Wij willen met u aan tafel zitten en in een openhartig gesprek uitvinden welke uitdagingen en vragen er bij u spelen om zo, gezamelijk, tot een beste oplossing te komen. Oftewel, hoe kan de techniek u ondersteunen in plaats van dat u de techniek moet ondersteunen.

Singapore-based budget hotel booking startup RedDoorz is tiny in comparison to fast-growing giant Oyo. But it is holding its ground and winning the trust of an ever growing number of investors.

On Monday, the four-year-old startup announced it has raised $ 70 million in Series C financing round, less than five months after it closed its $ 45 million Series B. The new round, which is ongoing, was led by Asia Partners and saw participation from new investors Rakuten Capital and Mirae Asset-Naver Asia Growth Fund.

The startup, which has raised $ 140 million to date, has been seeing “tremendous interest from investors, so it is decided to do a back-to-back rounds,” said Amit Saberwal, founder and CEO of RedDoorz, in an interview with TechCrunch.

Regardless, the new funds will help RedDoorz fight SoftBank-backed Oyo, which is already aggressively expanding to new markets. Oyo currently operates in more than 80 nations.

Saberwal isn’t necessarily threatened by Oyo, on the contrary, he sees Oyo’s success as a testament that there is room for more players to be in the space. He is confident that RedDoorz is “on the right track to create the next tech unicorn in Southeast Asia,” and trade in public exchange in the next two to three years.

RedDoorz operates a marketplace of “two-star, three-star and below” budget hotels, selling access to rooms to people. Currently it has 1,400 hotels on its network, said Saberwal. By the end of the year, the startup aims to grow this number to 2,000.

The startup operates in 80 cities across Indonesia, Singapore, the Philippines and Vietnam, and plans to use the new capital to expand its network in its existing markets, said Saberwal. At least for the next one year, RedDoorz has no plans to expand beyond the four markets where it currently operates, he said.

“Anything in the accommodation is our playground. We have all kinds of properties. We have three-star hotels, some hostels, so we will continue to go deeper and wider moving forward,” Saberwal, a former top executive at India’s travel giant MakeMyTrip, said.

It’s a great combination: Making the ubiquity of typically unorganized local guesthouse-style rooms with the more organized and efficient — but pricier — hotel option.

Some of the new capital will also go into broadening RedDoorz’s tech infrastructure, building a second engineering hub in Vietnam. (RedDoorz’s current regional tech hub is based in India.)


TechCrunch

Using Photoshop and other image manipulation software to tweak faces in photos has become common practice, but it’s not always made clear when it’s been done. Berkeley and Adobe researchers have created a tool that not only can tell when a face has been Photoshopped, but can suggest how to undo it.

Right off the bat it must be noted that this project applies only to Photoshop manipulations, and in particular those made with the “Face Aware Liquify” feature, which allows for both subtle and major adjustments to many facial features. A universal detection tool is a long way off, but this is a start.

The researchers (among them Alexei Efros, who just appeared at our AI+Robotics event) began from the assumption that a great deal of image manipulation is performed with popular tools like Adobe’s, and as such a good place to start would be looking specifically at the manipulations possible in those tools.

They set up a script to take portrait photos and manipulate them slightly in various ways: move the eyes a bit and emphasize the smile, narrow the cheeks and nose, things like that. They then fed the originals and warped versions to the machine learning model en masse, with the hopes that it would learn to tell them apart.

Learn it did, and quite well. When humans were presented with images and asked which had been manipulated, they performed only slightly better than chance. But the trained neural network identified the manipulated images 99 percent of the time.

What is it seeing? Probably tiny patterns in the optical flow of the image that humans can’t really perceive. And those same little patterns also suggest to it what exact manipulations have been made, letting it suggest an “undo” of the manipulations even having never seen the original.

Since it’s limited to just faces tweaked by this Photoshop tool, don’t expect this research to form any significant barrier against the forces of evil lawlessly tweaking faces left and right out there. But this is just one of many small starts in the growing field of digital forensics.

“We live in a world where it’s becoming harder to trust the digital information we consume,” said Adobe’s Richard Zhang, who worked on the project, “and I look forward to further exploring this area of research.”

You can read the paper describing the project and inspect the team’s code at the project page.


TechCrunch

Created by R the Company. Powered by SiteMuze.