Multimodal AI

Multimodal AI is a new AI paradigm, in which various data types (image, text, speech, numerical data) are combined with multiple intelligence processing algorithms to achieve higher performances. Multimodal AI often outperforms single modal AI in many real-world problems. Check the following demo video to see what Multimodal AI can do.

Aimenicorn Ecosystem

Aimenicorn is a Multimodal AI software ecosystem developed by Aimesoft. Aimenicorn simplifies the process of applying Multimodal AI technologies in various industries by providing Multimodal AI software packages.

Aimenicorn contains three main layers: The bottom layer is the Multimodal AI framework, which provides complicated data fusion algorithms and machine learning/inference technologies. The middle layer contains the core libraries/frameworks based on Multimodal AI, such as AimeCard for structure analysis and document image examinations of the OCR, with AimeFace being used for face recognition technologies and AimeFluent for natural language understanding. Finally, the top layer contains various applications for specific domains, which combine cutting-edge Multimodal AI technologies with deep domain knowledge to yield an impressive user experience. AimeHotel, AimeHospital, AimeBank, Aime AIShop are just some of these applications.



Aimesoft’s technologies to realize Multimodal AI

  • eco

    Natural Language Processing technologies

    Tokenization, POS tagging, keyword extraction, synonym/antonym detection, information extraction, relation extraction, semantic search, natural language understanding

  • Computer Vision technologies

    Object recognition, semantic segmentation, face recognition, gender/age recognition, OCR (optical character recognition), image search/retrieval, predictive analytics based on image

  • eco

    Speech Processing technologies

    Language model extraction from Web text, acoustic model creation, hot word/trigger word detection, noise cancellation, etc.

  • Data Mining technologies

    Big Data processing, KPI prediction, predictive analytics based on Big Data, recomendation algorithms, data generation for machine learning