Conference:  Transform X 2021

Authors: Marty Chavez

2021-10-07

R. Martin (“Marty”) Chavez, Ph.D., is Partner and Vice Chairman of Sixth Street Partners and former CEO and former CFO of Goldman Sachs. widely renowned as a trailblazer and leader who turned the Wall Street trading business into a software business, during his time at Goldman Sachs. Marty joins Scale AI CEO Alexandr Wang in a fireside chat to discuss how machine learning is impacting the world of finance and beyond. Together they discuss the 'digital revolution' of Wall Street and financial services which saw a transformation where traditional finance roles worked directly with programmers to build new tools that unlocked new opportunities. Join this session to hear how Financial Services transformed with AI, and how those lessons can be applied to other industries.

Conference:  Transform X 2021

Authors: David Ha

2021-10-07

Internal mental models, as well as consciousness and the concept of mind modeling, are major themes in neuroscience and psychology. However, we do not understand them well enough to create conscious artificial intelligence. In this talk, David Ha, Research Scientist at Google Brain, explores building "world models" for artificial agents. Such world models construct an abstract representation of the agent's environment that allows it to navigate it. David discusses artificial agents' use of world models and self-attention as a kind of limitation, connecting it in with computational evolution and artificial life ideas and methods. The goal of the presentation is to motivate scientists to create conscious machines by encouraging them to build artificial life that includes an internal mental model.

Conference:  Transform X 2021

Authors: Lt Gen Kirk S. Pierce

2021-10-07

Lt. Gen. Kirk S. Pierce is the Commander, Continental U.S. North American Aerospace Defense Command Region - 1st Air Force. He joins Mark Valentine, Head of Scale Federal to discuss the potential of AI to provide direct assistance to warfighters within the contexts of Information Dominance and Decision Superiority. They explore the operational risks that could slow down the adoption of AI to support broader joint All Domain Command and Control. Delivering artificial intelligence to warfighters is a strategic imperative but there are still many questions that remain unanswered. What will the adoption of AI mean for the United States military? How can military organizations take advantage of this emerging technology, as they look towards current and future national security imperatives? Join this session to hear an example-rich discussion of the opportunities for AI to support national defense.

Conference:  Transform X 2021

Authors: Catherine Williams

2021-10-07

Catherine Williams, Global Head of iQ at Qualtrics , discusses the key things that enterprises should think about, as they develop and grow an AI strategy for maximum business impact. She shares a framework for choosing which kinds of problems organizations should apply AI to, and the steps needed to operationalize AI successfully. Join this session to learn how you can best build an AI strategy for your own organization.

Conference:  Transform X 2021

Authors: Drew Conway, Cassie Kozyrkov, Deepna Devkar, Jaclyn Rice Nelson

2021-10-07

Hosted by Tribe AI. Poor data quality. Inability to access the right talent. Failure to get models into production. When it comes to moving up the AI adoption curve, what's really holding businesses back? In this panel, you'll learn how technical leaders at enterprises like Google, CNN, and TwoSigma think about building higher performing teams and operationalizing machine learning projects to deliver business value in production.

Conference:  Transform X 2021

Authors: David Carmona

2021-10-07

David Carmona, General Manager of AI and Innovation Marketing at Microsoft shares a demo-rich session on Artificial Intelligence (AI) with real-life business applications. He walks through how enterprises can both 'anticipate and innovate' in a fast-paced and challenging business environment, to stay competitive, through AI. Plus, a 4-step framework to building a comprehensive Responsible AI strategy.

Conference:  Transform X 2021

Authors: Chris Hazard

2021-10-07

The presentation discusses the importance of privacy in data synthesis and the use of synthetic data to enhance privacy while unlocking the value of data. It also highlights the challenges and potential risks associated with synthetic data and the need for proper application of privacy techniques.

• Privacy affects behavior and is crucial for building trust and value in a brand
• Synthetic data can be used to unlock the value of data while maintaining privacy
• Proper application of privacy techniques is necessary to avoid potential risks and challenges associated with synthetic data
• Synthetic data can be generated using various techniques such as Bayesian networks and GANs
• Synthetic data sets should be generated with distributions that have the same analytic outcome as the original data
• Synthetic data sets should be generated with caution to avoid leaking privacy
• Synthetic data sets can be generated multiple times with different levels of fidelity as long as privacy is maintained
• Validation of privacy and value is necessary when using synthetic data

Conference:  Transform X 2021

Authors: Selcuk Kopru

2021-10-07

In this session, Dr. Kopru explores why AI-driven similarity clustering, indexing, and search are essential to creating the user experiences needed for today's online marketplaces. Some of these vital AI-driven experiences include search and ranking by text, metadata or images, product review filtering and summarization, fraud detection, and member-to-member communications. He discusses how AI can be applied to text and image similarity searches - or event multi-modal searches, for example, measuring the similarity of a product image and its listing title. Dr. Kopru dives into how eBay uses transformer models to drive similarity capabilities. What are the advanced use-cases enabled by powerful similarity searches? How do you similarity search across billions of items? How can you use smaller models for individual product categories without significantly losing accuracy? Join this session to learn how to apply off-the-shelf transformer models to drive large-scale similarity searches.

Conference:  Transform X 2021

Authors: Jeff Wilke

2021-10-07

Before Jeff Wilke became Chairman and Co-Founder of Re:Build Manufacturing, he led Amazon’s global retail business, deploying lean manufacturing techniques to create the infrastructure and technology that became Amazon Prime. He now integrates new technologies like AI to help rebuild and transform manufacturing. Jeff joins Scale AI CEO Alexandr Wang in a fireside chat to discuss how AI can accelerate the transformation of enterprises across industries like manufacturing, retail, and others. Jeff joined Amazon in 1999 to increase productivity and excellence. At the time Amazon's revenue was $2 billion a year. By the time he left, Amazon’s revenue had skyrocketed to over $1 billion a day. During his time leading the world's largest retailer, Jeff experienced first-hand the transformational impact of AI, from internal operations to customer experience. But as AI continues to impact and transform every industry, how should enterprises think about prioritizing their AI use-cases between internal process optimizations or customer-facing experiences? What challenges should business leaders be cognizant of?

Conference:  Transform X 2021

Authors: Jack Guo, Anitha Vijayakumar, Vishnu Rachakonda, Oleg Avdeëv

2021-10-07

Hosted by MLOps Community. Panelist to be announced soon. Demetrios Brinkmann, founder of MLOPs.community leads a panel managing the increasing compute requirements of AI models, whilst striking the right balance between flexibility for experimentation and stability in production. As enterprises collect more training data, and in many cases label it with Scale AI, they face the challenge of their models growing in both size and compute complexity. Join this session to learn how companies can develop robust and maintainable pipelines to ensure that ML experimentation remains possible, despite increasing model sizes and longer training times. This session will also cover compute for lifecycle phases from experimentation to scaling (with Metaflow, TFX, etc.) pipelines that are ready to deploy to production, including via microservices.