When you think about Artificial Intelligence, or its commonly abbreviated form, AI, what comes to mind? Do you think of a futuristic world where robots replace many of the jobs currently done by humans? Or, do you consider our present world, where AI tools are capable of creating extensive, human-like written content, images, code, music, and even videos in a matter of seconds? Does the thought of AI make you worried or fearful about the implications for teaching, learning, creating, and working? Or, does it intrigue you to want to explore and learn more?
If you're like us, AI brings up a mixture of thoughts, emotions, and concerns - some realistic, and some perhaps not. But whether you are excited by generative AI's potential or apprehensive about it, developing a better understanding of it is a good first step. This is where we will begin in this research guide. We will cover some basic AI literacy, looking at where we are with AI today and how we got here, and considering some of AI's abilities as well as limitations.
Whether you realized it or not, you have likely already been using AI in your daily routine for some time. AI is woven into everyday tools such as the 'digital assistants' Siri, Alexa, and Google, in the personalized recommendations you receive on platforms like Netflix or Spotify, as well as in the GPS directions guiding your way. You have likely interacted with AI-driven chatbots on business and educational websites, or with AI voice responders when you've called a customer service line. And if you have used Google search autocomplete and/or Outlook email autocomplete as well as texting next-word prediction—you've used AI!
In this course, when we talk about AI, we refer to computer systems that can recognize and replicate patterns through extensive data training, often utilizing machine learning algorithms. The AI that falls under the category of 'generative AI' refers to Large Language Models (LLMs) such as Chat GPT, as well as those capable of generating prompt-based original art, video, and audio.
Generative AI refers to a type of AI that uses inputs, predicts the outputs, and generates "new" information.
Neural networks are a fundamental component of generative AI. Neural networks are a type of machine learning process, that uses interconnected nodes, or neurons, in a layered structure that resembles the human brain. Neural networks are a type of mathematical model.
Artificial Intelligence (AI) refers to the capacity of machines to exhibit intelligence, which includes processes such as perception, synthesis, and inference. This is distinct from the intelligence displayed by humans and non-human animals. The leading AI scholar Kate Crawford, in her book "Atlas of AI," highlights that this form of "Artificial Intelligence" is not truly artificial or intelligent. It heavily relies on human labor and human-generated data, primarily focusing on predicting outcomes rather than engaging in reasoning or understanding as humans do. In response to this, there is a growing trend to adopt the term "augmented intelligence" more widely. However, it's important to note that there are nuanced yet significant distinctions between "augmented intelligence" and AI, which some argue should not be overlooked.
(Optional) If you have some time, watch this well-researched video Artificial Intelligence: Last Week Tonight with John Oliver—a sometimes humorous but highly informative take on AI (Length: 27:52).
Caveat: This video includes a fair bit of swearing.
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