2) Exploring Artificial Intelligence Use Cases and Applications Flashcards
(24 cards)
What does AI encompass?
The development of intelligent systems capable of performing tasks that typically require human intelligence
What is the focus of Machine Learning (ML)?
Developing algorithms and statistical models so that computer systems can learn from data and make predictions or decisions without being explicitly programmed
What is Deep Learning (DL) based on?
The concept of neurons and synapses similar to how our brain is wired
Define Generative AI.
Generative AI is capable of generating new data based on the patterns and structures learned from training data
When are AI and ML appropriate solutions?
When coding the rules is challenging and the scale of the project is challenging
What are the two subcategories of Supervised Learning?
- Classification
- Regression
What is the goal of Supervised Learning?
To learn a mapping function that can predict the output for new, unseen input data
What is Classification in Supervised Learning?
Learning patterns from training data to predict the class or category for new unlabeled data instances
List some use cases for Classification.
- Fraud detection
- Image classification
- Customer retention
- Diagnostics
What is Regression in Supervised Learning?
A technique used for predicting continuous or numerical values based on one or more input variables
List some use cases for Regression.
- Advertising popularity prediction
- Weather forecasting
- Market forecasting
- Estimating life expectancy
- Population growth prediction
What are the two subcategories of Unsupervised Learning?
- Clustering
- Dimensionality reduction
What is the goal of Unsupervised Learning?
To discover inherent patterns, structures, or relationships within the input data
What is Clustering in Unsupervised Learning?
Grouping data into different clusters based on similar features or distances between the data points
List some use cases for Clustering.
- Customer segmentation
- Targeted marketing
- Recommended systems
What is Dimensionality Reduction?
Reducing the number of features or dimensions in a dataset while preserving the most important information or patterns
List some use cases for Dimensionality Reduction.
- Big data visualization
- Meaningful compression
- Structure discovery
- Feature elicitation
What are some capabilities of Generative AI?
- Adaptability
- Responsiveness
- Simplicity
- Creativity and exploration
- Data efficiency
- Personalization
- Scalability
What are some challenges of Generative AI?
- Regulatory violations
- Social risks
- Data security and privacy concerns
- Toxicity
- Hallucinations
- Interpretability
- Nondeterminism
What is User Satisfaction in the context of Generative AI?
User feedback to assess their satisfaction with the AI-generated content or recommendations
What does Average Revenue Per User (ARPU) measure?
The average revenue generated per user or customer attributed to the generative AI application
What is Cross-Domain Performance?
Measures the generative AI model’s ability to perform effectively across different domains or industries
What does the Conversion Rate monitor?
The conversion rate to generate content or recommend desired outcomes, such as purchases, sign-ups, or engagement metrics
What does the Efficiency metric evaluate?
The generative AI model’s efficiency in resource utilization, computation time, and scalability
