Reaching Earth's orbit is an extremely energetic event. To remain in orbit requires a velocity of about 8 km/s, which corresponds to a specific energy of 30 MJ per kilogram. Since 1957 humans have been able to launch themselves as well as artificial satellites into orbit at increasing frequencies. This talk will give an update on the current state of launch vehicle technologies and this important and enabling part of the space sector. In 2023, for example, there were 222 launch attempts worldwide, of which 95% were successful. Assuming a CAGR of 12%, we predict that by 2027, there will be daily launches to space from somewhere on Earth's surface. This talk will summarize some of the physics, technologies, and economics of the launch vehicle industry.
MIT ARCLab focuses on space traffic management, space situational awareness, and space sustainability. This talk will analyze space security issues related to space management and orbital debris. It will also cover topics in space awareness, including behavior estimation, behavior characterization, and learning. Furthermore, the talk will discuss the Department of the Air Force's AI Accelerator, which has a focused project dedicated to space awareness and the development of AI techniques to address space security issues.
Availability and reliability of public electric vehicle charging infrastructure is an important factor for EV adoption. Professors Alex Jacquillat and Dan Freund provide an overview of their findings from an MIT Mobility Initiative research project that leverages computer vision and optimization to support public EV charging infrastructure within a dense urban context. Where can EV charging stations be feasibly located? Should fewer charging stations be offered each with more charging ports, or should more charging stations be offered each with fewer ports? Where should urban EV chargers be located? What is the ideal charging speed (power level)? This work focuses on the immediate neighborhood of Corktown in Detroit, Michigan, with support from Michigan Central.
No model or mathematical formula alone can capture the complexity of our world, with all its emotional, cultural, and human variables that are difficult to define and measure. Therefore, we must design. To cope with complexity, we often oversimplify and seek quick models to make sense of the world and predict outcomes. However, this approach can hinder creative problem-solving and contradict the essence of innovation.
As a method of synthesis, design is a fundamental human ability that relies on intuition, prediction, and facts to envision and create pathways to a better future. Designing generates meaning by inventing new wholes that exceed the sum of their parts through an interactive, collaborative process. By involving stakeholders in the design process to deeply understand their needs and the context of innovation, design uncovers opportunities for problem-solving that conventional analytical methods alone cannot achieve. The design process reveals hidden opportunities within complex situations, enabling a creative way forward. Thus, design is essential in our quest for a more sustainable and equitable future alongside science and technology.
The MIT community relies on our enterprise systems for a range of activities — everything from hiring and evaluating employees to managing research grants and facilities projects to maintaining student information. Our vision in updating our systems is 1) to create easy-to-use and well-integrated systems, streamlined processes, and comprehensible and accessible data for reporting and analysis; 2) to simplify our business processes to improve efficiency and effectiveness; 3) to modernize our enterprise systems and data architecture to take advantage of more innovative technology and functionality; and 4) to make our data accessible and actionable by implementing more robust data governance through clear ownership and accountability.
This talk shares both our plan and some best practices from recent efforts at transforming a complex collection of digital and non-digital assets into a more cohesive landscape, including a) addressing systems, processes, and data wholistically; b) developing a thoughtful and actionable multi-year roadmap of digital transformation projects; and c) engaging and assisting our entire community every step of the way.
Corporate culture is one of the most important enablers—or obstacles—to innovation, but culture is notoriously difficult to measure. Recent advances in LLMs enable leaders to mine employee feedback to understand and improve their corporate cultures. This session will discuss how to leverage AI to measure culture and share insights from an ongoing study of innovative culture at companies including NVIDIA, SpaceX, and Novo Nordisk.
Firms always face a choice for where to source their innovation: do they hire internal researchers? Work with startups or external companies? There are many options. In this talk, I will present results from research on how firms are sourcing digital innovations, and then I will speak specifically about AI and how to view it in this framework.