Welcome to our comprehensive LibGuide on Artificial Intelligence (AI), a transformative field that has captivated the world with its immense potential and thought-provoking implications. In this guide, we will embark on an enlightening journey through the history of AI, explore the ethical considerations surrounding its development and implementation, and delve into its fascinating applications in academia.
Join us as we trace the roots of AI, from its inception as a visionary concept to its current state as a driving force behind cutting-edge technologies. Uncover the milestones, breakthroughs, and key figures that have shaped the evolution of AI, forever altering the way we interact with machines and the world around us.
While AI offers boundless possibilities for progress and innovation, it simultaneously raises ethical dilemmas that require careful contemplation. We will explore the ethical considerations surrounding AI, such as privacy concerns, bias and fairness issues, and the impact of AI on society. Delving into these complexities will help us navigate the responsible and equitable use of AI technology.
In academia, AI has emerged as a potent tool, revolutionizing research, teaching, and learning. We will showcase how AI is transforming various academic disciplines, enabling more efficient data analysis, personalized learning experiences, and groundbreaking discoveries. From natural language processing to machine learning algorithms, discover how AI is enhancing academic pursuits and shaping the future of education.
Whether you're a student, researcher, educator, or curious individual, this LibGuide is your gateway to understanding the multifaceted realm of AI. Let's embark on this illuminating expedition into the history, ethics, and academic applications of Artificial Intelligence. Embrace the opportunities, grapple with the challenges, and expand your knowledge in this dynamic and ever-evolving domain. Happy exploring!
This introduction was created using an AI text generator named ChatGPT - specifically ChatGPT 3.0.
If you were not told that text was generated, more than likely you thought the author of this guide wrote it from scratch. The output generated by these predictive generators is complex, comprehensive, and surprising in how natural it can appear. The above was generated by a Large Language Model (LLM) text generating chat bot called ChatGPT. Below is the prompt I used with the chat bot. Read below to learn how these bots (and prompts) work.
Generative AI Chatbots (such as ChatGPT) are not:
connected to the internet in real time - some can access Internet search, but not spontaneously
generating discrete work - your prompts can generate different results with identical prompts
unbiased - the chatbots are as biased as the training materials (data set)
encyclopedias - chatbots are not authoritative sources and are by nature incomplete and/or inaccurate
The term "artificial intelligence" (AI) is an overly broad term. The "AI" we can interact with today are mostly "Large Language Models" (LLMs), which are a derivative of "machine learning" (ML) algorithms & processes. While these are under the umbrella of artificial intelligence, instances of these chat bots are no more intelligent than your phone assistant. These chat bots (and similar applications of AI) are generative and predictive, but have no true intelligence.
Glossary:
Artificial Intelligence (AI) - "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention" - Shubhendu, Vijay, International Journal of Scientific Engineering and Research. (Source)
Artificial General Intelligence (AGI) - "Algorithms that perform a wide variety of tasks and switch simultaneously from one activity to another in the manner that humans do." (Source)
example: we do not currently have any publicly known comprehensive AGI
Artificial Narrow Intelligence (ANI) - most of currently publicly accessible AI that perform a single task, but cannot generalize & synthesize the outcome outside of the AI's given function.
example: Looking for a particular face in iOS's Photos app or asking Google Assistant the weather.
Deep Learning - "A subset of machine learning that relies on neural networks with many layers of neurons... deep learning employs statistics to spot underlying trends or data patterns and applies that knowledge to other layers of analysis." Deep Learning requires large amounts of power and data. (Source)
Machine Learning (ML) - a way for artificial intelligence to classify data, pictures, text, objects, or other media without detailed instruction, and is able to continually learn on new data sets introduced. New data sets are comprehended in the context of older data sets.
Large Language Model (LLM) - generates text based on large data sets. LLMs use deep neural networks (Deep Learning & Machine Learning) to process and create text based on specific criteria.
Generative Pretrained Transformer (GPT) - a state-of-the-art language model based on the Transformer architecture. It is "pretrained" on a vast corpus of text data, learning patterns and language representations in an unsupervised manner. GPT can "generate" human-like text by predicting the next word in a sequence, making it capable of completing sentences, answering questions, and performing language-based tasks. (Prompt from ChatGPT. [In 100 words or less, explain what a generative pretrained transformer (GPT) is.])
Chatbots - interactive artificial narrow intelligence tools based on large language models and a generative pretrained transformer. Chatbots are interacted with via a text prompt or dialog box. Chatbots create responses using a vast corpus of "digested" data from large amounts of data sets.
This is not an exhaustive list of terminology related to AI. A very thorough source of terminology can be accessed through AIPRM's Ultimate Generative AI Glossary. While AIPRM is a for-profit business-oriented company, the glossary is informational, easily understandable, and thorough.
- Written by ChatGPT
Chat bots such as ChatGPT work on a prompt system. The user, such as yourself, writes a prompt into a chat box. The chat bot - we'll use ChatGPT as the main example - will then process your prompt. What it attempts to do is a "reasonable" procedural continuation of words to mimic what is in its data set(s). ChatGPT uses probabilistic analysis to rank each following or upcoming word in a response.
The chat bot is asking itself "what's the word with the highest probability rank" after each word choice. This probability stems from a corpus of data - e.g. data scraped from the Internet and websites such as Reddit or Wikipedia - processed by machine learning.
However, if AI only chose the top ranked word in association, the responses would be one-note (you could say dry or boring) and deterministic. There is then some wiggle-room in how the AI chooses the next word - sometimes the fifth "best" word or the eighth "best" word - to create a form of difference.
For an extremely detailed outline of how these Large Language Models work when paired with a GPT, look to Stephen Wolfram's explanation of how GPT-2 - OpenAI's transformer from several years (and software generations) ago - and LLMs work.
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You already experience the effects of generative AI in your life.
This form of AI suggests words you may insert in your text field. It uses a data set to predict the most common words to follow the previous words, keeping other words in your text field in context.
This guide is representative of the library's view on Generative AI in Education, with De Anza being the context. This is not representative of the College's perspective on the matter. Please ask your instructors and refer to College communications on up-to-date policies on the use of generative AI. The College has an Academic Integrity Policy. The use of Generative AI can be considered plagiarism, cheating, or a study tool - it depends on how you use it and cite it.