By dictating the information consumed by hundreds of millions of Americans, the machine learning (ML) algorithms employed by these platforms are …

Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models which have been used in the recent past to resolve many problems in computer vision, in particular optical flow estimation. Measuring displacement and strain fields can be regarded as a particular case of this problem. However, it seems that CNNs have never been used so far to perform such measurements. This work is aimed at implementing a CNN able to retrieve displacement and strain fields from pairs of reference and deformed images of a flat speckled surface, as Digital Image Correlation (DIC) does. This paper explains how a CNN called StrainNet can be developed to reach this goal, and how specific ground truth datasets are elaborated to train this CNN. The main result is that StrainNet successfully performs such measurements, and that it achieves competing results in terms of metrological performance and computing time. The conclusion is that CNNs like StrainNet offer a viable alternative to DIC, especially for real-time applications.

As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about---summary quality. In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models. We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.

Television game shows have made dating a form of entertainment for the masses for decades. More recently reality TV has adopted the genre, with hits like Love Island and First Dates. Now the phenomenon has moved online - and anyone can play. "I remember it as if it were yesterday. I was so nervous to even say, 'Hi'. The first time I saw him smiling, my heart told me. 'That's the one, I'm going to make that man happy whatever it takes."

Machine learning developers face an expanded set of management issues beyond merely getting the code right, including the testing and validation …

Ghosh will discuss the applications of quantum computing in the field of machine learning and electronic structure calculations in quantum chemistry.

Google noted that machine learning has a few maintenance challenges on top of all of the challenges of traditional code: unique hardware and software …

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market Market 2019-2029.

Constellation Research named CognitiveScale on the Constellation ShortList™ for Artificial Intelligence and Machine Learning Best-of-Breed …

Cable TV news is a primary source of information for millions of Americans each day. The people that appear on cable TV news and the topics they talk about shape public opinion and culture. While many newsrooms and monitoring organizations routinely audit the content of news broadcasts, these efforts typically involve manually counting who and what is on the air. But now researchers at the Brown Institute for Media Innovation at Stanford University have launched the Stanford Cable TV News Analyzer, an interactive tool that gives the public the ability to not only search transcripts but also compute the screen time of public figures in nearly 24/7 TV news broadcasts from CNN, Fox News and MSNBC dating back to January 2010. The site is updated daily with the previous day's coverage, and enables searches of over 270,000 hours of news footage. The Analyzer increases transparency around these broadcasters' editorial decisions by using modern AI techniques to automatically measure who is on the news and what they talk about.