Agenda

March 23
Day 1: The Business of AI

Organizations can gain and keep a competitive advantage with AI by clearly defining the problem space and understanding the people and strategies needed to produce results. Get inside businesses to discover successful, clear-eyed approaches to the problem, essential elements of an AI solution, and how real-world organizations are making AI happen.

March 24
Day 2: AI in the Lab

At its core, AI is an engineering problem. Successful implementation requires knowledge of the fundamentals of the technology. Gain insight from the technologists and researchers developing the newest innovations inside AI laboratories, the ways algorithms are getting smarter, and how hardware improvements are creating the next generation of AI.

March 25
Day 3: AI in our Daily Lives

AI is infusing its way into our lives in ever more meaningful ways. What does that mean for the way we eat, commute, and work? And while progress has been substantial, it hasn’t always been equal. We discuss what safeguards need to be put in place to ensure AI treats all equally.

*Times listed in ET. Schedule subject to change.

The Age of Implementation (12:00 p.m. - 1:20 p.m.)

AI is hard. While there are some great success stories, most are struggling. Is the promise of AI paying off or do we need to start rethinking our approach to the problem? 

12:10 p.m.

AI is transforming industries, yours included. How can you ensure you’re keeping up and hopefully ahead? Where do you start? What resources do you need? What procedures should be followed? What problems do you tackle? What about the data? The models? And bias? And equity? We ask the man who’s seen, and done, it all.

12:40 p.m.

The Machine Learning Solutions lab has worked with NASA, the NFL, AstraZeneca, and others to create machine learning solutions across all industries. This session explores the lessons learned from all those that have gone before you.

MIT Inside Track (1:20 p.m. - 1:50 p.m.)

Join the Inside Track sessions to engage more deeply with our content, speakers, and your fellow attendees during mainstage programming breaks.

1:20 p.m.

Learn how AI is helping with public transportation in underserved communities by pooling passengers for better fleet management for cities, transit authorities, and urban planners.

Essential Elements (1:50 p.m. - 3:05 p.m.)

Why do some organizations succeed while others struggle? We’ll break down examples of successful AI implementations in healthcare, finance, and retail and extract the essential elements that lead to success. 

1:50 p.m.

Consumers demand smart products that exactly fit their needs now. This compels companies to radically rethink their design, development, and manufacturing processes, and they are turning to AI to meet the challenge. Generative design and virtual electronics exploration have started the revolution. But we are fast approaching a world where AI can find the optimal product design, determine manufacturability, source parts, and reconfigure factories to produce one-off products quickly and efficiently.

2:20 p.m.

The massive amounts of transactions in the financial industry are like an all-you-eat data buffet for AI. Mastercard has become an expert at mining that data to improve its global network, processing platforms, technology hubs, information security, and technology operations. 

2:45 p.m.

Sepsis is a clinical condition that is common, dangerous, and expensive. Patients diagnosed with sepsis have a 28% mortality rate, and sepsis constitutes the single greatest cost to Medicare for inpatient hospitalizations. This is the story of how Duke University used deep learning to detect at risk patients, saving money and lives. 

MIT Inside Track (3:05 p.m. - 3:40 p.m.)

Join the Inside Track sessions to engage more deeply with our content, speakers, and your fellow attendees during mainstage programming breaks.

3:05 p.m.

Get an inside look at the fascinating synergy between theoretical physics principles and machine learning architectures using examples from high-energy particle collisions.

Deliver (3:40 p.m. - 4:55 p.m.)

How do we make AI happen? For those with strong in-house capabilities, it’s about the process. For those without the expertise, it’s about finding all the help you can get.

3:40 p.m.

What does it take to get engineers and business experts to better understand what each other needs? And how do we turn this understanding into actionable work that results in the building of better AI models?

4:05 p.m.

Software development used to be “write code, deploy code, evaluate.” Now it’s “label data, train model, evaluate."  Overnight, labeled data has become the bottleneck to the growth of the machine learning industry. Is it actually possible to use machine learning to label datasets and train models faster?

4:30 p.m.

Intuit has cut its model development lifecycle dramatically. What used to take six full months now takes less than a week. We’ll find out how.

Last Call (4:55 p.m. - 5:00 p.m.)

Before we send everyone home for the night, join our last call with all of our editors to get their analysis on the day’s topics, themes, and guests.

11:00 a.m.

Digital transformation is a constant process fueled by new innovations and emerging technology, and enabled by the right organizational capabilities. How has AI and digital transformation affected your business? How are you and others driving digital transformation? Join this discussion group, ask your questions, and get strategic insights to bring back to the office.

*Available to all-access ticket holders only*

Inside the Leading Labs (12:00 p.m. - 1:30 p.m.)

What’s going on in the labs of AI giants like Amazon, IBM, and others? What are their latest projects? Where are they headed? We’ve invited their leaders to answer your questions. 

12:00 p.m.

IBM Research is one of the world’s largest and most influential corporate research labs, with over 3,000 researchers. We’ll hear from their leader on their latest AI advancements and achievements.

12:30 p.m.

For many digital businesses, Amazon Web Services is the cloud. We’ll peek into their labs to see how they are working to make the cloud smarter by putting machine learning into the hands of every developer.

1:00 p.m.

When it was unveiled to the world, GPT-3, with its 175 billion parameters, was the largest neural network in the world. It started as an author, able to create human-level text, and has since become an artist, creating images from text requests. How does it work, and what’s next?

MIT Inside Track (1:30 p.m. - 2:00 p.m.)

Join the Inside Track sessions to engage more deeply with our content, speakers, and your fellow attendees during mainstage programming breaks.

Algorithms (2:00 p.m. - 3:30 p.m.)

Algorithms are the brains of AI. The smarter the algorithm, the more accurate the model, the better the insights. We explore the software side of AI. 

2:00 p.m.

Partial differential equations are used to model weather on earth, the wind over wings, and planetary motion. But they are notoriously difficult and time-consuming to solve, until AI found a faster way.

2:30 p.m.

There are significant holes in the fundamental assumptions we've been making in the AI training process, resulting in models that work in the lab, but not in practice. This problem is called underspecification, and there is no known cure.

2:55 p.m.

Microsoft’s Azure AI engineering and research teams are working to make machines see, hear, and understand human beings. We’ll get a progress update from their CTO.

MIT Inside Track (3:30 p.m. - 4:00 p.m.)

Join the Inside Track sessions to engage more deeply with our content, speakers, and your fellow attendees during mainstage programming breaks.

3:30 p.m.

How are MIT researchers using AI to make robots curious? Learn about the active learning-by-synthesis approach in which an AI-enabled robot reasons about where it is most uncertain in order to guide its curious exploration.

Learn how encoding symmetries and invariances into machine learning architectures can enable applications to extreme-scale problems, such as first-principles nuclear physics calculations.

Chips (4:00 p.m. - 5:00 p.m.)

AI is a hungry beast, demanding massive amounts of compute and energy. What innovations in AI hardware are on the horizon that might turbo-charge our algorithms and tame our energy consumption? 

4:00 p.m.

Computing is moving to the edge, so if you could just build an ultra-fast low energy chip that can run audio, 2D, and 3D image models, that’d be great. Challenge accepted.

4:30 p.m.

Photonic (or optical) computers have long been considered a holy grail for information processing due to the potential for high bandwidth and low power computation. This combination of electronics, photonics, and new algorithms is creating a next-generation computing platform purpose-built for artificial intelligence.

Last Call (5:00 p.m. - 5:10 p.m.)

Join this informal wrap-up session with the MIT Technology Review editorial team and get their reflections and insights on the day’s proceedings.

11:00 a.m.

The digital health field is growing, enabled by advances in AI that give computers (including software agents, robots, and interactive games) the skills of emotional intelligence. Have questions about how this AI works? Join our special pre-conference session and talk to the MIT researcher who wrote the book on affective computing.

*Available to all-access ticket holders only*

Guardrails (12:00 p.m. - 1:15 p.m.)

If we are going to build AI for the future, how do we make sure it works equally for all? What policies are needed? What practices need to become standard? And what just simply needs to stop? 

12:00 p.m.

Left unregulated, AI has the potential to clash with the public good. Overregulate AI, and you stifle innovation. Where is the balance?

12:20 p.m.

There is a hidden labor force driving a lot of AI innovation. Thousands of workers are being used by tech companies to label data to improve AI algorithms, but there are serious concerns about empowerment, emotional trauma, and more that we need to talk about.

12:35 p.m.

The ubiquitous technologies interwoven into our personal, social, political, and economical spheres are shaping what it means to be a person. We need to ensure the algorithms controlling our lives are by design, ethical and equal for all. 

MIT Inside Track (1:15 p.m. - 1:45 p.m.)

Join the Inside Track sessions to engage more deeply with our content, speakers, and your fellow attendees during mainstage programming breaks.

1:15 p.m.

Hear lessons learned related to deploying intelligent autonomous driving, from integrating AI trucking technology into yard vehicles for improved efficiency to enhancing safety in distribution logistics.

Life (1:45 p.m. - 3:10 p.m.)

On a daily basis, AI gets us from point A to B, selecting the music we play along the way and - for some - even parking the car when we get there. What are the next areas in which AI is going to fundamentally change the way we live? 

1:45 p.m.

Once the source of incremental improvements in customer experience or operational efficiency, AI initiatives today drive company-wide innovation, competitive moats, disruption, and dominance in the market. In our post-COVID world, embracing AI has become an existential challenge. Leading with AI requires a purposeful strategy that is honed at the top of the house and designed not just to close the gap with digital natives but to lead an industry.

2:10 p.m.

In just three decades, the global population will reach 10 billion people, requiring significant efficiency gains from agriculture operations to feed one and all. Learn how artificial intelligence, computer vision, and machine learning will power the food chain of the future.

2:40 p.m.

After 5 years of driving over 2 million autonomous miles on the streets of San Francisco, Cruise’s zero-emission autonomous vehicles are driving the city’s streets without human backup drivers. Don’t be fueled, this session maps out the road to a driverless future.

MIT Inside Track (3:10 p.m. - 3:40 p.m.)
Join the Inside Track sessions to engage more deeply with our content, speakers, and your fellow attendees during mainstage programming breaks.
3:10 p.m.

With only one in ten companies reporting significant financial benefits when implementing AI, bring your questions about the technical, cultural, and strategic challenges you may be facing to this small discussion group to get practical advice from a researcher and industry expert.

*Available to all-access ticket holders only*

An interview with Senior AI Reporter Karen Hao and News Editor Niall Firth providing a behind-the-scenes look into the revealing story on the AI that populates your Facebook newsfeed for maximum engagement and maximum enragement.  We explore the story, the reaction and try to understand why socially responsible AI is proving to be such a challenge.

Work (3:40 p.m. - 5:10 p.m.)

Whether you live to work or work to live, the fact is: you work. What possibilities lie ahead, as AI begins to re-engineer the workplace? What does an AI-augmented workforce look like, and just how much more can it achieve? 

3:40 p.m.

Artificial Intelligence and its impacts are consistently misunderstood as automation, but the future of AI is the augmentation of human endeavors. The incredible advances in AI technology need to be matched by equivalent advancement in human attitudes and perspectives. 

4:05 p.m.

In the gig economy, your boss is the algorithm, relentlessly pressuring for more, faster and cheaper, often at your expense. It’s an unsustainable model, and we’ll explore what needs to be done to fix it.

4:25 p.m.

AI is transforming the workplace, with significant implications for wages, skillsets, and the pace of innovation. For success, we must foster institutional innovations that complement technological change.

4:55 p.m.
Closing Remarks

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