We are in the midst of an impending step change, and again, schools like MIT are in competition to lead this change. It has led to an "arms race" in higher education that will shape the future people that work with/for you. Hundreds of millions of dollars are being spent by universities in a competition to create innovation ecosystems that produce technology innovators that have making + innovation skill sets.
You're going to want to know about these people, who is best at educating/creating them, and how to gain a competitive advantage in hiring them. In this talk, I'm going to help you figure that out.
An open question in artificial intelligence is how to endow agents with common sense knowledge that humans naturally seem to possess. A prominent theory in child development posits that human infants gradually acquire such knowledge through the process of experimentation. According to this theory, even the seemingly frivolous play of infants is a mechanism for them to conduct experiments to learn about their environment. Inspired by this view of biological sensorimotor learning, I will present my work on building artificial agents that use the paradigm of experimentation to explore and condense their experience into models that enable them to solve new problems. I will discuss the effectiveness of my approach and open issues using case studies of a robot learning to push objects, manipulate ropes, finding its way in office environments and an agent learning to play video games merely based on the incentive of conducting experiments.
Every team has top performers -- people who excel at working in a team to find the right solutions in complex, difficult situations. These top performers include nurses who run hospital floors, emergency response teams, air traffic controllers, and factory line supervisors. While they may outperform the most sophisticated optimization and scheduling algorithms, they cannot often tell us how they do it. Similarly, even when a machine can do the job better than most of us, it can’t explain how. In this talk I share recent work investigating effective ways to blend the unique decision-making strengths of humans and machines. I discuss the development of computational models that enable machines to efficiently infer the mental state of human teammates and thereby collaborate with people in richer, more flexible ways. Our studies demonstrate statistically significant improvements in people’s performance on military, healthcare and manufacturing tasks, when aided by intelligent machine teammates.
Our work addresses the planning, control, and mapping issues for autonomous robot teams that operate in challenging, partially observable, dynamic environments with limited field-of-view sensors. In such scenarios, individual robots need to be able to plan/execute safe paths on short timescales to avoid imminent collisions. Performance can be improved by planning beyond the robots’ immediate sensing horizon using high-level semantic descriptions of the environment. For mapping on longer timescales, the agents must also be able to align and fuse imperfect and partial observations to construct a consistent and unified representation of the environment. Furthermore, these tasks must be done autonomously onboard, which typically adds significant complexity to the system. This talk will highlight three recently developed solutions to these challenges that have been implemented to (1) robustly plan paths and demonstrate high-speed agile flight of a quadrotor in unknown, cluttered environments; and (2) plan beyond the line-of-sight by utilizing the learned context within the local vicinity, with applications in last-mile delivery. We further present a multi-way data association algorithm to correctly synchronize partial and noisy representations and fuse maps acquired by (single or multiple) robots, showcased on a simultaneous localization and mapping (SLAM) application.
Spatial perception has witnessed an unprecedented progress in the last decade. Robots are now able to detect objects, localize them, and create large-scale maps of an unknown environment, which are crucial capabilities for navigation and manipulation. Despite these advances, both researchers and practitioners are well aware of the brittleness of current perception systems, and a large gap still separates robot and human perception. While many applications can afford occasional failures (e.g., AR/VR, domestic robotics) or can structure the environment to simplify perception (e.g., industrial robotics), safety-critical applications of robotics in the wild, ranging from self-driving vehicles to search & rescue, demand a new generation of algorithms. This talk discusses two efforts targeted at bridging this gap. The first focuses on robustness: I present recent advances in the design of certifiably robust spatial perception algorithms that are robust to extreme amounts of outliers and afford performance guarantees. These algorithms are “hard to break” and are able to work in regimes where all related techniques fail. The second effort targets metric-semantic understanding. While humans are able to quickly grasp both geometric and semantic aspects of a scene, high-level scene understanding remains a challenge for robotics. I present recent work on real-time metric-semantic understanding, which combines robust estimation with deep learning. I discuss these efforts and their applications to a variety of perception problems, including mesh registration, image-based object localization, and robot Simultaneous Localization and Mapping.
Moderator: David Keith Panelists (4-minute statement each): Kent Larson Carlo Ratti Sarah Williams Jinhua Zhao
Given the severe mobility challenges in urbanizing areas, numerous visions for designing urban mobility systems are discussed by policymakers, planners, and industry. These visions must anticipate technological and sociodemographic developments, while accounting for the constraints of operator business models and environmental concerns. In this session, MIT faculty will share and discuss their ideas for urban mobility systems around the globe, considering both promising technologies as well as heterogeneities among the world’s urban centers.
Moderator: Randall Field Speakers: - Matthias Winkenbach (14 minutes) "Opportunities and Challenges of Urban Delivery of Goods" - John Hansman (14 minutes) "Opportunities and challenges for urban air mobility"
Urban areas around the globe face increasing mobility challenges. Demand for both passenger and freight services continue to increase, straining already congested systems. Opportunities to build new infrastructure to address these challenges are limited. Therefore, novel system designs are needed to support mobility in future urban environments. For passenger transportation, Urban Air Mobility systems could create additional capacity through largely decoupling transportation from the confinements of the ground. For freight transportation, existing ground infrastructure (e.g. metro systems) could be leveraged systematically and autonomous systems in combination with additive manufacturing techniques for localized production could disrupt urban logistics.
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.