The Massachusetts Institute of Technology has come up what it thinks is a better way to teach robots general purpose skills. Derived from LLM techniques, the method provides robot intelligence access to an enormous amount of data at once, rather than exposing it to individual programs for specific tasks. Faster and more cost efficient, the approach has been referred to as a “brute force” approach to problem-solving, and machine learners have taken to it in lieu of individualized, task-specific “imitation learning.” Early tests show it outperforming traditional training by more than 20 percent under simulation and real-world conditions. Continue reading MIT Intros LLM-Inspired Teacher for General Purpose Robots

AI startup Liquid, founded by alums of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), has released its first models. Called Liquid Foundation Models, or LFMs, the multimodal family approaches “intelligence” differently than the pre-trained transformer models that dominate the field. Instead, the LFMs take a path of “first principles,” which MIT describes as “the same way engineers build engines, cars, and airplanes,” explaining that the models are large neural networks with computational units “steeped in theories of dynamic systems, signal processing and numeric linear algebra.” Continue reading MIT Spinoff Liquid Eschews GPTs for Its Fluid Approach to AI

The list of potential risks associated with artificial intelligence continues to grow. “Global AI adoption is outpacing risk understanding,” warns the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL), which has joined with the MIT multidisciplinary computer group FutureTech to compile the AI Risk Repository, a “living database” of more than 700 unique risks extracted across 43 source categories. Organized by cause, classifying “how, when and why these risks occur,” the repository is comprised of seven risk domains (for example, “misinformation”) and 23 subdomains (such as “false or misleading information”). Continue reading MIT’s AI Risk Assessment Database Debuts with 700 Threats

Researchers from the Massachusetts Institute of Technology and Adobe have unveiled a new AI acceleration tool that makes generative apps like DALL-E 3 and Stable Diffusion up to 30x faster by reducing the process to a single step. The new approach, called distribution matching distillation, or DMD, maintains or enhances image quality while greatly streamlining the process. Theoretically, the technique “marries the principles of generative adversarial networks (GANs) with those of diffusion models,” consolidating “the hundred steps of iterative refinement required by current diffusion models” into one step, MIT PhD student and project lead Tianwei Yin says. Continue reading New Tech from MIT, Adobe Advances Generative AI Imaging

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have introduced a computer vision system that combines image recognition and image generation technology into one training model instead of two. The result, MAGE (short for MAsked Generative Encoder) holds promise for a wide variety of use cases and is expected to reduce costs through unified training, according to the team. “To the best of our knowledge, this is the first model that achieves close to state-of-the-art results for both tasks using the same data and training paradigm,” the researchers said. Continue reading MAGE AI Unifies Generative and Recognition Image Training

Waiting for a video to buffer may become an annoyance of the past. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are working on streaming algorithms that use AI to improve load rates and, thus, reduce buffering. Dubbed Pensieve, the new technology relies on machine learning to navigate the often-chaotic and ever-changing conditions of networks in real-time, based on a system of rewards (when the video loads smoothly) and penalties (when it’s interrupted). Meanwhile, Netflix is working on its own AI solution to address buffering. Continue reading MIT and Netflix Testing AI-Based Algorithms to Curb Buffering

MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), with Israel’s Weizmann Institute of Science, released the prototype of a 3D display technology, for use in movie theaters, that doesn’t require glasses. Other glasses-free 3D displays have been available, most notably with the Nintendo 3DS, but they are designed for use by a single user and only work when the content is viewed at a specific angle. A research paper on the technology, dubbed “Cinema 3D,” will be given at the SIGGRAPH conference this week. Continue reading MIT Prototypes Glasses-Free 3D for Motion Picture Theaters