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Snapchat applies for patent to serve ads by recognizing objects in your snaps


Snapchat has filed a patent application for a system of advertising that uses object recognition to serve users sponsored filters. The technology outlined by the company would identify items in users' pictures, and then offer them image overlays from brands related to these objects. It's the visual equivalent of buying advertising space based on keywords in Google searches -- but instead of looking for textual data in searches like "headphones" or "shoes," it's looking for the objects themselves. The patent application was filed in January last year, published by the US patent office earlier this month, and first spotted by Business Insider. Although it includes details about the advertising system described above, its primary purpose is outlining a more general system of "object recognition based photo filters."

Why Facial Recognition Providers Must Take Consumer Privacy Seriously


Consumer privacy has made big headlines in the recent years with the Facebook Cambridge Analytica Scandal, Europe's GDPR and high-profile breaches by companies like Equifax. It's clear that the data of millions of consumers is at risk every day, and that companies that wish to handle their data must do so with the highest degree of protection around both security and privacy of that data, especially for companies that build and sell AI-enabled facial recognition solutions. As CEO of an AI-enabled software company specializing in facial recognition solutions, I've made data security and privacy among my top priorities. Our pro-privacy stance goes beyond mere privacy by design engineering methodology. We regularly provide our customers with education and best practices, and we have even reached out to US lawmakers, lobbying for sensible pro-privacy regulations governing the technology we sell.

Malong Technologies: Winner in G20 “Olympics of Startups”


SHENZHEN, China--(BUSINESS WIRE)--Malong Technologies, an artificial intelligence startup and international provider of product recognition technology, was named winner in the "Olympics of Startups" held at the G20 Young Entrepreneurs' Alliance, in Berlin. Malong Technologies' breakthrough ProductAI platform is a high performance'intelligent eye' that enables machines to have human-like visual perception of products, including non-rigid objects such as fashion and fabrics, which have historically been infeasible for computers to recognize accurately without barcodes. Malong Technologies beat startups from every nation at the G20, which represents the 20 major economies of the world. Judged by officials of the European Union and other international organizations, the competition, run by the "Get in the Ring" foundation, led participants through a series of six rounds held over two days, and included'head-to-head' oratory matchups inside a replica boxing ring. The three winning nations include China, represented by Malong Technologies, Canada, and France.

Accurate Visual Localization for Automotive Applications Artificial Intelligence

Accurate vehicle localization is a crucial step towards building effective Vehicle-to-Vehicle networks and automotive applications. Yet standard grade GPS data, such as that provided by mobile phones, is often noisy and exhibits significant localization errors in many urban areas. Approaches for accurate localization from imagery often rely on structure-based techniques, and thus are limited in scale and are expensive to compute. In this paper, we present a scalable visual localization approach geared for real-time performance. We propose a hybrid coarse-to-fine approach that leverages visual and GPS location cues. Our solution uses a self-supervised approach to learn a compact road image representation. This representation enables efficient visual retrieval and provides coarse localization cues, which are fused with vehicle ego-motion to obtain high accuracy location estimates. As a benchmark to evaluate the performance of our visual localization approach, we introduce a new large-scale driving dataset based on video and GPS data obtained from a large-scale network of connected dash-cams. Our experiments confirm that our approach is highly effective in challenging urban environments, reducing localization error by an order of magnitude.

How Much Does a Facial Recognition System Cost


With the help of a facial recognition system, federal agents could capture a man suspected of abuse. The tool detected him in the background of someone else's photo at the gym, in the mirror. So, the agents were able to get to that gym, ask about the man, and eventually capture him. This real-life story, and many others, encourage businesses to benefit from AI services and deploy facial recognition systems. The global facial recognition market size was evaluated at $3.8 billion in 2020 and is expected to reach $8.5 billion in 2025, growing at a CAGR of 17.2%.