合成資料：人工智慧和新生態系統的未來

改變企業利用資料並產生有意義的見解的方式

合成資料是根據從現實世界中發生的事件收集的資料人工產生的資料。人工生成的文字、表格、圖像、影片等形式的資料。合成資料產生解決了低效資料集和隱私問題的挑戰。

它使用演算法生成，允許組織測試營運資料並有效訓練人工智慧 (AI)/機器學習 (ML) 模型。它對於檢驗數學模型和訓練深度學習模型也很有用。鑑於全球範圍內採用人工智慧/機器學習模型來改善營運，這項技術很可能在未來五年內成為主流。我們不斷研究、開發和增強以標準化格式建立合成資料。

在這項研究中，Frost & Sullivan 透過人工產生的資料來評估資料驅動的轉型。

本次調查對象

• 產生合成資料的模型與技術
• 現有和新的生態系統
• 科技相關發展與全球趨勢
• 成長機會
• 戰略見解和觀點

目錄

戰略衝動

• 為什麼成長如此困難？策略要務 8 (TM)：阻礙成長的因素
• The Strategic Imperative 8（TM）
• 戰略要務對合成資料產業的影響
• 成長機會推動Growth Pipeline Engine（TM）
• 調查方法

成長機會分析

• 分析範圍
• 分割
• 生長促進因子
• 成長阻礙因素

技術吸引力儀表板

• 技術吸引力儀表板

綜合資料影響評估

• 合成資料及其故事生成框架
• 合成資料的類型以及如何創建它們
• 人工智慧與合成資料之間的關係
• 合成資料的應用與影響評估
• 生態系：科技顛覆多個產業
• 改變合成資料使用的頂尖研究
• 將合成資料模型收益
• 確保公平使用虛假資料的法規環境
• 合成資料技術的專利形勢
• 資金籌措及投資場景
• 策略夥伴關係：B2B 對接會
• 區域趨勢和見解
• 為什麼公司需要合成資料？

成長機會宇宙

• 成長機會一：跨產業合作的開放原始碼舉措
• 成長機會2：多模態綜合資料
• 成長機會2：多模態綜合資料
• 成長機會3：制定標準化協議

藍圖和策略見解

• 策略洞察力

附錄

• 技術完備等級(TRL)：說明

下一步

Transforms the Way Businesses Use Data and Generate Meaningful Insights

Synthetic data is data generated artificially based on data collected from real-world occurrences. It is artificially generated data in the form of text, tables, images, and videos, among others. Synthetic data generation will address the challenge of inefficient datasets and privacy concerns.

Generated using algorithms, it enables organizations to test operational data and train artificial intelligence (AI)/machine learning (ML) models efficiently. It also helps validate mathematical models and train deep learning models. The technology will go mainstream in the next 5 years, considering the global adoption of AI/ML models to elevate operations. There is constant R&D and reinforcement for building synthetic data in a standardized format.

In this study, Frost & Sullivan will assess the transformation due to data usage caused by artificially generated data.

This research covers the following:

• Models and techniques to generate synthetic data
• Existing and emerging ecosystems
• Technology-related developments and global trends
• Growth opportunities
• Strategic insights and viewpoints

Table of Contents

Strategic Imperatives

• Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
• The Strategic Imperative 8™
• The Impact of the Top 3 Strategic Imperatives on the Synthetic Data Industry
• Growth Opportunities Fuel the Growth Pipeline Engine™
• Research Methodology

Growth Opportunity Analysis

• Scope of Analysis
• Segmentation
• Growth Drivers
• Growth Restraints

Technology Attractiveness Dashboard

• Technology Attractiveness Dashboard

Synthetic Data: Impact Assessment

• Synthetic Data and Its Story-Generation Frameworks
• Types of Synthetic Data and Their Creation
• Relationship Between AI and Synthetic Data
• Synthetic Data Applications and Impact Assessment
• Ecosystem-Technologies That Disrupt Multiple Industries
• Top Research Transforming the Use of Synthetic Data
• Monetization of Synthetic Data Models
• Regulatory Environment to Ensure Fair Usage of Fake Data
• Patent Landscape for Synthetic Data Technologies
• Funding and Investment Scenario
• Strategic Partnerships-B2B Matchmaking
• Regional Trends and Insights
• Why Do Businesses Need Synthetic Data?

Growth Opportunity Universe

• Growth Opportunity 1: Open-source Initiatives for Cross-industry Collaboration
• Growth Opportunity 2: Multi-modal Synthetic Data
• Growth Opportunity 2: Multi-modal Synthetic Data
• Growth Opportunity 3: Setting Up Standardization Protocols

Roadmap and Strategic Insights

• Strategic Insights

Appendix

• Technology Readiness Levels (TRL): Explanation

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