March 23
Day 1: The Business of AI

What are the challenges that must be met to successfully implement AI in an organization? While AI is - on the surface - a computing technology, its successful implementation depends on people, strategies, and a clear definition of the problem space.

March 24
Day 2: AI in the Lab

At its core, AI is an engineering problem. With smarter algorithms and more powerful chips, AI can deliver incredible insights at greater speeds and lower energy costs. Successful implementation requires an understanding of the fundamental engineering challenges of AI hardware and software.

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. - 1:15 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, but how do you create an AI strategy that will transform your organization and set you on the path for successful AI adoption and implementation?

12:40 p.m.

The AWS Solution lab has worked with NASA, the NFL, AstraZeneca and others to create ML solutions across all industries. This session explores the crowdsourced fundamentals necessary to identify opportunities and implement machine learning in any organization.

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

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1:15 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 (1:45 p.m. - 3:10 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. 

1:45 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.

2:05 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. 

2:40 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 (3:10 p.m. - 3:40 p.m.)
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3:10 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. - 5:00 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 the help and getting AI as a service. 

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?

4:10 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?

4:35 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 (5:00 p.m. - 5:10 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.

Inside the Leading Labs (12:00 p.m. - 1:35 p.m.)

What’s going on in the labs of AI giants like Microsoft, 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.

12:55 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 (1:35 p.m. - 2:05 p.m.)
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1:35 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.

Algorithms (2:05 p.m. - 3:35 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. 

2:05 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 (3:35 p.m. - 4:05 p.m.)
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3:35 p.m.

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 (4:05 p.m. - 5:20 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? 

4:25 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.

4:50 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 (5:20 p.m. - 5:30 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.

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? 

2:05 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.

2:30 p.m.

After 5 years of driving over 2 million miles on the streets of San Francisco, Cruise zero-emission cars received a permit from the California DMV to begin testing their autonomous vehicles 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.)
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Work (3:40 p.m. - 5:05 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. 

4:30 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.

5:00 p.m.
Closing Remarks


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