1. Foundations of AI Flashcards

Question

What is Artificial NARROW Intelligence? What's another name for this? Example?

Answer 1

ANI is designed to perform a single or a narrow set of related tasks at a high level of proficiency. ANIs operate under a narrow set of constraints and limitations, which is why this type of AI is commonly referred to as weak AI Can help boost productivity and efficiency by automating repetitive tasks, enabling smarter decision making and optimization through trend analysis Ex) AI system designed to play chess

Answer 2

AGI, is also known as Strong, Deep or Full AI. It is intended to closely mimic human intelligence. AGI has been a goal of AI development for decades but, as of today, it remains beyond our reach. Experts expect AGI systems will do the following things at a level 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

Answer 3

ASI, is a category of AI systems with intellectual powers beyond those of humans across a comprehensive range of categories and fields of endeavor. Thus, it is capable of outperforming humans. Like AGI, ASI does not yet exist. However, experts expect this type of system would be self-aware: able to understand human emotions and experiences and evoke its own, thus experiencing reality like humans.

Answer 4

Broad artificial intelligence is a category of AI more advanced in scope than ANI. It is 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 that are capable of working together and combining decision-making abilities, but still lacking the full human-like capabilities of AGI

Answer 5

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. An example of supervised learning is a model that analyzes images of road signs labeled to define the sign’s meaning or purpose

Answer 6

1) Classification models produce outputs in the form of a specific categorical response; for example, whether an image contains a puppy. EX) Support Vector Machine (SVM) 2) Regression models predict a continuous value; for example, estimating a stock price. EX) Support Vector Regression (SVR)

Answer 7

1) Clustering - automatically groups data points that share similar or identical attributes; for example, similarities or patterns in DNA samples. 2) Association rule learning - identifies relationships and associations between data points; for example, consumer buying habits

Answer 8

Unsupervised learning models tend to be more cost-efficient and require less effort, but are susceptible to producing less accurate outputs and displaying unpredictable behaviors.

Answer 9

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 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.

Answer 10

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.

Answer 11

These models 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.

Answer 12

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.

Answer 13

Industry 4.0 (Fourth Industrial Revolution) - industry and manufacturing advancements, enabled by increased interconnectivity and smart automation Machine Perception - systems are trained to process sensory information and mimic human senses. enables systems to sift through data, eliminate noise and analyze and categorize information at a much faster rate and order of magnitude than humans Robotic Process Automation - using software robots to automate repetitive and rule-based tasks in business processes. RPAs are designed to mimic human actions on digital systems

Answer 14

a form of AI intended to mimic the decision-making abilities of a human expert in a specific field. These systems draw inferences from a knowledge base and rely on AI to replicate the judgment and behavior of a human with specific expertise

Answer 15

1) Knowledge Base - typically an organized collection of facts and information from human experts, focused on a specific field or domain. In some cases, the system is also allowed to gather additional information from external sources 2) Inference Engine - extracts relevant information from the knowledge base and uses it appropriately to solve a problem. Expert systems normally use a rule-based approach that maps data from the knowledge base to a series of rules, which the system uses to make decisions in response to input. Expert systems often include a module that allows users to review its decision-making process 3) User Interface - allows the end user to interact with the expert system. The user provides an input, like a problem or question, and obtains an output (resolution).

Answer 16

Linguistic variables describe concepts in natural language terms, such as “low,” “medium,” or “high”, and “warm,” “hot,” or “very hot.” Fuzzy rules express relationships between variables using if-then statements. For example, a fuzzy rule might state, “If the temperature is ‘very hot,’ then set the fan speed to ‘high.’”

Answer 17

1) Fuzzification - input data is converted into fuzzy data sets 2) Rule evaluation - which determines the degree of matching between the rules and input data 3) Aggregation - where rule outputs are combined 4) Defuzzification -process by which fuzzy outputs are converted back into specific values EX) climate control systems, image recognition systems and traffic management systems

Answer 18

AI Platform: Software used to develop, test, deploy and refresh AI applications. EX) Microsoft Azure, AWS AI Application: How an AI system is used. EX) Chatbots, Self-driving, Marketing, etc.

Answer 19

Centralize data analysis Streamline development and production workflows Facilitate collaboration Automate systems-development tasks Monitor models and systems in production

Answer 20

Linear/statistical models Decision trees Neural networks (such as computer vision and speech recognition models) Language models (like NLP) Reinforcement learning models Robotics applications

Answer 21

Linear and Statistical Models

Answer 22

Decision Trees

Answer 23

Neural networks

Answer 24

Language models

Answer 25

Reinforcement learning models

Answer 26

Robotics applications