Generative AI and Its Applications in Industry

Opening and Speaker Introduction

• Speaker: Pak Maulana Akbar Diwijaya, Technical Consultant at Ctpt Indonesesia Global Solisindo (ISGS).
• Topic: Generative AI and applications in the industry.
• Agenda:
  1. Introduction to Generative AI
  2. Utilization in the industry
  3. Turning points of Generative AI
  4. Conclusion

Introduction to Generative AI

• **Technological Evolution Highlights:"
  • 1993: Internet revolution with Mosaic web browser.
  • 2007: Apple introduced the smartphone.
  • 2008: Growth of cloud computing.
  • 2022: Introduction of CGCtpt (Generative AI) by Open AI.
• **Core Concepts:"
  • Generative AI uses algorithms to create new data based on existing data.
  • Types of outputs: text, images, audio, video, source code.
  • Difference from other AI: Focuses on generating new content versus only analyzing existing data.

Applications of Generative AI

• Popular tools:
  • CGCtpt: Generates text like articles, stories, poems.
  • DALL-E: AI model generates images based on text descriptions.
• Industry Impact:
  • Creativity: Helps in content creation and innovation.
  • Efficiency and Automation: Automates tasks to improve operational efficiency.
  • Business Innovation: Helps businesses compete with innovative solutions.
• Examples by Industry:
  • Creative industries: Generates creative content.
  • Decision-making: Simulate scenarios and forecast outcomes.
  • Healthcare: Develops new hypotheses and treatment recommendations.
  • Finance: Analyzes and predicts financial trends.
  • IT: Assists in automating tasks and upskilling workforce.

Real-life Use Case

• Example: Internal chatbot for a company’s knowledge base.
  • Problem: Inefficiency in document search.
  • Solution: Internal chatbot using CGCtpt model for efficient document access.
• Implementation Process:
  • Aggregation of documents.
  • Utilization of two language models (chat generation and text preprocessing).
  • Establish a user interface for interacting with the knowledge base.

Turning Points and Challenges of Generative AI

• **Key Challenges:"
  • Hallucination: AI generating outputs without context leading to misinformation.
  • Domain-specific Data: Struggles with non-generalized data and operational accuracy.
  • Cost: High computational resource needs, especially for training AI systems.
  • Latency: Network delays impacting real-time response.
• **Risks:"
  • Misinformation due to inaccurate outputs.
  • Privacy and data security concerns.
• **Mitigation Strategies:"
  • Identify potential AI impacts through assessments.
  • Align resource capabilities with organizational needs.
  • Establish strong governance for data security and error mitigation.

Conclusion

• Generative AI Value: Offers significant advancements and creative potential across multiple industries.
• Call to Action: Encourages exploring Generative AI while considering its challenges and responsibly utilizing its outputs.

Key Takeaways

• Generative AI is revolutionizing how content and solutions are created and applied in industries.
• It’s vital to understand and mitigate the associated challenges and risks.
• Continuous evolution and responsible use will maximize the benefits of AI technologies.

Q&A Highlights

• Recording and Privacy: CGCtpt records interactions for improving AI but raises potential data security concerns.
• Avoiding Poor Responses: Maintain context constraints and careful model selection for accurate AI outputs.
• Job Evolution: AI automation may shift job roles, necessitating adaptability and continuous learning.
• Plagiarism Risk: AI-generated content must be carefully monitored to avoid legal and ethical issues.

Administrative Notes

• Attendance and participation were recorded, and evaluations were requested.