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. Finally, 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", and 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.