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AI Development in the Era of ChatGPT: Challenges and Key Considerations

  • Report

  • 4 Pages
  • July 2023
  • Region: Global
  • Market Intelligence & Consulting Institute (MIC)
  • ID: 5851671

The rapid development of generative AI software, such as ChatGPT, has the potential to bring significant benefits. However, it also brings forth far-reaching consequences that need careful considerations. The training of AI models relies heavily on large amounts of data, necessitating the implementation of robust data governance measures and the adaptation of guidelines to address ethical and legal concerns.

This report offers a brief overview of the challenges and issues associated with AI and highlights a few key considerations that should be taken into account.

Table of Contents

1. Background
2. Challenges and Issues
3. Key Considerations

Methodology

Primary research with a holistic, cross-domain approach

The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.

Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:

Method

  • Component supplier interviews
  • System supplier interviews
  • User interviews
  • Channel interviews
  • IPO interviews
  • Focus groups
  • Consumer surveys
  • Production databases
  • Financial data
  • Custom databases

Methodology

  • Technology forecasting and assessment
  • Product assessment and selection
  • Product life cycles
  • Added value analysis
  • Market trends
  • Scenario analysis
  • Competitor analysis

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