Types of AI Flashcards
What is Artificial Narrow Intelligence? What are the benefits? Provide an example.
AI designed to perform a single or a narrow set of related tasks at a high level of proficiency. (AKA “Weak AI”)
Benefits:
- Boost productivity and efficiency by automating repetitive tasks
- Enabling smarter decision making
- Optimization through trend analysis
Example: A system designed to play chess.
What is Artificial General Intelligence?
AI designed to closely mimic human intelligence (still has not been achieved). Experts expect AGI systems will do the following things at a level that is similar to or on par with human capabilities:
- Have strong generalization capabilities
- Be able to think, understand, learn and perform complex tasks
- Achieve goals in different contexts and environments
What is Artificial Super Intelligence?
A category of AI systems with intellectual powers beyond those of humans across a comprehensive range of categories and fields of endeavor (still has not been achieved). Once developed, the expectation is that it would be:
- Self-aware
- Capable of understanding human emotions and experiences
- Able to experience reality like humans
What is Broad Artificial Intelligence?
A category of AI more advanced in scope than artificial narrow intelligence, capable of performing a broader set of tasks, but not sophisticated enough to be considered AGI. Broad artificial intelligence often involves reliance on a group of artificial intelligence systems, capable of working together and combining decision-making capabilities, but still lacking the full human-like capabilities experts expect of AGI.
What is Machine Learning?
A branch of AI that leverages the use of data and algorithms to enable systems to learn and make decisions repeatedly. It improves over time without being explicitly instructed or programmed to do so.
What are the characteristics of a Supervised Learning Model? Provide an example.
A Machine Learning model which learns from a pre-labeled and classified data set. As part of a supervised learning training process, an algorithm analyzes the input data and associated labels to produce an inferred function, which can then become the basis for the system to make predictions based on new, previously unseen inputs. Supervised learning models can also compare their outputs with the correct or intended output, to identify errors and improve their prediction skills.
Example: A model that analyzes images of road signs labeled to define the sign’s meaning or purpose.
What are the subcategories of Supervised Learning?
1) Classification Models
The models produce outputs in the form of a specific categorical response; for example, whether an image contains a puppy.
2) Regression Models predict a continuous value; for example, estimating a stock price.
Name a couple of widely used Supervise Learning Models.
1) Support Vector Machine (SVM):
Used for classification and regression tasks but most widely used for classification objectives.
2) Support Vector Regression (SVR):
Most commonly used to produce continuous values.
What are the characteristics of an Unsupervised Learning Model? What are the weaknesses?
Unsupervised learning models do not rely on labeled datasets. Instead, they are designed to identify differences, similarities and other patterns without the aid of human supervision. These models tend to be more cost-efficient and require less effort, but are susceptible to producing less accurate outputs and can display unpredictable behaviors.
What are the categories of Unsupervised Learning Models? Name some use cases.
1) Clustering:
Automatically groups data points that share similar or identical attributes; for example, looking for similarities or patterns in DNA samples.
2) Association Rule Learning: Identifies relationships and associations between data points; for example, understanding consumer buying habits.
Use cases:
- Anomaly detection for mechanical faults
- Fraud detection
- Consumer segmentation
- Marketing strategies
- Genetics
What are the characteristics of a Reinforced Learning Model? Provide an example.
Reinforcement learning models use a reward and punishment matrix to determine a correct or optimal outcome. They rely on trial and error to determine what to do or not to do and are rewarded or punished accordingly. These models do not ingest pre-labeled data sets and learn solely through action and repetition, changing or not changing state or by getting feedback from their environment.
Example: Amazon’s Warehouse Supply Chain Optimization
Describe two models that are in between Supervised and Unsupervised.
1) Semi-supervised learning models: Use a combination of supervised and unsupervised learning processes. This approach generally uses a small amount of labeled data and a large amount of unlabeled data. The aim is to leverage the benefits of both models, improving reliability while reducing costs. They are particularly helpful in scenarios where it is challenging to find or create a large, pre-labeled dataset. Image and speech analysis or categorization and ranking of web page search results are classic examples.
2) Large Language Models (LLMs):
Often rely on semi-supervised learning models. They are a form of AI using deep learning algorithms to create models trained on massive text data sets to analyze and learn patterns and relationships among characters, words and phrases.
What is the aim of robotics?
Robotics stems from engineering and computer science and aims to design machines that can perform tasks, usually specific tasks or duties, without human intervention.
Explain the Fourth Industrial Revolution (Industry 4.0).
The use of AI in manufacturing and robotics is ushering in the next stage of industry and manufacturing advancements, enabled by increased interconnectivity and smart automation.
What is Machine Perception? Provide a use case.
A field in which systems are trained to process sensory information and mimic human senses.
Use case: Improving food production, preparation or storage by creating a system that can touch, smell and taste produce.
