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CMU Researchers, Argo AI Predict Future With Lidar Data

CMU School of Computer Science

Researchers at Carnegie Mellon University's Argo AI Center for Autonomous Vehicle Research developed a system to use lidar data to visualize not just where other moving objects are on the roads now, but also where they are likely to be in a few seconds. This method enables autonomous vehicles (AVs) to better plan for the motions of cars, pedestrians and other moving objects around it. Using the moving object's current position, velocity and trajectory, their method, FutureDet, creates several possible future paths in lidar, ranks them by the confidence it has that the moving object will follow the predicted path, and shows all these paths to the AV as if they were occurring in real-time. FutureDet then sees how the AV plans to respond to each path. "This method basically takes in lidar measurements and spits out the possible future locations of objects," said Deva Ramanan, a professor in the School of Computer Science's Robotics Institute, head of the Argo AI Center, an Argo principal scientist and the supervising researcher on the paper.


$10.5M Army Contract to CMU Lab Will Expand Use of AI in Predictive Maintenance

CMU School of Computer Science

Yet large amounts of clinical data may not be sufficient for use in AI because little data is labeled, and it is not always apparent whether data represent healthy patients or sick patients without human guidance. One priority is thus to develop new, efficient methods of capturing human expertise so that machines can understand the contexts that may not be well represented in the available data. That is crucial for applying AI to health care, but also important for equipment maintenance in the military, which is seeing retirements of an entire generation of veteran maintainers.


Is the Future of Healthcare Machine Learning Tools

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Complex artificial intelligence programs promise to revolutionize medicine. Terms like "machine learning," "artificial intelligence," and "deep learning" have become scientific buzzwords in recent years. But can these technologies be applied to save lives? The answer to that is a resounding yes. Future developments in the health sciences may indeed depend on the integration of rapidly growing computing technologies and methods into medical practice. Cosmos spoke with researchers from the University of Pittsburgh, Pennsylvania, USA, who have just published a paper in Radiology about using machine learning techniques to analyze large datasets of brain trauma patients.


Core Challenges in Embodied Vision-Language Planning

Journal of Artificial Intelligence Research

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.


The Global Reach of CMU AI

CMU School of Computer Science

As intractable problems accrue and grow, artificial intelligence is increasingly being called upon as part of the solution. Carnegie Mellon University AI researchers have stepped up to help surmount these obstacles where large data sets must be analyzed and patterns discovered to find answers. Last year, the National Science Foundation teamed up with the U.S. Department of Agriculture, the U.S. Department of Homeland Security, as well as corporate sponsors Accenture, Amazon, Google and Intel to provide $220 million in grants to create 11 new institutes specifically dedicated to AI research across a wide range of sectors. CMU's School of Computer Science and College of Engineering faculty will work with four of these new institutes: the AI Institute for Resilient Agriculture, the AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups, the AI Institute for Future Edge Networks and Distributed Intelligence, and the USDA-NIFA Institute for Agricultural AI for Transforming the Workforce and Decision Support. Learn more about these institutes and meet the researchers leading the work in our magazine, The Link.


From The Link: Lessons Learned From the SubT Challenge

CMU School of Computer Science

As the countdown started, a boxy robot with four big wheels carrying a host of cameras, sensors, communication equipment, autonomy software and the computing power to make it all work together rolled down a ramp into a dark tunnel. It did not know where it was, what was ahead of it or where it was going. It was there to explore. Over the next hour, more robots followed: wheeled robots, drones and a dog-like quadruped. Team Explorer deployed eight robots for the final round of the Defense Advanced Research Projects Agency (DARPA) Subterranean, or SubT, Challenge -- a three-year competition during which teams from around the world raced to develop robotic systems that could autonomously operate in underground environments like caves, mines or subway stations for search and rescue missions.


Solving Sudoku With AI or Quantum?

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Established in Pittsburgh, Pennsylvania, US -- Towards AI Co. is the world's leading AI and technology publication focused on diversity, equity, and inclusion. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. All of our articles are from their respective authors and may not reflect the views of Towards AI Co., its editors, or its other writers.


SCS Ph.D. Students Designed, Taught New Course To Make Computer Science More Welcoming, Inclusive

CMU School of Computer Science

The Computer Science Department's new course focusing on issues of justice, equity, diversity and inclusion in computer science and society got its start when a group of graduate students decided to create the training they wished they had received. And after hundreds of hours of work by 15 Ph.D. students --pilot programs, countless conversations with faculty and students, data gathering, and developing and tweaking course material -- CS-JEDI: Justice, Equity, Diversity and Inclusion is now a required part of the curriculum for incoming Ph.D. students in computer science. It's also being looked at as a model by both other departments in the School of Computer Science and universities elsewhere. The course was created and taught by Abhinav Adduri, Valerie Chen, Judeth Choi, Bailey Flanigan, Paul Göelz, Anson Kahng, Pallavi Koppol, Ananya Joshi, Tabitha Lee, Sara McAllister, Samantha Reig, Ziv Scully, Catalina Vajiac, Alex Wang and Josh Williams -- all doctoral candidates in SCS who represent nearly every department in the school. The team received Carnegie Mellon University's 2022 Graduate Student Service Award and will be honored during the Celebration of Education Award Ceremony on Thursday, April 28.


The Only Domain AI Can't Crack

#artificialintelligence

Established in Pittsburgh, Pennsylvania, US -- Towards AI Co. is the world's leading AI and technology publication focused on diversity, equity, and inclusion. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. All of our articles are from their respective authors and may not reflect the views of Towards AI Co., its editors, or its other writers.


Waibel Elected a Fellow of the International Speech Communication Association

CMU School of Computer Science

Alex Waibel, a professor in Carnegie Mellon University's Language Technologies Institute, has been elected a fellow of the International Speech Communication Association (ISCA). The ISCA recognized Waibel for his pioneering contributions in multilingual and multimodal spoken language processing and translation. Waibel, also faculty at the Karlsruhe Institute of Technology in Germany, has worked on speech and machine translation for decades, developing systems that now can translate speech in real time. Waibel demonstrated the first speech translation systems in the 1990s and 2000s. By 2020, he had developed a system that outperformed humans in recognizing conversational speech on a public benchmark.