Challenges of GPT-based Models Like ChatGPT

Slide Content:

The PowerPoint slide outlines the various challenges associated with GPT (Generative Pre-trained Transformer) models such as ChatGPT. It lists out challenges including:

• Bias: Illustrating the dependency of content on the model's training data.
• Misinformation: Points out the vast but potentially unreliable internet text used for training.
• Privacy: Highlights security breaches in data sharing.
• Lack of Context Awareness: Denotes the issue with models not fully understanding context.
• Quality Control: Emphasizes the need to monitor for inappropriate or offensive content.
• Confrontational Vulnerability: Refers to the model's susceptibility to being tricked by certain prompts.
• Explainability & "Black Box" Model: Underscores the opaqueness of the GPT model's decision-making process.
• Performance: Denotes the significant computational resources required for real-time operations.

Graphical Look:

• The slide has a white background.
• The title is presented in a large, bold, dark grey font.
• There are three hexagonal icons with a shadow effect to the left of the slide:
  • One green labeled "True"
  • One orange labeled "Privacy & Transparency"
  • One red labeled "Performance"
• Each hexagon is accompanied by question mark icons that are grey and partially transparent.
• To the right, there is a text box with light blue horizontal lines beside each line of text, serving as bullet points.
• The text within the box is in black font and lists various challenges, with descriptive explanations beside each challenge.
• The top of the slide has a slim horizontal blue stripe.

The overall look is clean, professional, and uses a simple color-coding and icon scheme to emphasize the key points. The use of shapes, colors, and fonts facilitates an easy-to-follow presentation of information.

Use Cases:

• Presenting in a technology conference to discuss AI advancements and their associated challenges.
• Educating stakeholders in a business setting on the potential risks and considerations when implementing AI technologies.
• Training sessions for AI developers and data scientists to raise awareness of ethical and practical issues in AI model development.
• Discussions in academic or policy-making settings where the implications of AI are being debated or researched.