Tag Archives: world
#439380 Autonomous excavators ready for around ...
Researchers from Baidu Research Robotics and Auto-Driving Lab (RAL) and the University of Maryland, College Park, have introduced an autonomous excavator system (AES) that can perform material loading tasks for a long duration without any human intervention while offering performance closely equivalent to that of an experienced human operator. Continue reading
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#439168 The World’s Biggest AI Chip Now Comes ...
The world’s biggest AI chip just doubled its specs—without adding an inch.
The Cerebras Systems Wafer Scale Engine is about the size of a big dinner plate. All that surface area enables a lot more of everything, from processors to memory. The first WSE chip, released in 2019, had an incredible 1.2 trillion transistors and 400,000 processing cores. Its successor doubles everything, except its physical size.
The WSE-2 crams in 2.6 trillion transistors and 850,000 cores on the same dinner plate. Its on-chip memory has increased from 18 gigabytes to 40 gigabytes, and the rate it shuttles information to and from said memory has gone from 9 petabytes per second to 20 petabytes per second.
It’s a beast any way you slice it.
The WSE-2 is manufactured by Taiwan Semiconductor Manufacturing Company (TSMC), and it was a jump from TSMC’s 16-nanometer chipmaking process to its 7-nanometer process—skipping the 10-nanometer node—that enabled most of the WSE-2’s gains.
This required changes to the physical design of the chip, but Cerebras says they also made improvements to each core above and beyond what was needed to make the new process work. The updated mega-chip should be a lot faster and more efficient.
Why Make Giant Computer Chips?
While graphics processing units (GPUs) still reign supreme in artificial intelligence, they weren’t made for AI in particular. Rather, GPUs were first developed and used for graphics-heavy applications like gaming.
They’ve done amazing things for AI and supercomputing, but in the last several years, specialized chips made for AI are on the up and up.
Cerebras is one of the contenders, alongside other up-and-comers like Graphcore and SambaNova and more familiar names like Intel and NVIDIA.
The company likes to compare the WSE-2 to a top AI processor (NVIDIA’s A100) to underscore just how different it is from the competition. The A100 has two percent the number of transistors (54.2 billion) occupying a little under two percent the surface area. It’s much smaller, but the A100’s might is more fully realized when hundreds or thousands of chips are linked together in a larger system.
In contrast, the WSE-2 reduces the cost and complexity of linking all those chips together by jamming as much processing and memory as possible onto a single wafer of silicon. At the same time, removing the need to move data between lots of chips spread out on multiple server racks dramatically increases speed and efficiency.
The chip’s design gives its small, speedy cores their own dedicated memory and facilitates quick communication between cores. And Cerebras’s compiling software works with machine learning models using standard frameworks—like PyTorch and TensorFlow—to make distributing tasks among the chip’s cores fairly painless.
The approach is called wafer-scale computing because the chip is the size of a standard silicon wafer from which many chips are normally cut. Wafer-scale computing has been on the radar for years, but Cerebras is the first to make a commercially viable chip.
The chip comes packaged in a computer system called the CS-2. The system includes cooling and power supply and fits in about a third of a standard server rack.
After the startup announced the chip in 2019, it began working with a growing list of customers. Cerebras counts GlaxoSmithKline, Lawrence Livermore National Lab, and Argonne National (among others) as customers alongside unnamed partners in pharmaceuticals, biotech, manufacturing, and the military. Many applications have been in AI, but not all. Last year, the company said the National Energy Technology Laboratory (NETL) used the chip to outpace a supercomputer in a simulation of fluid dynamics.
Will Wafer-Scale Go Big?
Whether wafer-scale computing catches on remains to be seen.
Cerebras says their chip significantly speeds up machine learning tasks, and testimony from early customers—some of which claim pretty big gains—supports this. But there aren’t yet independent head-to-head comparisons. Neither Cerebras nor most other AI hardware startups, for example, took part in a recent MLperf benchmark test of AI systems. (The top systems nearly all used NVIDIA GPUs to accelerate their algorithms.)
According to IEEE Spectrum, Cerebras says they’d rather let interested buyers test the system on their own specific neural networks as opposed to selling them on a more general and potentially less applicable benchmark. This isn’t an uncommon approach AI analyst Karl Freund said, “Everybody runs their own models that they developed for their own business. That’s the only thing that matters to buyers.”
It’s also worth noting the WSE can only handle tasks small enough to fit on its chip. The company says most suitable problems it’s encountered can fit, and the WSE-2 delivers even more space. Still, the size of machine learning algorithms is growing rapidly. Which is perhaps why Cerebras is keen to note that two or even three CS-2’s can fit into a server cabinet.
Ultimately, the WSE-2 doesn’t make sense for smaller tasks in which one or a few GPUs will do the trick. At the moment the chip is being used in large, compute-heavy projects in science and research. Current applications include cancer research and drug discovery, gravity wave detection, and fusion simulation. Cerebras CEO and cofounder Andrew Feldman says it may also be made available to customers with shorter-term, less intensive needs on the cloud.
The market for the chip is niche, but Feldman told HPC Wire it’s bigger than he anticipated in 2015, and it’s still growing as new approaches to AI are continually popping up. “The market is moving unbelievably quickly,” he said.
The increasing competition between AI chips is worth watching. There may end up being several fit-to-purpose approaches or one that rises to the top.
For the moment, at least, it appears there’s some appetite for a generous helping of giant computer chips.
Image Credit: Cerebras Continue reading
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#437386 Scary A.I. more intelligent than you
GPT-3 (Generative Pre-trained Transformer 3), is an artificial intelligence language generator that uses deep learning to produce human-like output. The high quality of its text is very difficult to distinguish from a human’s. Many scientists, researchers and engineers (including Stephen … Continue reading
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#439110 Robotic Exoskeletons Could One Day Walk ...
Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.
Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.
One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.
Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.
Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.
Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.
According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.
In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”
In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .
Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.
However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading
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#439105 This Robot Taught Itself to Walk in a ...
Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.
And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.
It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.
This likely isn’t the first robot video you’ve seen, nor the most polished.
For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.
This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.
But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.
In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.
Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.
In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.
Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.
To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.
Once the algorithm was good enough, it graduated to Cassie.
And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.
Other labs have been hard at work applying machine learning to robotics.
Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.
And in the meantime, Boston Dynamics bots are testing the commercial waters.
Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”
The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.
Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading
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