AI & ML Applications Overview

AI & ML Applications

• AI and machine learning (ML) are used in everyday life.
• Common AI/ML problems exist, including ethical concerns.
• AI methods are used in various fields.

AI Methods in Fields and Industries

• Information Technology: Examples include Google Face ID, image recognition in Netflix, and game playing applications like OpenAI Five.
• Game-Playing: AlphaGo's chess success and OpenAI Five's games illustrate AI in strategy games.
• Healthcare: AI aids in diagnoses, prescreening, drug discovery (e.g., Exscientia), and robotic surgery.

AI Methods in Fields and Industries (continued)

• Media: AI is used for tasks like media analysis and creation (narrative science).
• Business: AI affects numerous business areas, including marketing, human resources, finance, and manufacturing.

Algorithms Behind AI/ML Applications

• This section likely discusses the specific algorithms used in various applications.

Search Applications

• Google Maps uses pathfinding algorithms.
• Other applications like video games utilize pathfinding and other search algorithms to find optimal routes or targets

Classification: Spam Mail Filter

• Spam mail filters use classification to identify spam messages based on past examples..

Classification in Other Applications

• AI can categorize images (e.g., COVID-19 diagnosis).
• Image categorization relies on algorithms like Grad-CAM for overlay displays.

Types of Classification Errors

• True Positive (TP): Correctly identified as positive.
• True Negative (TN): Correctly identified as negative.
• False Positive (FP): Incorrectly identified as positive.
• False Negative (FN): Incorrectly identified as negative.

Cancer Screening vs. Spam Mail Filter

• The note compares classification errors in cancer screening vs. spam mail filtering, using a table to show examples.

Recommendation: Netflix

• Netflix utilizes recommendation systems to suggest content based on user viewing history.

Recommendation Approaches

• Content-Based Recommendation: Based on content of items.
• Collaborative Filtering: Based on past user preferences.

Regression: Keypoint Detection

• Keypoint detection uses regression to identify specific body points in images (e.g., running person).
• Specific points like nose, eyes, and limbs are identified by index numbers.

Clustering: Customer Segmentation

• Clustering techniques, such as K-means, are used for clustering customers based on data characteristics.

Large Language Models (LLMs)

• LLMs power modern chatbots and creative applications.
• These models automate data visualization (e.g., LIDA) and generate images (e.g., DALL-E 3)
• LLMs utilize "Transformer" architecture.

How LLMs Work

• LLMs predict the next word in a sequence by analyzing previous words.
• These models use vast amounts of text data for learning.

Large Language Models (LLMs)

• Graph displaying growth of parameters in LLMs over time.
• Example of models like GPT-3, BERT, etc are shown and their parameter size is compared.

Why Large Models?

• Large language models show strong performance in "few-shot" learning.
• The models can perform effectively with minimal examples, demonstrating a benefit of size.

Ethical Considerations

• Potential misuse of AI technologies (e.g., deepfakes, autonomous weapons, bias in machine learning algorithms).
• Issues of bias and fairness in AI systems (e.g., recidivism risk assessment).
• Using existing code (co-pilot) has ethical concerns.

Ethical Considerations (Continued)

• The Trolley Problem illustrates complex ethical dilemmas involving AI decision-making.

This quiz explores the various applications of artificial intelligence and machine learning in everyday life. It covers the fields of information technology, healthcare, media, and business, detailing specific use cases and the ethical concerns associated with these technologies. Additionally, you'll learn about the algorithms that power AI and ML applications.