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”
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
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.
