NishMath - #mit

MIT researchers are making significant strides in artificial intelligence, focusing on enhancing AI's ability to learn and interact with the world more naturally. One project involves developing AI models that can learn connections between vision and sound without human intervention. This innovative approach aims to mimic how humans learn, by associating what they see with what they hear. The model could be useful in applications such as journalism and film production, where the model could help with curating multimodal content through automatic video and audio retrieval.

The new machine-learning model can pinpoint exactly where a particular sound occurs in a video clip, eliminating the need for manual labeling. By adjusting how the original model is trained, it learns a finer-grained correspondence between a particular video frame and the audio that occurs in that moment. The enhancements improved the model’s ability to retrieve videos based on an audio query and predict the class of an audio-visual scene, like the sound of a roller coaster in action or an airplane taking flight. Researchers also made architectural tweaks that help the system balance two distinct learning objectives, which improves performance.

Additionally, researchers from the National University of Singapore have introduced 'Thinkless,' an adaptive framework designed to reduce unnecessary reasoning in language models. Thinkless reduces unnecessary reasoning by up to 90% using DeGRPO. By incorporating a novel algorithm called Decoupled Group Relative Policy Optimization (DeGRPO), Thinkless separates the training focus between selecting the reasoning mode and improving the accuracy of the generated response. This framework equips a language model with the ability to dynamically decide between using short or long-form reasoning, addressing the issue of resource-intensive and wasteful reasoning sequences for simple queries.


References :

• learn.aisingapore.org: AI learns how vision and sound are connected, without human intervention | MIT News
• news.mit.edu: AI learns how vision and sound are connected, without human intervention
• www.marktechpost.com: Researchers from the National University of Singapore Introduce ‘Thinkless,’ an Adaptive Framework that Reduces Unnecessary Reasoning by up to 90% Using DeGRPO
• news.mit.edu: Learning how to predict rare kinds of failures
• MarkTechPost: Researchers from the National University of Singapore Introduce ‘Thinkless,’ an Adaptive Framework that Reduces Unnecessary Reasoning by up to 90% Using DeGRPO

Classification:

• HashTags: #MITAI #MachineLearning #AIRobotics
• Company: MIT
• Target: AI Researchers
• Feature: AI Learning
• Type: Research
• Severity: Informative

MIT researchers have unveiled a "periodic table of machine learning," a groundbreaking framework that organizes over 20 common machine-learning algorithms based on a unifying algorithm. This innovative approach allows scientists to combine elements from different methods, potentially leading to improved algorithms or the creation of entirely new ones. The researchers believe this framework will significantly fuel further AI discovery and innovation by providing a structured approach to understanding and developing machine learning techniques.

The core concept behind this "periodic table" is that all these algorithms, while seemingly different, learn a specific kind of relationship between data points. Although the way each algorithm accomplishes this may vary, the fundamental mathematics underlying each approach remains consistent. By identifying a unifying equation, the researchers were able to reframe popular methods and arrange them into a table, categorizing each based on the relationships it learns. Shaden Alshammari, an MIT graduate student and lead author of the related paper, emphasizes that this is not just a metaphor, but a structured system for exploring machine learning.

Just like the periodic table of chemical elements, this new framework contains empty spaces, representing algorithms that should exist but haven't been discovered yet. These spaces act as predictions, guiding researchers toward unexplored areas within machine learning. To illustrate the framework's potential, the researchers combined elements from two different algorithms, resulting in a new image-classification algorithm that outperformed current state-of-the-art approaches by 8 percent. The researchers hope that this "periodic table" will serve as a toolkit, allowing researchers to design new algorithms without needing to rediscover ideas from prior approaches.


References :

• news.mit.edu: Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.
• www.sciencedaily.com: After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists combine elements of different methods to improve algorithms or create new ones.
• techxplore.com: MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.
• learn.aisingapore.org: This article discusses “Periodic table of machine learning†could fuel AI discovery | MIT News

Classification:

• HashTags: #MachineLearning #PeriodicTable #AIDiscovery
• Company: MIT
• Target: AI Researchers
• Feature: Algorithm Discovery
• Type: Research
• Severity: Informative

NASA astronaut Chris Williams, who earned a doctorate in physics from MIT in 2012, is set to launch to the International Space Station (ISS) on a Russian Soyuz rocket no earlier than November 2025. The rookie astronaut, a member of NASA's 23rd astronaut class, will join cosmonauts Sergey Kud-Sverchkov and Sergei Mikaev aboard the Soyuz MS-28, launching from Baikonur Cosmodrome in Kazakhstan. Williams will spend approximately eight months on the ISS as part of Expedition 74, serving as a flight engineer and continuing ongoing microgravity investigations into the effects of spaceflight.

This mission marks Williams' first assignment to space after completing his training. Prior to joining NASA, Williams completed a Medical Physics Residency training at Harvard Medical School and conducted research as a clinical physicist at Brigham and Women’s Hospital. His selection for the Soyuz MS-28 mission follows the recent launch of fellow classmate Nichole Ayers on SpaceX's Crew-10 mission and the selection of Andre Douglas as part of NASA's Artemis 2 backup crew. The MS-28 crew will replace the MS-27 astronauts, including NASA's Jonny Kim, who arrived at the ISS in April.

Separately, a new physics model has been developed to analyze the intricacies of bowling and determine the optimal conditions for achieving strikes. The model utilizes six differential equations relating to a rotating rigid body. This research considers several factors that influence a bowling ball's trajectory, including the composition and application of oil on bowling lanes, as well as the inherent asymmetries of bowling balls. The team of physicists involved in the study includes individuals with a strong background in bowling, including one who serves as a coach for Team England at the European Youth Championships. The study aims to move beyond statistical analysis of empirical data and provide a more comprehensive understanding of the physics behind bowling.


References :

• arstechnica.com: "This isn't 'Nam, this is bowling, there are rules..." The physics of bowling strike after strike. New model uses 6 differential equations relating to a rotating rigid body for best strike conditions.
• physics.mit.edu: Rookie NASA astronaut Chris Williams PhD ’12 will launch to the ISS on a Russian rocket later this year

Classification:

• HashTags: #MITNews #Astronaut #Physics
• Company: MIT
• Target: Innovation
• Product: Physics
• Feature: Research
• Type: News
• Severity: Medium