Every week, the top AI labs globally — Google, Facebook, Microsoft, Apple, etc. — release tons of new research work, tools, datasets, models, libraries and frameworks in artificial intelligence (AI) and machine learning (ML).
Interestingly, they all seem to have picked a particular school of thought in deep learning. With time, this pattern is becoming more and more clear. Read More
Artificial intelligence (AI) helps organizations to make timely and accurate decisions from data in almost every field of study.
The volume of data keeps growing. Statista believes that 59 Zettabytes were produced in 2020 and that 74 Zettabytes will be produced in 2021.
A Zettabyte is a trillion gigabytes! Read More
Facebook is pouring a lot of time and money into augmented reality, including building its own AR glasses with Ray-Ban. Right now, these gadgets can only record and share imagery, but what does the company think such devices will be used for in the future?
A new research project led by Facebook’s AI team suggests the scope of the company’s ambitions. It imagines AI systems that are constantly analyzing peoples’ lives using first-person video; recording what they see, do, and hear in order to help them with everyday tasks. Facebook’s researchers have outlined a series of skills it wants these systems to develop, including “episodic memory” (answering questions like “where did I leave my keys?”) and “audio-visual diarization” (remembering who said what when). Read More
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com, taunts users with GAN generated images that seem too real to believe. On the otherhand, GANs do leak information about their training data, as evidenced by membership attacks recently demonstrated in the literature. In this work, we challenge the assumption that GAN faces really are novel creations, by constructing a successful membership attack of a new kind. Unlike previous works, our attack can accurately discern samples sharing the same identity as training samples without being the same samples. We demonstrate the interest of our attack across several popular face datasets and GAN training procedures. Notably, we show that even in the presence of significant dataset diversity, an over represented person can pose a privacy concern. Read More
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