamlessly performing modality conversions (crossmodal).
Example: Generating an image from text and then combining that image with the text for further contextual analysis.
Dynamic Interactions:
Enables interactions between different modalities and facilitates holistic learning across modalities.
Example: Generating image captions and then creating additional images based on those captions.
Advanced Applications:
Combines the data fusion ability of multimodal AI with the transformation capability of crossmodal AI to handle complex tasks.
Example: In the medical field, analyzing an X-ray (image modality), generating a diagnostic report (text modality), and converting it into audio instructions (speech modality).
Potential Applications of Hybrid Modal AI
Education:
Answering student questions (text) by generating learning materials (images/videos) and providing verbal explanations (audio).
Healthcare:
Analyzing medical data across multiple modalities (e.g., X-rays → explanations → audio reports).
Entertainment:
Creating videos (visual modality) and sound effects (audio modality) based on a story (text modality).
Autonomous Driving:
Integrating vision (camera), sound (sensor alerts), and contextual decision-making in real time.
특징
특징
통합적 이해 + 변환 가능성:
여러 모달리티를 동시에 처리(멀티모달)하면서도 모달 간의 변환(크로스모달)을 유기적으로 수행.
예: 텍스트로 이미지를 생성하고, 생성된 이미지와 텍스트를 결합하여 새로운 맥락의 분석 결과를 도출.
다양한 상호작용:
서로 다른 모달리티 간의 상호작용과 정보의 통합적 학습을 실현.
예: 이미지 캡션 생성 + 텍스트를 기반으로 추가 이미지 생성.
고급 응용:
멀티모달의 데이터 융합 능력과 크로스모달의 변환 능력을 결합하여 복잡한 작업을 처리.
예: 의학 분야에서 의료 영상을 분석(이미지 모달), 해당 결과를 설명(텍스트 모달), 그리고 음성으로 의사에게 제공(음성 모달).
하이브리드 모달 AI의 잠재적 응용
교육:
학생 질문(텍스트)을 기반으로 학습 자료 생성(이미지/비디오) 후 음성으로 설명.
의료:
의료 데이터를 여러 모달로 분석하고 변환(예: X-ray → 설명 → 음성 리포트).
엔터테인먼트:
스토리(텍스트)를 기반으로 비디오(영상)와 음향 효과를 통합적으로 생성.
자율주행:
시각(카메라) + 음성(센서 데이터 경고) + 맥락적 결정을 통합적으로 처리.
特徴
主な特徴
統合的な理解 + 変換能力：
複数のモーダリティを同時に処理（マルチモーダル）しながら、シームレスにモーダリティ変換（クロスモーダル）を実行。
例：テキストから画像を生成し、その画像とテキストを組み合わせて、さらに文脈に基づいた分析結果を導き出す。
動的な相互作用：
異なるモーダリティ間の相互作用を可能にし、モーダリティを超えた統合的な学習を実現。
例：画像キャプションを生成し、そのキャプションを基に新たな画像を生成。
高度な応用：
マルチモーダルAIのデータ融合能力とクロスモーダルAIの変換能力を組み合わせ、複雑なタスクに対応。
例：医療分野では、医療画像を分析（画像モーダリティ）、診断結果をテキスト化（テキストモーダリティ）、さらに音声で医師に提供（音声モーダリティ）。
ハイブリッドモーダルAIの潜在的応用
教育：
学生の質問（テキスト）に基づき、学習資料（画像/ビデオ）を生成し、音声で説明を提供。
医療：
医療データを複数のモーダリティで分析（例：X線 → 診断 → 音声レポート）。
エンターテインメント：
ストーリー（テキスト）に基づき、ビデオ（映像モーダリティ）と効果音（音声モーダリティ）を統合的に生成。
自動運転：
視覚（カメラ）＋音声（センサーアラート）＋文脈的な意思決定をリアルタイムで統合的に処理。
Linkbricks Horiozn-AI Cross & Multi Modal Ai Technology
Text
text-based Cross Modal LLMs with varying parameter sizes
Voice
Synchronous, asynchronous, and real-time voice LLM
Image
Low-cost LLM for text-to-image, image-to-text, and image-to-image
Music
Fast and cost-effective LLM for music generation, music-to-music, and text-to-music
Video
Low-cost LLM for video generation, text-to-video, and video-to-text
Physical
Genesis and Omniverse Physics platform designed for general-purpose Robotics/Embodied AI/Physical AI with LLMs
