Alexandr Wang

Opening Remarks With Alex Wang, CEO & Founder of Scale AI (Day 1)

Ilya Sutskever is Co-founder and Chief Scientist of OpenAI, which aims to build artificial general intelligence (AGI) that benefits all of humanity. He is one of the world’s most respected, and cited, experts in machine learning. Ilya leads research at OpenAI and is one of the architects behind GPT models. He joins Scale AI CEO Alexandr Wang in a fireside chat to discuss recent developments in AI and what the future holds for language models and beyond.

Ilya Sutskever

What's Next for AI Systems & Language Models With Ilya Sutskever of OpenAI

Dr. Fei-Fei-Li,  Sequoia Professor of Computer Science at Stanford University and Denning Co-Director of the Stanford Institute for Human-Centered AI (HAI) explores the evolutionary origins of vision and how it is the 'cornerstone for intelligence', for both humans and machines alike. 

Dr. Li shares how vision is critical for first perceiving the physical world, and then interacting with it. She highlights recent advances in AI research which helps machines perceive the environment around them and then engage with it, to perform both short- and long-horizon tasks. See how BEHAVIOR, a benchmark of everyday activities, can help robots learn to perform increasingly complex tasks, by composing smaller actions to achieve more elaborate goals, for example, clearing up a table or putting away toys.

Fei-Fei Li

From Seeing to Doing: Understanding & Interacting With The Real World With Fei-Fei Li

Dmitri Dolgov is the co-CEO of Waymo, an autonomous driving technology company. He is also one of the founders of the Google self-driving car project, which began in 2009 and became Waymo in 2016. He joins Scale AI CEO Alexandr Wang in a fireside chat to discuss the challenges of data-driven artificial intelligence in enabling safe and effective autonomous vehicles.

Dmitri Dolgov

Data-Driven AI for Autonomous Vehicles With Dmitri Dolgov of Waymo

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?

Jeff Wilke

Jeff Wilke on Accelerating AI Adoption: From Operations to Customer Experience

Dr. Andrew Ng, Founder of DeepLearning.AI and Founder and CEO of Landing AI shares real-world examples to show how switching from a model-centric to a data-centric AI development approach, by 'engineering the data', helps improve the performance of machine learning models. He shares his views on key AI best practices for those considering data-centric model development to unlock greater AI performance and efficiencies.

Andrew Ng 

Founder of DeepLearning.AI, Founder and CEO

The Data-Centric AI Approach With Andrew Ng

Jesse Levinson co-founded Zoox in 2014 and is the company's Chief Technical Officer (CTO). He joins Scale AI’s Head of Nucleus, Russell Kaplan, for a fireside chat to discuss the challenges of bringing a completely autonomous vehicle stack (vehicle platform + sensors) from the lab to real-world streets for autonomous ride-sharing vehicles.

Together Jesse and Russell explore the biggest obstacles that need to be solved when making robot taxis. How do you train a model to generalize for the unknown? How can a model respond to new challenges safely or plan a new response in real-time? What are the limitations in perception for different sensor types? As you train a model to learn edge cases and scenarios, how do you prevent regression in other areas? Jesse shares how Zoox uses simulation to take advantage of real-world data to perform realistic testing at scale. Join this session to hear Zoox's insights on taking an entire end-to-end technology stack from experimentation to reality. 

Jesse Levinson

Russell Kaplan

The Challenges of Full-Stack AV Development With Jesse Levinson of Zoox

If you need to take a break from sessions, head over to the speed networking area to connect via 1:1 video calls with thousands of fellow practitioners, researchers, and business leaders across industries. Explore our virtual expo to connect with conference partners and learn about new solutions. 

👋 Networking + Virtual Expo

Chu-Cheng Hsieh, Chief Data Officer at Etsy, discusses how enterprises should build a data management framework that helps them translates data into revenue-generating products. He explores best practices for each stage of the entire data lifecycle, from data capture to business insights, to enable positive AI-driven outcomes. Join this session to learn how you should be thinking about getting value from data, in your own organization.

Chu-Cheng Hsieh

Rethinking The Framework for Data With Chu-Cheng Hsieh of Etsy

Clement Delangue, CEO of Hugging Face insightfully describes through the evolution of Transform models. These foundational models have generated unprecedented excitement in academia and industry, enabling huge advances in NLP like next sentence prediction, question answering, and text summarization. He shares why it's important to ensure they are used in an ethical and transparent manner. With that in mind, he discusses the work being done to ensure AI fairness in foundational models.

Clement Delangue

Are Transformers Becoming the Most Impactful Tech of the Decade?

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.

Catherine Williams

How to Build an Effective AI Strategy for Business

In this session, Stephen Balaban, CEO of Lambda, shares a playbook for standing up machine learning infrastructure. This session is intended for any organization that has to scale up its infrastructure to support growing teams of Machine Learning (ML) practitioners.

Stephen describes how large ML models are often built with on-premise infrastructure. He explores the pros and cons of this approach and how the workstations, servers, and other related resources could be scaled up to support larger workloads or numbers of users.

How do you scale from a single workstation to a number of shared or dedicated servers for each ML practitioner? How can you use a single software stack across laptops, servers, clusters, and the cloud? What are the network, storage, and power considerations of each step in that journey?

Join this session to hear some best practices for scaling up your machine learning platform, to serve the growing needs of your organization.

Stephen Balaban

A Machine Learning Infrastructure Playbook with Lambda

Great machine learning teams iterate quickly. Learn from experts at Aurora, GM, KeepTruckin, and Motional about the tools and techniques they use to accelerate their ML development at every stage—from building datasets, to trying new experiments, validating models, deploying ML in production, and improving model performance.

Siva Gurumurthy

Dr. Yanbing Li

Sammy Omari 

Gonen Barkan

Panel: Tools to Accelerate ML Development & Rate of Innovation

Dataset debugging, versioning, and augmentation is essential to building successful ML pipelines and models. Even with the robust training, optimization of AI models deployed with well defined CI/CD operational pipelines, high-quality data remains an essential and critical part of the whole AI development process. Learn how different organizations collaborate to improve their datasets and debug errors in their data. See how doing so helps then unlock higher accuracies as well as other key benefits and efficiencies. 

Elliot Branson 

Chun Jiang

Adrian Macneil

Ville Tuulos

Alessya Visnjic

Panel: Building a Resilient MLOps Strategy Through Dataset Management

Melisa Tokmak (GM, Scale AI) moderates a discussion with Nima Ghamsari (CEO, Blend), Jag Duggal (CPO, Nubank), and Jean Dennis Greze (CTO, Plaid). Together, they discuss the evolution of financial services as companies race to put customer experience at the center of their focus while trying to reduce risk in the age of Machine Learning.  

Melisa Tokmak 

Jag Duggal 

Nima Ghamsari

Jean-Denis Greze

Panel: Race to Better Customer Experience: Value of Data and ML in Fintech

Engineering leader, Aerin Kim, will present a brief summary of ongoing ML research at Scale AI, then deep dive into ML linters and other mechanisms that reliably enhance labeler productivity when labeling complex images and scenes.

Aerin will showcase complex scenarios like 3D LiDAR bounding box classification as well as 2D semantic segmentation. When assisted by an ML model, labelers typically generate higher quality data for our customers than they might consistently do on their own.

Aerin Kim

How Scale Uses ML to Improve Label Quality & Labeler Productivity

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.

David Ha

Building Better Reinforcement Learning With World Models & Self-Attention Methods

Justin Basilico, Director of Machine Learning and Recommender Systems at Netflix, where he leads an applied research team that creates algorithms used to personalize the Netflix homepage through machine learning, recommender systems, and large-scale software engineering. In this session, he discusses recent trends in personalization and the challenges of applying machine learning to recommendation systems to create better user experiences.

Justin Basilico

Director, ML & Recommendations Engineering

Trends in Recommendation & Personalization at Netflix 

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.

Selcuk Kopru

Building Similarity into an AI-Driven Marketplace with Dr. Selcuk Kopru of eBay

Kevin Scott is executive vice president of Technology & Research, and the chief technology officer of Microsoft. He is an innovative leader and is passionate about creating technologies that benefit everyone. He joins Scale AI CEO Alexandr Wang in a fireside chat to discuss the recent advances in AI that he considers the most impactful, and how AI can be democratized for the benefit of everyone in society, enabling a new wave of technology innovation. Together they explore a vision of an AI-enabled society where AI is accessible to all - and walk through what that means for the way we live and work.

Kevin Scott

Democratizing & Accelerating the Future of AI With Kevin Scott

In this keynote session, Raquel Urtasun, Founder and CEO of Waabi, offers a new approach to developing AI-based systems for autonomous vehicles and explains why it is critical in enabling self-driving autonomy at scale.

Raquel explores why autonomous self-driving is a complex problem to solve for AI engineers. She shares an in-depth explanation of the technical issues that must be solved, which commonly lead to scalability and cost challenges for today's AV manufacturers. She critiques current AI approaches and offers a different approach that is better able to learn and scale. 

How does an AI-first approach improve model interpretability and verification? How can you perform complex reasoning that is generalized across different sensor configurations and geographies? How does this AI-first approach reduce overall cost and complexity?

Join this keynote to learn how an AI-first approach differs dramatically from current industry methodologies and positively impacts all parts of the AV software stack, from mapping and perception to prediction, planning, and vehicular control. 

Raquel Urtasun 

An AI-First Approach to Enable Self-Driving at Scale With Raquel Urtasun of Waabi

Jiajun Zhu is the CEO and Co-Founder of Nuro. Prior to founding Nuro, Jiajun was a principal software engineer of Google and one of the founding team members of the company’s self-driving car project (now known as Waymo). He joins Scale AI CEO, Alexandr Wang, in a fireside chat to discuss the technical challenges of building autonomous delivery vehicles at scale. 

Together Jaijun and Alexandr explore why it's vital for society to automate the nearly 100 billion personal shopping trips we make each year in the US. Jaijun shares the key reasons autonomous goods delivery represents 'one of the hardest engineering challenges in history'. In particular, what are the challenges in managing data to optimize the learning process? How do you optimize for the 'long tail' of edge cases rather than solving the most common driving scenarios? How do you build a sensor strategy in conjunction with an AI strategy? Join this session to hear key lessons learned from the first autonomous vehicle company licensed to operate a driverless delivery service in the state of California.

Jiajun Zhu

Autonomous Delivery: Overcoming the Technical Challenges with Jiajun Zhu of Nuro

Marc Segura is the Managing Director of ABB's Consumer Segments & Service Robotics (CSSR) Business Line. He joins Scale AI CTO Brad Porter in a fireside chat to discuss the current state of AI in robotics and how AI can be used to advance the field to create robots that are more advanced, efficient, and safer.

Marc Segura

Group Senior Vice President Managing Director Consumer Segments & Service Robotics

Brad Porter

Formerly, VP of Robotics and Distinguished Engineer

Advances in AI Robotics With Marc Segura of ABB

Bret Taylor, President and Chief Operating Officer of Salesforce and Former CTO of Facebook, leads Salesforce’s global product vision, engineering, security, marketing and communications. He joins Scale AI CEO Alexandr Wang in a fireside chat, where the two discuss the adoption of AI at Salesforce - and what he thinks enterprises should consider as they begin the same journey.

Every enterprise has to develop an AI strategy, whether they are a garage developer, startup or multi-national enterprise like Salesforce. Bret shares practical advice on building an AI strategy and how to go beyond the hype to achieve real business outcomes for your business.

Bret Taylor

Enterprise AI Strategy: Bret Taylor on Lessons Learned at Salesforce

Daphne Koller, Founder, and CEO of insitro, shares how recent advances in machine learning and medicine enables us to 'rethink' drug discovery and reverse a long-standing trend of declining drug research effectiveness. She also shares how AI can make high-quality clinical predictions that are as good as human beings, if not better, leading to better patient outcomes.

Daphne Koller

Transforming Drug Discovery Using Machine Learning

Closing remarks by Brad Porter, Chief Technology Officer at Scale AI.

Closing Remarks - Day 1

Opening remarks by Alex Wang, Scale AI CEO and Founder.

Opening Remarks With Alex Wang, CEO & Founder of Scale AI (Day 2)

Eric Schmidt, Co-Founder of Schmidt Futures and Former CEO of Google, is an accomplished technologist, entrepreneur, and philanthropist. He joins Scale AI CEO Alexandr Wang in a fireside chat to discuss the intersection of AI with a wide range of topics including healthcare, national defense and how we can be better prepared to respond to the challenges of the future. Join this session to hear what a 'new world order' could look like with AI, and what a new 'industrial revolution' powered by AI could look like.

Eric Schmidt

A Global Perspective on AI With Eric Schmidt

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.

R. Martin Chavez

The Impact of AI in Financial Services & Beyond With R. Martin Chavez

In this keynote, Drago Anguelov, Head of Research at Waymo, discusses Waymo’s progress towards building a scalable technology stack for autonomous driving vehicles. With more than a decade of experience in solving autonomous driving, Waymo is now operating the world’s first commercial ride-hailing service Waymo One in Phoenix and has recently welcomed its first riders in San Francisco by kicking off the Trusted Tester program.

Drago will give an overview of the key autonomous driving challenges and describe how Waymo is leveraging the cutting edge ML systems across the stack to handle them. He will also outline promising avenues to keep expanding the scope of ML in the stack in the future and showcase some of Waymo’s work in the space. 

Dragomir Anguelov

Distinguished Scientist and Head of Research

ML at Waymo: Building a Scalable Autonomous Driving Stack with Drago Anguelov

Oskar Stal, VP of Personalization at Spotify, shares how Spotify ensures each of their 365 million listeners has a personal and unique experience as they each explore and enjoy the music they love.

In this keynote, Oskar shares Spotify's evolution in machine learning from a single recommendation feature that engineers worked on in their spare time, to the wide-scale deployment of multiple personalized content recommendations, for each Spotify user.  He shares how Spotify uses numerous sources of data to create a personal experience. Oskar also discusses how Spotify uses reinforcement learning to maximize short- and long-term recommendation objectives to build long-term user engagement.

What makes a good content recommendation? How should you optimize recommendations to build a lifetime of loyal membership? How can you use simulation to train better recommendation models? 

Join this session to learn Spotify uses AI to build personalized listening experiences. 

Oskar Stål

Creating Personalized Listening Experiences with Spotify

Hussein Mehanna, Head of Artificial Intelligence at Cruise, joins Scale AI CTO, Brad Porter, for a fireside chat to discuss how Cruise has approached the challenge of building safe autonomous vehicles that are ready for the real world.

Together they explore what it means to be 'AI-native' as opposed to 'AI-first' and how one can organize broad multi-disciplinary teams to build AI applications in a continuously iterative way.

In this discussion, Hussein explores the key questions that need to be answered when building safe autonomous vehicles. When creating AI models, how do you evaluate progress and ensure it's the correct type of progress? How do you design for safety when AV vehicles get safer, and potential learning examples become rarer? Is it possible to develop an AI model that can safely handle scenarios it has never seen before? Join this session to hear the key experiences and learnings from one of the leaders of autonomous vehicle development.

Hussein Mehanna

Designing for Autonomous Vehicles at Scale with Hussein Mehanna of Cruise

Steve Escaravage, Senior Vice President at Booz Allen Hamilton and Mark Valentine, Head of Federal at Scale AI, discuss how the Artificial Intelligence (AI) and Machine Learning (ML) community can help federal customers build, deploy and operationalize realistic AI/ML capabilities that bring tangible and measurable wins, within the strict compliance and governance limitations of federal IT environments. 

Steve Escaravage

Senior Vice President, Booz Allen Analytics and Artificial Intelligence Business

Mark Valentine

Formerly, General Manager, National Security

Developing Realistic Approaches to Deploying ML in Federal Environments

Richard Socher is the fifth most-cited researcher in Natural Language Processing (NLP) and the CEO of you.com, an ad-free, privacy-preserving search engine. Prior, he was the Chief Scientist (EVP) at Salesforce. He joins Scale AI CEO Alexandr Wang in a fireside chat to discuss how leaders should apply research to production applications. Richard discusses the shrinking gap between pure research and the building of revenue-generating products, where enterprises can avoid the common pitfalls of AI adoption (regardless of industry sector), and the areas of research he is most excited about.  He discusses how the changing shape of the internet that led him to create You.com, a new type of intelligent and privacy-preserving internet search engine.

Richard Socher

Building the Next Generation of NLP Applications With Richard Socher

Former House Armed Services Committee Chairman Representative Mac Thornberry will discuss the national security implications of adopting AI and ML capabilities with Scale’s Head of Federal, Mark Valentine. Mark and Mac will outline potential reforms within the national security community to accelerate adoption.

Mac Thornberry 

U.S. House of Representatives, 1995 - 2021

Building a Framework to Accelerate the Adoption of AI for National Security

Soumith Chintala Creator of PyTorch, AI Researcher at Facebook AI Research (FAIR) discusses how open-source best practices can be used to run a successful development project with a collaborative community. He shares how these practices helped make PyTorch one of the most popular and widely used machine learning frameworks today. Join this session to learn how you can use tools, processes, and communities to run a highly effective open source project.

Soumith Chintala

Growing With Open Source: From Torch to PyTorch With Soumith Chintala

Navrina Singh is the Founder & CEO of Credo AI which helps organizations build Artificial Intelligence with higher ethical standards. She discusses why AI governance is critical to the scaling of AI across and highlights the risks of not governing AI effectively. She shares how organizations can adopt AI governance practices effectively and continuously to build trust with internal and external stakeholders.

Navrina Singh 

The Governance of Artificial Intelligence

In this breakout session, Dr. Daniela Rus, Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, discusses the societal benefits and risks of widespread AI adoption and explores how those risks can be mitigated for the benefit of all.

Dr. Rus shares a snapshot of an AI-enabled future and how we can expect AI to improve our daily lives. She draws on real-life examples from today's research labs to explore several recent advances in AI and their future benefits for society. Dr. Rus also highlights some of the significant technical and societal challenges that remain with ubiquitous AI adoption.

What are the societal challenges to broader AI adoption? How can we address these challenges to ensure that AI is fair and equitable for everyone? How can recent advances in AI, like Neural Circuit Policies, improve the limitations of existing AI systems?

Join this session to hear some of the advanced future use-cases that could be delivered through the AI of tomorrow and what we should consider now to ensure that AI remains safe, fair, and valuable for all. 

Daniela Rus

Director, Computer Science and AI Laboratory (CSAIL); Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science; and Deputy Dean of Research, Schwarzman College of Computing

AI and Humanity: Opportunities and Perils with Daniela Rus of MIT CSAIL

Chris Hazard, CTO and co-founder at Diveplane Cooperation, discusses some of the different techniques that can be used to protect privacy including encryption, differential privacy and privacy budgets. He explores how enterprises should adopt a thoughtful privacy strategy that balances the risks and rewards of these privacy-enhancing technologies. Join this session to learn how you can develop a privacy strategy that considers the business value, liability and user privacy to unlock key use-cases. 

Dr. Chris Hazard

How to Use Privacy to Prevent Adverse Customer Outcomes

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.

Jaclyn Rice Nelson 

Cassie Kozyrkov 

Drew Conway

Deepna Devkar

Vice President, Machine Learning & Data Platform

Panel: Why Do Businesses Fail at Machine Learning?

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. 


Demetrios Brinkmann

Anitha Vijayakumar

Jack Guo 

Vishnu Rachakonda

Oleg Avdeëv

Panel: Managing Compute With MLOps to Handle Growing Datasets & Model Sizes

Hosted by The World Economic Forum.

With the continued acceleration and adoption of artificial intelligence, businesses and business leaders are facing more challenges to ensure AI systems are trustworthy, are being developed ethically and equitably while respecting individuals’ privacy. In this expert panel, we discuss policy frameworks to help realize the benefits of AI while mitigating the risks and how tech companies can and should partner with the public sector. 

Kay Firth-Butterfield

Head of Artificial Intelligence and a member of the Executive Committee

Beena Ammanath

Caroline Lair

Karen Silverman

Panel: Digital Transformation with Responsible AI

We are very familiar with the power of recommendation engines in eCommerce. The most dominant eCommerce companies in the world attribute much of their overall revenue to upselling and cross-selling powered by those recommendations. However, other AI use cases in personalization, logistics, and analytics are also transforming other parts of the eCommerce experience.

In this panel, we will discuss the current state of AI in eCommerce. The panel will explore the upcoming use cases primed to disrupt the industry and share their experiences on how they have enabled their organizations to be AI-ready.

Ethan Choi 

Minesh Shah

Kady Srinivasan

Greg Bowen

Panel: The State of AI in eCommerce

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.

David Carmona

General Manager, Artificial Intelligence & Innovation

Driving Competitive Edge with Responsible AI Innovation

Ya Xu, VP of Engineering and Head of Data Science at LinkedIn discusses how LinkedIn is applying responsible AI to create economic opportunities for every one of the nearly 800 million members of their platform, across 200 countries worldwide. She shares how AI can be most impactful in creating opportunities and focuses on best practices in fairness and privacy to build a better and more equitable user experience.


Ya Xu

A Responsible Approach to Creating Global Economic Opportunities With AI

Dr. Jonathan Laserson, Head of AI Research at Datagen Technologies, is an expert in the field of photorealistic synthetic images. He shares how Nural Radience Fields (NeRF) can be used to generate a nearly infinite number of synthetic assets to train AI models. Dr. Lasserson also explains how synthetic objects can be represented in a latent space where features can be perturbed to modify the shape and texture of each asset. Join this session to learn how NeRF can accelerate the curation of data for your computer vision use cases. 

Dr. Jonathan Laserson

Embedding Synthetic Assets to Train AI Models

Advancement in the robotics field has been slow going due to the costs and complications of innovating on a large scale. Progress on perception and intelligence has brought the industry to the verge of real application breakthroughs. Danny Lange, SVP of Artificial Intelligence and Machine Learning at Unity, will demonstrate how synthetic data is now helping robots learn rather than be programmed and the potential this has for advancing the industry. Simulation technology is highly effective and advantageous when testing applications in situations that are dangerous, expensive, or rare. Validating applications in simulation before deploying to a robot shortens iteration time by revealing potential issues early. In this session, you will see a powerful example of a system that learns instead of being programmed, and as it learns from synthetic data, it is able to capture much more nuanced patterns than any programmer ever could. Layering technologies together is making the future vision of what is possible a reality. Discover how AI is now proving the efficiencies possible in training robots.

Dr. Danny Lange 

Senior Vice President of Artificial Intelligence and Machine Learning

AI + Synthetic Data = Smarter Robots

Online commerce marketplaces, from groceries to furniture to real-estate and everything in between, are seeking to leverage artificial intelligence and machine learning to enhance buyer and seller experiences. In this panel, learn from executives with experience at Wayfair, Amazon, Flipkart, Flexe, Shipium, and Redfin about how they approach their AI/ML development to improve the customer experiences on their platforms.

Aatish Nayak

Dave Glick

Akshat Kaul

Dr. Ganapathy Krishnan

Jason Murray

Jim Miller

Panel: Enhancing Online Commerce with AI

Venture capital (VC) investors have dramatically increased the pace of dealmaking in the field of AI and ML. In 2020, over 1600 rounds worth over $27b were closed by US-based startups in this space, and 2021 is on track to set an even higher record. 

Navigating this landscape can be difficult for founders. In addition to the operational aspects—recruiting a team, building technology, working with prospects and customers, finding product-market fit—founders also face fundraising-related decisions with implications for the years to come. What are investors looking for? Which ones have experience with AI startups? What should be expected post-investment? 

In this panel, top VC investors in the AI and ML space will share how they evaluate startups, give advice to founders raising capital, and explain best ways to leverage your existing (and future) investors. 

Dean Shu

Caryn Marooney

Daniel Levine

Anu Hariharan

Panel: The VC Landscape for AI Startups & Advice for Founders

Join Nicol Turner-Lee, Senior Fellow at The Brookings Institution leads a panel discussion with Safia Noble Associate Professor at UCLA, Aylin Caliskan Assistant Professor at University of Washington and Mark MacCarthy  Senior Fellow and Adjunct Professor at Georgetown University.

AI enables an incredibly broad set of new use cases that are moving businesses forward. However, there have been several high-profile companies that have elected to discontinue investing in AI use cases that are especially vulnerable to bias. Recruiting, criminal justice, and facial recognition are just some examples where relying on existing training data can amplify today's prevalent inequalities. While AI systems can be game-changing, how should we think about managing the bias within AI?

Join this panel discussion to hear leading researchers dissect the impact of AI bias, discuss approaches to operationalize fairness and explore how the roles of government and private industry can support each other, when tackling bias.

Nicol Turner-Lee, Ph.D.

Director, The Center for Technology Innovation (CTI) & Senior Fellow, Governance Studies

Mark MacCarthy

Nonresident Senior Fellow in Governance Studies at the Center for Technology Innovation

Aylin Caliskan

Assistant Professor in the Information School

Safiya Noble

Associate Professor of Gender Studies and African American Studies 

Panel: A Discussion on the Consequences of AI Bias

Scale’s Head of Federal, Mark Valentine, will explore AI and ML applications for DoD and the IC with Lockheed Martin’s Mike Harasimowicz and Rachael Martin from the National Geospatial-Intelligence Agency. The panel will discuss the problem sets the Government seeks to use AI/ML to solve,  review the current state of government use of AI/ML, set a vision for the future, and examine the path to achieve it. 

TransformX 2021 Sessions