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RECENT VIDEOS

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11.14.2019
25 mins
ILP Video

Fake news: Why we fall for it and what to do about it

David Rand
Erwin H. Schell Associate Professor of Marketing
MIT Sloan School of Management
Why do people believe and share misinformation, including entirely fabricated news headlines (?fake news?) and biased or misleading coverage of actual events ("hyper-partisan" content)? The dominant narrative in the media and among academics is that we believe misinformation because we want to ? that is, we engage in motivated reasoning, using our cognitive capacities to convince ourselves of the truth of statements that align with our political ideology rather than to undercover the truth. In a series of survey experiments using American participants, my colleagues and I challenge this account. We consistently find that engaging in more reasoning makes one better able to identify false or biased headlines - even for headlines that align with individuals? political ideology. These findings suggest that susceptibility to misinformation is driven more by mental laziness and lack of reasoning than it is by partisan bias hijacking the reasoning process. We then build on this observation to examine interventions to fight the spread of misinformation. We find - given this smaller-than-believed role of partisan bias - that crowdsourcing can actually be a quite effective approach for identifying misleading news outlets and news content. We also demonstrate the power of making the concept of accuracy top-of-mind, thereby increasing the likelihood that people think about the accuracy of headlines before they decide whether to share them online. Our results suggest that reasoning is not held hostage by partisan bias, but that instead our participants do have the ability to tell fake or inaccurate from real - if they bother to pay attention. Our findings also suggest simple, cost-effective behavioral interventions to fight the spread of misinformation.
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11.14.2019
33 mins
ILP Video

Life 3.0: Being Human in the Age of AI

Max Tegmark
Professor of Physics
MIT Department of Physics
If AI succeeds in eclipsing human general intelligence within decades, as many leading AI researchers predict, then how can we make it the best rather than worst thing ever to happen to humanity? I argue that this will require planning and hard work, and explore challenges that we need to overcome as well as exciting opportunities. How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today?s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? How can we make machines understand, adopt and retain our goals, and whose goals should should they be? What future do you want? Welcome to the most important conversation of our time!
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11.14.2019
37 mins
ILP Video

Track 6: Efficient Computing for AI and Robotics

Vivienne Sze
Assistant Professor of Electrical Engineering and Computer Science
MIT Department of Electrical Engineering and Computer Science
Computing near the sensor is preferred over the cloud due to privacy and/or latency concerns for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to the throughput and accuracy requirements of the application. In this talk, we will describe how joint algorithm and hardware design can be used to reduce energy consumption while delivering real-time and robust performance for applications including deep learning, computer vision, autonomous navigation/exploration and video/image processing. We will show how energy-efficient techniques that exploit correlation and sparsity to reduce compute, data movement and storage costs can be applied to various tasks including image classification, depth estimation, super-resolution, localization and mapping.
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11.14.2019
39 mins
ILP Video

Track 6: Towards Deployable ML

Aleksander Madry
Associate Professor of Electrical Engineering and Computer Science
MIT Department of Electrical Engineering and Computer Science
Machine learning has made tremendous progress over the last decade. It's thus tempting to believe that ML techniques are a "silver bullet", capable of making progress on any real-world problem they are applied to.

But is that really so?

In this talk, I will discuss a major challenge in the real-world deployment of ML: making ML solutions robust, reliable and secure. In particular, I will survey the widespread vulnerabilities of state-of-the-art ML models to various forms of noise, and then outline promising approach to alleviating these deficiencies as well as to making models be more human-aligned.
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11.14.2019
42 mins
ILP Video

Track 6: Machine Intelligence for Manufacturing and Operations: Opportunities and Challenges

Duane Boning
Clarence J. LeBel Professor,
Electrical Engineering and Computer Science, MIT
The large amounts of both structured and unstructured data created in manufacturing and operations today present enormous opportunities to apply advanced analytics, machine learning and deep learning. This talk will describe specific use cases in process control and optimization; yield prediction and enhancement; defect inspection and classification and anomaly detection in time series data. Additionally, some of the unique manufacturing and operations challenges like: class imbalance, concept drift and complex multivariate time dynamics will be described. This research has led to the creation of MIT MIMO (Machine Intelligence for Manufacturing and Operations) which will be described during this talk.
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