1 year ago
Generative AI Takes Stage at Machine Learning Week 2023

Along with the usual coverage of ML’s deployment across industry sectors, Machine Learning Week 2023 also has generative AI on the agenda — read on for more details.

Generative AI has taken the world by storm, scaling machine learning to viably generate the written word, images, music, speech, video, and more. To the public, it is by far the most visible deployment of machine learning. To futurists, it is the most human-like. And to industry leaders, it has the widest, most untapped range of potential use cases.

Machine Learning Week (June 18-22 in Las Vegas) covers generative AI – including large language models like ChatGPT and image generators like DALL-E 2.

GENERATIVE AI AT MLW – 7 KEYNOTES & SESSIONS, PLUS A WORKSHOP:

Keynote: SupportGPT, an AI for Customer Support

Sami Ghoche, CTO & co-founder of Forethought will talk about advancements in LLMs such as ChatGPT and GPT4 in the context of deploying them for customer service applications including automation, agent augmentation, and insights & analytics. This talk includes an overview of Forethought’s suite of products for customer service, powered by their AI engine called SupportGPT™.

Large Language Models in the Enterprise

Large language models are taking the machine learning industry by storm. Amid an outpouring of new research and exciting updates is the business question of how we can actually take advantage of this cutting-edge technology in the business world. Glean, an enterprise search engine that helps people find what they need and unearth things they should know across all their apps, is one of the only companies in the enterprise world which leverages LLMs jointly with enterprise data. We’ll talk about lessons learnt from doing so, and how can enterprise companies build AI teams from scratch.

Deep Learning for Credit Risk: Reducing customer churn and credit losses

In fintech, it’s important for companies manage their credit risk because if customers don’t repay their credit, the lender loses money. In this talk, we’ll explore how to prevent credit risk using a neural network model. We’ll discuss not only what worked, but also what didn’t work, and how the building blocks of the full machine learning system. We start with one model and discuss how to layer on more of an ensemble approach to predict risky customers and take action in a way that doesn’t cause them to become even more risky business. We’ll discuss features, a neural network model, evaluation, serving, monitoring, and ideas to improve.

Demystifying Large Language Models

Language models have revolutionized the field of Natural Language Processing (NLP), and large language models in particular have opened up new avenues for research and applications. In this session, we will explore the key features and underlying mechanisms of large language models, and discuss their implications for NLP research, industry, and society. We will begin by introducing the basic building blocks of large language models and discuss the challenges of training and evaluating large language models, as well as their limitations and potential biases. We will then explore some of the key applications of large language models and the impact of large language models on industries such as healthcare, finance, and entertainment, as well as their potential for use in improving accessibility to information and services for marginalized communities. By the end of this session, participants will have a deeper understanding of the opportunities and challenges of large language models, and a broader awareness of their impact on NLP research, industry, and society.

Utilizing big language models to normalize job titles

At Paychex, we used big language models like SBERT to match client inputted job titles to a taxonomy of 800+ job categories provided by the US Bureau of Labor Statistics. Our multi-level architecture combines SBERT with neural network classifiers to achieve high matching accuracy. The SBERT model was fine-tuned in a Siamese network approach to improve its performance. The product will be available to our clients to recommend the best matching BLS codes for their inputted job titles. Normalizing job titles will provide Paychex clients with advanced wage or retention analytics.

Keynote: Six Current Trends in Generative AI and Deep Learning

In this informal but informative presentation, Dr. James McCaffrey – who leads Microsoft Research’s CEO-mandated initiative to transfer deep learning intelligence into all products, services, and supporting systems across the enterprise – will describe six of the latest trends in machine learning and deep neural systems. Today’s hottest trends include the deployment of generative AI systems like GPT-x/ChatGPT, Codex, Copilot, and DALL-E – yet they also include innovative deployments of more classical ML methods. The emphasis will be on information that is practical and can drive revenue, not theoretical ideas filled with math equations and Greek letters. Particular emphasis will be given to explaining the difference between systems that require massive resources or expensive licensing, and systems that can be successfully implemented by organizations with a limited budget.

Keynote: ​Maturing AI Governance and Ethics Practices

Google continues to take a bold and responsible approach to developing and deploying AI through the company’s infrastructure, tools, products, and services. Google brought further AI breakthroughs into the real world through Google Cloud’s launch of the next wave of generative AI across core areas of their business, and new partnerships and programs grounded in Google’s commitment to an open AI ecosystem. At the same time, AI, as a still-emerging technology, poses complexities and risks; and the development and use of AI must address these risks in a structured, transparent, and accountable way. A robust governance structure – and rigorous testing and ethics reviews — is necessary to put responsible AI principles into practice. And with AI regulation coming soon, Jen will share learnings, challenges, and practical tips on how Google is maturing its responsible AI practices, processes, and tools in advance of greater regulatory, global standards, and consumer expectations.

Generative AI: From Basic Concepts to Real-World Applications

In this workshop, participants will get an introduction to generative AI and its concepts and techniques. The workshop will cover different techniques for image, text, and 3D object generation, and so forth. Participants will also learn how prompts can be used to guide and generate output from generative AI models. Real-world applications of generative AI will be discussed, including image and video synthesis, text generation, and data augmentation. Ethical considerations when working with generative AI, including data privacy, bias, and fairness, will also be covered. Hands-on exercises will provide participants with practical experience using generative AI tools and techniques. By the end of the workshop, participants will have a solid understanding of generative AI and how it can be applied in various domains.

Check out the complete agenda

Secure Pre-Event price and save up to $400
— Ends May 5    

We are excited to see you in Las Vegas from June 18 to 22, 2023.

Register now

Machine Learning Week is a multi-conference event that includes: 
PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, 
PAW Climate, and Deep Learning World.