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Using robotics to supercharge health care

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Since its founding in 1998, Vecna Technologies has developed a number of ways to help hospitals care for patients. The company has produced intake systems to respond to Covid-19 patient surges, prediction systems to manage health complications in maternity wards, and telepresence robots that have allowed sick people to stay connected with friends and loved ones. The differences among those products have also led to a number of transformations and spinoffs, including material handling company Vecna Robotics and the health care nonprofit VecnaCares. Vecna Technologies co-founders Deborah Noel Theobald '95 and Daniel Theobald '95, SM '98 say each of those pivots has been driven by a desire to build a robotics company that makes a positive impact on the world. "We knew we wanted to do robotics and do something good in the world," Deborah says of the team's mindset.


Putting clear bounds on uncertainty

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In science and technology, there has been a long and steady drive toward improving the accuracy of measurements of all kinds, along with parallel efforts to enhance the resolution of images. An accompanying goal is to reduce the uncertainty in the estimates that can be made, and the inferences drawn, from the data (visual or otherwise) that have been collected. Yet uncertainty can never be wholly eliminated. And since we have to live with it, at least to some extent, there is much to be gained by quantifying the uncertainty as precisely as possible. Expressed in other terms, we'd like to know just how uncertain our uncertainty is.


Unpacking the "black box" to build better AI models

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When deep learning models are deployed in the real world, perhaps to detect financial fraud from credit card activity or identify cancer in medical images, they are often able to outperform humans. But what exactly are these deep learning models learning? Does a model trained to spot skin cancer in clinical images, for example, actually learn the colors and textures of cancerous tissue, or is it flagging some other features or patterns? These powerful machine-learning models are typically based on artificial neural networks that can have millions of nodes that process data to make predictions. Due to their complexity, researchers often call these models "black boxes" because even the scientists who build them don't understand everything that is going on under the hood.


3D Perception and Deep Learning - Intern at Bosch Group - Sunnyvale, CA, United States

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The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence (for example, human-assisted AI, natural language processing, robotics, 3D perception, and AI platform), energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design. Our global research on human-machine intelligence focuses on Big Data Visual Analytics, Explainable AI (XAI), Audio Analytics, Natural Language Processing, Knowledge Engineering, XR/AR/MR, 3D Perception, and Cloud Robotics. We develop intelligent and trustworthy AIoT solutions to enable inspiring UX for Bosch products and services in application areas such as autonomous driving, driver assistance systems (ADAS), robotics, smart manufacturing, health care, smart home and building solutions. As a part of our global research unit, our Mixed Reality and Autonomous System group is responsible for shaping the future user experience of Bosch products by developing cutting-edge technologies and prototype systems in the field of mixed reality and robotics, including object detection, segmentation, tracking and pose estimation, 3D reconstruction and understanding, visual localization, sensor fusion, reinforcement learning and adaptive robot control.


Robotics AI Intern at Bosch Group - Pittsburgh, PA, United States

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The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is part of the global Bosch Group (www.bosch.com), The Research and Technology Center North America (RTC-NA) is committed to providing technologies and system solutions for various Bosch business fields primarily in the areas of Human Machine Interaction (HMI), Robotics, Energy Technologies, Internet Technologies, Circuit Design, Semiconductors and Wireless, and MEMS Advanced Design. The two research groups at RTC-NA: Wireless connectivity & sensing (WCS), and Intelligent Internet of Things (IIoT) are currently working together with external partners to develop smart docking software for future lunar rovers as part of NASA funded project. The project team is looking for enthusiastic graduate student interns to work on product oriented research. We seek an ideal candidate with good theoretical background and strong desire for practical implementation in the area of multi-sensor fusion (camera, IMUs, wireless) in the context of autonomy/robotics applications.


Advice and responses from faculty on ChatGPT and A.I.-assisted writing - MIT Comparative Media Studies/Writing

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Several faculty members in CMS/W have expertise, both technological and pedagogical, in what the use of ChatGPT and other A.I. tools may mean for the instruction of academic writing. Because instructors at MIT and elsewhere have expressed some urgency in better understanding what the effects and ethics of ChatGPT may be, two pairs of our faculty have provided an advisory memo and a response. It should be noted that with tools like ChatGPT being both so new and so quickly evolving, these pieces are the faculty's take and don't yet represent official guidance from CMS/W or MIT. First is an excerpt from "Advice Concerning the Increase in AI-Assisted Writing", a memo from Edward Schiappa, Professor of Rhetoric, and Nick Montfort, Professor of Digital Media. The full document is available at Montfort's website.


Computers that power self-driving cars could be a huge driver of global carbon emissions

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In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today. That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted. The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.


2022-23 Takeda Fellows: Leveraging AI to positively impact human health

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The MIT-Takeda Program, a collaboration between MIT's School of Engineering and Takeda Pharmaceuticals Company, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry. With the aim of building a community dedicated to the next generation of AI and system-level breakthroughs, the MIT-Takeda Program is also creating educational opportunities. Every year Takeda funds fellowships to support graduate students pursuing research related to health and AI. This year's Takeda Fellows, described below, are working on projects ranging from electronic health record systems and robotic control to pandemic preparedness and traumatic brain injuries.


Program teaches US Air Force personnel the fundamentals of AI

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A new academic program developed at MIT aims to teach U.S. Air and Space Forces personnel to understand and utilize artificial intelligence technologies. In a recent peer-reviewed study, the program researchers found that this approach was effective and well-received by employees with diverse backgrounds and professional roles. The project, which was funded by the Department of the Air Force–MIT Artificial Intelligence Accelerator, seeks to contribute to AI educational research, specifically regarding ways to maximize learning outcomes at scale for people from a variety of educational backgrounds. Experts in MIT Open Learning built a curriculum for three general types of military personnel -- leaders, developers, and users -- utilizing existing MIT educational materials and resources. They also created new, more experimental courses that were targeted at Air and Space Forces leaders.


Making scientific publishing easier around the world

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If you've been at MIT long enough, you've probably heard grumblings about peer-reviewed journals that are slow or uncooperative. But those problems are trivial compared to the challenges faced by researchers in other parts of the world. Researchers in developing countries have to sift through lists of less familiar international journals that each have wildly different policies and review practices. That makes it more likely that papers will be sent back, which can delay publication times dramatically as they go back and forth with editors. Early-career researchers in particular are more likely to have their work sent back for corrections or fall victim to predatory journals that only care about collecting publication fees.