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Presentation: Reducing LLM Hallucinations In Production

Technologist Deep-Dive (Gen AI & Data Science) Track

AI Technologists

Data Science

Hallucination Prevention

AI Optimizations

Author:

Dat Ngo

Machine Learning Engineer

Arize AI

Dat Ngo is a data scientist and machine learning engineer who works directly with Arize AI users to evaluate and troubleshoot generative AI applications. Before Arize, Ngo led strategic data science efforts at PointPredictive, alliantgroup, and Wood Mackenzie. Ngo has a Master of Science in Applied Statistics from Texas A&M University.

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Navigating the Mirage: Understanding and Mitigating Hallucinations in Large Language Models

In an era where artificial intelligence is not just an asset but a necessity, understanding the intricacies of Large Language Models (LLMs) has become paramount for enterprises. This session, 'Understanding and Mitigating Hallucinations in Large Language Models', offers a deep dive into the phenomenon of LLM hallucinations – a critical challenge in the deployment of AI technologies in business environments.

We will explore the mechanics behind LLM hallucinations, shedding light on how these AI models, despite their sophistication, can generate inaccurate or misleading information. From the subtlety of input-conflicting hallucinations to the complexity of context and fact-conflicting errors, we will dissect various types of hallucinations with real-world examples, including notable instances from prominent LLMs.

This talk will not only focus on the identification and detection of such hallucinations but will also present effective strategies for mitigation. We will discuss the role of data quality, model fine-tuning, and advanced techniques like Reinforcement Learning with Human Feedback (RLHF) in reducing the risks of inaccuracies. Furthermore, the session will highlight the importance of balancing the creative potential of LLM hallucinations with the need for factual accuracy, especially in high-stakes business decisions.

Attendees will leave with a comprehensive understanding of the challenges and opportunities presented by LLM hallucinations. This knowledge is crucial for enterprises looking to leverage AI responsibly and effectively, ensuring that their use of these powerful tools aligns with the highest standards of accuracy and reliability in the business world.

Technologist Deep-Dive (Gen AI & Data Science) Track

Retail

Business Leader

Data Science

AI Technologists

Author:

Ved Upadhyay

Senior Data Scientist

Walmart Global Tech

Ved Upadhyay is a seasoned professional in the realm of data science and artificial intelligence (AI). With a focus on addressing complex challenges in data science on an enterprise scale, he boasts over 7 years of hands-on experience in crafting AI-powered solutions for businesses. Ved’s expertise spans diverse industries, including retail, e-commerce, pharmaceuticals, agrotech, and socio-tech, where he has successfully productized multiple machine learning pipelines. Currently serving as a Senior Data Scientist at Walmart, Ved spearheads multiple data science initiatives centered around customer propensity and responsible AI solutions at enterprise scale. Prior to venturing into the industry, Ved earned his master’s degree in Data Science from the University of Illinois at Urbana-Champaign and contributed as a Deep Learning researcher at IIIT Hyderabad. His research contributions are reflected in multiple publications in the field of applied AI.

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Winning The Marketing Strategy Battle With The Power Of AI

Less than 1 out of 10 marketing campaigns really work. How can AI help generate marketing campaigns that increase the chance of success? In this session, Abbas Arslan will talk us through the experience of The Coca-Cola Company to revitalize their marketing effectiveness with AI at its core.

Application & Gen AI Integration (Business Leaders) Track

Marketing

Business Leader

AI Optimizations

Data Analysis

Author:

Abbas Arslan

Senior Marketing Director

Coca-Cola

Abbas is a Global Marketing leader with expertise in Consumer Marketing, Digital Marketing, Creative Strategy, Innovation Success and Artificial Intelligence.

Over last 20 years, he has positively impacted businesses across 50+ countries through user base acceleration, market share gains and stronger brand building. Abbas has also led the Predictive Insights at Coca-Cola with focus on use of Artificial Intelligence. His work has led to stronger effectiveness while significantly reducing time and cost.

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Data Privacy Within Generative AI & Considerations For The Enterprise

Application & Gen AI Integration (Business Leaders) Track

Data Privacy

Ethical AI

C-Suite

Healthcare

Finance

Manufacturing

Moderator

Author:

Hira Dangol

Vice President, AI/ML & Automation

Bank Of America

Industry experience in AI/ML, engineering, architecture and executive roles in leading technology companies, service providers and Silicon Valley leading organizations. Currently focusing on innovation, disruption, and cutting-edge technologies through startups and technology-driven corporation in solving the pressing problems of industry and world.

Author:

Andy Lofgreen

AVP, Data Science Practice

DataRobot

Author:

Waheed Qureshi

Founder & CEO

WMQ Investments

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GAI and AI Platform Evolution: From LLMs to Distributed Multi-Agents, and Everything In-Between

GAI has driven a huge revolution in how AI platforms are designed, architected, and scaled for training, fine tuning, evaluation, inferencing and GAI application engineering needs using RAG, embeddings and distributed multi-agents frameworks. In this session we will deep dive into the (re)evolution of AI platforms and various technologies to scale this for next generation GAI needs.

AI Agents

C-Suite

Business Leader

AI Implementation

Author:

Animesh Singh

Executive Director, AI & Machine Learning

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications.

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.