At the recent GPU Technology Conference, held in San Jose, California, NVIDIA founder and CEO Jensen Huang stated that Kaldi had become “the most popular framework for speech recognition”. And was “now optimized for GPUs”. Kaldi, a toolkit for speech recognition, was created in 2009 at a Johns Hopkins University workshop titled “Low Development Cost, High Quality Speech […]
Scientific American reports on efforts to mine social media to forecast flu outbreaks. CS/CLSP’s Mark Dredze is quoted.
Johns Hopkins University’s annual celebration of engineers’ contributions to society begins today and runs through Friday, Feb. 23 on the Homewood campus.
In a Q&A with Inverse Innovation, Suchi Saria discusses her research in using machine learning algorithms to tailor treatment and improve outcomes for clinical patients.
During the six-week workshop, attendees collaborated to design a system to monitor neurodegenerative diseases over the phone, detect risk and monitor mental health through posts on social media, and build a speech recognition system from untranscribed data.
KITT.AI, a new Seattle-based artificial intelligence startup whose founders include two Whiting School alumni, has landed funding from Founders’ Co-Op, Amazon’s Alexa Fund, and Madrona Venture Group.
After actor Charlie Sheen disclosed his HIV-positive status on NBC’s Today show last November, millions took to the Internet to find out more about HIV, according to a new study led by computer scientist Mark Dredze and two Whiting School alumni.
The six-week workshop—which marks the 21st summer workshop in CLSP’s history—seeks to advance and promote machine learning for speech language and computer vision technology.
Predicting suicide risk by understanding language usage is exactly what Glen Coppersmith–data scientist, psychologist, and CLSP affiliate–has set out to do, reports Mashable.
“A momentous change in health care is under way,” says Suchi Saria, an assistant professor of Computer Science at Johns Hopkins University. Fast Company reports five ways that machine learning is poised to bring new rigor to medicine.
