年間契約型資訊服務

人工智慧(AI)和機器學習

AI & Machine Learning

出版商 ABI Research 商品編碼 486120
出版日期 年間契約型資訊服務 內容資訊 英文
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人工智慧(AI)和機器學習 AI & Machine Learning
出版日期: 年間契約型資訊服務內容資訊: 英文
簡介

ABI Research提供與人工智慧(AI)和機器學習(ML)相關的年約型資訊服務,此服務可分析IoT系統與各種應用領域的相關技術,涵蓋資料、趨勢、預測報告等多樣資訊,並解析開放原始碼在AI及ML的平台即服務、技術即服務、軟體授權、邊界設備等應用的多樣商業模式中扮演的角色。此服務的目的是提供對於引進使用語音辨識、圖像識別、機器視覺、自然語言處理、觸摸和觸覺、AI 和 ML的資料與分析的安全技術有用的可靠見解,在IoT基礎技術與解決方案拓展普及中,為亟需活用這些技術改善產業流程與商業流程的企業提供後援。

考慮及早採用先進技術企業的提問

  • 管理連上網路的數量龐大的裝置產生的海量資料時,IoT相關業界面臨的主要挑戰為何?
  • 未來應該考慮引入的高度分析方法為何?
  • 高度分析方法在哪些業種及應用領域擔任重要角色?
  • ML以何種形式參與IoT系統與應用技術?
  • 現在ML中使用的演算法的主要類型有哪些?
  • 人工智慧以何種形式簡化業務流程?
  • 說明性分析和預測解析之間的區別是什麼?為有效管理所生成的所有資料, 應執行哪些原則?
  • 從使用高度分析方法生成的資料中可以看到什麼?
  • 是否有必要意識到在全面引進高度分析方法的安全問題?
  • 活用AI所帶來的重要商機是什麼?
  • 該用什麼方法保護自己公司的資料和客戶資料?
  • 現實上來看,AI系統的各種元素需要多少時間才能成熟?
  • 該用什麼形式使開放原始碼商品化,才能從開放原始碼社群生出價值?
  • 最可靠並獲得成功的開放原始碼社群在何處?
  • 將AI整合至企業內生態系統的最佳方法?
  • 與雲端運算相比, 邊界運算的價值何在?
  • 最值得依賴並獲得成功的開放原始碼社群在何處?
  • 什麼方法可以使開放原始碼商品化並產生價值?
  • 應被設定為目標的新業種是什麼?收入機會有多大?
  • AI帶來的重要商機是什麼?
  • 在 AI 領域選擇合作夥伴時要考慮的標準是什麼?

服務範圍

  • 機器學習
  • 人工智慧
  • IoT
  • 網路連接的裝置
  • 資料生成
  • 高度分析方法
  • 預測分析
  • 處方分析
  • 開放原始碼在啟用新技術和商業模式中的作用
  • 語音和圖像識別, 機器視覺, 自然語言處理, 在觸摸和觸覺, 安全性等應用中的新趨勢
  • 邊界運算與雲端運算比較分析
目錄

AI technologies are moving fast into new areas, including machine learning, deep learning and augmented intelligence. Developments in these areas are opening new opportunities across different market sectors and use cases. ABI Research's AI and Machine Learning (ML) market intelligence service assesses the market opportunity created by AI related technology, while at the same time providing thought leadership for the industry. Our extensive coverage of these areas includes data, trends, forecast, benchmark and analysis reports, that asses the key technical and business factors that are essential for shaping AI and ML market activity and business models - including ML as a service, technology and platform as a service, software licensing, edge AI hardware and applications. We aim to provide technology implementers with visionary and authoritative insight into the various AI and ML applications and use cases they should leverage to best streamline industrial and business processes as AI technology becomes accessible. Our approach to market coverage is use-case centric as it looks at technology implementation for each use case studied. Aside from verticals that have existing AI implementation, such as consumer electronics and robotics, we also track AI and ML deployment in retail, manufacturing, energy, automotive, public safety and telecommunications. Special attention is dedicated to AI edge solutions.

TOP QUESTIONS WE RECEIVE FROM INDUSTRY INNOVATORS

Technology Suppliers

  • How are the different ML hardware and algorithms are mapped against requirements of the different use cases addressed?
  • What are the key verticals that will drive AI and ML applications?
  • Should I create my own AI frameworks and solutions or should I adopt existing open frameworks?
  • Who are competitors I should watch and who are those I should partner with?
  • What emerging verticals should my organization target? How big is the revenue opportunity?
  • What major challenges will the industry face when managing a myriad of data generated by billions of connected devices?
  • Who are the companies and organizations my company should partner with to create adequate solutions for the verticals are targeting?
  • Where does my company fit in the AI/ML competitive landscape?
  • How can my organization productize open source code? How can we stream value from it?
  • What are the most successful open-source communities and frameworks for my company to rely on?
  • Do I have any benefit from contributing and using open source and what are the risks?
  • What are the most invested in AI R&D projects and frameworks?
  • What impact will the move from cloud-based to edge based have on the market dynamics and supplier positioning?

Implementers

  • How can I implement AI in my current business activities?
  • How will AI create new market opportunities in my sector?
  • What is the realistic time to maturity of different AI components?
  • What is the best approach for integrating AI into my company's ecosystem?
  • What criteria should I consider when choosing an AI partner?
  • What advanced analytics techniques should my company consider adopting?
  • What are the main types of algorithms used in ML today and how this is going to evolve in the future?
  • How can my company utilize AI to simplify our business and operation processes?
  • What is the difference between predictive and prescriptive analytics, and what is the best course of action for my company to take to effectively keep tabs on all our generated data?
  • What can my company discern from our generated data through advanced analytics?
  • Are there any security concerns my company should be made aware of when relying on advanced analytics?
  • How can my company protect our data and our customers' data?
  • What is the value of edge computing versus cloud computing?
  • Should I be using an open-source AI framework to develop models, and which one would suit my needs?

COVERAGE AREAS

  • Machine learning
  • Artificial intelligence
  • Augmented Intelligence
  • Deep Learning
  • Data analytics
  • Predictive analytics
  • Prescriptive analytics
  • Algorithms and hardware technologies segmentation
  • Analysis of AI Tools and SDKs
  • AI and ML hot technology innovators
  • Edge AI and ML
  • Market segmentation and taxonomy of AI and ML use cases and applications
  • Different implementation approaches of AI and ML
  • AI and ML business models
  • AI and ML use cases in the telecoms industry
  • AI and ML use cases in the manufacturing industry
  • AI and ML use cases in the consumer market
  • AI and ML use cases in the IoT market
  • The role of open source in shaping new applications and business models
  • Emerging trends in speech and image recognition, machine vision, natural language processing, touch/haptics, Generative and Creative Adversarial Networks, automated reasoning and security applications
  • Analysis of edge AI versus cloud AI
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