Stationary energy storage seeks to disrupt the electricity markets on a global scale. Safe, inexpensive energy storage, the missing link in the electricity grid’s 140-year history, will: (1) supplement large capital infrastructure upgrades with low-cost storage; (2) pair with renewables to realize cost effective alternatives to fossil fuel generation; (3) upturn legislative and regulatory restrictions, ushering in digital and energy-sharing economic and societal opportunities.
PolyJoule has developed a non-lithium form of energy storage that is built purposely for the electricity grid. Safety is molecularly designed into our battery chemistry, streamlining permitting and usability. PolyJoule batteries can respond to both base loads and peak loads in microseconds, allowing the same energy storage system to participate in multiple power markets and deployment use cases. Upfront asset costs are low. Lifetime battery reliability is high. This lecture will introduce PolyJoule, our proprietary energy storage chemistry, its performance profile, and how congested electricity grids, renewable adaptation, and environmental tidal waves all benefit from low-cost, high-power energy storage assets.
Technology is changing the world at a blistering pace, and established industries are being rapidly displaced, often in a matter of years. Airbnb, Uber, Netflix, Spotify - all multi-billion dollar businesses which unseated highly entrenched incumbents. Nowhere is this more evident than the transportation sector, and the highly integrated and coordinated automotive industry.
The automotive industry is telling the world that it's reinventing itself too, but the supposedly groundbreaking new vehicles they offer are largely built on the same century-old vehicle designs. Global need for immediate order-of-magnitude improvements in vehicle efficiency combined with a rise in mobility-as-a-service preferences and autonomous systems mean the traditional incremental change in vehicle design will not be sufficient for auto makers to remain competitive.
Indigo Technologies is working to break this vehicle design paradigm. The vehicles of tomorrow need to be highly efficient, lightweight, modular, and flexible, using advanced manufacturing techniques and materials. Indigo unlocks this future with its core technology, an innovative in-wheel motor with integrated active suspension, which frees vehicle designers from the constraints of encapsulating a traditional powertrain and allows for efficient design. Marshall will explain how this technology, as well as Indigo's wireless power transfer system, is helping forward-thinking OEMs design and build the future of mobility.
The adherence to treatment in injectable therapies for chronic diseases (e.g. rheumatoid arthritis, multiple sclerosis, psoriasis, Crohn’s disease, etc.) is extremely low (45% - 60%) and in part due to the inconvenience and anxiety associated with using needles and syringes. Biological medicines treating those conditions cannot be formulated as pills and as such there is a huge opportunity for new technologies replacing needles and syringes to transform the perception, approachability and market penetration of such therapies. Portal has developed a next-generation needle-free drug delivery platform that is computer-controlled, easy to use and patient preferred. Real time injection tracking via cloud-based connectivity enables patients and their care teams to manage their condition better and take charge of their wellbeing. Issued from Professor Ian Hunter’s research at the MIT BioInstrumentation Lab, this technology leverages advances in multiple disciplines such as high-power density electromagnetic actuators, ARM-based micro-electronics and embedded software, and energy storage. The company is at the commercial stage, preparing to launch a drug/device combination product with Takeda Pharmaceuticals in the field of Inflammatory Bowel Diseases. A live demonstration of the Portal Device will be presented at the Symposium.
The traditional lecture and laboratory approach used in teaching science and engineering has dominated education at high schools and universities for centuries. Although classroom demonstrations are sometimes used to provide instructive and motivating examples of taught concepts, in large classes they are difficult to see and without direct “hands on” involvement of the students have limited effect. Our initiative to address this shortcoming is MICA (Measurement, Instrumentation, Control and Analysis) an educational approach designed for subjects in Science, Technology, Engineering and Mathematics (STEM). Students interact with an experimental workstation (MICA workstation) to conduct experiments, analyze data, undertake parameter estimation, and fit mathematical models, while learning the theory and relevant subject history under the guidance of a virtual tutor (MICA avatar). As students interact with the MICA workstations their skill level, rate of learning and progress is quantified. Based on these data, deep learning techniques and mathematical modelling are then used to generate an individualized model of a student’s state of knowledge which is augmented every time the student interacts with a MICA workstation. This ‘state of knowledge’ model is then used by the MICA tutor to personalize (and eventually optimize) the teaching pace as well as the way in which subject material is delivered.
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.