July 15, 2020


Presented by 

Technology and Automation Summit

One of the key applications of AI is to automate relatively less complex processes thereby making organizations more efficient and freeing up humans for more complex tasks. At the VB Transform Technology & Automation Summit, we discuss various topics such as AI-first application development, AI-led process optimization, identifying the right data to power AI and create business impact, the importance of hyper-personalization in AI, putting together the right team and getting pan-organization buy in to effectively implement AI, the major trends around AutoML, RPA and key algorithm and modeling trends such as transfer learning, reinforcement learning, synthetic data and more.

In implementing these AI technologies, organizations need to also focus on some of the big picture concerns around explainability, trust, accuracy, human eccentricity, and eliminating biases such as gender, race, etc.

Hear from industry leaders and luminaries who will talk about their journeys and learnings in implementing these technologies, how they unlocked value/ROI from them, and their thoughts about what the future holds.

DAY 1 Agenda

7:30 AM -

9:00 AM PT

Women in AI Breakfast | Presented by Capital One and Intel (Invitation only)

How Women are advancing AI and leading the trend of AI fairness, ethics & human-centered AI

When it comes to applied AI, women leaders and practitioners are generally leading the thinking in areas of empathy, fairness, ethics, and human centricity. AI models and algorithms need to learn from both men and women to be truly balanced and fair and this can only happen with a higher percentage of women in the AI workforce. Join us for this 'Women in AI' breakfast that includes a panel discussion where leading stalwarts from the industry come together to discuss and debate Women's role in AI.


Opening Remarks: Carla Saavedra Kochalski, Director of Conversational AI & Messaging Products, Capital One


Kay Firth-Butterfield, Head of AI and Machine Learning and Member of the Executive Committee, World Economic Forum

Dr. Timnit Gebru, Co-lead of Ethical AI Research Team, Google Brain

Francesca Rossi, IBM Fellow and AI Ethics Global Leader, IBM Research 

Moderated by Jaime Fitzgibbon, Founder & CEO, Insights


Closing Remarks: Huma Abidi, Senior Director of AI Products, Intel


July 15th

9:00 AM –

9:15 AM PT

Main Stage

Welcome from Emcee

Matt Marshall, CEOVentureBeat, Opening Remarks,
Kurt Muehmel, 
Chief Customer OfficerDataiku, Summit Chair Opening Remarks

9:15 AM –

9:40 AM PT

Main Stage

FIRESIDE CHAT From mobile-first to AI-first: How Intuit is adopting an AI-first approach in app development


With the move to mobile, we saw a shift in not only how apps served mobile users, but even how software was created to be mobile-first. Now with AI technologies becoming more readily available, Intuit CTO Marianna Tessel believes that AI will be the next major movement to change how apps are created. We’ll shift from simply adding AI functionality into existing user experiences, to designing entirely new apps using new tools with AI at the core.


Marianna Tessel, Chief Technology OfficerIntuit

In conversation with Jana Eggers, CEO, Nara Logics

9:40 AM –

10:05 AM PT 

Main Stage

FIRESIDE CHAT Banking on AI: How Chase invested early and big in AI & ML to enhance customer experiences

Chase bank was an early adopter of AI to enhance and optimize customer experiences, such as customer-centric hyper-personalization, faster response times, and proactive fraud monitoring. Because of its vast and diverse customer base, and the breadth of its financial services businesses, when Chase thinks about enhancing the customer experience, it’s an initiative tackled on numerous dimensions. Hear from Chase’s Chief Data & Analytics Officer, Sandra Nudelman, about how Chase used powerful technology levers including advanced analytics, machine learning, and artificial intelligence to enhance customer experiences.

Sandra Nudelman, Chief Data & Analytics OfficerChase

In conversation with Rajeev Chand, Partner and Head of ResearchWing Venture Capital

10:05 AM – 10:30 AM PT

Main Stage

FIRESIDE CHAT From raw data to business impact: Best practices on how organizations can put their data to work in building human-centric AI models

Companies today are generating insights from AI applications, though most aren’t realizing their full potential. Inefficient processes, lack of model flexibility, and outdated analysis are among the things hampering product design.

Join the Chief Customer Officer of Dataiku, one of the leading platforms used by companies to scale AI projects, who will explain how organizations need to embrace the following approaches for best-practice AI:

  • Speed across the organization via seamless processes, communication, etc. (remove blockers to go fast, otherwise you’ll never get there)

  • Maintaining optionality (stay flexible as the technology landscape evolves)

  • Excellent governance (so you can bring more people in and operate with confidence, and in line with your values)

  • The proper team: size, drive and abilities (engaged collaboration across skill sets and the desire to keep learning new techniques)

  • Constant evolution (never rely solely on the past; COVID has rendered most historical data/models useless)

Kurt Muehmel, Chief Customer OfficerDataiku

In conversation with Seth Colaner, Editorial Director, VentureBeat

10:30 AM –

10:45 AM PT

Virtual Networking & Break  Stand up, stretch, grab a coffee or tea and connect with speakers and attendees virtually 

10:30 AM –

11:15 AM PT

(Invite Only)

Executive Forum Roundtable  How AI & ML can help make Healthcare more efficient, reliable & affordable.


One of the most significant ways in which AI is contributing to humankind is in the field of healthcare. AI can contribute to better and more affordable health insurance coverage and better outcomes for all constituents, it can accelerate drug and vaccine development, making it safer, cheaper and more reliable, it can empower more accurate diagnostics, it can help perform medical procedures through the use of intelligent robotics, it can make the prescription filling process more efficient, and up skill pharmacies from mere drug dispensation centers to first ports of call for managing patient health and much more. Join industry leaders in discussing the latest trends and most significant applications of AI & ML in healthcare.

Moderated by Emil Protalinski, Executive Editor, VentureBeat

Chair Speakers:

Renee Yao, Global Healthcare AI Startups Lead, NVIDIA

10:45 AM –

11:10 AM PT

Main Stage

FIRESIDE CHAT Intelligent, Actionable Insights from Al; How Walmart is implementing AI & ML algorithms to make shopping easier 

Today’s consumers are looking beyond low prices to find the best shopping experience. And Walmart, serving more than 265 million customers each week uses AI technologies to provide that best experience, by managing its supply chain, distribution, and in-store operations.

In this session, Fiona Tan will showcase how Walmart implemented AI at scale, and discuss some of the lessons and challenges of doing this. She’ll explain the advantages, including improved customer experience, streamlined operations, and empowered associates who can make better decisions and provide even better customer service.


Tan will discuss how Walmart’s AI process has helped in numerous cases It has helped  75,000 Walmart associates more efficiently pick online grocery orders by optimizing the picker’s route through the store. If an item is out of stock, deep learning technology helps associates quickly make smart product substitutions to maintain high customer satisfaction and prevent revenue loss. To ensure that product availability, Walmart employs shelf-scanning robots that alert associates via their In-Stock Assistant app when an item needs restocking. It also feeds data back into forecasting, procurement, and other systems such as the FAST Unloader robot, which rapidly sorts products as they come off the truck and expedites products that need to be restocked., Tan will discuss how Walmart connected several ML-enhanced systems, including Global Integrated Fulfillment (picking), capacity and slot management (scheduling), and Resource Optimization and Vehicle Routing (delivery), to create a new Express Delivery service. 

Fiona Tan, Senior Vice PresidentWalmart U.S. Technology 

In conversation with Saurabh Gupta, Chief Research Officer, HFS Research

11:10 AM –

11:35 AM PT

Main Stage

FIRESIDE CHAT ML/AI lessons in finance, and what others can learn from them

AI and ML are helping traders accurately determine the rational price for a complex derivative, helping loan underwriters by estimating the probability of default, and generally helping to increase revenue, lower costs, and improve customer satisfaction in measurable ways. While everyone in financial services agrees that AI has tremendous applications, there are relatively few that have implemented AI effectively and derived significant ROI. Charles Elkan, the head of ML at Goldman Sachs, talks about examples and applications of AI & ML that are showing big promise in delivering ROI. He will offer a perspective on the 'how to' process to design and deploy an AI application successfully. He will talk about how an enterprise needs to align business, technology, and organization to choose the right AI use case and implement it successfully.

Charles Elkan, Global Head of MLGoldman Sachs

In conversation with Rajeev Chand, Partner and Head of ResearchWing Venture Capital

11:35 AM – 12:00 PM PT

Main Stage

FIRESIDE CHAT How Covid-19 is helping constituents re-imagine Healthcare using AI

The COVID-19 pandemic has accelerated many trends that have long been apparent in the healthcare ecosystem. Given the pace of innovation over the past several months, we are seeing a greater potential to reshape how people access and receive care across the continuum. This fireside chat will discuss how pragmatic, applied AI is impacting some of the biggest trends around 1) health care and social services, 2) technology sectors and access, 3) lab/pharma and care coordination, and 4) personalized preferences and primary care delivery. 

Rich Roth, SVP, Chief Strategic Innovation OfficerCommonSpirit Health

In conversation with Seth Colaner, Editorial Director, VentureBeat

12:00 PM – 12:25 PM PT

Main Stage

FIRESIDE CHAT How Visa harnessed data and AI to prevent $25B in annual fraud

Each time you tap your phone at the grocery store or click the “buy” button online with your Visa card, Visa’s AI analyzes hundreds of unique attributes about your purchase to predict whether it’s you, the genuine cardholder, or a fraudster with ill-begotten access to your credentials. At the same time, Visa needs to minimize “false positives,” which can frustrate cardholders by wrongly declining their transactions. Visa’s modelers must find a balance that keeps fraud low, while minimizing friction and lost revenue.

In this session, Melissa McSherry, Visa’s SVP & Global Head of Data, Security and Identity Products, will share the story behind Visa’s real time risk scoring machine learning engine—a capability that prevents an estimated $25B in fraud each year. She’ll share insights on the challenges, learnings and opportunities of operating an AI/ML platform at scale.

Melissa McSherry, SVP and Global Head of Data, Security and Identity ProductsVisa

In conversation with Lori Sherer, Partner, Bain & Company   

12:25 PM – 12:45 PM PT 

FIRESIDE CHAT  Disrupt AI Development Scale And Speed Through Automation

AI has continued to be critical and has been increasing in importance for enterprises. Over the past six months, however, organizations have been under tremendous pressure to deliver more results with fewer resources due to the economic downturn. How can we deliver hundreds of AI models to produce disruptive business outcomes in less time but with fewer resources? What if an organization doesn’t have enough data scientists? How do we keep AI projects from stalling? 


dotData CEO, Dr. Ryohei Fujimaki PhD will share the story of one of our clients and how they delivered hundreds of AI models and produced significant business impacts by leveraging AI automation. He will also share how automating AI development can disrupt the scale and speed at which AI projects are completed, enabling any organization to deliver more with less.

Ryohei Fujimaki, CEO & Founder, dotData Inc.

In conversation with Dean Takahashi, Lead WriterVentureBeat

12:45 PM – 12:55 PM PT

Main Stage 

12:55 PM – 1:40 PM PT 

Women in AI Breakfast Report Back

Carla Saavedra Kochalski, Director of Conversational AI & Messaging Products, Capital One

Huma Abidi, Senior Director of AI Products, Intel

Virtual Networking & Lunch Break – Refuel and connect virtually with industry executives

12:55 PM – 1:40 PM PT

(Invite Only)

Executive Forum Roundtable – AI's role in Social Responsibility. How AI can help make sure the content on social media and the internet in general is moderated so that all users are safe from abusive and negative behavior.

The current COVID-19 pandemic has reshaped our offline and online worlds in record time. More than ever before, people are turning to online channels to conduct business, communicate with friends and family, keep abreast of the latest news, play games, order food and much more. They are forming online communities to find entertainment, solace, and connection. How can AI help ensure user generated content that is shared online – especially in a time where we are seeing record volumes – is moderated so that all users are safe from abusive and negative behaviour.

Presented by Two Hat

Moderated by David Ryan Polgar, Author & Industry leader

Chair Speakers: 

Dr. Richard KhouryAssociate Professor, University Laval

Dr. Kim Voll, Co-Founder, Stray Bombay Company

Kate Shores, Player Dynamics Designer, Riot Games

Liza Wood, Director of Research & Data Science, Two Hat

1:40 PM – 2:25 PM PT

 Tech & Automation

PANEL The right data: Big trends on how Companies are identifying the right data to train AI & ML algorithms accurately to tackle various issues such as customer service, personalization, risk & fraud with increased speed and agility

As AI & ML gains prominence and becomes an integral part of businesses across industries, there is a need to ensure that the algorithms are accurate, explainable, and trustworthy to be able to solve business problems effectively, provide great ROI and ensure customer satisfaction. To do this, AI teams not only need to select the right AI models, but also need to train them on the right datasets and make sure they address issues such as over fitment of data, identifying and correcting cyclical trends in the data, ensuring the data is labeled accurately etc. Join our panel of industry stalwarts who will discuss and debate this.

Jaime Delange, Director of ProductSlack

Hui Wang, VP of Data SciencePayPal

Dimitris Tsementsiz, Senior Quantitative Researcher, Goldman Sachs

Jacob Wilson, Principal, PwC

Moderated by Jed Dougherty, VP of Field EngineeringDataiku

2:25 PM – 2:55 PM PT

 Tech & Automation

FIRESIDE CHAT The rewards and challenges of building and scaling a custom AI and analytics experimentation engine


When Yelp built its own analytics and experimentation engine, Bunsen, three years ago, it was controversial.  Most companies were buying AI solutions from the big software vendors, but Yelp’s leadership knew that ML and AI deployment at the scale required special attention, and decided to run everything on its custom-made Bunsen engine.  Justin Norman, VP of Data Science, will discuss the rewards and challenges of building and scaling custom AI, experimentation and analytics systems across your entire business. With tens of millions of users and businesses listed, Yelp has the ability to responsibly and effectively get the right data to the right people in a timely manner. Whether it’s analyzing local economies, delivering a curated web UI through ML, or testing hundreds of different in-app features simultaneously across multiple cities, Yelp thrives on data-driven decision making at scale -- using a “multi-armed bandit” approach in ML       


Deciding what to build, buy, invest in, or let die involved many different skills, tradeoffs, and collaboration across teams. The first steps on this journey involved making some tough decisions about which teams would build and run such systems.  Yelp then launched the Bunsen Experimentation Platform to serve the three use cases of AI, experimentation, and analytics. This would eventually form the heart of Yelp's Data ecosystem, supporting hundreds of controlled, randomly stratified digital experiments. Bunsen continues to be a massive technical and operational effort involving stakeholders from engineering to data science to product management all converging to propose experiments.


Justin Norman, VP of Data ScienceYelp

In conversation with Kyle Wiggers, AI Staff Writer, VentureBeat

2:55 PM – 3:10 PM PT 

Virtual Networking & Break – Take a short break, recaffeinate and network with fellow AI community peers  

2:55 PM – 3:40 PM PT 

(Invite Only)

Executive Forum Roundtable – How AI & ML is transforming the banking, financial services & insurance industry. The most impactful use cases. 

There's a reason the finance industry has been a first mover in leveraging the power of AI and machine learning. Money makes the world go around, and financial decisions are among the most important things we do. ML and AI has been defined by financial applications in customer service/personalization, new product development, credit decision making, fraud monitoring, risk management, claims processing, trend analysis, stock trading, reconciliations, and more. There is still a long way to go, and AI can help make this industry more efficient and secure. Join a select group of senior industry executives in this 45-minute roundtable to brainstorm and exchange ideas of where the industry is headed next.

Presented by Anaconda

Moderated by Alex Olshonsky, VP, Sales, VentureBeat

Chair Speakers:

Michael Grant, VP Of Services, Anaconda

Wei Li, Vice President and General Manager, Intel

3:10 PM – 3:35 PM PT

 Tech & Automation

FIRESIDE CHAT AI for Everyone: How LinkedIn Builds Holistic AI at Scale to ensure personalization, inclusivity, and fairness.

LinkedIn serves close to 700 million members in more than 200 countries, and AI is woven into virtually every experience on the site. Last year alone, members viewed nearly 400 billion feed updates, and the rate of content creation on the site is rapidly expanding. To maximize a useful, personalized experience, LinkedIn has to use AI to customize things like a user's feed, job notifications, and learning content.

Romer Rosales, senior director, at LinkedIn leads the AI team whose core responsibility is delivering that personalized experience at scale. We will discuss how he approaches that huge task and how the company is finding success with a uniquely holistic and AI-first approach to system design. This includes the main technology trends that are making his job easier, dealing with the challenges of balancing multiple competing/complementary objectives across products, and how the company is using AI to make their products more inclusive and fair.

He will share some business, technological and organizational insights, challenges and lessons learnt around AI product management that the audience can take away and implement in their businesses.

Romer Rosales, Senior Director and Head of Consumer AI, LinkedIn

In conversation with Hari Sivaraman, Head of AI Content Strategy, VentureBeat

3:35 PM – 4:00 PM PT

 Tech & Automation

PRESENTATION Case Study: How Zappos leveraged AI to deliver accurate personalization, better customer satisfaction and tangible ROI

Zappos leveraged AI to achieve a 10% drop in wrong-size related returns, as well as a corresponding increase in customer satisfaction by delivering highly personalized search results. E-commerce customers struggle with overwhelming selections and the risk of a poor fit generally. In turn, this results in Zappos suffering from lost conversion due to the paradox of choice, increased return rates due to sizing mishaps, and a less than ideal shopping experience overall. In a world where free returns and wide selections have become table stakes, this can prove to be a tremendous burden on your top and bottom-line revenue. In this talk, we share how the Zappos Machine Intelligence team obsessed over giving customers personalized search results coupled with just right-sizing recommendations.  Zappos’ head of ML/AI will walk through the software and hardware stacks that allow Zappos to leverage deep learning, machine learning, and genetic algorithms, at scale to solve these problems. These lessons can be abstracted for other executives to understand the opportunities and common pitfalls of ML in production. Ranging from the cross-functional skill-sets of the team, to the open-source technologies leveraged, the conversation walks through Zappos’ journey to becoming a company that uses ML & AI to shape the customers' digital experience.

Ameen Kazerouni, Head of ML/AI ResearchZappos

4:00 PM – 4:25 PM PT

 Tech & Automation

FIRESIDE CHAT How Pfizer successfully leveraged analytics and AI to scale their initiatives and achieve results


Pfizer has successfully created a culture of collaboration and co-creation around analytics and AI that has allowed them to significantly scale their initiatives and achieve results. By taking a broadly inclusive view on who should contribute to those processes, they've un-siloed their teams, and provided them with means to use the most advanced techniques on the largest datasets. In this fireside chat, Chris Kakkanatt, Senior Director of Data Science at Pfizer speaks with Kurt Muehmel, Chief Customer Officer at Dataiku, about the decade-long journey that they have taken to achieve this human-centric, AI-driven transformation and walks the audience through the business, technology and organizational lessons learnt and best practices that they can implement in their businesses.

Chris Kakkanat, Data Science Senior Director/Team Leader, Pfizer

In conversation with Kurt Muehmel, CCODataiku

4:25 PM – 4:50 PM PT

Main Stage

FIRESIDE CHAT Demystifying AI interpretability; Improving accuracy and predictability of AI models using reinforcement learning

Reinforcement Learning (RL) is a machine learning technique that is being quickly adopted by many organizations to solve large and complex problems, where labeled datasets are not readily available. Since RL learns by a continuous process of rewards and punishments on the actions it takes, it is able to train algorithms that interact with new environments. RL can also help in designing more explainable and accurate AI, which is one of the most important considerations in designing AI systems today. In this session, Ion Stoica, the director of RISELab (UC Berkeley) will discuss some of the most relevant advances and applications in this space.

Ion Stoica, Co-founderRise Labs (UC Berkeley)

In conversation with Jed Dougherty, VP of Field EngineeringDataiku

4:50 PM – 5:35 PM PT

VentureBeat AI Innovation Awards & Reception Private Chatroom

(VIP Only)

Transform 202o

When? July 14-17, 2020

Hosted Online
Questions about Transform 2020? Contact us at

  • Facebook Basic Black
  • Twitter Basic Black
  • Black Instagram Icon


© 2020 VentureBeat Transform, the AI event of the year.