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Archivo del Autor: Belen De Leon

The creator of Internet Explorer wants to read your mind with a bracelet

The idea of controlling a computer with your thoughts sounds amazing. A New York startup called CRTL-Labs has created a non-invasive device they claim will make this an everyday reality.

The post The creator of Internet Explorer wants to read your mind with a bracelet appeared first on Digital Trends.


Source: Digital trends

Batman Is Only Kinda Good at Crime Scene Investigation

Bruce Wayne’s gumshoe techniques are alright, but how does he stack up against ‘True Detective’?
Source: Wired

Microsoft backpedals on VR promise

If you were still waiting patiently for the virtual reality features that Microsoft promised in 2016, then I have some bad news for you. During E3 last week, Microsoft’s chief marketing officer for gaming, Mike Nichols, told GamesIndustry.biz that the company had no plans to fulfill that promise.

“We don’t have any plans specific to Xbox consoles in virtual reality or mixed reality,” Nichols told GamesIndustry.biz.

This goes against a promise that Microsoft made two years ago when Xbox chief Phil Spencer told the Verge that the Xbox One X (then dramatically known as Xbox Scorpio) would support “[the kind of] high-end VR that you see happening in the PC space.”

The release of the Xbox One X came and went without any news of VR integration, but in the interim, Microsoft did make strides toward VR and mixed reality tech for PC gaming with the release of the Windows Mixed Reality headsets for for Windows 10.

According to Nichols, it seems like Microsoft may be sticking to this PC gaming territory for awhile.

“PC is probably the best platform for more immersive VR and MR … but as it relates to Xbox, no,” he said.


Source: TechCrunch

Melania Trump wears jacket saying 'I really don't care' before visit with migrant kids – CNET

The visit comes as President Donald Trump’s administration has separated separated families that cross the US-Mexico border illegally.
Source: CNET

Carne y Arena puts you in VR shoes of scared fleeing immigrants – CNET

Carne y Arena, from Alejandro González Iñárritu, the Academy-Award winning filmmaker behind The Revenant, is mesmerizing audiences.
Source: CNET

MTV Studios reboots '90s TV shows Daria, The Real World – CNET

MTV gives fans a big dose of nostalgia by relaunching ’90s shows via the newly launched MTV Studios.
Source: CNET

Apple killing the Lightning port may be the next step to a truly wireless iPhone – CNET

The iPhones of the future may be wireless, portless and buttonless.
Source: CNET

From Elastigirl to Edna, Incredibles 2 a feminist statement – CNET

Elastigirl is out fighting crime, while Mr. Incredible stays home with the kids. And Edna, well, she does everything right.
Source: CNET

Species-identifying AI gets a boost from images snapped by citizen naturalists

Someday we’ll have an app that you can point at a weird bug or unfamiliar fern and have it spit out the genus and species. But right now computer vision systems just aren’t up to the task. To help things along, researchers have assembled hundreds of thousands of images taken by regular folks of critters in real life situations — and by studying these, our AI helpers may be able to get a handle on biodiversity.

Many computer vision algorithms have been trained on one of several large sets of images, which may have everything from people to household objects to fruits and vegetables in them. That’s great for learning a little about a lot of things, but what if you want to go deep on a specific subject or type of image? You need a special set of lots of that kind of image.

For some specialties, we have that already: FaceNet, for instance, is the standard set for learning how to recognize or replicate faces. But while computers may have trouble recognizing faces, we rarely do — while on the other hand, I can never remember the name of the birds that land on my feeder in the spring.

Fortunately, I’m not the only one with this problem, and for years the community of the iNaturalist app has been collecting pictures of common and uncommon animals for identification. And it turns out that these images are the perfect way to teach a system how to recognize plants and animals in the wild.

Could you tell the difference?

You might think that a computer could learn all it needs to from biology textbooks, field guides, and National Geographic. But when you or I take a picture of a sea lion, it looks a lot different from a professional shot: the background is different, the angle isn’t perfect, the focus is probably off, and there may even be other animals in the shot. Even a good computer vision algorithm might not see much in common between the two.

The photos taken through the iNaturalist app, however, are all of the amateur type — yet they have also been validated and identified by professionals who, far better than any computer, can recognize a species even when it’s occluded, poorly lit, or blurry.

The researchers, from Caltech, Google, Cornell, and iNaturalist itself, put together a limited subset of the more than 1.6 million images in the app’s databases, presented this week at CVPR in Salt Lake City. They decided that in order for the set to be robust, it should have lots of different angles and situations, so they searched for species that have had at least 20 different people spot them.

The resulting set of images (PDF) still has over 859,000 pictures of over 5,000 species. These they had people annotate by drawing boxes around the critter in the picture, so the computer would know what to pay attention to. A set of images was set aside for training the system, another set for testing it.

Examples of bounding boxes being put on images.

Ironically, they can tell it’s a good set because existing image recognition engines perform so poorly on it, not even reaching 70 percent first-guess accuracy. The very qualities that make the images themselves so amateurish and difficult to parse make them extremely valuable as raw data; these pictures haven’t been sanitized or set up to make it any easier for the algorithms to sort through.

Even the systems created by the researchers with the iNat2017 set didn’t fare so well. But that’s okay — finding where there’s room to improve is part of defining the problem space.

The set is expanding, as others like it do, and the researchers note that the number of species with 20 independent observations has more than doubled since they started working on the dataset. That means iNat2018, already under development, will be much larger and will likely lead to more robust recognition systems.

The team says they’re working on adding more attributes to the set so that a system will be able to report not just species, but sex, life stage, habitat notes, and other metadata. And if it fails to nail down the species, it could in the future at least make a guess at the genus or whatever taxonomic rank it’s confident about — e.g. it may not be able to tell if it’s anthopleura elegantissima or anthopleura xanthogrammica, but it’s definitely an anemone.

This is just one of many parallel efforts to improve the state of computer vision in natural environments; you can learn more about the ongoing collection and competition that leads to the iNat datasets here, and other more class-specific challenges are listed here.


Source: TechCrunch

A huge spreadsheet naming ICE employees gets yanked from GitHub and Medium

A massive database of current U.S. Immigration and Customs Enforcement (ICE) employees scraped from public LinkedIn profiles has been removed from the tech platforms hosting the data. The project was undertaken by Sam Lavigne, self-described artist, programmer and researcher in response to recent revelations around ICE’s detention practices at the southern U.S. border.

Lavigne posted the database to GitHub on Tuesday and by Wednesday the repository had been removed. The database included the name, profile photo, title and city area of every ICE employee who listed the agency as their employer on the professional networking site. A more in-depth version of the data pulled all public LinkedIn data from the pool of users, including previous employment, education history and any other information those users opted to make public. The total database lists this information for 1,595 ICE employees, from the company’s CTO on down to low-level workers.

The project accompanied a Medium post about the project’s aims that has since been removed by the platform:

While I don’t have a precise idea of what should be done with this data set, I leave it here with the hope that researchers, journalists and activists will find it useful…

I find it helpful to remember that as much as internet companies use data to spy on and exploit their users, we can at times reverse the story, and leverage those very same online platforms as a means to investigate or even undermine entrenched power structures. It’s a strange side effect of our reliance on private companies and semi-public platforms to mediate nearly all aspects of our lives.

The data set appears to have violated GitHub and Medium guidelines against doxing. Medium’s anti-harassment policy specifically forbids doxing and defines it broadly, preventing “the aggregation of publicly available information to target, shame, blackmail, harass, intimidate, threaten, or endanger.”

Because it doesn’t include personal identifying information like home addresses, phone numbers or other non-public details, Lavigne’s project isn’t really doxing in the normal sense of the word, though that hasn’t made it less controversial.

GitHub’s own policy leading to the data’s removal is less clear, though the company told The Verge the repository was removed due to “doxxing and harassment.” The platform’s terms of service forbid uses of GitHub that “violate the privacy of any third party, such as by posting another person’s personal information without consent.” This leaves some room for interpretation, and it is not clear that data from a public-facing social media profile is “personal” under this definition. GitHub allows researchers to scrape data from external sites in order to aggregate it “only if any publications resulting from that research are open access.”

While Lavigne’s aggregation efforts were deemed off-limits by some tech platforms, they do raise compelling questions. What kinds of public data, in aggregate, run afoul of anti-harassment rules? Why can this kind of data be scraped for the purposes of targeted advertising or surveillance by law enforcement but not be collected in a user-facing way? The ICE database raised these questions and plenty more, but for some tech companies the question of hosting the data proved too provocative from the start.


Source: TechCrunch

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