Trusted AI and Autonomy

Length: 2 Days

Trusted AI and Autonomy Short Course by Tonex

Trusted AI and Autonomy is a 2-day course where participants earn how AI-enabled Autonomy operates independent of direct human intervention to achieve a goal or solve a problem.

Artificial Intelligence (AI) and autonomous systems are revolutionizing industries across the globe.

From healthcare to automotive, AI’s ability to process data, make decisions, and even predict outcomes is creating a more efficient world. However, the increasing reliance on AI and autonomy highlights a critical need for trust in these systems.

In other words, without trust, the true potential of AI cannot be fully realized.

Trust in AI systems is paramount because AI often handles sensitive tasks, such as financial transactions, medical diagnoses, and autonomous driving. Any malfunction or biased decision-making can have severe consequences.

For instance, in healthcare, an inaccurate diagnosis due to faulty AI algorithms could lead to mistreatment, jeopardizing patient lives. Similarly, biased AI models in hiring or lending decisions can lead to unfair practices and perpetuate discrimination.

To ensure AI systems are trusted, they must be transparent, unbiased, and explainable. Users need to understand how decisions are made and be confident that these systems are reliable and fair.

Autonomy, which refers to machines or systems that operate independently, further intensifies the importance of trust. Autonomous vehicles, drones, and robots are becoming part of daily life, from self-driving cars to warehouse automation.

For these systems to be widely adopted, users must trust that they can operate safely and effectively in various environments without constant human oversight. Trusted autonomy requires rigorous testing, validation, and regulation to ensure that autonomous systems perform as expected and handle unexpected situations seamlessly.

Bottom line: Building trust in AI and autonomous systems also requires regulatory oversight and ethical considerations. Establishing clear guidelines, ethical standards, and robust cybersecurity measures are crucial to protect users’ privacy and security. As AI continues to evolve, fostering trust will not only drive its adoption but also ensure that its development benefits society as a whole.

Trusted AI and Autonomy Short Course by Tonex

Trusted AI and Autonomy Short Course is a 2-day introduction to Artificial Intelligence (AI) and how it relates to Autonomy, the engineering discipline that expands robots’ abilities to perform tasks while limiting the need for human interaction.

This course is especially beneficial for individuals who want to see the “big picture” in order to communicate with technical as well as non-technical individuals.

Trusted AI and Autonomy Short Course covers the most important aspects of AI and Autonomy without getting too involved in the technical nitty gritty.

Participants will learn how AI-enabled Autonomy operates independent of direct human intervention to achieve a goal or solve a problem.

Additionally, participants will learn about the benefits of AI and Autonomy and specific applications that lend themselves to AI and Autonomy technology now and in the future.

This cutting edge technology is important because an AI system processes a large amount of data in a short time, finding the best solution available at that time for the task at hand for the autonomous system or human-machine team. It can also perceive, assess, and make sense of new situations given very little data – also known as “sparse data” – which shortens decision-making timelines.

This ability to interpret information and make good decisions quickly in new situations is useful for any autonomous technology, whether it is used in civilian, industrial, or military environments. However, the challenges and potential consequences of those abilities are vital when in use by defense and intelligence agencies.

An autonomous military system (AMS), for example, not only will often need to perform complex tasks in disrupted, chaotic, or unanticipated environments, but may do so while being attacked by adversaries.

Who Should Attend

Who Should Attend?

Trusted AI and Autonomy Short Course is a 2-day course designed for:

Program and Project Managers
IT Leadership
IT Infrastructure Managers
CIOs / CTOs /CSOs
Configuration Managers
Developers and Application Team Members and Leads
IT Project & Program Managers
Product Owners and Managers
Military Personnel and Leaders
Military Contractors
Anybody Interested in the Potential of AI and Autonomy Technology

Outline

Introduction to Trusted AI and Autonomy

Defining AI and autonomy
Importance of trust in AI systems
Ethical considerations and challenges

Fundamentals of Artificial Intelligence

Overview of machine learning and deep learning
Algorithms and models for AI
Data acquisition and preprocessing

Trustworthy AI Principles

Transparency and explainability
Fairness and bias mitigation
Robustness and reliability
Privacy and security

Evaluation and Verification of AI Systems

Testing methodologies for AI models
Evaluation metrics for trustworthiness
Verification techniques and tools
Benchmarking and standards

Explainable AI (XAI)

Need for explainability in AI systems
Techniques for model interpretability
Visualizations and feature importance
Rule-based and symbolic approaches

Fairness, Bias, and Diversity

Understanding biases in data and algorithms
Measuring and mitigating bias in AI systems
Ensuring diversity and inclusivity
Ethical considerations in decision-making

Robustness and Resilience

Adversarial attacks and defenses
Secure and resilient AI architectures
Error handling and fault tolerance
System robustness in real-world scenarios

Privacy and Security in AI

Privacy-preserving techniques in data handling
Secure multi-party computation
Federated learning and differential privacy
Threat modeling and security practices

Human-AI Interaction

Designing intuitive and user-centric AI interfaces
Collaborative decision-making with AI
Trust building and user acceptance
User experience and feedback mechanisms

Regulatory and Ethical Considerations

Legal and policy frameworks for AI
Responsible AI practices and guidelines
Impact of AI on society and workforce
Future trends and emerging challenges

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