Moderator: Randall Field Speakers: - Sertac Karaman (17 minutes) "The path towards autonomous vehicles on our roads" - Sanjay Sarma (17 minutes) "Digitalization of the mobility value chain: opportunities and implications"
Over the past decade, new digital technologies have re-defined mobility in urban areas through new on-demand mobility services offered through a sharing economy model. This session will explore future opportunities associated with digital transformations of the mobility value chain and will assess the implications linked to these transformations from a strategic perspective. In particular, the technological foundations of in-vehicle digitalization will be explored for the case of autonomous vehicles, with a focus on assessing current technical implementations and potential technical solutions.
Moderator: Joanna Moody Speakers: - Sergey Paltsev (9 minuets) "Mobility policy, energy demand, and global scenarios" - Jessika Trancik (9 minuets) "Low-carbon mobility technology development: Measuring progress and predicting innovation opportunities using new models"
Mobility systems are constantly changing. Currently, the availability of on-demand mobility services and the development of new vehicle technologies (e.g. electric vehicles) is altering the way we travel in urban areas. At the same time, policymakers around the world tackle the environmental challenges associated with mobility systems through new policies including emission standards and driving restrictions. This session sets out to (i) provide an overview of emerging vehicle technologies for passenger ground transportation, especially with regard to fuels and powertrains, (ii) outline the interaction of technology adoption with different policy scenarios, and (iii) describe current adoption of new technologies and future innovation opportunities.
The MIT Integrated Learning Initiative (MITili) is a cross-disciplinary, Institute wide initiative to foster quantitative and rigorous research about how people learn and how knowledge from that research can enhance learning from school through adult professional education. MITili aims to integrate knowledge from psychology, economics, neuroscience, engineering, and public policy in pursuit of these goals. The work of the future will require life-long learning, and knowledge from learning science ought to enhance that learning. I will show how knowledge from learning science can enhance work-place learning. I will also review how technology might enable, and in some cases disable, learning. I will also share recent findings about how sleep matters for higher education. Finally, I will share some evidence about the brain bases of adult learning.
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.
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.
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.
How many Design Thinking workshops have you been to in the last 5 years? How many times have you seen the IDEO shopping cart video? User-Centered Design has changed how industry innovates and has taught us how to go beyond business needs and design for customer/user needs. But think about your favorite products—do they just give you satisfaction as a customer or user? Or do they see into your life and fulfill you at a deeper level? We founded Human Element to go beyond users and to design for humans. In this talk, we will present our proprietary methodology, Whole Human Design to show you how we do that.
It is an exciting time for computer vision. With the success of new computational architectures for visual processing, such as deep neural networks (e.g., ConvNets) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. Even when no examples are available, Generative Adversarial Networks (GANs) have demonstrated a remarkable ability to learn from images and are able to create nearly photorealistic images. The performance achieved by convNets and GANs is remarkable and constitute the state of the art on many tasks. But why do convNets work so well? what is the nature of the internal representation learned by a convNet in a classification task? How does a GAN represent our visual world internally? In this talk I will show that the internal representation in both convNets and GANs can be interpretable in some important cases. I will then show several applications for object recognition, computer graphics, and unsupervised learning from images and audio.
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.
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!