AI 180 refers to a fictional university course code for an introductory artificial intelligence class. In this article, we’ll explore key topics and learning objectives that might be covered in a survey course called AI 180.

Course Overview

AI 180 provides a broad introduction to artificial intelligence concepts, applications, techniques, and ethical considerations. It aims to build foundational literacy around AI systems and how they impact society.

Target students are those new to AI, including majors in computer science, data science, cognitive science, and other technical or scientific fields. No prerequisites are required beyond high school-level math.

The course curriculum interleaves theory with hands-on exploration through projects. Readings draw from a variety of texts, papers, and online resources to provide diverse perspectives on AI.

By the end, students possess basic AI knowledge to build upon in further studies or interact with AI systems thoughtfully as technological citizens.

History of AI

The course begins by surveying the origins and evolution of artificial intelligence:

Early Thinkers

  • Alan Turing – conceived the Turing test for intelligent behavior
  • John McCarthy – coined the term “artificial intelligence”

Cycles of Hype vs. Reality

  • Alternating periods of inflated expectations and disillusionment known as “AI winters”
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Milestones

  • 1997 – Deep Blue defeats world chess champion
  • 2011 – IBM Watson wins Jeopardy!
  • 2012 – AlexNet pioneers deep learning for image recognition

This history provides context for present-day AI capabilities and limitations.

Intelligent Agents

A core framework introduced is that of intelligent agents:

  • Agents perceive environments through sensors
  • Use knowledge and inference to choose actions
  • Act upon environments to achieve goals

Varieties of agents include simple reflex agents, goal-based agents, utility-based agents, and learning agents.

Environments may be fully observable or partially observable, single-agent or multi-agent, competitive or collaborative.

Problem Areas in AI

AI research encompasses a wide array of challenges and approaches:

Reasoning and Problem-Solving

  • Logical agents, search algorithms, planning under uncertainty

Knowledge Representation and Reasoning

  • Semantic networks, description logics, ontologies

Machine Learning

  • Statistical learning theory, supervised/unsupervised/reinforcement learning

Natural Language Processing

  • Speech recognition, machine translation, dialogue systems

Computer Vision

  • Image classification, object detection, image generation

Robotics

  • Motion, manipulation, navigation, swarm behaviors

And many other subfields feeding into creating intelligent systems.

Major AI Techniques

Students get hands-on practice applying fundamental AI techniques:

Machine Learning Algorithms

  • Linear models, neural networks, decision trees, clustering, etc.

Computer Vision Techniques

  • Convolutional neural networks, object detection

Natural Language Processing

  • Sentiment analysis, language models like n-grams

Search Algorithms

  • Uninformed search, A*, adversarial search

Logic-Based Methods

  • Propositional logic, first-order logic, inference

Through projects, students directly implement algorithms underlying AI systems.

AI Applications

The course surveys contemporary AI application areas:

Healthcare

  • Expert systems, treatment planning, personalized medicine

Transportation

  • Autonomous vehicles, intelligent traffic systems

Business

  • Recommender systems, predictive analytics, process automation
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Security

  • Biometrics, surveillance, network intrusion detection

Entertainment

  • Game-playing agents, generative art, music composition

And many more – AI is transforming nearly every industry and activity.

AI Ethics

With growing societal influence, AI raises many ethical considerations:

  • Privacy around data collection and monitoring
  • Potential biases perpetuated through training data or algorithms
  • Transparency and explainability of AI decision-making
  • Effects of automation on jobs and inequality
  • Development of artificial general intelligence

Students critically consider short-term ethical ramifications and speculate on long-term possibilities.

The Future of AI

The course concludes with reflections on the future trajectory of artificial intelligence:

  • AI likely to continue advancing in capability and commercial deployment
  • Possibility of artificial general intelligence manifesting
  • Potential benefits as well as risks to address
  • Importance of ongoing research, ethical foresight, and regulation

This future outlook motivates further education in AI to steer progress beneficially.

Conclusion

A fictional course like AI 180 aims to build a holistic perspective on artificial intelligence, from history and foundational concepts to algorithms, applications, ethics, and outlooks. Hands-on projects provide concrete experience applying AI techniques. By course’s end, students have a baseline understanding to thoughtfully participate in our increasingly AI-driven world.