Latest from MIT : New techniques efficiently accelerate sparse tensors for massive AI models

Researchers from MIT and NVIDIA have developed two techniques that accelerate the processing of sparse tensors, a type of data structure that’s used for high-performance computing tasks. The complementary techniques could result in significant improvements to the performance and energy-efficiency of systems like the massive machine-learning models that drive generative artificial intelligence. Tensors are data…

Latest from MIT : Accelerating AI tasks while preserving data security

With the proliferation of computationally intensive machine-learning applications, such as chatbots that perform real-time language translation, device manufacturers often incorporate specialized hardware components to rapidly move and process the massive amounts of data these systems demand. Choosing the best design for these components, known as deep neural network accelerators, is challenging because they can have…

Latest from MIT : The brain may learn about the world the same way some computational models do

To make our way through the world, our brain must develop an intuitive understanding of the physical world around us, which we then use to interpret sensory information coming into the brain. How does the brain develop that intuitive understanding? Many scientists believe that it may use a process similar to what’s known as “self-supervised…

Latest from MIT Tech Review – Joy Buolamwini: “We’re giving AI companies a free pass”

Joy Buolamwini, the renowned AI researcher and activist, appears on the Zoom screen from home in Boston, wearing her signature thick-rimmed glasses.  As an MIT grad, she seems genuinely interested in seeing old covers of MIT Technology Review that hang in our London office. An edition of the magazine from 1961 asks: “Will your son…

Latest from Google AI – Answering billions of reporting queries each day with low latency

Posted by Jagan Sankaranarayanan, Senior Staff Software Engineer, and Indrajit Roy, Head of Napa Product, Google Google Ads infrastructure runs on an internal data warehouse called Napa. Billions of reporting queries, which power critical dashboards used by advertising clients to measure campaign performance, run on tables stored in Napa. These tables contain records of ads…

Latest from Google AI – Measurement-induced entanglement phase transitions in a quantum circuit

Posted by Jesse Hoke, Student Researcher, and Pedram Roushan, Senior Research Scientist, Quantum AI Team Quantum mechanics allows many phenomena that are classically impossible: a quantum particle can exist in a superposition of two states simultaneously or be entangled with another particle, such that anything you do to one seems to instantaneously also affect the…

Latest from Google AI – Improving traffic evacuations: A case study

Posted by Damien Pierce, Software Engineer, and John Anderson, Senior Research Director, Google Research Some cities or communities develop an evacuation plan to be used in case of an emergency. There are a number of reasons why city officials might enact their plan, a primary one being a natural disaster, such as a tornado, flood,…

Latest from Google AI – Audioplethysmography for cardiac monitoring with hearable devices

Posted by Xiaoran “Van” Fan, Experimental Scientist, and Trausti Thormundsson, Director, Google The market for true wireless stereo (TWS) active noise canceling (ANC) hearables (headphones and earbuds) has been soaring in recent years, and the global shipment volume will nearly double that of smart wristbands and watches in 2023. The on-head time for hearables has…

Latest from Google AI – Supporting benchmarks for AI safety with MLCommons

Posted by Anoop Sinha, Technology and Society, and Marian Croak, Google Research, Responsible AI and Human Centered Technology team Standard benchmarks are agreed upon ways of measuring important product qualities, and they exist in many fields. Some standard benchmarks measure safety: for example, when a car manufacturer touts a “five-star overall safety rating,” they’re citing…

Latest from Google AI – Spoken question answering and speech continuation using a spectrogram-powered LLM

Posted by Eliya Nachmani, Research Scientist, and Alon Levkovitch, Student Researcher, Google Research The goal of natural language processing (NLP) is to develop computational models that can understand and generate natural language. By capturing the statistical patterns and structures of text-based natural language, language models can predict and generate coherent and meaningful sequences of words….