Many people fear artificial intelligence, but don’t understand what it is or how it is being used. Here is a glossary of key terms.
Many people fear artificial intelligence, but don’t understand what it is or how it is being used. In our Brookings Institution Press book, Turning Point: Policymaking in the Era of Artificial Intelligence , we discuss AI applications in healthcare, education, transportation, e-commerce, and defense, and present a policy and governance blueprint for responsible and trustworthy AI. Below is a glossary of key terms drawn from that book, which we present as a living document that will be updated as the AI conversation unfolds.
According to author Pedro Domingos, algorithms are “a sequence of instructions telling a computer what to do.” These software-based coding rules started with simple and routine tasks, but now have advanced into more complex formulations, such as providing driving instructions for autonomous vehicles, identifying possible malignancies in X-rays and CT scans, and assigning students to public schools. Algorithms are widely used in finance, retail, communications, national defense, and many other areas.
Artificial Intelligence (AI):
Indian engineers Shukla Shubhendu and Jaiswal Vijay define AI as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.” This definition emphasizes several qualities that separate AI from mechanical devices or traditional computer software, specifically intentionality, intelligence, and adaptability. AI-based computer systems can learn from data, text, or images and make intentional and intelligent decisions based on that analysis.
Augmented Reality (AR):
Augmented reality puts people in realistic situations that are augmented by computer-generated video, audio, or sensory information. This kind of system allows people to interact with actual and artificial features, be monitored for their reactions, or be trained on the best ways to deal with various stimuli.
Extremely large data sets that are statistically analyzed to gain detailed insights. The data can involve billions of records and require substantial computer-processing power. Data sets are sometimes linked together to see how patterns in one domain affect other areas. Data can be structured into fixed fields or unstructured as free-flowing information. The analysis of big data sets can reveal patterns, trends, or underlying relationships that were not previously apparent to researchers.
Automated tools for answering human questions. Chatbots are being used in retail, finance, government agencies, nonprofits, and other organizations to respond to frequently asked questions or routine inquiries.
Data storage and processing used to take place on personal computers or local servers controlled by individual users. In recent years, however, storage and processing have migrated to digital servers hosted at data centers operated by internet platforms, and people can store information and process data without being in close proximity to the data center. Cloud computing offers convenience, reliability, and the ability to scale applications quickly.
Computer Vision (CV):
Computers that develop knowledge based on digital pictures or videos. For example, cameras in automated retail outlets that are connected to CV systems can observe what products shoppers picked up, identify the specific items and their prices, and charge consumers’ credit card or mobile payment system without involving a cash register or sales clerk. CV also is being deployed to analyze satellite images, human faces, and video imagery.
Cars, trucks, and buses that communicate directly with one another and with highway infrastructure. This capacity speeds navigation, raises human safety, and takes advantage of the experiences of other vehicles on the road to improve the driving experience. […]
Read more: www.brookings.edu