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Artificial Intelligence System Safety (AISYS 23-1)
October 10 @ 8:00 am - October 14 @ 12:00 pm$3000
Artificial Intelligence and autonomous systems have become pervasive in software applications. However, the implications for AI safety has created new concerns and risk factors especially for autonomous vehicles. This course provides a thorough survey of AI, machine learning, optimization, and autonomous vehicle techniques followed by safety and hazard analysis methods. Along the way, we will bring clarity to definitions, actual capabilities of AI systems, and the current state of data science. Case studies and real-world incidents will also provide learnings and insights to further advance the goal of AI safety.
To provide a survey of current AI and autonomous vehicle techniques as well as discovering and mitigating their hazards. This course will also apply existing software safety methods to artificial intelligence and establish why AI is just another tool in a suite of software functionalities.
Who Should Attend
System managers and engineers, system safety engineers and software engineers who are involved with developing systems that possess major software components and are responsible for the safety of such systems. Attending the System Safety Engineering course and some understanding of software beforehand is highly recommended.
- Definitions and Concepts
- AI and ML Capabilities
- Optimization and routing
- Metaheuristics and tree search
- Machine Learning
- Deep Learning
- Reinforcement learning
- ML Explainability
- Data analysis and preparation
- Statistical methods
- Scientific method versus data mining
- Confusion matrices, ROC, and AUC
- P-values and P-hacking
- Sampling bias, outliers, and data rot
- Autonomous Vehicles
- Defining autonomous vehicles
- Levels of autonomy
- Air and ground vehicle instrumentation
- Air and ground vehicle automation methods
- Ego, connected, modular, and end-to-end systems
- Localization techniques
- Object detection techniques
- Environment mapping techniques
- Human factors in autonomous vehicles
- Data Science Talent Strategy
- Defining data science
- State of data science as a discipline
- Rubrics for hiring a data scientist
- AI Risk Assessment
- AI Hazard Analysis
- AI Safety testing/reliability/maintenance
Attendees should have an engineering or hard science background or completed the System Safety course (SSC).
Published on May 3rd, 2021
Last updated on April 28th, 2022