Tag Archives: communication
#432352 Watch This Lifelike Robot Fish Swim ...
Earth’s oceans are having a rough go of it these days. On top of being the repository for millions of tons of plastic waste, global warming is affecting the oceans and upsetting marine ecosystems in potentially irreversible ways.
Coral bleaching, for example, occurs when warming water temperatures or other stress factors cause coral to cast off the algae that live on them. The coral goes from lush and colorful to white and bare, and sometimes dies off altogether. This has a ripple effect on the surrounding ecosystem.
Warmer water temperatures have also prompted many species of fish to move closer to the north or south poles, disrupting fisheries and altering undersea environments.
To keep these issues in check or, better yet, try to address and improve them, it’s crucial for scientists to monitor what’s going on in the water. A paper released last week by a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new tool for studying marine life: a biomimetic soft robotic fish, dubbed SoFi, that can swim with, observe, and interact with real fish.
SoFi isn’t the first robotic fish to hit the water, but it is the most advanced robot of its kind. Here’s what sets it apart.
It swims in three dimensions
Up until now, most robotic fish could only swim forward at a given water depth, advancing at a steady speed. SoFi blows older models out of the water. It’s equipped with side fins called dive planes, which move to adjust its angle and allow it to turn, dive downward, or head closer to the surface. Its density and thus its buoyancy can also be adjusted by compressing or decompressing air in an inner compartment.
“To our knowledge, this is the first robotic fish that can swim untethered in three dimensions for extended periods of time,” said CSAIL PhD candidate Robert Katzschmann, lead author of the study. “We are excited about the possibility of being able to use a system like this to get closer to marine life than humans can get on their own.”
The team took SoFi to the Rainbow Reef in Fiji to test out its swimming skills, and the robo fish didn’t disappoint—it was able to swim at depths of over 50 feet for 40 continuous minutes. What keeps it swimming? A lithium polymer battery just like the one that powers our smartphones.
It’s remote-controlled… by Super Nintendo
SoFi has sensors to help it see what’s around it, but it doesn’t have a mind of its own yet. Rather, it’s controlled by a nearby scuba-diving human, who can send it commands related to speed, diving, and turning. The best part? The commands come from an actual repurposed (and waterproofed) Super Nintendo controller. What’s not to love?
Image Credit: MIT CSAIL
Previous robotic fish built by this team had to be tethered to a boat, so the fact that SoFi can swim independently is a pretty big deal. Communication between the fish and the diver was most successful when the two were less than 10 meters apart.
It looks real, sort of
SoFi’s side fins are a bit stiff, and its camera may not pass for natural—but otherwise, it looks a lot like a real fish. This is mostly thanks to the way its tail moves; a motor pumps water between two chambers in the tail, and as one chamber fills, the tail bends towards that side, then towards the other side as water is pumped into the other chamber. The result is a motion that closely mimics the way fish swim. Not only that, the hydraulic system can change the water flow to get different tail movements that let SoFi swim at varying speeds; its average speed is around half a body length (21.7 centimeters) per second.
Besides looking neat, it’s important SoFi look lifelike so it can blend in with marine life and not scare real fish away, so it can get close to them and observe them.
“A robot like this can help explore the reef more closely than current robots, both because it can get closer more safely for the reef and because it can be better accepted by the marine species.” said Cecilia Laschi, a biorobotics professor at the Sant’Anna School of Advanced Studies in Pisa, Italy.
Just keep swimming
It sounds like this fish is nothing short of a regular Nemo. But its creators aren’t quite finished yet.
They’d like SoFi to be able to swim faster, so they’ll work on improving the robo fish’s pump system and streamlining its body and tail design. They also plan to tweak SoFi’s camera to help it follow real fish.
“We view SoFi as a first step toward developing almost an underwater observatory of sorts,” said CSAIL director Daniela Rus. “It has the potential to be a new type of tool for ocean exploration and to open up new avenues for uncovering the mysteries of marine life.”
The CSAIL team plans to make a whole school of SoFis to help biologists learn more about how marine life is reacting to environmental changes.
Image Credit: MIT CSAIL Continue reading
#432181 Putting AI in Your Pocket: MIT Chip Cuts ...
Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.
Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.
That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.
Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.
Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.
This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.
The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.
The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.
This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.
“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.
“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”
It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.
Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.
The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.
Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.
Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.
Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.
Image Credit: Blue Planet Studio / Shutterstock.com Continue reading
#432018 Few jobs can be completely replaced by ...
Artificial intelligence, machine learning, and robotics can perform an increasingly wider variety of jobs, and automation is no longer confined to routine tasks. Nevertheless, the automation potential for non-routine tasks seems to remain limited, especially for tasks involving autonomous mobility, creativity, problem solving, and complex communication. Continue reading