Module 4: Vision & Language AI
Understanding Computer Vision & Natural Language Processing
Executive Summary
- Computer Vision (CV) enables machines to interpret visual data; Natural Language Processing (NLP) allows understanding and generating human language.
- Combined, CV and NLP power applications from automated quality inspection to chatbots and sentiment analysis.
- Ethical use requires addressing privacy, bias, and false-positive risks inherent in perception systems.
Key Concepts
Computer Vision (CV) lets machines interpret visual data; Natural Language Processing (NLP) allows understanding and generation of human language. These technologies power tools from automated image tagging to sentiment analysis and chatbots.
Computer Vision enables machines to:
- Detect and classify objects in images and videos
- Recognize faces and expressions
- Interpret medical imaging
- Enable autonomous navigation
Natural Language Processing allows systems to:
- Understand and generate human language
- Analyze sentiment and intent
- Extract key information from documents
- Translate between languages
- Generate coherent and contextually relevant text
Interactive Charts
This demo shows computer vision object detection. Click the button to detect objects in the image.
Select a sample text and analyze its sentiment to see how NLP models evaluate language.
This heatmap shows how AI attention mechanisms focus on different parts of text or images.
Real-World Examples
Computer Vision Applications
- Medical scan analysis for early disease detection
- Self-checkout systems in retail
- Security and surveillance systems
- Quality control in manufacturing
NLP Applications
- Customer support chatbots
- Automated contract review and analysis
- Content summarization tools
- Email categorization and prioritization
Combined Applications
- Content moderation (images + captions)
- Visual search with natural language queries
- Accessibility tools for visually impaired
- Augmented reality with voice commands
Discussion Prompts
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What tasks in your organization involve heavy visual or text review that could be automated?
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Where could human-AI collaboration enhance speed or accuracy in document processing or visual inspection?
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How do you manage ethical concerns like bias and false positives in vision and language systems?
Prompts for Real-World Use
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Image Recognition Test: Try image recognition with a mobile app or API demo to understand capabilities.
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Text Summarization: Use ChatGPT to summarize a lengthy report and evaluate the quality of the summary.
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Translation Exercise: Translate business materials to/from a second language and test clarity with native speakers.
Call to Action
Identify one internal process using visual or textual data. Meet with relevant stakeholders and assess whether AI tools could enhance speed or accuracy.