AIGP — Domain I: Understanding the Foundations of Artificial Intelligence — Part A
Domain-I tests your knowledge of AI and ML terms and concepts, different use cases and benefits of AI, OECD framework, different types of AI systems, AI technology stack, history of AI.
I have split this up into 3 parts. In this first part of Domain-I, will touch upon basic elements of AI & ML — common elements of AI/ML, AI system as socio-technical system, understanding need for cross-disciplinary collaboration, different AI use cases &some terminologies.
Disclaimer-This information is compiled from various resources and personal experience. While I aim to credit sources and ensure accuracy, some references may not be directly cited. Please let me know if you spot any errors or missing attributions. This content is meant as a helpful resource but isn’t a substitute for professional advice.
John McCarthy
Pioneer in the field of artificial intelligence and computer science.
Coined the term “AI” in 1950s => AI is a Science and engineering of making intelligent machines.
Alan Turing
Pivotal in shaping the field of artificial intelligence.
Turing Test developed by Alan Turing was a widely used measure to determine if machines can think intelligently as humans. A computer and human are asked questions to determine which among the two is really a human. If a computer cannot be distinguished from a human, it has passed the Turing test. This was used as a benchmark for measuring success of AI research [see Ref #4 below].
ELIZA, an early natural language processing computer program, is considered one of the first to have deceived a judge in a Turing Test.
Artificial Intelligence
Artificial Intelligence (AI) — Broad field with a variety of technologies that enables computers to mimic human intelligence.
There are many techniques within AI, such as machine learning, neural networks, and natural language processing, that aid computers in thinking like humans to performs tasks that involves reasoning and problem-solving skills.
Machine Learning
Machine learning (ML) — Subset of AI.
Involves algorithms that use large amounts of data to enable computers to make decisions. Just as we improve our skills by practicing repeatedly, machine learning teaches computers to get better by providing them with many examples.
How does AI and ML overlap
People often use AI and ML interchangeably, but they differ in several key ways.
AI is a broad field that simulates human intelligence that enables the machine to think and act like human. [Keyword : Intelligence]
ML is when computers learn to do tasks better by practicing with lots of data and examples. [Keyword : Machine learns].
AI and ML overlap as machine learning provides the techniques and methods used to achieve AI goals.
Lets take an example of voice assistant like Alexa. AI enables the assistant to understand and process your voice commands, while ML algorithms continuously learns from your interactions[i.e. lots of data] to improve its responses thereby increasing its efficiency.
Common Elements of AI/ML
Numerous definitions have been established, and different jurisdictions across the globe have formulated their own definitions [see Ref #2 below]. Although there are various definitions of AI, they all share four common elements:
Technology — Refers to the techniques that enable machines to think. E.g. Machine Learning, Neural Networks, Natural Language Processing etc.
Autonomy — Capability of machines to take decisions.
Human involvement — Humans are needed for designing, training and fine-tuning AI models so that complex & evolving challenges can be solved.
Output — Results or responses by the AI model based on processing and analysis of the data
AI system as socio-technical system
First lets split these words for better understanding.
Socio system is the way different parts of society interact and work together to shape how people live and behave.
Technical system is a set of tools and machines to solve problems.
Simply put, a socio-technical system blends social and technical elements to create systems where both work well together. AI as a socio-technical system means combining human factors & technology to create AI systems that work well with people and fit into their social environment.
Need for cross-disciplinary collaborations
As mentioned above, AI is considered as socio-technical system hence there is a need of experts from different fields to come together to build AI systems that consider both technical and human aspects. So insights needs to be combined from psychologists, computer scientist, sociologists, engineers, anthropologist while building a robust and ethical AI system.
AI Usecases
There are plethora of AI use cases to mention, including those in the medical field, entertainment, transportation, agriculture, home automation, and beyond.
Citing a few examples here for reference:
Healthcare — Early diagnosis of critical diseases through AI technologies to improve the survival rate.
Retail — Personalizing shopping experience to enhance customer satisfaction.
Customer Service — Chatbots & virtual assistants providing customer support.
Entertainment — Recommending movies, music, and other contents based on user’s viewing history.
Transportation — Optimizes route planning to improve traffic management.
Agriculture — Weather forecasting & yield prediction that help farmers to make better decisions on harvesting.
Home Automation — AI enhanced security cameras, AI powered appliances, smart TV's and speakers, all aid in enhancing quality of life.
Useful AI Terms
Ethical — Doing morally right. In the context of AI — Ethical AI is making sure that AI is used in fair & responsible way
AI Model — Computer program that is trained by giving lots of data to make certain decisions or predictions
Fine Tuning — Making small adjustments for improvement. In the context of AI — fine tuning a model means adjusting the model with additional data or making some tweaks to improve its performance for a particular task.
References & some additional reading
1.AIGP BoK Reference : https://iapp.org/media/pdf/certification/AIGP_BOK_EBP_FINAL-050724.pdf
2.AI Definitions across globes — https://hai.stanford.edu/sites/default/files/2020-09/AI-Definitions-HAI.pdf
3. Article on Turing Test published by Built In
Explore my other articles on AIGP on Medium, and keep an eye out for new ones I’ll be publishing
Preparing for IAPP’s AIGP [Artificial Intelligence Governance Professional] Certification
AIGP — Domain I: Understanding the Foundations of Artificial Intelligence — Part B
