AI Con USA 2024 - Business User

Generative AI (GenAI), exemplified by groundbreaking systems like ChatGPT and LLAMA, is revolutionizing the software landscape. These advanced technologies represent some of the most sophisticated software ever devised, capable of navigating an unprecedented range of prompts and questions, many of which have never been posed in human history. Their ability to generate varied responses to the same query and even fabricate answers when uncertain poses unique challenges in verification and testing. This talk delves into the intricacies of validating such systems and identifies areas needing...

It is evident that AI/ML is significantly impacting how businesses operate, transforming the enterprise landscape in various ways, enhancing customer experience, improving efficiency, and reducing costs. The desire to incorporate AI/ML is there, but enterprises often struggle with where to start or how to do it in a repeatable and sustainable manner. Hien will discuss the key challenges to AI/ML adoption and share practical strategies for overcoming them. He will explore the critical factors that drive successful implementation, including data readiness, organizational culture, and...

Let’s dive into what goes into creating expert-level prompts for ChatGPT and other LLMs. We will focus on the key ideas to take into account when prompting for any use case and how to better understand the results you get back. We’ll look at prompting 101, think about language, and explore advanced prompting frameworks. Some of the challenges we will address include: how to improve your results, how to process large text, why ChatGPT doesn't do what you want, what your prompt should look like, and many more!

Have you ever gotten into an argument on whether or not Die Hard is a Christmas movie and thought "If only machine learning could help" or "What could I do to automate my dog's daily routine and make his life easier" or "Is it possible to spend 3 months time to determine if I can sleep in 5 minutes tomorrow morning"? If so, you might get along with Matt Eland. Come join this lighthearted talk as he tours you through some ridiculous uses of AI and Machine Learning with topics ranging from large language models to reinforcement learning to simple regression algorithms. You'll get a...

In this keynote, we explore the transformative impact of AI tool adoption in software development, focusing on measuring the tangible benefits, implementing effective dashboarding for real-time insights, and linking time savings to strategic business outcomes. We delve into best practices for quantifying efficiency gains and how these advancements not only enhance immediate development processes but also contribute significantly to broader organizational goals. This discussion aims to equip attendees with actionable strategies for leveraging AI tools to achieve competitive advantage, drive...

The advent of AI Agents heralds a new era in computing, introducing a paradigm shift from conventional application-specific interactions to a holistic orchestration of digital workflows across various platforms and industries. This presentation will explore the concept of AI Agents as a pivotal innovation in artificial intelligence, capable of transforming user experiences by enabling seamless integration and management of tasks across multiple applications. Unlike their predecessors, AI Agents excel in providing a personalized, context-aware interface, learning from user interactions to...

The barrier to entry for AI, specifically Large Language Models (LLM), is low and we can take advantage of them in our own tools, projects, and apps. In this session, we’ll build a simple app that uses an LLM which will help us cover: The steps to using AI in a program. LLM Basics. Pick the right LLM. Data Preparation. Evals and Testing. Insights and issues to consider. Tips that you can use as you build your own. Join us for a hands-on session that will help you build your first LLM app or level up your current project!

How does an evolving environment affect the predictions of a Machine Learning (ML) system? Often, the characteristics of real-world environments change as time passes. When we build an AI system that looks for behavioral markers for a certain condition, what do we do when the behavioral markers change? They are no longer a good predictor, and we need to retrain our predictor system. As an environment changes and evolves, its evolutionary path, sometimes called 'drift,' can affect how a system in that environment functions. Environmental drift may adversely affect how well an ML system...