Global Information
회사소개 | 문의 | 비교리스트

AI 기술과 멀티모달 학습 : 기술개발과 이용 사례

AI Techniques: Multimodal Learning: Technology Development and Use Cases

리서치사 ABI Research
발행일 2019년 03월 상품 코드 817515
페이지 정보 영문 34 Pages, 4 Tables, 1 Chart, 14 Figures
가격
자세한 내용은 문의바랍니다.

AI 기술과 멀티모달 학습 : 기술개발과 이용 사례 AI Techniques: Multimodal Learning: Technology Development and Use Cases
발행일 : 2019년 03월 페이지 정보 : 영문 34 Pages, 4 Tables, 1 Chart, 14 Figures

멀티모달 학습(Multimodal Learning)에 이용되는 디바이스의 출하대수는 2018년의 2474만 대에서 2023년에는 5억 1412만 대로 증가할 것으로 예측됩니다. 자동차, 로보틱스, 소비자용 디바이스, 미디어 & 엔터테인먼트, 헬스케어 등의 산업 부문이 멀티모달 학습 시스템을 적극적으로 도입하고 있습니다.

멀티모달 학습의 기술개발 동향을 조사했으며, 멀티모달 학습의 필요성, 개발 현황과 향후 가능성, 주요 실현 기술, 주요 도입 영역, 이용 사례 등을 정리하여 전해드립니다.

제1장 개요

제2장 산업 멀티모달 학습의 필요성

  • 분류
  • 의사결정 시스템
  • 휴먼 머신 인터페이스

제3장 멀티모달 학습의 개발과 과제

  • 멀티모달 학습의 현황
  • 멀티모달 학습의 장래성
  • 기존 AI 시스템과의 호환성

제4장 멀티모달 학습의 실현 기술

  • 룰기반 vs DNN
  • 소프트웨어
  • 하드웨어

제5장 현재 상업적 개발

  • 주요 도입 영역·예측
  • 이용 사례·최종 시장

제6장 제안

제7장 결론

게재 기업

  • Amazon
  • ARM
  • CPU
  • DSP Group
  • Google
  • IBM Corp
  • Intel Corporation
  • Intuition Robotics
  • Ling Robotics
  • Microsoft Corporation
  • NN, Inc.
  • Nuance
  • NVIDIA
  • Processing Technologies
  • Qualcomm Inc
  • Unisound
  • Xilinx, Inc.
KSA 19.04.11

The primary objective of multimodal learning is to consolidate the learning process from heterogeneous data streamed from various sensors and other data inputs into a single model, either for prediction or inference. Multimodal learning systems can improve on unimodal ones because modalities can carry complementary information about each other, which will only become evident when they are both included in the learning process. Therefore, learning-based methods that combine signals from different modalities can generate more robust inference, or even new insights impossible in a unimodal system. Multimodal learning has been a research topic in computer science since the mid-1970s, but recent improvements in Deep Learning reignited interest in the field. In the initial phase of multimodal learning, rules-based approaches dominated implementations. However, increasingly, a hybrid mixture of rules-based and deep learning based multimodal learning is becoming the most popular style of software implementation, creating specific implementation requirements for multimodal learning systems.

The market is currently experiencing the first wave of multimodal learning applications and products that draw on Deep Learning techniques to both interrupt sensor data and increasingly inform the multimodal learning process itself. Multimodal learning exploits complementary aspects of modality data streams, making it a powerful technology and enabling new business applications that fall into three categories: classification, decision making, and HMI. Shipments of devices using multimodal learning will increase from 24.74 million in 2018 to 514.12 million in 2023. The market sectors most aggressively introducing multimodal learning systems include automotive, robotics, consumer devices, media and entertainment, and healthcare.

At present, several applications are driving the uptake of multimodal learning, creating demand for systems which can support it. Implementing multimodal learning is still challenging, as open source software efforts remain limited, while capable hardware platforms that bring multimodal learning inference to devices at the edge are only just starting to emerge. The inference of hybrid multimodal learning software has compute requirements that are best served by heterogeneous computing architectures. Consequently, some companies are now building specialized chips based on heterogeneous architectures.

Table of Contents

1. EXECUTIVE SUMMARY

  • 1.1. Commercial Momentum for Multimodal Learning
  • 1.2. The Future of Multimodal Learning

2. WHY DOES THE INDUSTRY NEED MULTIMODAL LEARNING?

  • 2.1. Classification
  • 2.2. Decision-Making Systems
  • 2.3. Human Machine Interfaces

3. MULTIMODAL LEARNING DEVELOPMENT AND CHALLENGES

  • 3.1. Current State of Multimodal Learning
  • 3.2. Potential Future of Multimodal Learning
  • 3.3. Compatibility with Existing AI Systems

4. TECHNOLOGY THAT ENABLES MULTIMODAL LEARNING

  • 4.1. Rule-Based versus DNN
  • 4.2. Software
  • 4.3. Hardware

5. CURRENT COMMERCIAL DEVELOPMENT

  • 5.1. Key Application Areas and Forecasts
  • 5.2. Use Cases/End Markets

6. RECOMMENDATIONS

7. CONCLUSION

Companies Mentioned:

  • Amazon
  • ARM
  • CPU
  • DSP Group
  • Google
  • IBM Corp
  • Intel Corporation
  • Intuition Robotics
  • Ling Robotics
  • Microsoft Corporation
  • NN, Inc.
  • Nuance
  • NVIDIA
  • Processing Technologies
  • Qualcomm Inc
  • Unisound
  • Xilinx, Inc.
Back to Top
전화 문의
F A Q