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Archivo del Autor: Belen De Leon

Shadow profiles: Facebook has information you didn't hand over – CNET

Facebook CEO Mark Zuckerberg told lawmakers you control all the data you give the social network. But the company gets information about you from many sources.
Source: CNET

Why Facebook's 2011 Promises Haven't Protected Users

Facebook didn’t notify the Federal Trade Commission when it learned that Cambridge Analytica had improperly obtained personal information of users.
Source: Wired

DroneShield is keeping hostile UAVs away from NASCAR events

If you were hoping to get some sweet drone footage of a NASCAR race in progress, you may find your quadcopter grounded unceremoniously by a mysterious force: DroneShield is bringing its anti-drone tech to NASCAR events at the Texas Motor Speedway.

The company makes a handful of products, all aimed at detecting and safely intercepting drones that are flying where they shouldn’t. That’s a growing problem, of course, and not just at airports or Area 51. A stray drone at a major sporting event could fall and interrupt the game, or strike someone, or at a race it may even cause a major accident.

Most recently it introduced a new version of its handheld “DroneGun,” which scrambles the UAV’s signal so that it has no choice but to safely put itself down, as these devices are generally programmed to do. You can’t buy one — technically, they’re illegal — but the police sure can.

Recently DroneShield’s tech was deployed at the Commonwealth Games in Brisbane and at the Olympics in PyeongChang, and now the company has announced that it was tapped by a number of Texas authorities for the protection of stock car races.

DroneShield’s systems in place in PyeongChang

“We are proud to be able to assist a high-profile event like this,” said Oleg Vornik, DroneShield’s CEO, in an email announcing the news. “We also believe that this is significant for DroneShield in that this is the first known live operational use of all three of our key products – DroneSentinel, DroneSentry and DroneGun – by U.S. law enforcement.”

It’s a big get for a company that clearly saw an opportunity in the growing drone market (in combating it, really) and executed well on it.


Source: TechCrunch

Apple starting to alert users that it will end 32-bit app support on the Mac

Tomorrow at midnight PT, Apple will begin issuing an alert box when you open a 32-bit app in MacOS 10.13.4. It’s a one-time (per app) alert, designed to help MacOS make the full transition to 64-bit. At some unspecified time in the future, the operating system will end its support for 32-bit technology… meaning those apps that haven’t been updated just won’t work. 

That time, mind you, is not tomorrow, but the company’s hoping that this messaging will help light a fire under users and developers to upgrade before that day comes. Says the company on its help page, “To ensure that the apps you purchase are as advanced as the Mac you run them on, all future Mac software will eventually be required to be 64-bit.”

It’s similar to the transition the company made on the mobile side with iOS 11. Of course, making the shift is a bit messier on the desktop. For one thing, the company’s desktop operating system has been around a lot longer than iOS. For another, while Apple does have a MacOS App Store, plenty of desktop apps are still downloaded from other channels.

As the company notes, the transition’s been a long time coming. The company started making it 10 or so years ago with the Power Mac G5 desktop, so it hasn’t exactly been an overnight ask for developers. Of course, if you’ve got older, non-supported software in your arsenal, the eventual end-of-lifing could put a severe damper on your workflow. For those users, there will no doubt be some shades of the transition from OS 9 to OS X in all of this.

You can skip the alert and just see for yourself by clicking the System Report button. For those apps that haven’t updated yet (I’m looking at you, Audacity), Apple recommends bugging the developers directly.


Source: TechCrunch

AI produces first new Brothers Grimm fairy tale in 200 years – CNET

An algorithm trained to mimic the Grimm style leads to a new story. “You might call it a form of literary cloning,” says the co-founder of the app that commissioned it.
Source: CNET

Who are Diamond and Silk, and what do they have to do with Facebook? – CNET

More than one member of Congress quizzed CEO Mark Zuckerberg about why his social network had banned the conservative siblings.
Source: CNET

Mark Zuckerberg's Congress Testimony Day Two: Republicans and Democrats Diverge

In his second day of Congressional testimony, Democrats wanted to know about privacy. Republicans wanted to hear about Diamond and Silk.
Source: Wired

Privacy imported: US weighs EU-style regulations to protect your data – CNET

Congressional hearings with Facebook’s Mark Zuckerberg get lawmakers talking openly this week about regulating how internet companies collect and use consumers’ data.
Source: CNET

Worldwide PC shipments flat in 2018's first quarter – CNET

HP and Dell grow their PC businesses during a stagnant quarter, market researchers find.
Source: CNET

Who’s a good AI? Dog-based data creates a canine machine learning system

We’ve trained machine learning systems to identify objects, navigate streets and recognize facial expressions, but as difficult as they may be, they don’t even touch the level of sophistication required to simulate, for example, a dog. Well, this project aims to do just that — in a very limited way, of course. By observing the behavior of A Very Good Girl, this AI learned the rudiments of how to act like a dog.

It’s a collaboration between the University of Washington and the Allen Institute for AI, and the resulting paper will be presented at CVPR in June.

Why do this? Well, although much work has been done to simulate the sub-tasks of perception like identifying an object and picking it up, little has been done in terms of “understanding visual data to the extent that an agent can take actions and perform tasks in the visual world.” In other words, act not as the eye, but as the thing controlling the eye.

And why dogs? Because they’re intelligent agents of sufficient complexity, “yet their goals and motivations are often unknown a priori.” In other words, dogs are clearly smart, but we have no idea what they’re thinking.

As an initial foray into this line of research, the team wanted to see if by monitoring the dog closely and mapping its movements and actions to the environment it sees, they could create a system that accurately predicted those movements.

In order to do so, they loaded up a Malamute named Kelp M. Redmon with a basic suite of sensors. There’s a GoPro camera on Kelp’s head, six inertial measurement units (on the legs, tail and trunk) to tell where everything is, a microphone and an Arduino that tied the data together.

They recorded many hours of activities — walking in various environments, fetching things, playing at a dog park, eating — syncing the dog’s movements to what it saw. The result is the Dataset of Ego-Centric Actions in a Dog Environment, or DECADE, which they used to train a new AI agent.

This agent, given certain sensory input — say a view of a room or street, or a ball flying past it — was to predict what a dog would do in that situation. Not to any serious level of detail, of course — but even just figuring out how to move its body and to where is a pretty major task.

“It learns how to move the joints to walk, learns how to avoid obstacles when walking or running,” explained Hessam Bagherinezhad, one of the researchers, in an email. “It learns to run for the squirrels, follow the owner, track the flying dog toys (when playing fetch). These are some of the basic AI tasks in both computer vision and robotics that we’ve been trying to solve by collecting separate data for each task (e.g. motion planning, walkable surface, object detection, object tracking, person recognition).”

That can produce some rather complex data: For example, the dog model must know, just as the dog itself does, where it can walk when it needs to get from here to there. It can’t walk on trees, or cars, or (depending on the house) couches. So the model learns that as well, and this can be deployed separately as a computer vision model for finding out where a pet (or small legged robot) can get to in a given image.

This was just an initial experiment, the researchers say, with success but limited results. Others may consider bringing in more senses (smell is an obvious one) or seeing how a model produced from one dog (or many) generalizes to other dogs. They conclude: “We hope this work paves the way towards better understanding of visual intelligence and of the other intelligent beings that inhabit our world.”


Source: TechCrunch

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