AI 202: Next-level AI Mastery for Java Developers

Abstract:

In this session, the presenter reviews and builds upon key Generative AI (GenAI) capabilities to show how to incorporate capabilities essential for delivering applications with real-world value.
Concepts covered will include some of all of the following, depending on time available:
* Spring AI and the models it supports
* Types of Messages and how to use them
* LLM responses, Spring AI outputs
* Prompts and PromptTemplates
* Tokens, the building block$ of AI
* Vector stores: unleashing AI on your data
* Conversation memory: 100x your AI's capabilities
* Images from text: can AI draw better than you?
* Deployment considerations for your Spring AI app
Come to this session for a fast-paced, live-coding adventure to learn how to put AI to work for you.

LIGHTNING TALK
Title: Beyond Static: Building Adaptive Systems with Reinforcement Learning using Java
Imagine a Java application that evolves with new challenges, learns from its experiences, and continuously adjusts its behavior to enhance performance. While this may sound futuristic, it’s the reality of reinforcement learning in Java.
In today’s fast-paced technological landscape, the need for software that can learn and adapt is growing across industries. Reinforcement learning (RL) provides a framework for developing intelligent, self-improving systems. Java, with its widespread use in enterprise environments, offers a solid foundation for implementing RL algorithms, thanks to its object-oriented structure, rich libraries, and strong community support.
This presentation explores how to leverage reinforcement learning within Java applications to optimize resource allocation, enhance decision-making processes, and build more adaptive, responsive systems. Discover the potential of RL in Java to unlock new levels of application performance and scalability.
Speaker - Omakoji Idakwoji
Omakoji is a Senior Software Engineer with an expertise in enterprise software engineering and machine learning applications. Currently based in Atlanta, Georgia, Omakoji is part of the enterprise search team at the Home Depot Store Support Centre where he contributes to the maintenance and enhancement of a reinforcement learning platform, leveraging reinforcement learning algorithms, specifically the multi-armed bandit to optimize testing processes. Beyond his technical expertise, Omakoji is an avid soccer player and enjoys playing FIFA in his spare time. He is also committed to volunteering and actively participates in the Atlanta Java User Group.

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