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Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. AI is often categorized into two types:
Narrow AI: Also known as weak AI, operates under a limited set of constraints and is designed to perform a narrow task, such as voice recognition or driving a vehicle. Most AI that we interact with today, like virtual assistants (e.g., Siri or Alexa), are considered narrow AI.
General AI: Also known as strong AI, this type of AI possesses the ability to perform any intellectual task that a human being can do. It can understand, learn, adapt, and implement knowledge in a way that's not limited to a specific domain.
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy (IBM - What is ML).
Large language models (LLMs) are a category of foundation 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 (IBM - What are LLMs).
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions. (IBM - What is a Neural Network).
Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs. Generative AI uses a number of techniques that continue to evolve. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. (Gartner - What is Gen AI).
Artificial Intelligence (AI) has significant potential in reshaping education, making it more personalized, efficient, and inclusive. Some examples include:
1. Personalized Learning: AI can adapt to a student's individual learning pace and style. By analyzing a student's strengths, weaknesses, and progress, AI can customize content delivery for optimal learning, resulting in personalized education for each student.
2. Tutoring and Support: AI-driven tutoring systems can provide additional support to students, helping them in subjects where they might struggle. These intelligent tutoring systems can explain concepts, answer questions, provide feedback, and even assess students' understanding of a subject.
3. Efficiency for Educators: AI can automate administrative tasks such as grading and scheduling, freeing up time for educators to focus on instruction and student interaction. AI can also assist in detecting plagiarism in assignments.
4. Data-Informed Insights: AI can analyze vast amounts of data to provide insights into learning patterns and trends, helping educators and policy-makers make informed decisions to improve teaching methods, curriculum design, and overall educational policies.
5. Accessibility: AI technologies can help make education more accessible for students with disabilities. For instance, speech-to-text and text-to-speech technologies can aid students with hearing or speech impairments, while AI-driven personalized learning systems can cater to students with learning difficulties.
6. Lifelong Learning and Upskilling: With the rapid pace of technological advancement, continuous learning has become essential. AI-powered platforms can provide personalized, on-demand learning for people at all stages of their career, making it easier for individuals to acquire new skills and adapt to changing job markets.
7. Virtual Reality (VR) and Augmented Reality (AR): Though not AI per se, these technologies often leverage AI for creating immersive learning experiences, making education more engaging and interactive.
While AI presents these remarkable opportunities, it's crucial to navigate potential challenges such as data privacy and security, ensuring AI's equitable use, and addressing concerns around the depersonalization of education. As with any technology, the goal should be to use AI to enhance human effort in education, not replace it.
Learn more about AI Resources at UNM.