AI KEYNOTES & FEATURED

Tuesday, October 26, 2021

- PDT
KEYNOTE (AI): LivePerson -- How AI Can Make the Future More Human
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Joe Bradley
Joe Bradley
LivePerson, Chief Scientist

While AI has plenty of potential, some of the earliest AI-based consumer experiences gave the technology a less-than-stellar reputation, and rightfully so. Too often, we see AI that provides de-personalized experiences, harms people due to bias, and lacks the human touch that powers good business, let alone good for humankind.

But, when done right, AI has the potential to not only transform the customer experience and enterprise, but to create positive change and make life easier for millions of people.

In this session, Joe Bradley will offer a compelling case for how AI can make the future more human.

MORE HUMAN BUSINESS. As he explores why and how companies must capitalize on today’s unprecedented opportunity to be connected to customers in new ways, Joe  will delve into why companies that focus on empathetic conversational AI experiences will own the future of commerce, leveraging case studies from household name brands. For example, when brides-to-be faced a myriad of pandemic-era wedding challenges - including dresses trapped in closed storefronts, cancelled appointments, and postponed event dates - David’s Bridal leveraged AI-driven messaging to transition customers from in-store associates to fully-online conversations that drove the bridal experience. AI-driven messaging, seamlessly orchestrated by their virtual assistant Zoey, accomplished everything from answering questions and making recommendations to facilitating the buying experience online, and the brand’s e-commerce revenue skyrocketed.

A MORE HUMAN WORLD. Joe will also explore why fighting bias in AI is more critical than ever, arguing that it isn’t enough for AI to help us be smarter, faster and more productive – it also needs to be a force for good in the world. Given AI’s growing role in high-stakes decision-making, companies need to expand their use of tools and technologies capable of fighting AI bias, going further than just standards and talk. Every company will soon have its own conversational AI to create more human connections with their customers, rather than rely on the Alexas, Siris, and Cortanas of the world that exist to keep them within the walls of Big Tech.

Wednesday, October 27, 2021

- PDT
FEATURED TALK: (AI): Responsible AI into Practice - Deliver Trust in Artificial Intelligence Solution
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Sweta Sinha
Sweta Sinha
Ascend Learning, Director of Data Science

AI has been a key driver in innovation in every industry Organizations have ramped up their effort on leveraging AI to gain a competitive advantage. However, AI solution comes with its own challenges and risk, particularly in regulated industries. There have been numerous instances when AI introduced bias. Organizations must use a balanced approach to accelerating the adoption of AI and prioritize AI governance to ensure trust in the AI system. While AI regulation landscape is still evolving, now is the time for organizations to start taking steps to understand and mitigate AI risks. Responsible AI framework provides guidelines around AI governance for building fair, transparent, ethical, and accountable AI solutions. In this session you will learn about how organizations can follow Responsible AI guidelines and operationalize trust in AI solutions by incorporating AI governance throughout the AI/ML life cycle.

- PDT
FEATURED TALK (AI): Scaling Machine Learning Models
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Chen Levkovich
Chen Levkovich
Habana Labs, Product Manager ML Developer experience

Starting with ML tutorials seems easy. But how do you scale your ML models from detecting cats and dogs to a full scale business ML model?

Thursday, October 28, 2021

- PDT
KEYNOTE (AI): John Snow Labs -- State of the Art Natural Language Processing at Scale
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David Talby
David Talby
John Snow Labs, CTO

NLP is a key component in many data science systems that must understand or reason about text. This hands-on tutorial uses the open-source Spark NLP library to explore advanced NLP in Python. Spark NLP provides state-of-the-art accuracy, speed, and scalability for language understanding by delivering production-grade implementations of some of the most recent research in applied deep learning. It's the most widely used NLP library in the enterprise today. You'll edit and extend a set of executable Python notebooks by implementing these common NLP tasks: named entity recognition, sentiment analysis, spell checking and correction, document classification, and multilingual and multi domain support. The discussion of each NLP task includes the latest advances in deep learning used to tackle it, including the prebuilt use of BERT embeddings within Spark NLP, using tuned embeddings, and 'post-BERT' research results like XLNet, ALBERT, and roBERTa. Spark NLP builds on the Apache Spark and TensorFlow ecosystems, and as such it's the only open-source NLP library that can natively scale to use any Spark cluster, as well as take advantage of the latest processors from Intel and Nvidia. You'll run the notebooks locally on your laptop, but we'll explain and show a complete case study and benchmarks on how to scale an NLP pipeline for both training and inference.

- PDT
FEATURED TALK (AI): ML Observability: A Critical Piece in Making Models Work
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Aparna Dhinakaran
Aparna Dhinakaran
Arize AI, Chief Product Officer

In this talk, Aparna Dhinakaran, Founder of Arize AI (Ex-Uber ML), will highlight common model failure modes including model drift, data quality issues, performance degradation, etc. The talk will also surface how ML Observability can address these challenges by monitoring for failures, providing tools to troubleshoot and identify the root cause, as well as playing an important part in the feedback loop to improving models. The talk will highlight best practices and share examples from across the industry.

- PDT
KEYNOTE (AI): Modzy -- Crossing the AI Valley of Death: Deploying and Monitoring Models in Production at Scale
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Seth Clark
Seth Clark
Modzy, Founder & Head of Product

It’s happened again. You built another AI model that will never see the light of day because it won’t make it past the AI “valley of death” – the crossover of model development to model deployment across your enterprise. The handoff between data science and engineering teams is fraught with friction, outstanding questions around governance and accountability, and who is responsible for different parts of the pipeline and process. Even worse? The patchwork approach when building an AI pipeline leaves many organizations open to risks because of a lack of a holistic approach to security and monitoring.Join us to learn about approaches and solutions for configuring a MLOps pipeline that’s right for your organization. You’ll discover why it’s never too early to plan for operationalization of models, regardless of whether your organization has 1, 10, 100, or 1,000 models in production.The discussion will also reveal the merits of an open container specification that allows you to easily package and deploy models in production from everywhere. Finally, new approaches for monitoring model drift and explainability will be revealed that will help manage expectations with business leaders all through a centralized AI software platform called Modzy®.