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Instagram Employs Machine Learning to Stop Bullying

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New Head of Instagram Adam Mosseri is only 10 days into the job and already introducing high-tech tools to limit bullying on the social network.

“Online bullying is complex,” Mosseri wrote in a blog post, recognizing that the current system of reporting and removing content that violates company guidelines isn’t enough.

“We know we have more work to do to further limit bullying and spread kindness on Instagram,” he said.

Enter machine learning.

A new algorithm, trained to detect harassment in photos and captions, proactively sends offending content to the Community Operations team for review.

“This change will help us identify and remove significantly more bullying,” Mosseri said. “And it’s a crucial next step since many people who experience or observe bullying don’t report it.”

Bullying comment filter on Live videos (via Instagram)

It is also aimed at protecting Instagram’s youngest members, since teens often experience high rates of online torment.

The new function is rolling out now, and is expected to reach all users over the coming weeks.

There are no further details about how the system works, or whether it differs at all from the usual means of complaint.

Even AI isn’t perfect, though. You can still manually report a post or profile for abuse via the mobile app or website.

Additionally, the platform is expanding its bullying comment filter to hide crude comments from the Feed, Explore, and Profile tabs, as well as live videos (above)—”to ensure that Live remains a safe and fun place to authentically connect with your friends and interests,” Mosseri wrote.

Maddie Ziegler’s kindness camera filter (via Instagram)

Sure, taking down trolls is important and self-affirming. But so is spreading kindness—a concept Instagram is celebrating with Maddie Ziegler. The teen dancer (best known for Dance Moms, Sia music videos, and being Millie Bobby Brown’s BFF) helped launch a kindness camera effect “to spread positivity.”

“Maddie has been speaking out against online bullying since she started experiencing it and is one of many people in our community that uses Instagram to promote positivity,” the blog said.

Ziegler’s 12.4 million followers can automatically access the effect; simply open the camera, tap the face icon at the bottom, and click “spread kindness.”

In selfie mode, hearts fill the screen; switch to the rear camera to see kind comments in various languages. You are encouraged to tag a friend to support; they in turn receive a notification, and can share it to their own story or use the filter to pay it forward.

It’s no coincidence that Instagram announced its latest efforts at the beginning of October—National Bullying Prevention Month in the US.

Parent company Facebook recently revealed how it uses artificial intelligence, machine learning, and computer vision to purge the site of hate speech, nudity, and violence. Stay up to date with all things Instagram here.

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New Florida Law Nixes Need for Autonomous Vehicle Operators

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Florida Gov. Ron DeSantis last week signed a bill removing “unnecessary obstacles that hinder the development of autonomous vehicle technology”—including backup drivers.

The new law, which takes effect July 1, will allow a self-driving car (meeting all insurance requirements) to run without a human operator.

It also exempts occupants from laws against texting and other distractions.

“Signing this legislation paves the way for Florida to continue as a national leader in transportation innovation and technological advancement,” DeSantis said in a statement.

Flanked by smiling supporters, the governor on Thursday signed House Bill 311 at the SunTrax transportation center, used to test autonomous vehicles.

Also in attendance were Republican bill sponsors Sen. Jeff Brandes and Rep. Jason Fischer.

Florida Gov. Ron DeSantis signed a bill allowing autonomous vehicle tests with no human operator (via Gov. DeSantis Press Office)

“We here in Florida are pioneering the most exciting innovations in transportation,” Fischer said. “This bill on self-driving cars will usher in a new era of smart cities that will not only expand our economy but increase road safety and decrease traffic congestion.”

And permit “active display” of TV or video in the car.

The Sunshine State isn’t exactly breaking new ground: Last year, the California DMV introduced new regulations allowing automakers to test and deploy fully driverless vehicles.

We still have a long way to go, though, before folks can start napping behind the wheel.

High-profile accidents—an autonomous Uber struck and killed a pedestrian in Arizona; Tesla’s Autopilot feature was engaged at the time of a fiery Model X crash in California—have left some mistrustful of self-driving cars.

Others, meanwhile, are ready and willing to move forward with the unpredictable technology.

“Autonomous vehicles are the way of the future and Florida is leading the charge through the research, testing, and development of autonomous vehicles,” according to state Department of Transportation Secretary Kevin Thibault. “And now with this bill signed into law … Florida is ready to lead the nation with this innovative transportation advancement.”

Brandes agreed, adding that “With the signing of this legislation we reaffirm our bold commitment to lead the country as we transition to a shared, electric and driverless future.”

In February 2018, Ford unveiled a self-driving delivery pilot program that would see autonomous vehicles roaming the streets of Miami—notorious for its traffic congestion.

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MIT Robot Learns to ID Objects by Sight, Touch

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Humans’ five senses work together to reveal what we see, hear, smell, taste, and touch.

But robots are still learning to understand different tactile signals.

To move the process along, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a predictive AI that can learn to see by touching, and learn to feel by seeing.

The system creates realistic signals from visual inputs to predict with which object it is making physical contact.

Using a KUKU robot arm and GelSight tactile sensor (designed by another group at MIT), researchers recorded nearly 200 objects—tools, household products, fabrics, etc.—being touched more than 12,000 times.

By breaking down those video clips into static frames, the team compiled a dataset of more than 3 million visual/tactile-paired images, known as “VisGel.”

“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge,” lead study author Yunzhu Li, a CSAIL PhD student, said in a statement.

“By blindly touching around, our model can predict the interaction with the environment purely from tactile feelings,” he continued. “Bringing these two senses together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects.”

During testing, if the model was fed tactile data on a shoe, for instance, it could produce an image of where the shoe was most likely to be touched. The same goes for a computer mouse, box, cup, T-shirt, hammer—whatever its automated heart desires.

This type of ability, CSAIL said, could be useful for tasks in which there is no visual data: like when a light is off, or someone is indiscriminately reaching into an unknown area.

Moving forward, the team plans to increase the size and diversity of its dataset  by collecting input in more unstructured areas (i.e. outside a controlled environment). Or by using a new MIT-designed tactile smart-glove.

Even with the help of a sensor-packed mitt, there are certain details that can be tricky to infer from switching models—details that even humans can’t ascertain without using more than one sense. Like identifying the color of an object by touching it or determining how soft a sofa is without actually pressing on it.

CSAIL invented a similar system earlier this year: The “RoCycle” uses a soft Teflon hand covered in tactile sensors to detect an object’s size and stiffness—no visual cues necessary.

Basically, it squeezes cups, boxes, and cans to determine their makeup, and, ultimately, their recyclability.

A collaboration with Yale University, RoCycle demonstrates the limits of sight-based sorting; it can distinguish between two identical-looking Starbucks cups made of paper and plastic that would give vision systems (and the human eye) trouble.

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Deepfake Tool Makes It Easy to Put Words Into Someone’s Mouth

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Changing what someone says in a video is now as easy as “copy and paste.”

Researchers developed new software that uses machine learning to let users edit the text transcript of a video, altering the very words coming out of a person’s mouth.

The team—from Stanford University, the Max Planck Institute for Informatics, Princeton University, and Adobe Research—envision their technology being used by film and television editors.

“Much like word processing, the editor could easily add new words, delete unwanted ones, or completely rearrange the pieces by dragging and dropping them as needed to assemble a finished video that looks almost flawless to the untrained eye,” according to a Stanford press release.

A new algorithm allows video editors to modify talking-head videos as if they were editing text—copying, pasting, adding and deleting words (via Stanford University)

The algorithm works best with talking-head videos, which show speakers only from the shoulders up; hand gestures and other body movements are a dead giveaway.

“The work could be a boon for video editors and producers but does raise concerns as people increasingly question the validity of images and videos online,” the authors said.

Say, for example, an actor flubs their line: The editor can simply rewrite the transcript, and the application will assemble the right word from various phrases spoken elsewhere in the recording.

It’s a bit like when surgeons transplant skin from one area of the body to another. It’s the skin grafting of video production.

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The machine-learning element then converts those sounds into a final video that appears natural to the viewer. Intelligent smoothing and Neural Rendering also work to create a photorealistic video in perfect lip-synch.

“Visually, it’s seamless. There’s no need to re-record anything,” lead researcher Ohad Fried, a postdoctoral scholar at Stanford, said in a statement.

In a crowd-sourced study with 138 participants, the team’s edits were rated as “real” almost 60 percent of the time. There is, of course, still room for improvement.

The algorithm currently requires at least 40 minutes of original video as input, and won’t yet work with just any sequence.

In an era of fake news, Internet hoaxes, and revenge porn, letting this technology fall into the wrong hands could be disastrous.

“This technology is really about better storytelling,” Fried said, acknowledging concerns about the software being used for illicit purposes.

Editing video is as easy as editing text (via Stanford University)

“Unfortunately, technologies like this will always attract bad actors,” he added. “But the struggle is worth it given the many creative video-editing and content-creation applications this enables.”

In an effort to curb rabble-rousers, researchers have proposed guidelines for using these tools that would alert viewers and performers that a video has been manipulated.

An opt-in watermarking system, perhaps, to identify edited content. Or digital/non-digital fingerprinting techniques.

None of these solutions are comprehensive, though; viewers must remain skeptical and cautious, Fried said.

The most pressing matter, he suggested, is to raise public awareness and education on video manipulation, so people are better equipped to question and assess synthetic content.

The full report—available online—will be published in the journal ACM Transactions on Graphics.

Watch: ‘Minority Report’-Like AI Can Detect Shoplifting

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