Tag Archives: system
#437529 Magnetic FreeBOT balls make giant leap ...
A unique type of modular self-reconfiguring robotic system has been unveiled. The term is a mouthful, but it basically refers to a robotic enterprise that can construct itself out of modules that connect to one another to achieve a certain task. Continue reading
Posted in Human Robots
#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
#437477 If a Robot Is Conscious, Is It OK to ...
In the Star Trek: The Next Generation episode “The Measure of a Man,” Data, an android crew member of the Enterprise, is to be dismantled for research purposes unless Captain Picard can argue that Data deserves the same rights as a human being. Naturally the question arises: What is the basis upon which something has rights? What gives an entity moral standing?
The philosopher Peter Singer argues that creatures that can feel pain or suffer have a claim to moral standing. He argues that nonhuman animals have moral standing, since they can feel pain and suffer. Limiting it to people would be a form of speciesism, something akin to racism and sexism.
Without endorsing Singer’s line of reasoning, we might wonder if it can be extended further to an android robot like Data. It would require that Data can either feel pain or suffer. And how you answer that depends on how you understand consciousness and intelligence.
As real artificial intelligence technology advances toward Hollywood’s imagined versions, the question of moral standing grows more important. If AIs have moral standing, philosophers like me reason, it could follow that they have a right to life. That means you cannot simply dismantle them, and might also mean that people shouldn’t interfere with their pursuing their goals.
Two Flavors of Intelligence and a Test
IBM’s Deep Blue chess machine was successfully trained to beat grandmaster Gary Kasparov. But it could not do anything else. This computer had what’s called domain-specific intelligence.
On the other hand, there’s the kind of intelligence that allows for the ability to do a variety of things well. It is called domain-general intelligence. It’s what lets people cook, ski, and raise children—tasks that are related, but also very different.
Artificial general intelligence, AGI, is the term for machines that have domain-general intelligence. Arguably no machine has yet demonstrated that kind of intelligence. This summer, a startup called OpenAI released a new version of its Generative Pre-Training language model. GPT-3 is a natural language processing system, trained to read and write so that it can be easily understood by people.
It drew immediate notice, not just because of its impressive ability to mimic stylistic flourishes and put together plausible content, but also because of how far it had come from a previous version. Despite this impressive performance, GPT-3 doesn’t actually know anything beyond how to string words together in various ways. AGI remains quite far off.
Named after pioneering AI researcher Alan Turing, the Turing test helps determine when an AI is intelligent. Can a person conversing with a hidden AI tell whether it’s an AI or a human being? If he can’t, then for all practical purposes, the AI is intelligent. But this test says nothing about whether the AI might be conscious.
Two Kinds of Consciousness
There are two parts to consciousness. First, there’s the what-it’s-like-for-me aspect of an experience, the sensory part of consciousness. Philosophers call this phenomenal consciousness. It’s about how you experience a phenomenon, like smelling a rose or feeling pain.
In contrast, there’s also access consciousness. That’s the ability to report, reason, behave, and act in a coordinated and responsive manner to stimuli based on goals. For example, when I pass the soccer ball to my friend making a play on the goal, I am responding to visual stimuli, acting from prior training, and pursuing a goal determined by the rules of the game. I make the pass automatically, without conscious deliberation, in the flow of the game.
Blindsight nicely illustrates the difference between the two types of consciousness. Someone with this neurological condition might report, for example, that they cannot see anything in the left side of their visual field. But if asked to pick up a pen from an array of objects in the left side of their visual field, they can reliably do so. They cannot see the pen, yet they can pick it up when prompted—an example of access consciousness without phenomenal consciousness.
Data is an android. How do these distinctions play out with respect to him?
The Data Dilemma
The android Data demonstrates that he is self-aware in that he can monitor whether or not, for example, he is optimally charged or there is internal damage to his robotic arm.
Data is also intelligent in the general sense. He does a lot of distinct things at a high level of mastery. He can fly the Enterprise, take orders from Captain Picard and reason with him about the best path to take.
He can also play poker with his shipmates, cook, discuss topical issues with close friends, fight with enemies on alien planets, and engage in various forms of physical labor. Data has access consciousness. He would clearly pass the Turing test.
However, Data most likely lacks phenomenal consciousness—he does not, for example, delight in the scent of roses or experience pain. He embodies a supersized version of blindsight. He’s self-aware and has access consciousness—can grab the pen—but across all his senses he lacks phenomenal consciousness.
Now, if Data doesn’t feel pain, at least one of the reasons Singer offers for giving a creature moral standing is not fulfilled. But Data might fulfill the other condition of being able to suffer, even without feeling pain. Suffering might not require phenomenal consciousness the way pain essentially does.
For example, what if suffering were also defined as the idea of being thwarted from pursuing a just cause without causing harm to others? Suppose Data’s goal is to save his crewmate, but he can’t reach her because of damage to one of his limbs. Data’s reduction in functioning that keeps him from saving his crewmate is a kind of nonphenomenal suffering. He would have preferred to save the crewmate, and would be better off if he did.
In the episode, the question ends up resting not on whether Data is self-aware—that is not in doubt. Nor is it in question whether he is intelligent—he easily demonstrates that he is in the general sense. What is unclear is whether he is phenomenally conscious. Data is not dismantled because, in the end, his human judges cannot agree on the significance of consciousness for moral standing.
Should an AI Get Moral Standing?
Data is kind; he acts to support the well-being of his crewmates and those he encounters on alien planets. He obeys orders from people and appears unlikely to harm them, and he seems to protect his own existence. For these reasons he appears peaceful and easier to accept into the realm of things that have moral standing.
But what about Skynet in the Terminator movies? Or the worries recently expressed by Elon Musk about AI being more dangerous than nukes, and by Stephen Hawking on AI ending humankind?
Human beings don’t lose their claim to moral standing just because they act against the interests of another person. In the same way, you can’t automatically say that just because an AI acts against the interests of humanity or another AI it doesn’t have moral standing. You might be justified in fighting back against an AI like Skynet, but that does not take away its moral standing. If moral standing is given in virtue of the capacity to nonphenomenally suffer, then Skynet and Data both get it even if only Data wants to help human beings.
There are no artificial general intelligence machines yet. But now is the time to consider what it would take to grant them moral standing. How humanity chooses to answer the question of moral standing for nonbiological creatures will have big implications for how we deal with future AIs—whether kind and helpful like Data, or set on destruction, like Skynet.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#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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