2022 EmTechDigital @MIT, March 29-30, 2022
Real Time Coverage: Aviva Lev-Ari, PhD, RN
#EmTechDigital
@AVIVA1950
@pharma_BI
@techreview
SPEAKERS
https://event.technologyreview.com/emtech-digital-2022/speakers
Ali
Alvi
Turing Group Program Manager
Microsoft
Refik
Anadol
CEO, RAS Lab; Lecturer
UCLA
Lauren
Bennett
Group Software Engineering Lead, Spatial Analysis and Data Science
Esri
Elizabeth
Bramson-Boudreau
CEO
MIT Technology Review
Tara
Chklovski
Founder & CEO
Technovation
Sheldon
Fernandez
CEO
DarwinAI
David
Ferrucci
Founder, CEO, & Chief Scientist
Elemental Cognition
Anthony
Green
Podcast Producer
MIT Technology Review
Agrim
Gupta
PhD Student, Stanford Vision and Learning Lab
Stanford University
Mike
Haley
VP of Research
Autodesk
Will Douglas
Heaven
Senior Editor for AI
MIT Technology Review
Natasha
Jaques
Senior Research Scientist
Google Brain
Tony
Jebara
VP of Engineering and Head of Machine Learning
Spotify
Clinton
Johnson
Racial Equity Unified Team Lead
Esri
Danny
Lange
SVP of Artificial Intelligence
Unity Technologies
Julia (Xing)
Li
Deputy General Manager
Baidu USA
Darcy
MacClaren
Senior Vice President, Digital Supply Chain
SAP North America
Haniyeh
Mahmoudian
Global AI Ethicist
DataRobot
Andrew
Moore
GM and VP, Google Cloud AI
Google
Mira
Murati
SVP, Research, Product, & Partnerships
OpenAI
Prem
Natarajan
Vice President Alexa AI, Head of NLU
Amazon
Andrew
Ng
Founder and CEO
Landing AI
Amy
Nordrum
Editorial Director, Special Projects & Operations
MIT Technology Review
Kavitha
Prasad
VP & GM, Datacenter, AI and Cloud Execution and Strategy
Intel Corporation
Bali
Raghavan
Head of Engineering
Forward
Rajiv
Shah
Principal Data Scientist
Snorkel AI
Sameena
Shah
Managing Director, J.P. Morgan AI Research
JP Morgan Chase
David
Simchi-Levi
Director, Data Science Lab
MIT
Jennifer
Strong
Senior Editor for Podcasts and Live Journalism
MIT Technology Review
Fiona
Tan
CTO
Wayfair
Zenna
Tavares
Research Scientist, Columbia University; Co-Founder
Basis
Nicol
Turner Lee
Director, Center for Technology Innovation
Brookings Institution
Raquel
Urtasun
Founder & CEO
Waabi
Oriol
Vinyals
Principal Scientist
DeepMind
MIT Inside Track
David
Cox
IBM Director
MIT-IBM Watson AI Lab
Luba
Elliott
Curator, Producer, and Researcher
Creative AI
Charlotte
Jee
Reporter, News
MIT Technology Review
Naveen
Kamat
Executive Director, Data and AI Services
Kyndryl
Joseph
Lehar
Senior Vice President, R&D Strategy
Owkin
Stefanie
Mueller
Associate Professor
MIT CSAIL
Jianxiong
Xiao
Founder and CEO
AutoX
TUESDAY, MARCH 29
Data-Centric AI
Better Data, Better AI
Data powers AI. Good data can mean the difference between an impactful solution or one that never gets off the ground. Re-assess the foundational AI questions to ensure your data is working for, not against, you.
Innovation to Reality
The challenges of implementing AI are many. Avoid the common pitfalls with real-world case studies from leaders who have successfully turned their AI solutions into reality.
Harness What’s Possible at the Edge
With its potential for near instantaneous decision making, pioneers are moving AI to the edge. We examine the pros and cons of moving AI decisions to the edge, with the experts getting it right.
Generative AI Solutions
The use of generative AI to boost human creativity is breaking boundaries in creative areas previously untouched by AI. We explore the intersection of data and algorithms enabling collaborative AI processes to design and create.
Day 1: Data-Centric AI (9:00 a.m. – 5:20 p.m.)
Day 1: Data-Centric AI (9:00 a.m. – 5:20 p.m.)
9:00 AM
Welcome Remarks
Will Douglas Heaven
Senior Editor for AI, MIT Technology Review
Better Data, Better AI (9:10 a.m. – 10:35 a.m.)
Data powers AI. Good data can mean the difference between an impactful solution or one that never gets off the ground. Re-assess the foundational AI questions to ensure your data is working for, not against, you.
9:10 AM
Empowering Data-Centric AI
Data is the most under-valued and de-glamorized aspect of AI. Learn why shifting the focus from model/algorithm development to quality of the data is the next and most efficient, way to improve the decision-making abilities of AI.
Andrew Ng
Founder and CEO, Landing AI
9:40 AM
The Mechanics of Data-First AI
Data labeling is key to determining the success or failure of AI applications. Learn how to implement a data-first approach that can transform AI inference, resulting in better models that make better decisions.
Rajiv Shah
Principal Data Scientist, Snorkel AI
10:10 AM
Thought Leadership in Responsible AI
Question the status quo. Build stakeholder trust. These are foundational elements of thought leadership in AI. Explore how organizations can use their data and algorithms in ethical and responsible ways while building bigger and more effective systems.
Haniyeh Mahmoudian
Global AI Ethicist, DataRobot
Mainstage Break (10:35 a.m. – 11:05 a.m.)
Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.
10:35 AM
MIT Inside Track: From AI Startup to Tech “Unicorn” (available online only)
With its next-generation machine learning models fueling precision medicine, French biotech company, Owkin, captured the attention of the pharma industry. Learn how they did it and get tips to navigate the complex task of scaling your innovation.
Joseph Lehar
Senior Vice President, R&D Strategy, Owkin
Networking Break
Networking and refreshments for our live audience.
Innovation to Reality (11:05 a.m. – 12:30 p.m.)
The challenges of implementing AI are many. Avoid the common pitfalls with real-world case studies from leaders who have successfully turned their AI solutions into reality.
11:05 AM
Secrets of Successful AI Deployments
Deploying AI in real-world environments benefits from human input before and during implementation. Get an inside look at how organizations can ensure reliable results with the key questions and competing needs that should be considered when implementing AI solutions.
Andrew Moore
GM and VP, Google Cloud AI, Google
11:35 AM
From Research Lab to Real World
AI is evolving from the research lab into practical real world applications. Learn what issues should be top of mind for businesses, consumers, and researchers as we take a deep dive into AI solutions that increase modern productivity and accelerate intelligence transformation.
Julia (Xing) Li
Deputy General Manager, Baidu USA
12:00 PM
Closing the 20% Performance Gap
Getting AI to work 80% of the time is relatively straightforward, but trustworthy AI requires deployments that work 100% of the time. Unpack some of the biggest challenges that come up when eliminating the 20% gap.
Bali Raghavan
Head of Engineering, Forward
Lunch and Networking Break (12:30 p.m. – 1:30 p.m.)
12:30 PM
Lunch and Networking Break
Lunch served at the MIT Media Lab and a selection of curated content for those tuning in virtually.
Harness What’s Possible at the Edge (1:30 p.m. – 3:15 p.m.)
With its potential for near instantaneous decision making, pioneers are moving AI to the edge. We examine the pros and cons of moving AI decisions to the edge, with the experts getting it right.
1:30 PM
AI Integration Across Industries – Presented by Intel
To create sustainable business impact, AI capabilities need to be tailored and optimized to an industry or organization’s specific requirements and infrastructure model. Hear how customers’ challenges across industries can be addressed in any compute environment from the cloud to the edge with end-to-end hardware and software optimization.
Kavitha Prasad
VP & GM, Datacenter, AI and Cloud Execution and Strategy, Intel Corporation
Elizabeth Bramson-Boudreau
CEO, MIT Technology Review
1:55 PM
Explainability at the Edge
Decision making has moved from the edge to the cloud before settling into a hybrid setup for many AI systems. Through the examination of key use-cases, take a deep dive into understanding the benefits and detractors of operating a machine-learning system at the point of inference.
Sheldon Fernandez
CEO, DarwinAI
2:25 PM
AI Experiences at the Edge
Enable your organization to transform customer experiences through AI at the edge. Learn about the required technologies, including teachable and self-learning AI, that are needed for a successful shift to the edge, and hear how deploying these technologies at scale can unlock richer, more responsive experiences.
Prem Natarajan
Vice President Alexa AI, Head of NLU, Amazon
2:50 PM
The Road Ahead
Reimagine AI solutions as a unified system, instead of individual components. Through the lens of autonomous vehicles, discover the pros and cons of using an all-inclusive AI-first approach that includes AI decision-making at the edge and see how this thinking can be applied across industry.
Raquel Urtasun
Founder & CEO, Waabi
Mainstage Break (3:15 p.m. – 3:45 p.m.)
Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.
3:15 PM
Networking Break
Networking and refreshments for our live audience.
MIT Inside Track: The Impact of Creative AI (available online only)
Advances in machine learning are enabling artists and creative technologists to think about and use AI in new ways. Discuss the concept of creative AI and look at project examples from London’s art scene that illustrate the various ways creative AI is bridging the gap between the traditional art world and the latest technological innovations.
Luba Elliott
Curator, Producer, and Researcher, Creative AI
Generative AI Solutions (3:45 p.m. – 5:10 p.m.)
The use of generative AI to boost human creativity is breaking boundaries in creative areas previously untouched by AI. We explore the intersection of data and algorithms enabling collaborative AI processes to design and create.
3:45 PM
Enhancing Design through Generative AI
Change the design problem with AI. The creative nature of generative AI enhances design capabilities, finding efficiencies and opportunities that humans alone might not conceive. Explore business applications including project planning, construction, and physical design.
Mike Haley
VP of Research, Autodesk
4:15 PM
Using Synthetic Data and Simulations
Deep learning is data hungry technology. Manually labelled training data has become cost prohibitive and time-consuming. Get a glimpse at how interactive large-scale synthetic data generation can accelerate the AI revolution, unlocking the potential of data-driven artificial intelligence.
Danny Lange
SVP of Artificial Intelligence, Unity Technologies
4:40 PM
The Art of AI
Push beyond the typical uses of AI. Explore the nexus of art, technology, and human creativity through the unique innovation of kinetic data sculptures that use machines to give physical context and shape to data to rethink how we engage with the physical world.
Refik Anadol
CEO, RAS Lab; Lecturer, UCLA
Last Call with the Editors (5:10 p.m. – 5:20 p.m.)
5:10 PM
Last Call with the Editors
Before we wrap day 1, join our last call with all of our editors to get their analysis on the day’s topics, themes, and guests.
Networking Reception (5:20 p.m. – 6:20 p.m.)
WEDNESDAY, MARCH 30
Evolving the Algorithms
What’s Next for Deep Learning
Deep learning algorithms have powered most major AI advances of the last decade. We bring you into the top innovation labs to see how they are advancing their deep learning models to find out just how much more we can get out of these algorithms.
AI in Day-To-Day Business
Many organizations are already using AI internally in their day-to-day operations, in areas like cybersecurity, customer service, finance, and manufacturing. We examine the tools that organizations are using when putting AI to work.
Making AI Work for All
As AI increasingly underpins our lives, businesses, and society, we must ensure that AI must work for everyone – not just those represented in datasets, and not just 80% of the time. Examine the challenges and solutions needed to ensure AI works fairly, for all.
Envisioning the Next AI
Some business problems can’t be solved with current deep learning methods. We look at what’s around the corner at the new approaches and most revolutionary ideas propelling us toward the next stage in AI evolution.
Day 2: Evolving the Algorithms (9:00 a.m. – 5:25 p.m.)
9:00 AM
Welcome Remarks
Will Douglas Heaven
Senior Editor for AI, MIT Technology Review
What’s Next for Deep Learning (9:10 a.m. – 10:25 a.m.)
Deep learning algorithms have powered most major AI advances of the last decade. We bring you into the top innovation labs to see how they are advancing their deep learning models to find out just how much more we can get out of these algorithms.
9:10 AM
Transforming Traditional Algorithms
Transformer-based language models are revolutionizing the way neural networks process natural language. This deep dive looks at how organizations can put their data to work using transformer models. We consider the problems that business may face as these massive models mature, including training needs, managing parallel processing at scale, and countering offensive data.
Ali Alvi
Turing Group Program Manager, Microsoft
9:35 AM
Human-like Problem Solving
Critical thinking may be one step closer for AI by combining large-scale transformers with smart sampling and filtering. Get an early look at how AlphaCode’s entry into competitive programming may lead to a human-like capacity for AI to write original code that solves unforeseen problems.
Oriol Vinyals
Principal Scientist, DeepMind
10:00 AM
Aligning AI Technologies at Scale
As advanced AI systems gain greater capabilities in our search for artificial general intelligence, it’s critical to teach them how to understand human intentions. Look at the latest advancements in AI systems and how to ensure they can be truthful, helpful, and safe.
Mira Murati
SVP, Research, Product, & Partnerships, OpenAI
Mainstage Break (10:25 a.m. – 10:55 a.m.)
Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.
10:25 AM
Networking Break
Networking and refreshments for our live audience.
Business-Ready Data Holds the Key to AI Democratization – Presented by Kyndryl
Good data is the bedrock of a self-service data consumption model, which in turn unlocks insights, analytics, personalization at scale through AI. Yet many organizations face immense challenges setting up a robust data foundation. Dive into a pragmatic perspective on abstracting the complexity and untangling the conflicts in data management for better AI.
Naveen Kamat
Executive Director, Data and AI Services, Kyndryl
AI in Day-To-Day Business (10:55 a.m. – 12:20 p.m.)
Many organizations are already using AI internally in their day-to-day operations, in areas like cybersecurity, customer service, finance, and manufacturing. We examine the tools that organizations are using when putting AI to work.
10:55 AM
Improving Business Processes with AI
Effectively operationalized AI/ML can unlock untapped potential in your organization. From enhancing internal processes to managing the customer experience, get the pragmatic advice and takeaways leaders need to better understand their internal data to achieve impactful results.
Fiona Tan
CTO, Wayfair
11:25 AM
Accelerating the Supply Chain
Use AI to maximize reliability of supply chains. Learn the dos and don’ts to managing key processes within your supply chain, including workforce management, streamlining and simplification, and reaping the full value of your supply chain solutions.
Darcy MacClaren
Senior Vice President, Digital Supply Chain, SAP North America
David Simchi-Levi
Director, Data Science Lab, MIT
11:55 AM
Putting Recommendation Algorithms to Work
Machine and reinforcement learning enable Spotify to deliver the right content to the right listener at the right time, allowing for personalized listening experiences that facilitate discovery at a global scale. Through user interactions, algorithms suggest new content and creators that keep customers both happy and engaged with the platform. Dive into the details of making better user recommendations.
Tony Jebara
VP of Engineering and Head of Machine Learning, Spotify
Lunch and Networking Break (12:20 p.m. – 1:15 p.m.)
12:20 PM
Lunch and Networking Break
Lunch served at the MIT Media Lab and a selection of curated content for those tuning in virtually.
Making AI Work for All (1:15 p.m. – 2:35 p.m.)
As AI increasingly underpins our lives, businesses, and society, we must ensure that AI must work for everyone – not just those represented in datasets, and not just 80% of the time. Examine the challenges and solutions needed to ensure AI works fairly, for all.
1:15 PM
Mapping Equity
Walk through the practical steps to map and understand the nuances, outliers, and special cases in datasets. Get tips to ensure ethical and trustworthy approaches to training AI systems that grow in scope and scale within a business.
Lauren Bennett
Group Software Engineering Lead, Spatial Analysis and Data Science, Esri
Clinton Johnson
Racial Equity Unified Team Lead, Esri
1:45 PM
Bridging the AI Accessibility Gap
Get an inside look at the long- and short-term benefits of addressing inequities in AI opportunities, ranging from educating the tech youth of the future to a 10,000-foot view on what it will take to ensure that equity top is of mind within society and business alike.
Tara Chklovski
Founder & CEO, Technovation
2:10 PM
The AI Policies We Need
Public policies can help to make AI more equitable and ethical for all. Examine how policies could impact corporations and what it means for building internal policies, regardless of what government adopts. Identify actionable ideas to best move policies forward for the widest benefit to all.
Nicol Turner Lee
Director, Center for Technology Innovation, Brookings Institution
Mainstage Break (2:35 p.m. – 3:05 p.m.)
Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.
2:35 PM
Networking Break
Networking and refreshments for our live audience.
MIT Inside Track: Accelerating the Advent of Autonomous Driving (available online only)
From the U.S. to China, the global robo-taxi race is gaining traction with consumers and regulators alike. Go behind the scenes with AutoX – a Level 4 driving technology company – and hear how it overcame obstacles while launching the world’s second and China’s first public, fully driverless robo-taxi service.
Jianxiong Xiao
Founder and CEO, AutoX
Envisioning the Next AI (3:05 p.m. – 4:50 p.m.)
Some business problems can’t be solved with current deep learning methods. We look at what’s around the corner at the new approaches and most revolutionary ideas propelling us toward the next stage in AI evolution.
3:05 PM
How AI Is Powering the Future of Financial Services – Presented by JP Morgan Chase
The use of AI in finance is gaining traction as organizations realize the advantages of using algorithms to streamline and improve the accuracy of financial tasks. Step through use cases that examine how AI can be used to minimize financial risk, maximize financial returns, optimize venture capital funding by connecting entrepreneurs to the right investors; and more.
Sameena Shah
Managing Director, J.P. Morgan AI Research, JP Morgan Chase
3:30 PM
Evolution of Mind and Body
In a study of simulated robotic evolution, it was observed that more complex environments and evolutionary changes to the robot’s physical form accelerated the growth of robot intelligence. Examine this cutting-edge research and decipher what this early discovery means for the next generation of AI and robotics.
Agrim Gupta
PhD Student, Stanford Vision and Learning Lab, Stanford University
4:00 PM
A Path to Human-like Common Sense
Understanding human thinking and reasoning processes could lead to more general, flexible and human-like artificial intelligence. Take a close look at the research building AI inspired by human common-sense that could create a new generation of tools for complex decision-making.
Zenna Tavares
Research Scientist, Columbia University; Co-Founder, Basis
4:25 PM
Social Learning Bots
Look under the hood at this innovative approach to AI learning with multi-agent and human-AI interactions. Discover how bots work together and learn together through personal interactions. Recognize the future implications for AI, plus the benefits and obstacles that may come from this new process.
Natasha Jaques
Senior Research Scientist, Google Brain
Closing Segment (4:50 p.m. – 5:25 p.m.)
4:50 PM
Pulling Back the Curtain on AI
David Ferrucci was the principal investigator for the team that led IBM Watson to its landmark Jeopardy success, awakening the world to the possibilities of AI. We pull back the curtain on AI for a wide-ranging discussion on explicable models, and the next generation of human and machine collaboration creating AI thought partners with limitless applications.
David Ferrucci
Founder, CEO, & Chief Scientist, Elemental Cognition
5:15 PM
Closing Remarks
Closing Toast (5:25 p.m. – 5:45 p.m.)
Read Full Post »
2012pharmaceutical
sjwilliamspa