1.2
Course objectives
- Identify examples of real-world AI applications
- Know when and when it is not useful
- Know the capabilites of the AI
AI:
Use cases:
- Retail: Product review summaries, pricing optimization, virtual try-ons, layout optimization
- Healthcare: Personalize medicine and scan medical imaging
- Life Sciences: Scanning protein folds and other complex biological formations en masse for better understanding, drug discovery
- Financial Services: Fraud detection, debt collection, portfolio management
- Manufacturing: Product optimization, predictive maintinence, product design
Examples: - Intelligent document processing (IDP):
- Application that classifies information from unstructured data into summaries
- Fraud detection:
- By scanning many transactions it can flag those that are suspicious
ML:
More useful when rules are complex and not very binary (Like determining what is spam in email)
Use cases:
- Supervised:
- Classification (identifying new data by analyzing old) and regression (predicting new instances based on existing data)
- Fraud detection and image classification
- Weather prediction or any forward predictions
- Classification (identifying new data by analyzing old) and regression (predicting new instances based on existing data)
- Unsupervised
- Clustering (Finds similar features between points) and dimensionality reduction
- Clustering includes recommended systems
- Dimensionality is for compression
- Clustering (Finds similar features between points) and dimensionality reduction
- Reinforcement
- Useful when you can give correct answer but path is not known
Generative AI:
Automate tedious tasks and can analyze data freeing up time for workers to do more creative work
Use cases:
- Wider range as this AI is more adaptable
- Very responsive and can be used in live service
- Good at simplifying complex tasks
Challanges:
- Regulatory violations:
- Can generate outputs that exposes personally identifiable information (PII)
- To reduce risk, make sure data is anonymized and the agent is only given whats needed
- Social risk:
- Might create unwated content
- Just test
- Data security:
- Ensure data that you share with model does not violate privacy laws
- Hallucinations:
- These models can't be trusted 100% off the time.
- Users should be aware that they are using an FM and this can result in halucinations
- Nondeterminism:
- These are nondeterministic models and will give different outputs per input
- You can run the model multiple times for consistency
Business metrics for generative AI:
- User satisfaction: Gather feedback and see what customers think
- Average revenue per user (ARPU): Calculated profits per user attributed to AI. Sees if this product is generating more money if it cost
- Cross-domain performance: Ensures that different uses are all efective. (Ex: montoring if AI is effective on a multidomain e-commerce platform)
- Conversion Rate: convert clicks or sign ups to purchases
- Efficiency: How effective the model is getting its answers