A field of computer science focused on creating smart machines that can perform tasks that typically require human intelligence, like learning, reasoning, and problem-solving.
Author: Saher Prakash
Attributing human characteristics, emotions, or behaviors to non-human entities like animals, objects, or deities.
Author: Xavier Baptiste
The creation and application of technology to monitor and control the production and delivery of products and services.
Source: International Society of Automation (https://www.isa.org/about-isa/what-is-automation)
A system that automatically controls some aspects of a vehicle's motion (steering, speed, or both) without continuous manual input from the operator.
The term originated in aviation, where autopilot systems would maintain heading, altitude, and course, and has since been adapted to maritime, rail, and automotive contexts. Most commercial automotive autopilot systems are not fully autonomous and lie around Level 4 of the SAE Levels (see definition below).
Author: Wilson Liang
People who believe in/promote the idea that the advancement of AI is "inevitable" and that the result will be "autonomous and powerful." Boosters, unlike Doomers (see below), think this advancement is massively beneficial and will bring a solution to everything. Many of these people are or attempting to profit off of the tech their promoting. According to Hanna and Bender, "Doomerism/Boosterism serves to obscure, rather than illuminate, what’s at stake when it comes to the current AI boom."
Source: Chapter 6 of AI Con: How to Fight Big Tech’s Hype and Create the Future We Want by Alex Hanna and Emily M. Bender
Author: Olivia Reese
Data is the body of evidence we use to inform how knowledge is produced and circulated. It includes not only numbers and market projections, but the words, headlines, advertisements, films, social media posts, testimonials, categorizations, and other cultural artifacts that reveal patterns in language, imagery, and persuasion. In this project, data means any trace that helps us understand how claims about AI are being made, repeated, and normalized.
Author:Elyas Berger
The process of parsing through sources and motivations to extract the truth behind strategic and systemically reproduced claims; syn- demystifying.
Author: Valeria Briones Herrera
a subset of machine learning driven by multilayered neural networks whose design is inspired by the structure of the human brain
Source: International Business Machines Corporations (IBM)
People who believe in/promote the idea that the advancement of AI is "inevitable" and that the result will be "autonomous and powerful." Doomers, unlike Boosters (see above), think this advancement is massively problematic, which comes from an existential fear surrounding the tech. According to Hanna and Bender, "Doomerism/Boosterism serves to obscure, rather than illuminate, what’s at stake when it comes to the current AI boom."
Source: Chapter 6 of AI Con: How to Fight Big Tech’s Hype and Create the Future We Want by Alex Hanna and Emily M. Bender
The action of intentionally trying to make people afraid of something when this is not necessary or reasonable
Source: Cambridge Disctionary
Hype is the process by which a technology is made to feel bigger, more transformative, and more inevitable than the evidence can actually support. It works through repeated promises, fears, and dramatic framings that amplify expectations. In this project, hype is not just excitement; it is a cultural narrative that shapes how people imagine AI.
Author: Elyas Berger
The generation of an idea or conclusion based on a set of pre existing information or data
Author: Saher Prakash
The subset of artificial intelligence (AI) focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data
Source: International Business Machines Corporations (IBM)
The means of communication that reach large numbers of people, such as television, newspapers, and radio
Source: Dictionary.com
A subfeild of cumputer science and artificial intelligence that enables computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics, the rule-based modeling of human language together with statistical modeling, machine learning and deep learning.
Source: International Business Machines Corporations (IBM)
An American artificial intelligence (AI) research organization that developed the GPT family of large language models.
Source: Wikipedia
Short for ‘probability of doom’, a term used in conversations about AI in politics and tech development "referring to a popular trope that machines with minds of their own will [...] kill us all" to discuss the statistical probabilities of AI technologies creating an apocalyptic/dystopian scenario.
Source: AI Con: How to Fight Big Tech’s Hype and Create the Future We Want by Alex Hanna and Emily M. Bender
Representation of something using human-like properties such as intelligence or emotions, often to create an atmosphere of intimacy or closeness.
Author: Andrew Mazier
Capacity to sense or feel; the existence of an inner-world.
Source: Merriam-Webster Dictionary
The application of scientific knowledge to the practical aims of human life—or, as it is sometimes phrased, to the change and manipulation of the human environment.
Source: Britannica.com
The Society of Automotive Engineers provide 6 levels of driving automation based on the division of responsibility between the human driver and the automatic driving system.
ODD - Operational Design Domain
OEDR - Object and Event Detection and Response
DTT - Dynamic Driving Task
ADS - Autonomous Driving System
SAE Levels:
Level 0: No Driving Automation
The performance by the driver of the entire DTT, even when enhanced by active safety systems.
Level 1: Driver Assistance
The sustained and ODD-specific execution by a driving automation system of either the lateral or the longitudinal vehicle motion control subtask of the DDT (but not both simultaneously) with the expectation that the driver performs the remainder of the DDT.
Level 2: Partial Driving Automation
The sustained and ODD-specific execution by a driving automation system of both the lateral and longitudinal vehicle motion control subtasks of the DDT with the expectation that the driver completes the OEDR subtask and supervises the driving automation system.
Level 3: Conditional Driving Automation
The sustained and ODD-specific performance by an ADS of the entire DDT under routine/normal operation (see 3.27) with the expectation that the DDT fallback-ready user is receptive to ADS-issued requests to intervene, as well as to DDT performance-relevant system failures in other vehicle systems, and will respond appropriately
Level 4: High Driving Automation
The sustained and ODD-specific performance by an ADS of the entire DDT and DDT fallback’
Level 5: Full Driving Automation
The sustained and unconditional (i.e., not ODD-specific) performance by an ADS of the entire DDT and DDT fallback.
Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. (2021).
SAE International. https://doi.org/10.4271/j3016_202104
Author: Wilson Liang
a category of deep learning models trained on immense amounts of data, making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.
Source: International Business Machines Corporations (IBM)