All posts in “machine learning”

Hinge dating app will begin using machine learning to make better matches

The Most Compatible feature factors how people act on the app (such as who you previously liked) and wants to serve as a virtual matchmaker.
The Most Compatible feature factors how people act on the app (such as who you previously liked) and wants to serve as a virtual matchmaker.

Image: golden sikorka/shutterstock

Hinge, the Facebook friend-based dating app, has been testing machine learning to make better matches for singles.

The feature is called Most Compatible and according to multiple reports, plans to use all your data (there’s a whole trove of it on Facebook) to match people with each other. Most Compatible has been tested once a week for at least this past month, but it will now become a daily feature.

Hinge founder Justin McLeod said that this new feature mainly relies on the classic item matching algorithm Gale-Shapley, which was developed in 1962 and is nickname the stable marriage algorithm. It basically tries making successful matches by choosing the most seemingly compatible person.

Although that algorithm and methodology of using all cumulative data to create matches seems smart — and almost even obvious — the gradual change of Facebook’s role in your life could lead to spotty results. People rarely update their likes now and mainly use it as a tool for keeping tabs on others or curating a social media presence.

How successful would a match over Justin Bieber and One Direction be, when one person liked those pages in high school, while the other un-ironically sings Baby and What Makes You Beautiful in the shower? What about Sports on Facebook, which many people were forced to like in order to access some dumb quiz three years ago?

The Most Compatible feature does, however, take into account how people act on the app (such as who you previously liked). It wants to serve as a virtual matchmaker and aims to find people similar to those you previously matched with on the platform.

Who knows whether this will work. Hinge claims that although it likes having many users, it hopes people get off the app as soon as possible (assuming you leave because your needs have been met). Maybe AI and machines are the friends you never knew you needed to make that first introduction or connection happen.

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Your next summer DIY project is an AI-powered doodle camera

With long summer evenings comes the perfect opportunity to dust off your old boxes of circuits and wires and start to build something. If you’re short on inspiration, you might be interested in artist and engineer Dan Macnish’s how-to guide on building an AI-powered doodle camera using a thermal printer, Raspberry pi, a dash of Python and Google’s Quick Draw data set.

“Playing with neural networks for object recognition one day, I wondered if I could take the concept of a Polaroid one step further, and ask the camera to re-interpret the image, printing out a cartoon instead of a faithful photograph.” Macnish wrote on his blog about the project, called Draw This.

To make this work, Macnish drew on Google’s object recognition neural network and the data set created for the game Google Quick, Draw! Tying the two systems together with some python code, Macnish was able to have his creation recognize real images and print out the best corresponding doodle in the Quick, Draw! data set

But since output doodles are limited to the data set, there can be some discrepancy between what the camera “sees” and what it generates for the photo.

“You point and shoot – and out pops a cartoon; the camera’s best interpretation of what it saw,” Macnish writes. “The result is always a surprise. A food selfie of a healthy salad might turn into an enormous hot dog.”

If you want to give this a go for yourself, Macnish has uploaded the instructions and code needed to build this project on GitHub.

Booksy, the worldwide booking system, raises $13.2 million

Booksy, a Poland-based booking application for the beauty business, has raised $13.2 million in a series B effort to drive global growth. The company, founded in 2014 by Stefan Batory and Konrad Howard, is currently seeing 2.5 million bookings per month.

The company raised from Piton Capital, OpenOcean, Kulczyk Investments, and Zach Coelius.

Batory, an ultramarathoner, also co-founded iTaxi, Poland’s popular taxi hailing app. Booksy came about when he was trying to schedule physiotherapy appointments after long runs. He would come home sore and plan on calling his physiotherapist but it was always too late.

“I didn’t want to bother him after I was done with my workout late night, and it was virtually impossible to contact him during day time as his hands were busy massaging people and he did not answer my calls,” he said.

Booksy launched in the US in 2017 and “rapidly become the number one booking app in the world,” said Batory.

“We will use the funding to drive global growth, recruit high profile talent and develop proprietary technologies that will further support beauty businesses,” he said. “That includes the implementation of one-click booking, a feature that uses machine learning and AI technologies, to determine each user’s buying pattern and offer them the best dates with their favorite stylists, thus simplifying user experience for both merchants and their customers.”

MeetFrank nets $1.1M for its passive job matching chatbot

MeetFrank, aka a ‘secret’ recruitment app that uses machine learning plus a chatbot wrapper to take the strain out of passive job hunting and talent-to-vacancy matching, has closed a €1 million (~$1.1M) seed funding round to fuel market expansion in Europe.

Hummingbird VC, Karma VC, and Change Ventures are the investors.

The Estonian startup was only founded last September but says it has ~125,000 active users in its first markets: Estonia, Finland, Sweden, Latvia, Lithuania, plus its most recent market addition, Germany, an expansion this seed has financed.

Around 2,000 companies are using the app to try to attract talent. In Germany employers on board with MeetFrank include Daimler, Eon, Delivery Hero, SumUp, Blinkist, High Mobility and MyTaxi.

“The average company profile we have at the moment is a start-up/scale-up company that develops their product in-house,” says co-founder Kaarel Holm.

“At the moment we are mainly focused on technology related companies — so positions you can find from average start-up or a scale-up,” he tells TechCrunch. “Around 50% of the position are engineering and other 50% is marketing, sales, customer support, legal, data science, product/project management etc.”

He names TransferWise, Taxify, Testlio, Smartly and High-Mobility as other early customers.

Here’s how MeetFrank works on the talent side: The person downloads the app and goes through a relatively quick onboarding chat with ‘Frank’ (the emoji-loving chatbot) where they are asked to specify their skills and experience — choosing from pre-set lists, rather than needing to type — plus to state their current job title and salary.

So while MeetFrank’s target is passive job seekers, these people do still need to actively download the app and input some data.

Hence the chatbot having a strong emoji + GIF game to convince talent that a little upfront effort will go a long way…

The bot also asks what would convince them to switch jobs — offering options to choose from such as a higher salary, more flexible or remote working working, relocation, a startup culture and so on.

The anonymous aspect comes in because there’s no requirement for users to provide their real name or any other identifying personal information in order to get matches with potential positions.

Talent is therefore assessed on its merits, at least at this stage of the job hunt.

And while people are asked up front to specify their current salary, which you might think puts them at a potential disadvantage during any pay negotiations, Holm says the aim of MeetFrank’s platform is also to encourage greater openness from employers and steer away from traditional pay negotiation situations.

“We use salary as one datapoint for matching and we try to make sure that offers we make to the user are match their preferences. In lot of cases the salary is the main deal breaker and we would like to present the information as early as possible,” he explains. “Companies on the other side of the marketplace disclose their salary for the users as well — in that case we can avoid the negotiating disadvantage.”

“The policy of MeetFrank platform is that companies have to be extremely open about the position they are trying to fill — this also includes the salary information,” he adds.

Employers are not at all anonymous on the platform. On the contrary, they have to write detailed job advertisements — including levels of pay for advertised roles.

And a pay range will be disclosed to applicants that the app deems potentially suitable — i.e. after its matching process — by displaying a percentage of how much more they could earn above their current salary.

So employers need to be comfortable showing their hand to people who may just be curious what’s out there.

For employers, MeetFrank takes over the ad placement process — using its machine learning to algorithmically match potential candidates to positions. So its proposition is automatic pre-selection across “thousands” of potential job applicants.

And also the possibility of reaching talent which might otherwise not realize that company is hiring. Or think about working for a certain brand.

The app is mainly focused on a “passive talent pool” — aka “currently or recently employed talent that is open for offers”, as Holm puts it. So it’s certainly cherrypicking easier types of jobs to match and fill.

“Entry level jobs is bit out of reach for us at the moment but we will launch a beta project with couple of universities in the autumn this year,” he adds when we ask if the app is open to matching people who don’t currently have a job or are looking for a first job.

Holm says MeetFrank is currently showing 50% MRR growth. It’s already out of the pre-revenue phase — so is charging employers to advertise (the service remains free for the talent side).

The main monetization model is a daily subscription, with employers being charged on a pay-as-you-go basis. Holm says the price per day for employers is €9, and MeetFrank lets them cancel at any time — with no minimum time commitment required to sign up.

“We believe that the new-aged classifieds will only monetize on that kind of on-demand model and should only pay when they find us useful. This also lowers the barrier of entry to most of the start-ups and allows them to vet the market and get visibility with low budgets,” he adds.

Original Stitch’s new Bodygram will measure your body

After years of teasing, Original Stitch has officially launched their Bodygram service and will be rolling it out this summer. The system can scan your body based on front and side photos and will create custom shirts with your precise measurements.

“Bodygram gives you full body measurements as accurate as taken by professional tailors from just two photos on your phone. Simply take a front photo and a side photo and upload to our cloud and you will receive a push notification within minutes when your Bodygram sizing report is ready,” said CEO Jin Koh. “In the sizing report you will find your full body measurements including neck, sleeve, shoulder, chest, waist, hip, etc. Bodygram is capable of producing sizing result within 99 percent accuracy compared to professional human tailors.”

The technology is a clever solution to the biggest problem in custom clothing: fit. While it’s great to find a service that will tailor your clothing based on your measurements, often these measurements are slightly off and can affect the cut of the shirt or pants. Right now, Koh said, his team offers free returns if the custom shirts don’t fit.

Further, the technology is brand new and avoids many of the pitfalls of the original body-scanning tech. For example, Bodygram doesn’t require you to get into a Spandex onesie like most systems do and it can capture 40 measurements with only two full-body photos.

“Bodygram is the first sizing technology that works on your phone capable of giving you highly accurate sizing result from just two photos with you wearing normal clothing on any background,” said Koh. “Legacy technologies on the market today require you to wear a very tight-fitting spandex suit, take 360 photos of you and require a plain background to work. Other technologies give you accuracy with five inches deviation in accuracy while Bodygram is the first technology to give you sub-one-inch accuracy. We are the first to use both computer vision and machine learning techniques to solve the problem of predicting your body shape underneath the clothes. Once we predicted your body shape we wrote our proprietary algorithm to calculate the circumferences and the length for each part of the body.”

Koh hopes the technology will reduce returns.

“It’s not uncommon to see clothing return rates reaching in the 40-50 percent range,” he said. “Apparel clothing sales is among the lowest penetration in online shopping.”

The system also can be used to measure your body over time in order to collect health and weight data as well as help other manufacturers produce products that fit you perfectly. The app will launch this summer on Android and iOS. The company will be licensing the technology to other providers that will be able to create custom fits based on just a few side and front photos. Sales at the company grew 175 percent this year and they now have 350,000 buyers that are already creating custom shirts.

A number of competitors are in this interesting space, most notably ShapeScale, a company that appeared at TechCrunch Disrupt and promised a full body scan using a robotic scale. This, however, is the first commercial use of standard photos to measure your appendages and thorax and it’s an impressive step forward in the world of custom clothing.