Skip to main content

Facebook Starts Testing Dating Product Internally!

In a bid to take on mobile dating apps like Tinder and Bumble, Facebook has begun testing its dating project internally with employees. According to a report in The Verge on Friday, an independent app researcher Jane Manchun Wong found evidence of the dating feature testing and posted it on Twitter. "This product is for US Facebook employees who have opted-in to dogfooding Facebook's new dating product. The purpose for this dogfooding is to test the end-to-end product experience for bugs and confusing UI (user interface). This is not meant for dating your co-workers," read a screenshot. Facebook has asked employees to use fake data for their dating profiles and plans to delete all data before the public launch, said the report. "Dogfooding this product is completely voluntary and has no impact on your employment," the screenshot further read, adding that the product is confidential.


  The social media giant later confirmed the dating product is in testing within the main Facebook app but declined to comment further. Facebook had announced the dating product - which will not be a standalone dating app – during its F8 developer conference in the US in May. "This is going to be for building real, long-term relationships and not just for hookups," Facebook CEO Mark Zuckerberg said during his keynote. "We have designed this with privacy and safety in mind from the beginning. Your friends aren't going to see your profile, and you're only going to be suggested to people who are not your friends," he added. Facebook Product Chief Chris Cox showed a design of the dating project to the audience. A feature called "unlocking" will let any user of Facebook's dating platform make his or her profile visible to other attendees of events or members of groups.
Labels:

Comments

Post a Comment

Popular posts from this blog

Everything you should know about the coronavirus: COVID-19

  What are coronaviruses? SARS-CoV-2 belongs to a family of single-stranded RNA viruses known as coronaviridae, a common type of virus which affects mammals, birds and reptiles. In humans, it commonly causes mild infections, similar to the common cold, and accounts for 10–30% of upper respiratory tract infections in adults. More serious infections are rare, although coronaviruses can cause enteric and neurological disease. The incubation period of a coronavirus varies but is generally up to two weeks. Previous coronavirus outbreaks include  Middle East respiratory syndrome (MERS ), first reported in Saudi Arabia in September 2012, and severe acute respiratory syndrome (SARS), identified in southern China in 2003. MERS infected around 2,500 people and led to more than 850 deaths while SARS infected more than 8,000 people and resulted in nearly 800 deaths. The case fatality rates for these conditions were 35% and 10%, respectively. SARS-CoV-2 is a new strain of co...
Post a Comment
Read more

The First Step Towards Responsible AI Needs To Be About People Not Strategy!

Article By Charles Radclyffe:  I was recently consulting for an organisation that was looking to implement a framework to govern the implementation of Artificial Intelligence (AI)  technologies. Like many organisations in their sector, they had been running various ‘lab’ experiments for some time, and had seen positive results; but there was still something holding them back from wholesale investment. A major consulting firm had encouraged them to ‘accelerate’ their innovation by using a framework to govern the roll-out. I asked them where they felt it needed more focus, and they responded saying that it felt somewhat vanilla, a re-hashing of any-old IT project management best practice. “Surely there is something different about AI”, they asked? I couldn’t agree more. There is no magic to AI. Today’s AI is a collection of methodologies that apply extreme reductionism to Big Data in order to elicit patterns, calculate probabilities or make predictions. Wha...
Post a Comment
Read more

AI Can Recognize Images. But Can It Understand This Headline?

In 2012, artificial intelligence researchers revealed a big improvement in computers’ ability to recognize images by feeding a neural network millions of labeled images from a database called ImageNet. It ushered in an exciting phase for computer vision, as it became clear that a model trained using ImageNet could help tackle all sorts of image-recognition problems. Six years later, that’s helped pave the way for self-driving cars to navigate city streets and Facebook to automatically tag people in your photos. In other arenas of AI research, like understanding language, similar models have proved elusive. But recent research from fast.ai , OpenAI , and the Allen Institute for AI suggests a potential breakthrough, with more robust language models that can help researchers tackle a range of unsolved problems. Sebastian Ruder, a researcher behind one of the new models, calls it his field’s “ImageNet moment.” The improvements can be dramatic. The most widely tested mode...
Post a Comment
Read more