Paris seeks $14M from Airbnb for illegal adverts
Advertisements must include a registration number to help ensure properties are not rented out for longer.
via FOX BUSINESS NEWS https://fxn.ws/2MXe4sc
Tuesday, February 12, 2019
FOX BIZ NEWS: Paris seeks $14M from Airbnb for illegal adverts
Paris seeks $14M from Airbnb for illegal adverts
Advertisements must include a registration number to help ensure properties are not rented out for longer.
via FOX BUSINESS NEWS https://fxn.ws/2MXe4sc
The Best and Worst of the Grammys
By JON PARELES, JON CARAMANICA, JOE COSCARELLI and CARYN GANZ from NYT Arts https://nyti.ms/2DqGOFo
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In Closed Hearing, a Clue About ‘the Heart’ of Mueller’s Russia Inquiry
By SHARON LaFRANIERE, KENNETH P. VOGEL and SCOTT SHANE from NYT U.S. https://nyti.ms/2GAwmOK
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New Show Hacker News story: Launch HN: Aura Vision (YC W19) – Google Analytics for Physical Stores
Launch HN: Aura Vision (YC W19) – Google Analytics for Physical Stores
78 by jblok | 35 comments on Hacker News.
We're Daniel, Jaime, and Jonathon - the founders of Aura Vision. ( https://auravision.ai ) Aura Vision is like Google Analytics for physical retail stores. Our mission is to ensure that physical retailers can innovate and improve their stores with data, in the same way their eCommerce counterparts do, while protecting customer privacy. Retail teams often know very little about what shoppers do in-store leading up to a purchase. To try to increase sales, they change layouts, products, and media in their shops based on anecdotal knowledge, and experience. That’s because it’s hard to get good quality data about what consumers actually do in their stores at the moment. Many retailers periodically place people in doorways with clipboards recording shopper demographics and behaviours, which of course is costly and not very scalable. We use existing security cameras in stores to detect the demographics (age, gender, staff/customer) and behaviour of all visitors using our proprietary computer vision technology. This creates an anonymised feed of aggregated data for the retailer, giving them new tools to improve their stores. E.g. - To increase footfall, retailers can A/B test window displays, selecting the one with the highest peel off rate (the ratio of entries to people walking by) - To uncover why a product is underselling, retailers can learn about the movement and dwell times of different demographics around products. - To increase sales, they can select products that are suited to the demographics in that store. - To increase conversion rates, retailers can identify where customers spend most of their time in-store and locate staff accordingly. We started out in the UK during the birth of GDPR, so we’re acutely aware of the need to protect customer privacy. Video is deleted as part of the processing, and never stored thereafter, and our system never identifies people, nor stores identities. All data is aggregated into 15 minute chunks, which fully anonymises the counts, so you are left with information on the behaviours that the camera observed in that period. Those chunks are supplied back to the retailer through our dashboard and API as heatmaps and counts. We don’t rely on facial recognition, instead taking in visual cues from all features across the body. In contrast many other retail tracking solutions, like Bluetooth and WiFi, aren’t GDPR compliant as they store MAC addresses, or other phone IDs without consent, which count as personal data. This means they can re-identify you when you come back to the store, or another store on their network. While regulation will do a good job at getting rid of these tracking solutions, we want to help by showing retailers there’s an option that gives them more useful data anyway. Daniel and Jaime studied under the same supervisor at the University of Southampton during their computer vision PhDs. They saw plenty of opportunities for using deep learning in people tracking. A key part of Daniel's PhD was estimating people's demographics from CCTV footage and this led to the end result we are running now. Myself and Daniel went to primary school together, and my background is in APIs and frontends. Thanks for reading! We know the HN community has many people interested and knowledgeable in computer vision and deep learning, so we're looking forward to hearing your thoughts. If you or someone you know has experienced similar challenges in retail, please reach out! jonathon@auravision.ai
78 by jblok | 35 comments on Hacker News.
We're Daniel, Jaime, and Jonathon - the founders of Aura Vision. ( https://auravision.ai ) Aura Vision is like Google Analytics for physical retail stores. Our mission is to ensure that physical retailers can innovate and improve their stores with data, in the same way their eCommerce counterparts do, while protecting customer privacy. Retail teams often know very little about what shoppers do in-store leading up to a purchase. To try to increase sales, they change layouts, products, and media in their shops based on anecdotal knowledge, and experience. That’s because it’s hard to get good quality data about what consumers actually do in their stores at the moment. Many retailers periodically place people in doorways with clipboards recording shopper demographics and behaviours, which of course is costly and not very scalable. We use existing security cameras in stores to detect the demographics (age, gender, staff/customer) and behaviour of all visitors using our proprietary computer vision technology. This creates an anonymised feed of aggregated data for the retailer, giving them new tools to improve their stores. E.g. - To increase footfall, retailers can A/B test window displays, selecting the one with the highest peel off rate (the ratio of entries to people walking by) - To uncover why a product is underselling, retailers can learn about the movement and dwell times of different demographics around products. - To increase sales, they can select products that are suited to the demographics in that store. - To increase conversion rates, retailers can identify where customers spend most of their time in-store and locate staff accordingly. We started out in the UK during the birth of GDPR, so we’re acutely aware of the need to protect customer privacy. Video is deleted as part of the processing, and never stored thereafter, and our system never identifies people, nor stores identities. All data is aggregated into 15 minute chunks, which fully anonymises the counts, so you are left with information on the behaviours that the camera observed in that period. Those chunks are supplied back to the retailer through our dashboard and API as heatmaps and counts. We don’t rely on facial recognition, instead taking in visual cues from all features across the body. In contrast many other retail tracking solutions, like Bluetooth and WiFi, aren’t GDPR compliant as they store MAC addresses, or other phone IDs without consent, which count as personal data. This means they can re-identify you when you come back to the store, or another store on their network. While regulation will do a good job at getting rid of these tracking solutions, we want to help by showing retailers there’s an option that gives them more useful data anyway. Daniel and Jaime studied under the same supervisor at the University of Southampton during their computer vision PhDs. They saw plenty of opportunities for using deep learning in people tracking. A key part of Daniel's PhD was estimating people's demographics from CCTV footage and this led to the end result we are running now. Myself and Daniel went to primary school together, and my background is in APIs and frontends. Thanks for reading! We know the HN community has many people interested and knowledgeable in computer vision and deep learning, so we're looking forward to hearing your thoughts. If you or someone you know has experienced similar challenges in retail, please reach out! jonathon@auravision.ai
OUT OF THE DARK by Gregg Hurwitz
The fourth book in the Orphan X series.
#15 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#15 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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JUDGMENT by Joseph Finder
Juliana Brody, a judge in the Superior Court of Massachusetts, has a one-night stand that might be her undoing.
#13 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#13 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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MAGIC HOUR by Kristin Hannah
A child psychiatrist helps a 6-year-old girl found in the Olympic National Forest.
#11 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#11 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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FREE by Kristen Ashley
Members of the Chaos Motorcycle Club have more battles to fight.
#10 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#10 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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THE WRONG HIGHLANDER by Lynsay Sands
The seventh book in the Highland Brides series. While her ailing father needs her help, Lady Evina Maclean is distracted by Rory Buchanan.
#5 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#5 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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THE LOST GIRLS OF PARIS by Pam Jenoff
Grace Healey investigates the fates of 12 women who were sent to occupied Europe to help the resistance during World War II.
#3 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#3 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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VENGEANCE ROAD by Christine Feehan
The second book in the Torpedo Ink series. Complications rev up between Breezy and Steele.
#2 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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#2 on the NYT Best Sellers List for Combined Print & E-Book Fiction
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FOX NEWS: Glenn Greenwald: Amazon one of the world's biggest threats to privacy
Glenn Greenwald: Amazon one of the world's biggest threats to privacy
After Amazon founder and CEO Jeff Bezos went on the offensive against the National Enquirer and its parent company AMI, saying they threatened to release intimate photos of himself and his girlfriend, Lauren Sanchez, many in the media lauded the billionaire for his stance and his defense of privacy -- but some critics found the situation ironic.
via FOX NEWS https://fxn.ws/2DqS2cQ
FOX NEWS: Bezos' personal privacy concerns raise questions about privacy of Amazon's customers
Bezos' personal privacy concerns raise questions about privacy of Amazon's customers
Co-founding editor for The Intercept, Glenn Greenwald, weighs in on privacy concerns for Amazon's customers.
via FOX NEWS http://bit.ly/2BudIF6
FOX NEWS: Tucker: Mainstream media loves Jeff Bezos
Tucker: Mainstream media loves Jeff Bezos
Journalists rally to defend Amazon CEO Jeff Bezos after National Enquirer reportedly threaten to leak private photos.
via FOX NEWS http://bit.ly/2TJBCU7
FOX NEWS: Howard Kurtz: Why the brother of Jeff Bezos' girlfriend consulted Carter Page, Roger Stone
Howard Kurtz: Why the brother of Jeff Bezos' girlfriend consulted Carter Page, Roger Stone
'MediaBuzz' host Howard Kurtz weighs in on the backstage battle brewing among the people advising Jeff Bezos about how he should proceed in his public spat with The National Enquirer.
via FOX NEWS http://bit.ly/2GEFq55
FOX NEWS: Facebook must take 'real action' against disinformation, Mozilla Foundation and 32 civil society groups demand
Facebook must take 'real action' against disinformation, Mozilla Foundation and 32 civil society groups demand
Facebook needs to stop paying lip service to the fight against disinformation and take more concrete actions.
via FOX NEWS https://fxn.ws/2Dx8jx1