Artificial Intelligence (AI)

AI (artificial intelligence) tools have become widely available and embedded in more and more aspects of our daily lives. Knowing and understanding these tools - like all information systems tools (eg web pages, databases, search engines, etc.)- represents knowledge fundamental to thinking critically, accurately, and effectively about the world. AI's growing presence makes guides like this essential.

"It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. " -- John McCarthy, who coined the term in 1955, from his Stanford University FAQ page.

The American Psychological Association defines intelligence as "the ability to derive information, learn from experience, adapt to the environment, understand, and correctly utilize thought and reason." (APA Dictionary of Psychology)

McCarthy adds to this: "Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines."

From the National Artificial Intelligence Act of 2020: "The term ‘artificial intelligence’ means a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments."

Generative AI describes a broad category of any artificial intelligence (AI) that can create new text, images, video, audio, code, or synthetic data.

"Any time an AI technology can generate something on its own, it can be referred to as “generative AI.” This umbrella term includes learning algorithms that make predictions as well as those that can use prompts to autonomously write articles and paint pictures." (from Technopedia)

From Generative AI: Perspectives from Stanford's HAI: "The current wave of generative AI is a subset of artificial intelligence that, based on a textual prompt, generates novel content. ChatGPT might write an essay, Midjourney could create beautiful illustrations, or MusicLM could compose a jingle. Most modern generative AI is powered by foundation models, or AI models trained on broad data using self-supervision at scale, then adapted to a wide range of downstream tasks."

LLMs or Large Language Models represent "a type of machine learning model that can perform a variety of natural language processing (NLP) tasks such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another. The label “large” refers to the number of values (parameters) the language model can change autonomously as it learns. Some of the most successful LLMs have hundreds of billions of parameters." (Technopedia)

From "Language models: A guide for the perplexed" from Cornell:

"Language models as they exist today are the result of research in various disciplines, including information theory, machine learning, speech processing, and natural language processing.1 This work’s authors belong to the community of natural language processing (NLP) researchers, members of which have been exploring the relationship between computers and natural languages since the 1960s.2 Two fundamental and related questions asked in this community are: “In what ways can computers understand and use natural language?”
and “To what extent can the properties of natural languages be simulated computationally?” The first question has been approached mainly by attempts to build computer programs that show language-understanding and language-use behavior (such as holding a conversation with a person); it is largely treated as an engineering pursuit that depends heavily on advances in hardware. The second question brings NLP into contact with the fields of linguistics, cognitive science, and psychology. Here, language tends to be viewed through a scientific lens (seeking to experimentally advance the construction of theories about natural language as an observable phenomenon) or sometimes through a mathematical lens (seeking formal proofs). Because these two questions are deeply interconnected, people interested in either of them often converse and collaborate, and many are interested in both questions."

LLM tools:

• ChatGPT
• Consensus
• CORE-GPT
• DataSeer
• GDELT Project
• Hum
• Iris.ai
• LASER AI
• Perplexity.ai
• Prophy
• Scholarcy
• Scite.ai
• SOMA
• Writefull