Artificial Intelligence (AI) for City Planning

• An understanding of city planning
• A basic understanding of programming concepts

What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?

In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.

Audience

• City planners
• Architects
• Developers
• Transportation officials

Format of the Course

• Part lecture, part discussion, and a series of interactive exercises.

Note

• To request a customized training for this course, please contact us to arrange.

Introduction

• AI for city planning

Uses and Opportunities for City Service Providers

• Architecture, transportation, public safety, land use, environment, etc.

Applications for AI

• Computer Vision, Natural Language Procession (NLP), Voice Recognition, etc.

The Data Behind AI

• Data as the enabler of AI
• Gaining access to the data

The Computation behind AI

• Probability and Statistics as the Core
• How Algorithms Enable Intelligence

The Logic Behind AI

• Programming Language used in AI
• Needed skillsets

Teaching Machines How to Learn

•  Understanding machine learning
• Applying machine learning libraries to develop intelligent systems

Advanced Approaches to Machine Learning

• Deep Learning

Case Study

• Predicting traffic bottlenecks with machine learning

The Tooling behind AI

• Different databases for different purposes
• Data processing engines
• Building the infrastructure on premise or in the cloud

Analyzing the Data

• Handling large volumes of data
• Aggregating data across agencies
• Data preparation, staging, analysis and reporting
• Data mining approaches

Case Study

• Collecting, filtering and analyzing demographic data by neighborhood

The Interplay of AI and IoT

• Cameras, sensors, actuators, etc.
• Assessing the city's network infrastructure

Autonomous Decision Making and Execution

• Using rules engines and expert systems to make decisions
• Programming machines to take actions on their own

Case Study

• Responding to emergencies based on real-time data

Automating Human Processes

• The interplay of humans and machine
• Optimizing processes in municipal departments

Bringing it All Together

• The low-hanging fruit for city planners
• Constructing a city wide digital platform

Planning and Communicating an AI Strategy

• Needs assessment and return on investment
• Bringing together city leaders, agencies, businesses and universities

Summary and Conclusion