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#437504 A New and Improved Burger Robot’s on ...
No doubt about it, the pandemic has changed the way we eat. Never before have so many people who hated cooking been forced to learn how to prepare a basic meal for themselves. With sit-down restaurants limiting their capacity or shutting down altogether, consumption of fast food and fast-casual food has skyrocketed. Don’t feel like slaving over a hot stove? Just hit the drive through and grab a sandwich and some fries (the health implications of increased fast food consumption are another matter…).
Given our sudden immense need for paper-wrapped burgers and cardboard cartons of fries, fast food workers are now counted as essential. But what about their safety, both from a virus standpoint and from the usual risks of working in a busy kitchen (like getting burned by the stove or the hot oil from the fryer, cut by a slicer, etc.)? And how many orders of burgers and fries can humans possibly churn out in an hour?
Enter the robot. Three and a half years ago, a burger-flipping robot aptly named Flippy, made by Miso Robotics, made its debut at a fast food restaurant in California called CaliBurger. Now Flippy is on the market for anyone who wishes to purchase their own, with a price tag of $30,000 and a range of new capabilities—this burger bot has progressed far beyond just flipping burgers.
Flippy’s first iteration was already pretty impressive. It used machine learning software to locate and identify objects in front of it (rather than needing to have objects lined up in specific spots), and was able to learn from experience to improve its accuracy. Sensors on its grill-facing side took in thermal and 3D data to gauge the cooking process for multiple patties at a time, and cameras allowed the robot to ‘see’ its surroundings.
A system that digitally sent tickets to the kitchen from the restaurant’s front counter kept Flippy on top of how many burgers it should be cooking at any given time. Its key tasks were pulling raw patties from a stack and placing them on the grill, tracking each burger’s cook time and temperature, and transferring cooked burgers to a plate.
The new and improved Flippy can do all this and more. It can cook 19 different foods, including chicken wings, onion rings, french fries, and even the Impossible Burger (which, as you may know, isn’t actually made of meat, and that means it’s a little trickier to grill it to perfection).
Flippy’s handiwork. Image Credit: Miso Robotics
And instead of its body sitting on a cart on wheels (which took up a lot of space and meant the robot’s arm could get in the way of human employees), it’s now attached to a rail along the stove’s hood, and can move along the rail to access both the grill and the fryer (provided they’re next to each other, which in many fast food restaurants they are). In fact, Flippy has a new acronym attached to its name: ROAR, which stands for Robot on a Rail.
Flippy ROAR in action, artist rendering. Image Credit: Miso Robotics
Sensors equipped with laser make it safer for human employees to work near Flippy. The bot can automatically switch between different tools, such as a spatula for flipping patties and tongs for gripping the handle of a fryer basket. Its AI software will enable it to learn new skills over time.
Flippy’s interface. Image Credit: Miso Robotics
The first big restaurant chain to go all-in on Flippy was White Castle, which in July announced plans to pilot Flippy ROAR before year’s end. And just last month, Miso made the bot commercially available. The current cost is $30,000 (plus a monthly fee of $1,500 for use of the software), but the company hopes to bring the price down to $20,000 within the next year.
According to Business Insider, demand for the fast food robot is through the roof, probably given a significant boost by the pandemic—thanks, Covid-19. The pace of automation has picked up across multiple sectors, and will likely continue to accelerate as companies look to insure themselves against additional losses.
So for the immediate future, it seems that no matter what happens, we don’t have to worry about the supply of burgers, fries, onion rings, chicken wings, and the like running out.
Now if only Flippy had a cousin—perhaps named Leafy—who could chop vegetables and greens and put together fresh-made salads…
Maybe that can be Miso Robotics’ next project.
Image Credit: Miso Robotics Continue reading
Posted in Human Robots
#437491 3.2 Billion Images and 720,000 Hours of ...
Twitter over the weekend “tagged” as manipulated a video showing US Democratic presidential candidate Joe Biden supposedly forgetting which state he’s in while addressing a crowd.
Biden’s “hello Minnesota” greeting contrasted with prominent signage reading “Tampa, Florida” and “Text FL to 30330.”
The Associated Press’s fact check confirmed the signs were added digitally and the original footage was indeed from a Minnesota rally. But by the time the misleading video was removed it already had more than one million views, The Guardian reports.
A FALSE video claiming Biden forgot what state he was in was viewed more than 1 million times on Twitter in the past 24 hours
In the video, Biden says “Hello, Minnesota.”
The event did indeed happen in MN — signs on stage read MN
But false video edited signs to read Florida pic.twitter.com/LdHQVaky8v
— Donie O'Sullivan (@donie) November 1, 2020
If you use social media, the chances are you see (and forward) some of the more than 3.2 billion images and 720,000 hours of video shared daily. When faced with such a glut of content, how can we know what’s real and what’s not?
While one part of the solution is an increased use of content verification tools, it’s equally important we all boost our digital media literacy. Ultimately, one of the best lines of defense—and the only one you can control—is you.
Seeing Shouldn’t Always Be Believing
Misinformation (when you accidentally share false content) and disinformation (when you intentionally share it) in any medium can erode trust in civil institutions such as news organizations, coalitions and social movements. However, fake photos and videos are often the most potent.
For those with a vested political interest, creating, sharing and/or editing false images can distract, confuse and manipulate viewers to sow discord and uncertainty (especially in already polarized environments). Posters and platforms can also make money from the sharing of fake, sensationalist content.
Only 11-25 percent of journalists globally use social media content verification tools, according to the International Centre for Journalists.
Could You Spot a Doctored Image?
Consider this photo of Martin Luther King Jr.
Dr. Martin Luther King Jr. Giving the middle finger #DopeHistoricPics pic.twitter.com/5W38DRaLHr
— Dope Historic Pics (@dopehistoricpic) December 20, 2013
This altered image clones part of the background over King Jr’s finger, so it looks like he’s flipping off the camera. It has been shared as genuine on Twitter, Reddit, and white supremacist websites.
In the original 1964 photo, King flashed the “V for victory” sign after learning the US Senate had passed the civil rights bill.
“Those who love peace must learn to organize as effectively as those who love war.”
Dr. Martin Luther King Jr.
This photo was taken on June 19th, 1964, showing Dr King giving a peace sign after hearing that the civil rights bill had passed the senate. @snopes pic.twitter.com/LXHmwMYZS5
— Willie's Reserve (@WilliesReserve) January 21, 2019
Beyond adding or removing elements, there’s a whole category of photo manipulation in which images are fused together.
Earlier this year, a photo of an armed man was photoshopped by Fox News, which overlaid the man onto other scenes without disclosing the edits, the Seattle Times reported.
You mean this guy who’s been photoshopped into three separate photos released by Fox News? pic.twitter.com/fAXpIKu77a
— Zander Yates ザンダーイェーツ (@ZanderYates) June 13, 2020
Similarly, the image below was shared thousands of times on social media in January, during Australia’s Black Summer bushfires. The AFP’s fact check confirmed it is not authentic and is actually a combination of several separate photos.
Image is more powerful than screams of Greta. A silent girl is holding a koala. She looks straight at you from the waters of the ocean where they found a refuge. She is wearing a breathing mask. A wall of fire is behind them. I do not know the name of the photographer #Australia pic.twitter.com/CrTX3lltdh
— EVC Music (@EVCMusicUK) January 6, 2020
Fully and Partially Synthetic Content
Online, you’ll also find sophisticated “deepfake” videos showing (usually famous) people saying or doing things they never did. Less advanced versions can be created using apps such as Zao and Reface.
Or, if you don’t want to use your photo for a profile picture, you can default to one of several websites offering hundreds of thousands of AI-generated, photorealistic images of people.
These people don’t exist, they’re just images generated by artificial intelligence. Generated Photos, CC BY
Editing Pixel Values and the (not so) Simple Crop
Cropping can greatly alter the context of a photo, too.
We saw this in 2017, when a US government employee edited official pictures of Donald Trump’s inauguration to make the crowd appear bigger, according to The Guardian. The staffer cropped out the empty space “where the crowd ended” for a set of pictures for Trump.
Views of the crowds at the inaugurations of former US President Barack Obama in 2009 (left) and President Donald Trump in 2017 (right). AP
But what about edits that only alter pixel values such as color, saturation, or contrast?
One historical example illustrates the consequences of this. In 1994, Time magazine’s cover of OJ Simpson considerably “darkened” Simpson in his police mugshot. This added fuel to a case already plagued by racial tension, to which the magazine responded, “No racial implication was intended, by Time or by the artist.”
Tools for Debunking Digital Fakery
For those of us who don’t want to be duped by visual mis/disinformation, there are tools available—although each comes with its own limitations (something we discuss in our recent paper).
Invisible digital watermarking has been proposed as a solution. However, it isn’t widespread and requires buy-in from both content publishers and distributors.
Reverse image search (such as Google’s) is often free and can be helpful for identifying earlier, potentially more authentic copies of images online. That said, it’s not foolproof because it:
Relies on unedited copies of the media already being online.
Doesn’t search the entire web.
Doesn’t always allow filtering by publication time. Some reverse image search services such as TinEye support this function, but Google’s doesn’t.
Returns only exact matches or near-matches, so it’s not thorough. For instance, editing an image and then flipping its orientation can fool Google into thinking it’s an entirely different one.
Most Reliable Tools Are Sophisticated
Meanwhile, manual forensic detection methods for visual mis/disinformation focus mostly on edits visible to the naked eye, or rely on examining features that aren’t included in every image (such as shadows). They’re also time-consuming, expensive, and need specialized expertise.
Still, you can access work in this field by visiting sites such as Snopes.com—which has a growing repository of “fauxtography.”
Computer vision and machine learning also offer relatively advanced detection capabilities for images and videos. But they too require technical expertise to operate and understand.
Moreover, improving them involves using large volumes of “training data,” but the image repositories used for this usually don’t contain the real-world images seen in the news.
If you use an image verification tool such as the REVEAL project’s image verification assistant, you might need an expert to help interpret the results.
The good news, however, is that before turning to any of the above tools, there are some simple questions you can ask yourself to potentially figure out whether a photo or video on social media is fake. Think:
Was it originally made for social media?
How widely and for how long was it circulated?
What responses did it receive?
Who were the intended audiences?
Quite often, the logical conclusions drawn from the answers will be enough to weed out inauthentic visuals. You can access the full list of questions, put together by Manchester Metropolitan University experts, here.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Simon Steinberger from Pixabay Continue reading
Posted in Human Robots
#437471 How Giving Robots a Hybrid, Human-Like ...
Squeezing a lot of computing power into robots without using up too much space or energy is a constant battle for their designers. But a new approach that mimics the structure of the human brain could provide a workaround.
The capabilities of most of today’s mobile robots are fairly rudimentary, but giving them the smarts to do their jobs is still a serious challenge. Controlling a body in a dynamic environment takes a surprising amount of processing power, which requires both real estate for chips and considerable amounts of energy to power them.
As robots get more complex and capable, those demands are only going to increase. Today’s most powerful AI systems run in massive data centers across far more chips than can realistically fit inside a machine on the move. And the slow death of Moore’s Law suggests we can’t rely on conventional processors getting significantly more efficient or compact anytime soon.
That prompted a team from the University of Southern California to resurrect an idea from more than 40 years ago: mimicking the human brain’s division of labor between two complimentary structures. While the cerebrum is responsible for higher cognitive functions like vision, hearing, and thinking, the cerebellum integrates sensory data and governs movement, balance, and posture.
When the idea was first proposed the technology didn’t exist to make it a reality, but in a paper recently published in Science Robotics, the researchers describe a hybrid system that combines analog circuits that control motion and digital circuits that govern perception and decision-making in an inverted pendulum robot.
“Through this cooperation of the cerebrum and the cerebellum, the robot can conduct multiple tasks simultaneously with a much shorter latency and lower power consumption,” write the researchers.
The type of robot the researchers were experimenting with looks essentially like a pole balancing on a pair of wheels. They have a broad range of applications, from hoverboards to warehouse logistics—Boston Dynamics’ recently-unveiled Handle robot operates on the same principles. Keeping them stable is notoriously tough, but the new approach managed to significantly improve all digital control approaches by radically improving the speed and efficiency of computations.
Key to bringing the idea alive was the recent emergence of memristors—electrical components whose resistance relies on previous input, which allows them to combine computing and memory in one place in a way similar to how biological neurons operate.
The researchers used memristors to build an analog circuit that runs an algorithm responsible for integrating data from the robot’s accelerometer and gyroscope, which is crucial for detecting the angle and velocity of its body, and another that controls its motion. One key advantage of this setup is that the signals from the sensors are analog, so it does away with the need for extra circuitry to convert them into digital signals, saving both space and power.
More importantly, though, the analog system is an order of magnitude faster and more energy-efficient than a standard all-digital system, the authors report. This not only lets them slash the power requirements, but also lets them cut the processing loop from 3,000 microseconds to just 6. That significantly improves the robot’s stability, with it taking just one second to settle into a steady state compared to more than three seconds using the digital-only platform.
At the minute this is just a proof of concept. The robot the researchers have built is small and rudimentary, and the algorithms being run on the analog circuit are fairly basic. But the principle is a promising one, and there is currently a huge amount of R&D going into neuromorphic and memristor-based analog computing hardware.
As often turns out to be the case, it seems like we can’t go too far wrong by mimicking the best model of computation we have found so far: our own brains.
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Posted in Human Robots
#437466 How Future AI Could Recognize a Kangaroo ...
AI is continuously taking on new challenges, from detecting deepfakes (which, incidentally, are also made using AI) to winning at poker to giving synthetic biology experiments a boost. These impressive feats result partly from the huge datasets the systems are trained on. That training is costly and time-consuming, and it yields AIs that can really only do one thing well.
For example, to train an AI to differentiate between a picture of a dog and one of a cat, it’s fed thousands—if not millions—of labeled images of dogs and cats. A child, on the other hand, can see a dog or cat just once or twice and remember which is which. How can we make AIs learn more like children do?
A team at the University of Waterloo in Ontario has an answer: change the way AIs are trained.
Here’s the thing about the datasets normally used to train AI—besides being huge, they’re highly specific. A picture of a dog can only be a picture of a dog, right? But what about a really small dog with a long-ish tail? That sort of dog, while still being a dog, looks more like a cat than, say, a fully-grown Golden Retriever.
It’s this concept that the Waterloo team’s methodology is based on. They described their work in a paper published on the pre-print (or non-peer-reviewed) server arXiv last month. Teaching an AI system to identify a new class of objects using just one example is what they call “one-shot learning.” But they take it a step further, focusing on “less than one shot learning,” or LO-shot learning for short.
LO-shot learning consists of a system learning to classify various categories based on a number of examples that’s smaller than the number of categories. That’s not the most straightforward concept to wrap your head around, so let’s go back to the dogs and cats example. Say you want to teach an AI to identify dogs, cats, and kangaroos. How could that possibly be done without several clear examples of each animal?
The key, the Waterloo team says, is in what they call soft labels. Unlike hard labels, which label a data point as belonging to one specific class, soft labels tease out the relationship or degree of similarity between that data point and multiple classes. In the case of an AI trained on only dogs and cats, a third class of objects, say, kangaroos, might be described as 60 percent like a dog and 40 percent like a cat (I know—kangaroos probably aren’t the best animal to have thrown in as a third category).
“Soft labels can be used to represent training sets using fewer prototypes than there are classes, achieving large increases in sample efficiency over regular (hard-label) prototypes,” the paper says. Translation? Tell an AI a kangaroo is some fraction cat and some fraction dog—both of which it’s seen and knows well—and it’ll be able to identify a kangaroo without ever having seen one.
If the soft labels are nuanced enough, you could theoretically teach an AI to identify a large number of categories based on a much smaller number of training examples.
The paper’s authors use a simple machine learning algorithm called k-nearest neighbors (kNN) to explore this idea more in depth. The algorithm operates under the assumption that similar things are most likely to exist near each other; if you go to a dog park, there will be lots of dogs but no cats or kangaroos. Go to the Australian grasslands and there’ll be kangaroos but no cats or dogs. And so on.
To train a kNN algorithm to differentiate between categories, you choose specific features to represent each category (i.e. for animals you could use weight or size as a feature). With one feature on the x-axis and the other on the y-axis, the algorithm creates a graph where data points that are similar to each other are clustered near each other. A line down the center divides the categories, and it’s pretty straightforward for the algorithm to discern which side of the line new data points should fall on.
The Waterloo team kept it simple and used plots of color on a 2D graph. Using the colors and their locations on the graphs, the team created synthetic data sets and accompanying soft labels. One of the more simplistic graphs is pictured below, along with soft labels in the form of pie charts.
Image Credit: Ilia Sucholutsky & Matthias Schonlau
When the team had the algorithm plot the boundary lines of the different colors based on these soft labels, it was able to split the plot up into more colors than the number of data points it was given in the soft labels.
While the results are encouraging, the team acknowledges that they’re just the first step, and there’s much more exploration of this concept yet to be done. The kNN algorithm is one of the least complex models out there; what might happen when LO-shot learning is applied to a far more complex algorithm? Also, to apply it, you still need to distill a larger dataset down into soft labels.
One idea the team is already working on is having other algorithms generate the soft labels for the algorithm that’s going to be trained using LO-shot; manually designing soft labels won’t always be as easy as splitting up some pie charts into different colors.
LO-shot’s potential for reducing the amount of training data needed to yield working AI systems is promising. Besides reducing the cost and the time required to train new models, the method could also make AI more accessible to industries, companies, or individuals who don’t have access to large datasets—an important step for democratization of AI.
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