Our study introduces an automated system leveraging large language models (LLMs) to assess the effectiveness of five key tutoring strategies: 1. giving effective praise, 2. reacting to errors, 3. determining what students know, 4. helping students manage inequity, and 5. responding to negative self-talk. Using a public dataset from the Teacher-Student Chatroom Corpus, our system classifies each tutoring strategy as either being employed as desired or undesired. Our study utilizes GPT-3.5 with few-shot prompting to assess the use of these strategies and analyze tutoring dialogues. The results show that for the five tutoring strategies, True Negative Rates (TNR) range from 0.655 to 0.738, and Recall ranges from 0.327 to 0.432, indicating that the model is effective at excluding incorrect classifications but struggles to consistently identify the correct strategy. The strategy \textit{helping students manage inequity} showed the highest performance with a TNR of 0.738 and Recall of 0.432. The study highlights the potential of LLMs in tutoring strategy analysis and outlines directions for future improvements, including incorporating more advanced models for more nuanced feedback.

We look at consciousness through the lens of Theoretical Computer Science, a branch of mathematics that studies computation under resource limitations. From this perspective, we develop a formal machine model for consciousness. The model is inspired by Alan Turing's simple yet powerful model of computation and Bernard Baars' theater model of consciousness. Though extremely simple, the model aligns at a high level with many of the major scientific theories of human and animal consciousness, support ing our cl aim that machine consciousness is inevitable.

A wheeled robot set loose on a college campus has figured out how to open all kinds of doors and drawers while rolling around in the real world. The robot adapted to new challenges on its own – paving the way for machines capable of independently interacting with physical objects. "You want the robots to work autonomously… without relying on humans to keep giving examples at test time for every new kind of scenario that you're in," says Deepak Pathak at Carnegie Mellon University (CMU) in Pennsylvania. Pathak and his colleagues initially trained the robot through imitation learning, providing visual examples of how to open objects such as doors, cabinets, drawers and refrigerators. They then turned it loose on the CMU campus to try opening doors and cabinets it had never encountered before.

We welcome applicants from a variety of backgrounds and an undergraduate degree in Computer Science is not required. Incoming students must have a strong background in computer science, including a solid understanding of complexity theory and good programming skills, as well as a good background in mathematics. Specifically, the first-year courses assume at least one year of college-level probability and statistics, as well as matrix algebra and multivariate calculus. For our introductory ML course, there's a self-assessment test [PDF] which will give you some idea about the background we expect students to have (for the MS you're looking at the "modest requirements"). Generally, you need to have some reasonable programming skills, with experience in Matlab/R/scipy-numpy especially helpful, and Java and Python being more useful than C, and a solid math background, especially in probability/statistics, linear algebra, and matrix and tensor calculus.

On this episode of Yinzer Backstage Pass, I paid a visit to the Robotics Institute at Carnegie Mellon University in Oakland. We followed faculty member Matt Travers down a set of stairs into the basement of the building. It was a cavernous space -- lots of piled boxes and miscellaneous storage -- but that turns out to be the perfect space to train the robots that he has been developing. We turned the corner and entered the "MattLab," which was buzzing with activity. More than a dozen grad students were typing on desktops, tinkering with remote controls and referencing huge monitors displaying incomprehensible charts and data.

Aarti Singh, an associate professor in the Machine Learning Department, will use her research on decision-making algorithms to study the evolution of the universe as part of the Simons Collaboration on Learning the Universe. This international collaboration includes researchers from CMU, Columbia University, Harvard University, Princeton University, Lawrence Berkeley National Labs, the Flatiron Institute and international partners from Canada, France, Germany and Sweden. For scientists to understand how the universe evolved, they must know its initial conditions and the physical laws governing those conditions. Since these aren't knowable, they can only be inferred through observation. The collaboration -- directed by Greg Bryan, a professor of astronomy at Columbia University, and made possible by the Simons Foundation -- will repeatedly select sets of initial conditions, predict how they would be observed now, compare that to real observations of galaxies and gas, and then compute the likelihood of those initial conditions.

It's two weeks until the competition, and 17-year-old Ella Maier is ecstatic her robot can finally do a pull-up. "Oh, that's so exciting," the Girls of Steel member said, as her robot latched on to a bar at the team's practice facility and hoisted itself to the second rung. "I'm in charge of that subsystem, and I'm really pleased it works. There's always a fear that it might not perform. There are no guarantees on this stuff, ever."

The Carnegie Mellon University Libraries latest exhibition highlights the history of robotics at CMU and the ongoing work of The Robotics Project to preserve the legacy of the field. The exhibition, "Looking Back To Move Forward / A Re:collection of Robotics at Carnegie Mellon," opened Jan. 19 and runs through Friday, March 18, in the Hunt Library gallery. A virtual tour is available for visitors to explore the exhibition remotely. Curated by archivist and oral historian Katherine Barbera and Kathleen Donahoe, the Robot Archive processing archivist, "Looking Back To Move Forward" invites viewers to explore the history and the wide variety of research areas that CMU is known for, including field robotics, artificial intelligence and human-robot interaction, among others. Visitors will see more than 40 robots and archival artifacts -- such as soccer robots, snake robots, a nurse robot called "Pearl," a "Snackbot" autonomous food-delivery robot, and "Terregator," one of the first outdoor autonomous vehicles -- along with personal recollections from the people who made it all happen.

Imagine flying a small, robotic aircraft from goal post to goal post on an American football field. Now, repeat the flight 470 more times, and you'll match the record-setting 32-mile autonomous drone flight recorded by Aakash Sinha's industry-leading startup based in New Delhi. "It's only the beginning," said Sinha, a 2003 School of Computer Science graduate with a master's degree in robotics. "I'm super excited about how drones can change things, not just here in India but globally." From delivering vaccines in hard-to-reach areas to limiting fossil fuel leaks in expansive pipelines, the possibilities for positive change are endless.

A virtual, autonomous racing challenge launching this week will enable aspiring racers to head to the track without building a car, knowing how to brake and accelerate through a corner, or leaving their computer. And as teams tackle the demands of high-speed and safe driving that pushes race cars to their limits, they will improve the safety of autonomous vehicles and the learning algorithms teaching them to drive. The Learn-to-Race Autonomous Racing Virtual Challenge started Monday, Dec. 6. Competitors use the Learn-to-Race environment to teach an artificially intelligent agent how to race. The challenge is coupled with a workshop on Safe Learning for Autonomous Driving, which is accepting research paper submissions.