In an attempt to silence Twitter, the Kremlin appears to have developed novel techniques to restrict online content.
Russia has implemented a novel censorship method in an ongoing effort to silence Twitter. Instead of blocking the social media site outright, the country is using previously unseen techniques to slow traffic to a crawl and make the site all but unusable for people inside the country.
Research published Tuesday says that the throttling slows traffic traveling between Twitter and Russia-based end users to a paltry 128 kbps. Whereas past internet censorship techniques used by Russia and other nation-states have relied on simple blocking, slowing traffic passing to and from a widely used internet service is a relatively new technique that provides benefits for the censoring party. Read More
Daily Archives: April 8, 2021
Are Feature Stores The Next Big Thing In Machine Learning?
According to a Gartner study, 85 percent of AI projects will flatline by 2022. Even the most diligent machine learning models may not meet expectations when deployed in an enterprise setting, mainly due to two reasons — inadequate data infrastructure and talent scarcity.
In the machine learning pipeline, search for appropriate data and dataset preparation are among the most time-consuming processes. A data scientist spends around 80 percent of his/her time in managing and preparing data for analysis. The demand-supply gap for qualified data scientists is another pressing challenge.
Enter, feature store. Read More
Deep Learning May Not Be The Silver Bullet for All NLP Tasks Just Yet
Why You Should Still Learn Heuristics and Rule-Based Methods
Deep Learning; The solution to the problems of mankind. Over the past few years, Deep Learning has advanced humanity in novel ways. One of these beneficiaries is the entire field of Natural Language Processing (NLP). … Despite the monstrous success of Deep Learning, it’s still not yet the silver bullet for every NLP task. Therefore, practitioners shouldn’t rush to build the biggest RNN or transformer when faced with a problem in NLP. Read More
Generative Adversarial Transformers
We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model’s strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and out-door scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency. Further qualitative and quantitative experiments offer us an insight into the model’s inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. Read More
How Audio Pros ‘Upmix’ Vintage Tracks and Give Them New Life
Experts are using AI to pick apart classic recordings from the 50s and 60s, isolate the instruments, and stitch them back together in crisp, bold ways.
When James Clarke went to work at London’s legendary Abbey Road Studios in late 2009, he wasn’t an audio engineer. He’d been hired to work as a software programmer. One day not long after he started, he was having lunch with several studio veterans of the 1960s and ’70s, the pre-computer era of music recording when songs were captured on a single piece of tape. To make conversation, Clarke asked a seemingly innocent question: Could you take a tape from the days before multitrack recording and isolate the individual instruments? Could you pull it apart?
The engineers shot him down. It turned into “several hours of the ins and outs of why it’s not possible,” Clarke remembers. You could perform a bit of sonic trickery to transform a song from one-channel mono to two-channel stereo, but that didn’t interest him. Clarke was seeking something more exacting: a way to pick apart a song so a listener could hear just one element at a time. Maybe just the guitar, maybe the drums, maybe the singer.
“I kept saying to them that if the human ear can do it, we can write software to do it as well,” he says. To him, this was a challenge. “I’m from New Zealand. We love proving people wrong.” Read More
A Study of Face Obfuscation in ImageNet
Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark. Most categories in the ImageNet challenge are not people categories; however, many incidental people appear in the images, and their privacy is a concern. We first annotate faces in the dataset. Then we demonstrate that face blurring—a typical obfuscation technique—has minimal impact on the accuracy of recognition models. Concretely, we benchmark multiple deep neural networks on face-blurred images and observe that the overall recognition accuracy drops only slightly (≤0.68%). Further,we experiment with transfer learning to 4 downstream tasks (object recognition, scene recognition, face attribute classification, and object detection) and show that features learned on face-blurred images are equally transferable. Our work demonstrates the feasibility of privacy-aware visual recognition, improves the highly-used ImageNet challenge benchmark,and suggests an important path for future visual datasets. Read More
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