1. Artificial Intelligence (AI)
- AI making PC behave like human intelligence.
- Teach PC to do task for us.
- PC predicts using patterns and can act. And good at looking for anomalies.
- PC uses camera/photos to look for patterns.
- Engage in useful conversations, use multiple sources of knowledge.
2. Machine Learning (ML)
- Train PC's to see patterns and see patterns, and look for anomalies.
- Example. predict stock prices by looking at factors that affect stock price.
- Anomaly Detection - Detects unusual patterns e.g. CC used in Asia when normally in Europe, but transactions 10 min apart. Therefore likely to be fraudulent. Sort rubbish.
- Predictive models by finding relationships. Give model data and train the model.
- Example: flowers have features/characteristics e.g. colour, size, no petals, ...
- Using data to teach machine
- Supervise ML - need quality data including labels. Avg humidity, hrs sunshine, rainfall, temp, month of year (features), ice creams sold(label/class), so we feed in temp is Regression ML. Patient has features (weight, sex, age, bmi,...) give value btwn 0-1 of the person developing diabetes. is Classification ML
- Unsupervised ML - data is not labelled. Just feature provided, will group into clusters. Pulls data out and figures out it's own criteria is Clustering ML. Useful for fraud detection.
- Training - good data based on a training set and a validation set. Train model, with most data, then check with remaining - allows us to see how close to what happened. Service tries to figure out relationships. Model is used by test data - see how close/useful model is.
3. Compute Vision
- Self driving cars, sorting. Sort rubbish.
- Facial recognition, object recognition,..
- How do computers see? picture is cut up into pixels, data is pulled and used to find possible ans.
- Some types on Azure: object detection i.e. car, bike, car, bus. Image classification i.e horse, car. Semantic segmentation i.e. Teams blur background. Image analysis contect by bring various tougher. Face detection. OCR - read image and converts to text.
4. Natural Language Processing (NLP)
- interpret e.g grammerly, spam check, Alexia,
- Knowledge Mining - Extract info from knowledge and gain insights e.g. social media marketing.
Principals:
- Fairness - ensure bias is excluded e.g based on gender.
- Reliable & Safety - need high confidence and in certain systems cannot fail e.g. health systems, autonomous cars.
- Privacy & Security - Ensure data is protected and not giving away sensitive data.
- Inclusiveness - should be fair i.e. VI users
- Transparency - what is the model based on, what could be an issue.
- Accountability - who is liable for AI decisions
Azure:
- Scalability & Reliability
- AI Resources: sit in an Azure Resource Group
AI Services in Azure:
- Azure Machine Learning - Developers to train, test and deploy ML models. Within a subscription, create a Azure ML Workspace (consists of: compute, data, jobs, models) can then publish as a service. Azure ML Designer, used for creating ML pipeline, data in to train model. Automated Machine Learning user only needs to provide the data and select the model to use, service figures it out.
- Cognitive Services - vision, speak, language, decision. Rest API endpoints - have already been trained, choose the model. Can deploy multiple parts individually or together.
4. Azure Cognitive Search - Data extraction, & enrichment for indexing. Makes data searchable.
Anomaly detector resource - wizard to setup - Add Keys and endpoints to allow access.
Create a new Azure Machine Learning Service, will create a Workspace. Use multiple azure services such as key-vault, AI, storage accounts.
- Launch Studio
- Add Compute Cluster
- Add Data (csv, spreadsheets, nearly any form,...)
- AutomateML (figure it out without me) and run job
- Will show trends
- Deploy the model (i.e. to a web service)
- Shows "Endpoints" - get url and a test rig.
