AGENDA Generative AI Applications Summit 2024

Phoenix, AZ June 4-7, 2024

In this keynote, you’ll learn how open-source models can help you build high-quality AI applications, generative or not, while giving you more flexibility, control, and ROI than closed-model APIs. We’ll highlight the latest and greatest models, and show you how to get started with them in minutes. Along the way, you’ll also learn about the technical ecosystem that Hugging Face is fostering, from models and datasets, to cloud integrations and hardware acceleration.

The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption.

In this keynote, Machine Learning Week Founder Eric Siegel presents a six-step practice called bizML for ushering machine learning projects from conception to deployment. This disciplined approach, which is the topic of Siegel’s new book, The AI Playbook, serves both sides: It empowers business professionals and it establishes a sorely needed strategic framework for data professionals.

For those of us who’ve spent any time arguing with customer service chatbots or screaming “REPRESENTATIVE!” to an interactive voice response (IVR) line, the hype around generative AI improving customer experience might seem hard to believe. Sure, ChatGPT can do some impressive things, but do we really think AI can make the painful process of dealing with our insurance companies, wireless carriers or utilities any easier? The answer is yes, but not by building a better bot.

This presentation will explore how the real breakthrough created by GenAI is making the entire operation smarter and more personalized, so there are fewer reasons to deal with customer service in the first place.

Generative AI (Gen AI) is transforming investment management, enhancing operational efficiency, personalizing client services, and fostering innovative asset management strategies. By utilizing language models and multimodal LLMs, firms can effectively extract valuable signals from news, filings, and various unstructured data sources, driving market share growth and streamlining research processes to make advanced analytics more accessible. As Gen AI can significantly improve risk management and enrich client interactions, its adoption has become essential for firms seeking competitive advantages in a dynamic financial environment. Drawing from my experience at the intersection of Gen AI and investment management, I will share the industry viewpoint and practical insights on harnessing Gen AI’s transformative potential.

Building successful, interactive generative AI applications for troubleshooting or assistive knowledge management system from complex data that is originally meant for human readability is not a trivial task. In an organization documents can range from trouble shooting technical manuals, knowledge articles on specific product descriptions with lot of tables, images, links, and some machine generated outputs like call transcripts. This presentation highlights the key lessons learnt in implementing complex infobots and trouble shooting bots in a multi-tired way to meet use case scenarios for enhancing productivity of technicians and customer facing agents.

Paychex manages a diverse range of textual data, including phone call transcripts, emails, chat logs, and survey feedback. A primary opportunity is to leverage this data effectively for actionable insights to improve the customer experience, and help small businesses succeed. The Paychex Data Science team employs a suite of large language models (LLM), covering traditional methods like word embeddings and phrase matching, to cutting-edge technology such as generative AI. Through these methods, we identify clients at retention risk, address economic distress signals, and categorize communications for product improvement. Our advanced LLM technology promises continuous enhancement to deliver tangible business value.

The Generative AI Accelerator It is a unique exercise that guides participants through leveraging Generative AI to drive business innovation. It is a proven framework, tested with over 25 organizations, ranging from small to medium-sized businesses to large corporations. In a two-step process, we first identify which problems are most suitable for solving with generative AI models. This is achieved by providing participants with prompting templates to test the feasibility of cases against AI models in real-time. We then draw shortlisted use cases on the GenAI Architecture canvas, accompanied by guiding questions, which aids participants in drafting their GenAI solution architecture. This process elucidates how a GenAI architecture can be optimally integrated into organizations, addressing the most pertinent use cases. Participants leave with a comprehensive understanding of how a GenAI solution would function within their specific organization, including which data to leverage and the essential elements of cloud services needed to build the solution.

Requirements: 

• Be aware of potential use cases in your area. 
• Bring a laptop. 
• Have an internet connection
• Have access to an intelligent chatbot e.g. Opus, ChatGPT, Gemini, or open-source variants.

Discover the cutting-edge methodologies for preventing hallucinations in large language models (LLMs) used for chatbot development. This talk highlights the journey and key learnings from creating a custom chatbot, emphasizing the deployment of anti-hallucination strategies. Over the session you will explore the causes of LLM hallucinations, their impact on user trust, and effective strategies to mitigate them. This session offers a comprehensive toolkit for developers and researchers aiming to enhance the reliability of LLM applications, ensuring your projects maintain accuracy and credibility.

In today’s digital age, the quality and accessibility of knowledge assets are paramount. Aramco recognizes this and has established a comprehensive corporate knowledge base that consolidates validated knowledge assets across various disciplines. But how do we ensure the quality of these assets and prevent the tedious cycle of reviewer rejections and resubmissions?

Amjad will unveil Aramco’s approach to maximize knowledge value unsing an in-house developed virtual assistant that not only provides users with guidelines and templates but also harnesses the power of generative AI to review the content and structure of knowledge assets, ensuring they meet a predefined criteria specific to each knowledge type. Moreover, it enhances the writing quality, creates abstracts, references, etc. making it more readable and reusable, thus amplifying its business value.

By participating in this session, attendees will:

• Gain insights into Aramco’s AI-Empowered Knowledge Base.
• Understand the role of AI in enhancing knowledge asset quality.
• Learn how to leverage AI capabilities to optimize content for maximum business impact.

Natural Language Generation has made substantial progress – you can easily generate most types of text without sacrificing creativity. But the more creative your prompts, the more likely you are to generate non-compliant language. The challenge is how to comfortably generate text in regulated industries like finance. Last discusses potential solutions.

Although LLM-powered applications are all the rage, the best practices to make them safe, reliable, and robust are still being determined. It is critical that high-value brands be able to experiment with these technologies without compromising their brand.

In this session, we’ll share a behind-the-scenes look at how Harvard Business Review launched a brand-aligned generative AI experience in their consumer and business-to-business markets. We’ll unveil the various concerns, approaches, and tradeoffs that our team of researchers and engineers explored, balancing principled research with pragmatic product development. We’ll leave attendees with a checklist of concerns to address for their own experiments.

Over the last year, generative AI has exploded out of the dark corners of the tech industry to become a central object of public attention, imagination and investment. As generative AI tools are increasingly integrated into widely used products, ML engineers shoulder significant responsibility for the safety, risks and social impact of the systems they build. On the other hand, data engineers are expected to integrate a variety of new AI based tools into development workflows and data pipelines. In this talk, I will address the longstanding cultural gap between the two disciplines, and share thoughts on how data and machine learning engineers can work together to meet the challenges and opportunities created by the AI boom.

Meet new people for dinner at a restaurant in walking distance and network in a relaxed setting with good food and favorite beverages (everyone pays for their own dinner). We will publish the restaurants closer to the date.

Dive deeper into the practical aspects of Generative AI with this Hands-On Lab. This session offers a unique opportunity for participants to experiment directly with generative AI models in a collaborative environment. Building on the foundational knowledge from the Generative AI Accelerator, attendees will engage hands-on to solidify their understanding of AI-driven problem-solving. 

Participants will dig into their own real-world scenarios, using prompting templates to develop and test AI models. By the end of the session, you’ll be working on a prototype and will have gained practical insights into the iterative process of refining AI models to meet specific business needs.

Requirements:
– Completion of the Generative AI Accelerator session on Day 1 (or reach out to Chairs in the app).
– Bring a laptop.
– Have an internet connection.
– Access to an intelligent chatbot (e.g., Opus, ChatGPT, Gemini, or open-source variants).

This talk summarizes current trends and use cases that apply state-of-the-art natural language processing in healthcare systems, pharmaceuticals, and health IT companies. It covers unlocking use cases that become possible with current large language models, enabling medical domain experts to train & tune models in a co-code environment, and testing NLP models across aspects of Responsible AI in preparation for regulatory oversight. In this talk, David also shares free and open-source tools you can use today in these three areas.

In the chat-bot and generative AI industry, there is strong demand for Text-to-SQL tool that can take user input questions, create SQL queries, and return results of those queries back to the user. Successfully creating a training dataset then comparing and choosing the right LLM to put into production can be difficult. This session demonstrates how to successfully curate a Text-to-SQL training dataset and compare, evaluate, and fine-tune various Generative AI LLMs to ensure the highest accuracy and customer satisfaction.

This presentation discusses a case study for leveraging AI-based knowledge engagement techniques for development and use of Immunization Information Systems (IIS) guidelines. The knowledge base developed by the IIS community is voluminous and growing rapidly, resulting in a valuable yet difficult to navigate resource. Initial results of employing a generative AI tool (ChatGPT) at the CDC’s National Center for Immunization and Respiratory Diseases (NCIRD) are promising and indicate increased accessibility and discoverability of information. By making all guidance documents searchable at once, discoverability and presentation of content are enhanced. The AI tool uses a limited set of trusted in-house sources: guidance manuals, community standards, emails, and meeting notes. This ensures accuracy of responses and guarantees information is only from verified sources. Additional capabilities include support for new content development with information synthesis across multiple resources.

Dive deeper into the practical aspects of Generative AI with this Hands-On Lab. This session offers a unique opportunity for participants to experiment directly with generative AI models in a collaborative environment. Building on the foundational knowledge from the Generative AI Accelerator, attendees will engage hands-on to solidify their understanding of AI-driven problem-solving. 

Participants will dig into their own real-world scenarios, using prompting templates to develop and test AI models. By the end of the session, you’ll be working on a prototype and will have gained practical insights into the iterative process of refining AI models to meet specific business needs.

Requirements:
– Completion of the Generative AI Accelerator session on Day 1 (or reach out to Chairs in the app).
– Bring a laptop.
– Have an internet connection.
– Access to an intelligent chatbot (e.g., Opus, ChatGPT, Gemini, or open-source variants).

This discussion will explore practical strategies for leveraging  technologies top LLMs and their capabilities, visual language models, and other AI models like transcription e.g. Whisper by OpenAI. Let’s discover diverse applications from banking to novel sectors, uncovering both readily accessible opportunities and impactful, lesser-known use cases. We’ll also hear from the panelists real-world examples that illustrate how these technologies have been effectively implemented across various industries.

Join Paul in this engaging talk as we demystify Gen AI and uncover strategic approaches for traditional retailers to navigate the ever-changing retail landscape. While Gen AI offers incredible opportunities, it comes with its set of challenges and concerns that we’ll address together. We’ll delve into the complexities of integrating AI in retail, discussing issues like data privacy, ethical considerations, and the imperative to upskill our workforce. Together, we’ll explore the vast opportunities Gen AI presents, from crafting hyper-personalized experiences to leveraging data-driven insights that can redefine customer interactions. This talk is not just about strategies; it’s a journey into understanding and harnessing the power of Gen AI, embracing its potential, addressing concerns, and ensuring traditional retailers thrive in this transformative era.

This final conference session focuses on the strategic integration of Generative AI into existing business processes. Participants will learn from industry experts and peers about the best practices and pitfalls in adopting AI technologies. Through group discussion, attendees will explore how to align AI initiatives with business objectives, streamline workflows, and measure the impact of AI on business performance. The session will also cover the organizational change management necessary for successful AI adoption, ensuring that your AI strategy is not only innovative but also sustainable.