LAb41 discusses next steps on their VOiCES project.

LAb41 discusses VOiCES, the first open source speech data-set recorded in real environments with far-field microphones, capturing reverberant acoustics and background noise.

Lab41 recently started tackling the question of how much information is a model memorizing and how can we better understand this?

Machine Learning (ML) models are increasingly becoming more ubiquitous in our everyday lives, many of the models built using personal and sensitive data. For instance, ML models now regularly use unique biometric data to identify us, such as our voices, our fingerprints, faces or iris’.

Last year, we announced the creation of Squiggle, an algorithm for intuitive DNA sequence visualization and analysis along with an accompanying software package to enable interactive exploration of DNA sequences. Next, we created DNAvisualization.org so that it requires only a web browser to operate.

Discusses the VOiCES project and removal of dynamic noise, sources of noise which do not repeat, or do so only sporadically.

Discusses day three highlights from RECOMB 2019, a conference focusing on computational molecular biology.

Discusses day two highlights from RECOMB 2019, a conference focusing on computational molecular biology. There were two really interesting presentations today that show just how far deep learning has come in one of the hardest research areas in computational biology: proteins.

Discusses highlights from this year’s Research in Computational Molecular Biology (RECOMB) conference.

SRI International and Lab41 are proud to release the Voices Obscured in Complex Environmental Settings (VOICES) corpus, a collaborative effort that brings speech data in acoustically challenging reverberant environments to the researcher.

In this post, we investigate methods of redacting information from a machine learning classifier.

B.Next and Lab41 set out to answer the following question: Given a DNA sequence, can we use machine learning to determine if it was purposefully modified?