The Layers of AI

• What is Artificial Intelligence (AI) ?

Machines that perform jobs that mimic human behavior.

• What is Machine Learning (ML) ?

Machines that get better at a task without explicit programming. It is a subset of artificial intelligence that uses technologies (such as deep learning) that enable machines to use experience to improve at tasks. 

• What is Deep Learning (DL) ?

Machines that have an artificial neural network inspired by the human brain to solve complex problems. It is a subset of machine learning that's based on artificial neural network.

• What is a Data Scientist ?

A person with Multi-Disciplinary skills in math, statistics, predictive modeling and machine learning to make future predictions.

Principle of AI

• Bias can affect results
• Errors can cause harm
• Data could be exposed
• Solutions may not work for everyone
• Users must trust a complex system
• Who's liable for AI driven decision ?

1.  Reliability and Safety : Ensure that AI systems  operate as they were originally designed, respond to unanticipated conditions and resist harmful manipulation. If AI is making mistakes it is important to release a report quantified risks and harms to end-users so they are informed of the short comings of an AI solution.

• AI-based software application development must be subjected to rigorous testing and deployment management processes to ensure that they work as expected before release.
• Good Examples : while developing an AI system for a self-driving car?

2.  Fairness : Implementing processes to ensure that decisions made by AI systems can be override by humans.

• Harm of Allocation : AI Systems that are used to Allocate or Withhold:
  
• Opportunities
  
• Resources
• Information
• Harm of Quality-of-Service : AI systems can reinforce existing stereotypes.
  
• An AI system does not work well for one group of people as it does for another. As example is a voice recognition system which works well for men but not well for women.
• Reduce bias in the model as we will live in an unfair world.
• Fair learn is an open-source python package that allows machine learning systems developers to assess their systems' fairness and mitigate the observed the observed fairness issues.

3.  Privacy and Security : Provide customers with information and controls over the collection, use and storage of the data. 

• Example: On device machine learning
• AI security Aspects: Data Origin and Lineage , Data Use : Internal vs External
• Anomaly Detection API is good example for the above use case.

4.  Inclusiveness: AI systems should empower everyone and engage people especially minority groups based on:

• Physical Ability
• Gender
• Sexual orientation
• Ethnicity
• Other factors
• Microsoft Statement- "We firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer."
  

5. Transparency : AI systems should be understandable. Interpretability / intelligently is when end-users can understand the behavior of UI. Adopting an open source framework for AI can provide transparency (at least from the technical perspective) on the internal working of an AI systems.

• AI systems should be understandable. Users should be made fully aware of the purpose of the system, how it works, and what limitations may be expected.
• Example : Detail Documentation of Code for debugging 

6. Accountability : People should be responsible for AI systems. The structure put in place to consistently enact AI principles and taking them into account. AI systems should work with the :

• Framework of governance
• Organizational principles
• Ethical and legal standards
• That are clearly defined
• AI-Based solutions meets ethical and legal standards that advocate regulations on people civil liberties and works within a framework of governance and organizational principles.
• Designers and developers of AI-based solution should work within a framework of governance and organizational principles that ensure the solution meets ethical and legal standards that are clearly defined.
• AI-Based solutions meets ethical and legal standards that advocate regulations on people civil liberties and works withing a framework of governance and organizational principles.
• Ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems.

Dataset : A dataset is a logical grouping of units of data that are closely related and/or share the same data structure.

Data labeling : process of identifying raw data and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn.

Ground Truth : a properly labeled dataset to you use as the objective standard to train and assess a given model is often called as ‘ground truth’. The accuracy of your trained model will depend on the accuracy of the ground truth.

Machine learning in Microsoft Azure

Microsoft Azure provides the Azure Machine Learning service - a cloud-based platform for creating, managing, and publishing machine learning models. Azure Machine Learning provides the following features and capabilities:

Other Features of Azure Machine Learning Services :

A service that simplifies running AI/ML related workloads allowing you to build flexible Automated ML Pipelines. Use Python, R, Run DL workloads such as TensorFlow.

• build and document your machine learning models as you build them, share and collaborate.

2. Azure Machine Learning SDK for Python

• As SDK designed specifically to interact with Azure Machine Learning Services.

3. MLOps

• End to End Automation of ML Model pipelines eg. CI/CD, training, inference.

4. Azure Machine Learning Designer

• drag and drop interface to visually build, test, and deploy machine learning models.

5. Data Labeling Service

• Ensemble a team of humans to label your training data.

6. Responsible Machine Learning

• Model fairness through disparity metrics and mitigate unfairness.

Performance/Evaluation Metrics are used to evaluate different Machine Learning Algorithms

For different types of problems different metrics matters

• Classification Metrics (accuracy, precision, recall, F1-Score, ROC, AUC)
• Regression Metrics (MSE, RMSE, MAE)
• Ranking Metrics (MRR, DCG, NDCG)
• Statistical Models (Correlation)
• Computer Vision Models (PSNR, SSIM, IoU)
• NLP Metrics (Perplexity, BLEU, METEOR, ROUGE)
• Deep Learning Related Metrics (Inception Score, Frechet Inception Distance)

There are two categories of evaluation metrics:

• Internal Metrics : metrics used to evaluate the internals of the ML Model
• The Famous Four - Accuracy, Precision, Recall, F1-Score 
• External Metrics : metrics used to evaluate the final prediction of the ML Model