1. Foundations Flashcards
What is AI?
Hallmarks of human intelligence: ability to think creatively, consider various possibilities, and keep
a goal in mind while making short term decisions.
What are common definitions of AI?
- Machines performing tasks that normally require human intelligence (A branch of computer science concerned with creating technology to do things that
normally require human intelligence) - Alan Turing, a cryptographer and mathematician, 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)
- Definitions include common elements of AI: technology, autonomy, human involvement and
output
What are common elements of AI?
- Technology: use of technology and specified objectives for the technology to achieve
- Autonomy: level of autonomy by the technology to achieve defined objectives
- Human involvement: need for human input to train the technology and identify objectives for it to follow
- 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: Labeled data that is grouped or classified into categories via the AI system. Used for text recognition, detecting spam in email, etc.
- Unsupervised learning: Unlabeled data; typically used for pattern detection.
- 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.
What is an example of supervised learning?
For email filtering, the algorithm is trained using a labeled dataset
containing both spam and legitimate emails. It extracts the relevant information to create patterns to predict whether future emails are spam or legitimate.
What is an example of unsupervised learning?
Outliers in the data such as banking data
Reviewing transactions for any fraudulent behavior
What is an example of reinforcement learning?
Self-driving cars: the system is rewarded when it keeps a car on the road and gets it to the destination where it is supposed to go. It is personalized if the car goes off the road or hits another object. The system learns over time to maximize the rewards, resulting in a better performing self-driving car.
What are the risks in the use of AI?
- AI systems are implemented in vast and complex environments
- The data used for AI will change over time
What are the 5 OECD’s main dimensions for AI?
- People and planet
- Economic context
- Data and input
- AI model
- Tasks and output
What does the OECD’s ‘AI model’ dimension refer to?
Technical type and how the model is built and used.
Who are the relevant stakeholders to consider when working with AI?
- Individuals who look at the broader societal influences of AI, such as anthropologists, sociologist or others who work in social sciences.
- Individuals who develop and implement AI systems.
What does the OECD’s ‘people and planet’ dimension refer to?
Identifies individuals and groups that might be affected by the AI system. (ex, human rights, the environment, and society –> privacy comes into play here)
What does the OECD’s ‘economic context’ dimension refer to?
The AI system is looked at according to the economic and sectoral
environment in which it operates.
Characteristics include:
- The sector where the AI system operates (ex, financial, health care or education)
- The business function or model for the AI system
- Necessity of the AI system to operations
- How it is deployed and the impact of the deployment
- Scale of the system
- Technological maturity of the AI system (a newer system may not have been tested on as much data over time; more mature systems may be more effective)
What does the OECD’s ‘data and input’ dimension refer to?
The type of data used and expert input.
