Collaborating Authors


How to Kickstart an AI Venture Without Proprietary Data


A few years ago, I learned about the billions of dollars banks lose to credit card fraud on an annual basis. Better detection or prediction of fraud would be incredibly valuable. And so I considered the possibility of convincing a bank to share their transactional data in the hope of building a better fraud detection algorithm. The catch, unsurprisingly, was that no major bank is willing to share such data. They feel they're better off hiring a team of data scientists to work on the problem internally. My startup idea died a quick death.

Tinder sees massive rise in mentions of 'courting' and 'flirting' in bios


Tinder has released data showing a dramatic rise in mentions of the words "courting" and "flirting" in dating app bios, spelling a return to good old fashioned wooing. According to Tinder, "courting" has been included in 81 percent more Tinder bios this year, compared to February 2020. Interestingly, that data pertains to users aged between 18 and 25, meaning Gen Z daters seem to be showing an interest in more traditional forms of romancing. The dating app thinks that the popularity of period dramas like Netflix's Bridgerton are the reason for this. The term "flirting" has also seen a massive increase in 2021, with 132 percent more mentions in bios than the previous year.



The graph represents a network of 8,138 Twitter users whose tweets in the requested range contained "forbes", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 23 February 2021 at 21:51 UTC. The requested start date was Tuesday, 23 February 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 0-hour, 54-minute period from Saturday, 20 February 2021 at 07:41 UTC to Monday, 22 February 2021 at 08:35 UTC.

Battling the Weaponizing of AI


"I don't use Facebook anymore," she said. I was leading a usability session for the design of a new mobile app when she stunned me with that statement. It was a few years back, when I was a design research lead at IDEO and we were working on a service design project for a telecommunications company. The design concept we were showing her had a simultaneously innocuous and yet ubiquitous feature -- the ability to log in using Facebook. But the young woman, older than 20, less than 40, balked at that feature and went on to tell me why she didn't trust the social network any more. This session was, of course, in the aftermath of the 2016 Presidential election. An election in which a man who many regarded as a television spectacle at best and grandiose charlatan at worst had just been elected to our highest office. Though now in 2020, our democracy remains intact.

Dating apps: Data shows an increase in Saturday installs, but bots cause problems


Berlin, Germany-based SaaS platform Adjust has released its dating app marketing guide. The guide has benchmarks, spotlights on industry leaders, and has tips on how app developers can retain users by the use of gender targeting, and in-app video streaming. Simple steps can make the difference between losing your online accounts or maintaining what is now a precious commodity: Your privacy. Over 270 million adults worldwide used dating apps in 2020 and almost two in five (39%) of US adults reported meeting their partner online. However, a major risk to an app's reputation is the presence of bots on the platform which frustrate the users or exposes them to scams.

Valentine's Day scams: Beware phony romances, fake shopping offers

USATODAY - Tech Top Stories

Unfortunately, scams trying to steal your heart and money are, too. As Valentine's Day nears, potential scammers are attempting to take advantage, focused on stealing personal information or money. Whether you're looking for love on social networks or dating sites or looking to buy a special gift for your loved one, scammers are lurking to trick you. This season in particular, as many Americans remain homebound due to COVID-19 outbreaks, the number of scams related to romance or Valentine's Day is on the rise. Lynette Owens, global director of internet safety at Trend Micro, said scams related to romance are up 20% over last year, caused by the "double whammy" of people staying online more due to the pandemic and increased isolation.

Facebook improves AI photo descriptions for the visually impaired


Facebook has long been using AI to describe photos for the visually impaired, but it's stepping up its efforts in 2021. The social media giant has detailed a new version of automatic alternative text (AAT) that promises much more information. Instead of relying on heavily supervised AI learning, Facebook is now using weak supervision based on "billions" of Instagram photos and hashtags. The method lets Facebook expand beyond just 100 concept descriptions to include over 1,200, such as different kinds of food and national monuments. It's also more culturally inclusive -- it can recognize weddings that don't involve white wedding dresses, for example. A new object detection system can also recognize where people are in the frame as well as the number of people in the scene.

Interpol warns of romance scam artists using dating apps to promote fake investments


Interpol has warned of a new investment scam targeting users of mobile dating apps. As COVID-19 continues to severely restrict our daily lives and in many places, makes social interaction and meeting new people in person impossible, dating apps have experienced a surge in users. As the only possible method of anything akin to dating at the current time, scam artists have decided to capitalize on this trend in order to push an investment-based scam that deprives victims of their cash. According to Arkose Labs research, four million online dating fraud & abuse-related attacks were recorded in 2020, with many taking place through fake account registrations. On Tuesday, the International Criminal Police Organization (Interpol) said the agency had issued a "purple notice" -- the provision of data on criminal groups' methods, objects, devices, and concealment methods -- to 194 member countries.

Facebook enhances AI used to describe photos for visually impaired users


Facebook has announced new improvements to its artificial intelligence (AI) technology that is used to generate descriptions of photos posted on the social network for visually impaired users. The technology, called automatic alternative text (AAT), was first introduced by Facebook in 2016 to improve the experience of visually impaired users. Up until then, visually impaired users who checked their Facebook newsfeed and came across an image would only hear the word "photo" and the name of the person who shared it. With AAT, visually impaired users have been able to hear things like "image may contain: three people, smiling, outdoors". Facebook said, with the latest iteration of AAT, the company has been able to expand the number of concepts that the AI technology can detect and identify in a photo, as well as provide more detailed descriptions to include activities, landmarks, food types, and types of animals, like "a selfie of two people, outdoors, the Leaning Tower of Pisa" instead of "an image of two people".

Dissonance Between Human and Machine Understanding Artificial Intelligence

Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models that correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is, therefore, crucial to understand how and which models conform to human understanding of tasks. In this paper, we present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding, through the lens of an image classification task. In particular, we seek to answer the following questions: Which (well-performing) complex ML models are closer to humans in their use of features to make accurate predictions? How does task difficulty affect the feature selection capability of machines in comparison to humans? Are humans consistently better at selecting features that make image recognition more accurate? Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans.