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AI 101: A Starter Guide

Understanding the AI Landscape

Understanding the AI Landscape

Artificial Intelligence (AI) is rapidly changing how we access, use, and interact with information and technology, making it essential to grasp its foundational concepts. From personalized Netflix recommendations to fraud detection in banking, AI already shapes many aspects of our lives.

The Student Perspective

As students, understanding AI can empower you to engage critically with these systems, enhance your learning experience, and recognize when AI-generated information may require deeper scrutiny. At the same time, AI presents challenges, such as the risk of relying on biased or inaccurate outputs, making it crucial to develop the skills needed to navigate these tools responsibly and ethically. 

Beyond college, understanding AI can give you a competitive edge in the professional world, where businesses increasingly rely on AI-driven insights and innovation to make informed decisions and stay ahead in rapidly evolving industries.

The Faculty Perspective

As instructors, you can leverage AI to enhance your teaching methods and engage students in new and meaningful ways. AI tools can help you personalize learning experiences, providing tailored resources and adaptive learning paths that meet the diverse needs of your students. Automated administrative tools can also reduce workload, allowing more time for focused, high-quality student interactions.

However, the integration of AI in education is not without its challenges. One significant concern is the potential over-reliance on AI tools, which may inadvertently undermine critical thinking skills if students or instructors fail to question AI-generated content. Additionally, the ethical implications of using AI—such as ensuring equity in the face of the digital divide and protecting student data privacy—demand careful consideration and continuous vigilance.

Educators must therefore stay informed and critically engaged with AI advancements to effectively guide students in both the use and ethical understanding of these technologies. By doing so, you not only foster a deeper comprehension of AI but also prepare students to thoughtfully navigate and contribute to a world increasingly influenced by artificial intelligence.

Successfully navigating this landscape requires a clear understanding of the distinctions between key terms like artificial intelligence (AI), machine learning, and generative AI, as these are closely related concepts with overlapping boundaries, each contributing to different aspects of how AI systems are developed and applied.

What are Artificial Intelligence, Machine Learning, and Generative AI?

AI is the broad field that enables machines to perform tasks autonomously, without constant human input. Everyday technologies that make use of AI include:

  • Voice Assistants (e.g., Siri, Alexa, Google Assistant)
  • Spam Filters (in email services like Gmail)
  • Facial Recognition (e.g., unlocking phones or on social media platforms)
  • Recommender Systems (e.g., Netflix, YouTube, Amazon recommendations)
  • Autocorrect and Predictive Text (e.g., in smartphones and word processing apps)
  • Navigation Apps (e.g., Google Maps, Waze)
  • Smart Home Devices (e.g., smart thermostats, lighting systems)
  • Fraud Detection Systems (e.g., in banking and credit card transactions)

Machine learning is a subset of AI that allows systems to learn from data, improving their performance over time, making AI an adaptable technology. For example, Netflix uses machine learning to analyze your watch history, the genres you prefer, and the ratings you've given to shows and movies. Based on this data, Netflix adapts its recommendations by identifying patterns in your viewing habits and comparing them to similar users. Over time, the more you watch and interact with the platform, the more personalized and accurate the recommendations become, continuously updating to match your evolving preferences. 

Generative AI, like ChatGPT or Google’s Gemini, is a specific type of AI that uses machine learning to generate new, original content based on patterns it has learned from large datasets. This distinction helps highlight how generative AI builds upon machine learning to produce creative outputs.

Flip through the following slides to see how AI, machine learning, and generative are related.

Generative AI Explained

To further understand how generative AI is a distinct type of artificial intelligence, watch the following 2-minute video.

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