2024 AI Fast Track

The Actuarial Data Science team at Akur8

Demystifying Artificial Intelligence

Dispelling Myths and Identifying Transformative Applications in Actuarial Work

In an era of inflated buzz around artificial intelligence, it becomes essential to discern hype from genuine breakthroughs. This presentation demystifies AI, clarifying that at its core, it's a collection of sophisticated algorithms. We will disentangle fact from fiction and spotlight truly transformative applications, providing a grounded perspective on how AI can genuinely revolutionize actuarial practice.

Learning Objectives: 

• Distinguish between common myths and genuine capabilities of AI, facilitating an understanding of its core as a collection of clever algorithms. 
• Identify and evaluate transformative applications by appreciating their potential impact in advancing actuarial practice. 
• Apply insights to recognize potential AI applications in actuarial work, fostering an environment for innovation and enhanced problem-solving.

The Actuarial Data Science team at Akur8

AI Search Techniques

From Classical Search to Retrieval-Augmented Generation

Search algorithms form the bedrock of artificial intelligence, powering everything from simple queries to complex decision-making processes. This presentation delves into the evolution of AI search techniques, starting from classic search algorithms and advancing to the latest in retrieval-augmented generation (RAG). By understanding these methods, we can appreciate their applications and impact on actuarial science and beyond. This comprehensive overview will empower actuaries to harness these techniques in optimizing their workflows and enhancing predictive models.

Learning Objectives: 

• Gain insights into foundational search algorithms such as Depth-First Search (DFS), Breadth-First Search (BFS), Iterative Deepening, and A*.
• Explore modern search techniques like randomized optimization, genetic algorithms, and RAG. 
• Assess the transformative potential of these algorithms in enhancing efficiency, accuracy, and innovation in actuarial practice.
   

The Actuarial Data Science team at Akur8

From Rules to Rewards

The Role of Domain Knowledge in Rules-Based AI and Reinforcement Learning

Rules-based AI has long been a cornerstone of artificial intelligence, allowing us to encode domain knowledge into intelligent agents through explicit rules and logic. By leveraging knowledge engineering, we can create powerful systems that make decisions based on well-defined criteria. However, reinforcement learning represents a modern evolution of this approach. Instead of manually encoding rules, we engineer agents to learn optimal behaviors by interacting with their environment and receiving rewards that reflect our domain knowledge. This presentation explores the relationship between these methodologies, highlighting how they can be applied to solve complex problems in actuarial science and beyond. By understanding the strengths and applications of both approaches, actuaries can drive innovation and improve decision-making.

Learning Objectives: 

• Understand the fundamentals of rules-based systems and their role in creating transparent, explainable AI solutions.
• Grasp the basics of reinforcement learning, including key concepts like rewards, policies, and value functions. 
• Identify specific use cases where these AI techniques can be applied to actuarial tasks such as underwriting, risk modeling, and fraud detection.

The Actuarial Data Science team at Akur8

Machine Learning with GBMs and Modern GLMs
 
The Philosophy and Application of the Actuary’s Most Powerful Tools
 
Machine learning has revolutionized the actuarial profession, offering powerful tools for predictive modeling and data analysis. This presentation delves into two key machine learning techniques: Generalized Linear Models (GLMs) and Gradient Boosting Machines (GBMs). By understanding these methods, actuaries can enhance their analytical capabilities, improve model accuracy, and drive data-driven decision-making. This session will provide a thorough exploration of GLMs and GBMs, highlighting their applications and benefits in actuarial work.
 
Learning Objectives:

• Explore the role of supervised learning in actuarial analytics and relate to machine learning philosophies.
• Understand the fundamentals of GLMs and GBMs and how they can be used in actuarial domains.
• Evaluate the strengths and limitations of each method, and how to use both in conjunction to build models for current actuarial tasks as well as other insurance related problems.

The Actuarial Data Science team at Akur8

The Frontier of Artificial Intelligence
 
Exploring Generative AI, Deep Learning, Large Language Models, and Future Trends
 
The landscape of artificial intelligence is rapidly evolving, with generative AI, deep learning, and large language models leading the charge. This presentation explores these cutting-edge technologies, shedding light on their underlying mechanisms and transformative potential. Actuaries will gain insights into how these advancements can be leveraged to innovate, streamline processes, and solve complex problems in their field. This session will also delve into the future of AI, highlighting emerging trends and their implications for actuarial practice.
 
Learning Objectives:

• Discover the math and capabilities and limitations of Generative AI, Deep Learning, and LLMs.
• Recognize how these advanced AI technologies can be applied to actuarial tasks like risk assessment, process automation, and data-driven decision-making.
• Develop strategies to stay ahead of AI trends, fostering a culture of continuous learning and innovation within the actuarial field.