Module-1

• What is artificial intelligence (AI)?

  Machines performing tasks that normally require human intelligence
• Discuss Alan Turing‘s test?

  A cryptographer and mathematician who developed a test to determine whether a machine is intelligent (1950)
• A machine was considered intelligent if it produces responses to human interviewer that fool the interviewer into thinking the responses are human
• What are the common elements of the OECD AI definition

  • Technology
  • Autonomy
  • Human involvement
  • Output
• What is Technology?

  Use of technology and specified objectives for the technology to achieve
• What is Autonomy?

  Level of autonomy by the technology to achieve defined objectives
• What is Human involvement?

  Need for human input to train the technology and identify objectives for it to follow
• What is Output?

  Technology produces output, e.g., performing tasks, solving problems, producing content
• What is Machine learning?

  The process of training machines to display AI behavior
• What are the main types of machine learning?

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
• What is Supervised learning?

  Labeled data that is grouped or classified into categories via the AI system. Used for text recognition, detecting spam in email, etc.
• What is Unsupervised learning?

  Unlabeled data; typically used for pattern detection
• What is Reinforcement learning?

  An AI system is rewarded for performing a task well and penalized for not performing it well. Over time, learning to maximize the rewards and develop a system that works.
• AI as a socio-technical system

  AI influences society and society influences AI: strong relationship between the technology and human beings
• Who are the Relevant stakeholders to consider when working with AI?

  • Individuals who look at the broader societal influences of AI, such as anthropologists, sociologists or others who work in social sciences
  • Individuals who develop and implement AI systems
• The data used and AI will change over time
• The model may need to be changed, upgraded, or both, to reflect what is being done with the new data in the environment
• Five dimensions to classify AI systems

  • People and planet
  • Economic context
  • Data and input
  • AI model
  • Tasks and output
• People and planet


  Identifies individuals and groups that might be affected by the AI system. For example, human rights, the environment and society. Privacy comes into play here.
• Economic context


  The AI system is looked at according to the economic and sectoral environment in which it operates.
• What is Data and input?

  What type of data was used in the model and any expert input used.
• What is Expert input?

  Human knowledge that gets codified into rules
• Characteristics of data and input

  • How data was collected and what collection method was used (by machine or by human)
  • Structure of the data and data format
• AI model


  Discusses the technical type; how the model is built and used.
• What are Tasks and output?

  Tasks that AI systems perform, its outputs and resulting actions from those outputs.
• What are the Characteristics of tasks and output?

  • System tasks
  • Systems that combine tasks and actions
  • Evaluation methods used to look at how tasks and systems perform
• List the Categories of AI?

  • Recognition
  • Detection
  • Personalization
  • Interaction support
  • Goal-driven optimization
  • Recommendation

  
• Types of Recognition?


  Typically, image, speech or facial recognition. Facial recognition: determining if an individual's face can be matched to another picture of that individual.
• Examples of Recognition use cases?

  • Retailer product matches: sending a picture of a desired product to a retailer's online system. The system looks for a product match based on the description of the picture received, then notifies the consumer of product matches.
  • Manufacturing machines learn to see defects that impact product development
  • Plagiarism detectors, often used in education
• List examples of AI patterns?

  Credit card transaction fraud detection or fraud detection when applying for government services or benefits: looking for patterns of fraudulent behavior within the system
• Detection use cases


  • Events and sports video: for example, reviewing at a particular activity such as a touch down or goal
  • Cyber events and systems management help organizations better respond to incidents.
• Forecasting


  Predict sales and revenue, as well as potential product or service demand
• Forecasting use cases


  • Ridesharing apps: determine when there might be a higher demand for rides; when demand is high, prices can increase
  • Weather forecasting
• Personalization


  Unique online customer profiles: AI systems can help develop a profile based on an individual's previous activity and create a unique experience that better meets the individual's needs. Personalization can also improve customer engagement and sales.
• Interaction support


  Virtual assistants or chatbots that assist customers with transactions. Commonly used in private industry.
• Interaction support use cases


  • Used in the public sector as well, chatbot sometimes assist students applying for government student loans, such as answering frequently asked questions.
• Goal-driven optimization


  Used to optimize a particular problem and find solutions; for example, it can be used to optimize a supply chain. If you are having supply chain issues and want to get a product out faster, AI can be used to help you figure out how.
• Goal-driven optimization use cases

  • Optimizing driving routes and idle time for vehicles: for example, with bus routes or a trucking company trying to get products out in a timely manner.
• Recommendation


  Product recommendations or viewing recommendations for customers based on predictive analytics.
• Recommendation use cases


  • Can also be used for decision support systems. AI can help humans make better decisions in general. For example, AI can help health care providers make diagnoses based on past information about similar types of diseases, symptoms, and previous diagnoses.
  • Government use for adjudicating disability cases: trying to figure out the best way to give an individual access to their benefits for disability cases.