The team is testing robots that can help out in workplace environments, though right now, these early robots are focused on learning how to sort trash. Here’s what one of them looks like — it reminds me of a very tall, one-armed Wall-E (ironic, given what the robots are tasked to do):

Here’s a GIF of a robot actually sorting a recyclable can from a compost pile to a recycling pile. This is wild — check out how the arm actually grasps the can:

The concept of grasping something comes pretty easily to most humans, but it’s a very challenging thing to teach a robot, and Everyday Robot’s robots get their practice in both the physical world and the virtual world. In a tour of X’s offices, Wired described how a “playpen” of nearly 30 of the robots (supervised by humans) spend their daytime hours sorting trash into trays for compost, landfill, and recycling. At night, Everyday Robot has virtual robots practice grabbing things in simulated buildings, according to Wired. That simulated data is then combined with the real world data, which is given to the robots in a system update every week or two.

With all that practice, X says the robots are actually getting pretty good at sorting, apparently putting less than 5 percent of trash in the wrong place (X’s humans put 20 percent of trash in the wrong pile, according to X).

That doesn’t mean they’re remotely ready to replace human janitors, though. Wired observed one robot grasping thin air instead of the bowl in front of it, then attempting to put the “bowl” down. Another lost one of its “finger” during the demo. Engineers also told Wired that, at one point, some robots weren’t moving through a building because some types of light caused their sensors to hallucinate holes in the floor.

There are whole startups dedicated to the problem of teaching a robot how to grasp, such as Embodied Intelligence and the nonprofit OpenAI. And Google, also owned by Alphabet, has done research into grasping — check out this 2016 video of some Google-made robot arms trying to grab differently-sized objects:

But progress is being made beyond the work X and Google are doing. For example, Boston Dynamics (formerly owned by Google) released this video in 2018 of its SpotMini robot grabbing a doorknob to open a door for a friend:

And research from Google from this March showed off a robot that could pick up objects and, over time, learn the best way to throw a specific shape:

Despite all this research, Google and Alphabet have a troubled history with robotics. Google’s last serious attempt at robotics work started in 2013 in a division led by Android co-founder Andy Rubin. Though that division made some high-profile acquisitions, including Boston Dynamics, nothing concrete came from it, and Rubin departed from Google in 2014 following allegations of sexual harassment. Google is apparently dipping its toes back into robotics, though, based on a report from March of this year, and its new robots are also learning how to grab, but it seems Google’s work is different from that of Everyday Robot’s.

Everyday Robot lead Hans Peter Brondmo told Wired that he hopes to one day make a robot that can assist the elderly. But he also acknowledged something like that might be a few years out — so for now, it seems the robots will keep getting better at sorting trash.

The post Alphabet X’s new Everyday Robot project wants to build robots that can learn from the world around them appeared first on Artificial Intelligence.

Robots are coming to steal your reviews.

Researchers at the University of Chicago have trained a neural network, or artificial intelligence system, to write fake reviews on Yelp – and it’s pretty hard to tell them apart from a human review.

Their study, which will be presented at the ACM Conference on Computer and Communications Security in October, aimed to highlight how easily these systems can mimic human reviews – and how damaging they can become if they’re not monitored properly.

Since many small businesses rely on online reviews to help grow and sustain their reputation, a future where someone – like a rival or angry customer — could hypothetically spam their page with negative reviews written by a machine is pretty worrying.

And, according to the research team, the threat goes far beyond a bunch of fake reviews on Yelp.

“In general, the threat is bigger,” Ben Y. Zhao, one of the authors of the study, told Business Insider. “I think the threat towards society at large and really disillusioned users and to shake our belief in what is real and what is not, I think that’s going to be even more fundamental.”

To test how believable these reviews came across, the researchers recruited 40 volunteers and had AI generate five fake reviews for 40 actual restaurants. The volunteers were asked how useful they thought the review was and whether or not they thought it was fake. The AI reviews ranked as “effectively indistinguishable” from real reviews, according to the study. Further, the fake reviews were given a 3.15 “usefulness” rating, compared to a 3.28 rating for human reviews.

Below are four five-star reviews written for the same NYC restaurant, can you figure out which ones were written by a human?

1. “I love this place. I have been going here for years and it is a great place to hang out with friends and family. I love the food and service. I have never had a bad experience when I am there.”
2. “Easily my favorite Italian restaurant. I love the taster menu, everything is amazing on it. I suggest the carpaccio and the asparagus. Sadly it has become more widely known and becoming difficult to get a reservation for prime times.
3. “I had the grilled veggie burger with fries!!!! Ohhhh and taste. Omgggg! Very flavorful! It was so delicious that I didn’t spell it!!”
4. “My family and I are huge fans of this place. The staff is super nice and the food is great. The chicken is very good and the garlic sauce is perfect. Ice cream topped with fruit is delicious too. Highly recommended!”

Number two is the only review written by a human; numbers one, three and four were all generated by the AI.

“It is going to progress to greater attacks, where entire articles written on a blog may be completely autonomously generated along some theme by a robot,” said Zhao. “and then you really have to think about where does information come from, how can you verify.”

The researchers plan to use the study to start developing technology that can detect and get rid of AI-generated text.

The post Robots learned how to write fake Yelp reviews like a human appeared first on Artificial Intelligence.