Audio watermarking algorithm is first to solve “second-screen problem” in real time

Audio watermarking is the process of adding a distinctive sound pattern — undetectable to the human ear — to an audio signal to make it identifiable to a computer. It’s one of the ways that video sites recognize copyrighted recordings that have been posted illegally. To identify a watermark, a computer usually converts a digital file into an audio signal, which it processes internally.Read More

Identifying and eliminating bugs in learned predictive models

One in a series of posts explaining the theories underpinning our research. Bugs and software have gone hand in hand since the beginning of computer programming. Over time, software developers have established a set of best practices for testing and debugging before deployment, but these practices are not suited for modern deep learning systems. Today, the prevailing practice in machine learning is to train a system on a training data set, and then test it on another set. While this reveals the average-case performance of models, it is also crucial to ensure robustness, or acceptably high performance even in the worst case. In this article, we describe three approaches for rigorously identifying and eliminating bugs in learned predictive models: adversarial testing, robust learning, and formal verification.Machine learning systems are not robust by default. Even systems that outperform humans in a particular domain can fail at solving simple problems if subtle differences are introduced. For example, consider the problem of image perturbations: a neural network that can classify images better than a human can be easily fooled into believing that sloth is a race car if a small amount of carefully calculated noise is added to the input image.Read More

Adversarial training produces synthetic data for machine learning

Sentiment analysis is the attempt, computationally, to determine from someone’s words how he or she feels about something. It has a host of applications, in market research, media analysis, customer service, and product recommendation, among other things. Sentiment classifiers are typically machine learning systems, and any given application of sentiment analysis may suffer from a lack of annotated data for training purposes.Read More

Machine-labeled data + artificial noise = better speech recognition

Although deep neural networks have enabled accurate large-vocabulary speech recognition, training them requires thousands of hours of transcribed data, which is time-consuming and expensive to collect. So Amazon scientists have been investigating techniques that will let Alexa learn with minimal human involvement, techniques that fall in the categories of unsupervised and semi-supervised learning.Read More

TF-Replicator: Distributed Machine Learning for Researchers

At DeepMind, the Research Platform Team builds infrastructure to empower and accelerate our AI research. Today, we are excited to share how we developed TF-Replicator, a software library that helps researchers deploy their TensorFlow models on GPUs and Cloud TPUs with minimal effort and no previous experience with distributed systems. TF-Replicators programming model has now been open sourced as part of TensorFlows tf.distribute.Strategy. This blog post gives an overview of the ideas and technical challenges underlying TF-Replicator. For a more comprehensive description, please read our arXiv paper.A recurring theme in recent AI breakthroughs – from AlphaFold to BigGAN to AlphaStar – is the need for effortless and reliable scalability. Increasing amounts of computational capacity allow researchers to train ever-larger neural networks with new capabilities. To address this, the Research Platform Team developed TF-Replicator, which allows researchers to target different hardware accelerators for Machine Learning, scale up workloads to many devices, and seamlessly switch between different types of accelerators.Read More

Innovations from the 2018 Alexa Prize

The 2018 Alexa Prize featured eight student teams from four countries, each of which adopted distinctive approaches to some of the central technical questions in conversational AI. We survey those approaches in a paper we released late last year, and the teams themselves go into even greater detail in the papers they submitted to the latest Alexa Prize Proceedings. Here, we touch on just a few of the teams’ innovations.Read More