Tag Archives: the brain

#435676 Intel’s Neuromorphic System Hits 8 ...

At the DARPA Electronics Resurgence Initiative Summit today in Detroit, Intel plans to unveil an 8-million-neuron neuromorphic system comprising 64 Loihi research chips—codenamed Pohoiki Beach. Loihi chips are built with an architecture that more closely matches the way the brain works than do chips designed to do deep learning or other forms of AI. For the set of problems that such “spiking neural networks” are particularly good at, Loihi is about 1,000 times as fast as a CPU and 10,000 times as energy efficient. The new 64-Loihi system represents the equivalent of 8-million neurons, but that’s just a step to a 768-chip, 100-million-neuron system that the company plans for the end of 2019.

Intel and its research partners are just beginning to test what massive neural systems like Pohoiki Beach can do, but so far the evidence points to even greater performance and efficiency, says Mike Davies, director of neuromorphic research at Intel.

“We’re quickly accumulating results and data that there are definite benefits… mostly in the domain of efficiency. Virtually every one that we benchmark…we find significant gains in this architecture,” he says.

Going from a single-Loihi to 64 of them is more of a software issue than a hardware one. “We designed scalability into the Loihi chip from the beginning,” says Davies. “The chip has a hierarchical routing interface…which allows us to scale to up to 16,000 chips. So 64 is just the next step.”

Photo: Tim Herman/Intel Corporation

One of Intel’s Nahuku boards, each of which contains 8 to 32 Intel Loihi neuromorphic chips, shown here interfaced to an Intel Arria 10 FPGA development kit. Intel’s latest neuromorphic system, Pohoiki Beach, is made up of multiple Nahuku boards and contains 64 Loihi chips.

Finding algorithms that run well on an 8-million-neuron system and optimizing those algorithms in software is a considerable effort, he says. Still, the payoff could be huge. Neural networks that are more brain-like, such as Loihi, could be immune to some of the artificial intelligence’s—for lack of a better word—dumbness.

For example, today’s neural networks suffer from something called catastrophic forgetting. If you tried to teach a trained neural network to recognize something new—a new road sign, say—by simply exposing the network to the new input, it would disrupt the network so badly that it would become terrible at recognizing anything. To avoid this, you have to completely retrain the network from the ground up. (DARPA’s Lifelong Learning, or L2M, program is dedicated to solving this problem.)

(Here’s my favorite analogy: Say you coached a basketball team, and you raised the net by 30 centimeters while nobody was looking. The players would miss a bunch at first, but they’d figure things out quickly. If those players were like today’s neural networks, you’d have to pull them off the court and teach them the entire game over again—dribbling, passing, everything.)

Loihi can run networks that might be immune to catastrophic forgetting, meaning it learns a bit more like a human. In fact, there’s evidence through a research collaboration with Thomas Cleland’s group at Cornell University, that Loihi can achieve what’s called one-shot learning. That is, learning a new feature after being exposed to it only once. The Cornell group showed this by abstracting a model of the olfactory system so that it would run on Loihi. When exposed to a new virtual scent, the system not only didn't catastrophically forget everything else it had smelled, it learned to recognize the new scent just from the single exposure.

Loihi might also be able to run feature-extraction algorithms that are immune to the kinds of adversarial attacks that befuddle today’s image recognition systems. Traditional neural networks don’t really understand the features they’re extracting from an image in the way our brains do. “They can be fooled with simplistic attacks like changing individual pixels or adding a screen of noise that wouldn’t fool a human in any way,” Davies explains. But the sparse-coding algorithms Loihi can run work more like the human visual system and so wouldn’t fall for such shenanigans. (Disturbingly, humans are not completely immune to such attacks.)

Photo: Tim Herman/Intel Corporation

A close-up shot of Loihi, Intel’s neuromorphic research chip. Intel’s latest neuromorphic system, Pohoiki Beach, will be comprised of 64 of these Loihi chips.

Researchers have also been using Loihi to improve real-time control for robotic systems. For example, last week at the Telluride Neuromorphic Cognition Engineering Workshop—an event Davies called “summer camp for neuromorphics nerds”—researchers were hard at work using a Loihi-based system to control a foosball table. “It strikes people as crazy,” he says. “But it’s a nice illustration of neuromorphic technology. It’s fast, requires quick response, quick planning, and anticipation. These are what neuromorphic chips are good at.” Continue reading

Posted in Human Robots

#435593 AI at the Speed of Light

Neural networks shine for solving tough problems such as facial and voice recognition, but conventional electronic versions are limited in speed and hungry for power. In theory, optics could beat digital electronic computers in the matrix calculations used in neural networks. However, optics had been limited by their inability to do some complex calculations that had required electronics. Now new experiments show that all-optical neural networks can tackle those problems.

The key attraction of neural networks is their massive interconnections among processors, comparable to the complex interconnections among neurons in the brain. This lets them perform many operations simultaneously, like the human brain does when looking at faces or listening to speech, making them more efficient for facial and voice recognition than traditional electronic computers that execute one instruction at a time.

Today's electronic neural networks have reached eight million neurons, but their future use in artificial intelligence may be limited by their high power usage and limited parallelism in connections. Optical connections through lenses are inherently parallel. The lens in your eye simultaneously focuses light from across your field of view onto the retina in the back of your eye, where an array of light-detecting nerve cells detects the light. Each cell then relays the signal it receives to neurons in the brain that process the visual signals to show us an image.

Glass lenses process optical signals by focusing light, which performs a complex mathematical operation called a Fourier transform that preserves the information in the original scene but rearranges is completely. One use of Fourier transforms is converting time variations in signal intensity into a plot of the frequencies present in the signal. The military used this trick in the 1950s to convert raw radar return signals recorded by an aircraft in flight into a three-dimensional image of the landscape viewed by the plane. Today that conversion is done electronically, but the vacuum-tube computers of the 1950s were not up to the task.

Development of neural networks for artificial intelligence started with electronics, but their AI applications have been limited by their slow processing and need for extensive computing resources. Some researchers have developed hybrid neural networks, in which optics perform simple linear operations, but electronics perform more complex nonlinear calculations. Now two groups have demonstrated simple all-optical neural networks that do all processing with light.

In May, Wolfram Pernice of the Institute of Physics at the University of Münster in Germany and colleagues reported testing an all-optical “neuron” in which signals change target materials between liquid and solid states, an effect that has been used for optical data storage. They demonstrated nonlinear processing, and produced output pulses like those from organic neurons. They then produced an integrated photonic circuit that incorporated four optical neurons operating at different wavelengths, each of which connected to 15 optical synapses. The photonic circuit contained more than 140 components and could recognize simple optical patterns. The group wrote that their device is scalable, and that the technology promises “access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.”

Now a group at the Hong Kong University of Science and Technology reports in Optica that they have made an all-optical neural network based on a different process, electromagnetically induced transparency, in which incident light affects how atoms shift between quantum-mechanical energy levels. The process is nonlinear and can be triggered by very weak light signals, says Shengwang Du, a physics professor and coauthor of the paper.

In their demonstration, they illuminated rubidium-85 atoms cooled by lasers to about 10 microKelvin (10 microdegrees above absolute zero). Although the technique may seem unusually complex, Du said the system was the most accessible one in the lab that could produce the desired effects. “As a pure quantum atomic system [it] is ideal for this proof-of-principle experiment,” he says.

Next, they plan to scale up the demonstration using a hot atomic vapor center, which is less expensive, does not require time-consuming preparation of cold atoms, and can be integrated with photonic chips. Du says the major challenges are reducing cost of the nonlinear processing medium and increasing the scale of the all-optical neural network for more complex tasks.

“Their demonstration seems valid,” says Volker Sorger, an electrical engineer at George Washington University in Washington who was not involved in either demonstration. He says the all-optical approach is attractive because it offers very high parallelism, but the update rate is limited to about 100 hertz because of the liquid crystals used in their test, and he is not completely convinced their approach can be scaled error-free. Continue reading

Posted in Human Robots

#435591 Video Friday: This Robotic Thread Could ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.

Eight engineering students from ETH Zurich are working on a year-long focus project to develop a multimodal robot called Dipper, which can fly, swim, dive underwater, and manage that difficult air-water transition:

The robot uses one motor to selectively drive either a propeller or a marine screw depending on whether it’s in flight or not. We’re told that getting the robot to autonomously do the water to air transition is still a work in progress, but that within a few weeks things should be much smoother.

[ Dipper ]

Thanks Simon!

Giving a jellyfish a hug without stressing them out is exactly as hard as you think, but Harvard’s robot will make sure that all jellyfish get the emotional (and physical) support that they need.

The gripper’s six “fingers” are composed of thin, flat strips of silicone with a hollow channel inside bonded to a layer of flexible but stiffer polymer nanofibers. The fingers are attached to a rectangular, 3D-printed plastic “palm” and, when their channels are filled with water, curl in the direction of the nanofiber-coated side. Each finger exerts an extremely low amount of pressure — about 0.0455 kPA, or less than one-tenth of the pressure of a human’s eyelid on their eye. By contrast, current state-of-the-art soft marine grippers, which are used to capture delicate but more robust animals than jellyfish, exert about 1 kPA.

The gripper was successfully able to trap each jellyfish against the palm of the device, and the jellyfish were unable to break free from the fingers’ grasp until the gripper was depressurized. The jellyfish showed no signs of stress or other adverse effects after being released, and the fingers were able to open and close roughly 100 times before showing signs of wear and tear.

[ Harvard ]

MIT engineers have developed a magnetically steerable, thread-like robot that can actively glide through narrow, winding pathways, such as the labyrinthine vasculature of the brain. In the future, this robotic thread may be paired with existing endovascular technologies, enabling doctors to remotely guide the robot through a patient’s brain vessels to quickly treat blockages and lesions, such as those that occur in aneurysms and stroke.

[ MIT ]

See NASA’s next Mars rover quite literally coming together inside a clean room at the Jet Propulsion Laboratory. This behind-the-scenes look at what goes into building and preparing a rover for Mars, including extensive tests in simulated space environments, was captured from March to July 2019. The rover is expected to launch to the Red Planet in summer 2020 and touch down in February 2021.

The Mars 2020 rover doesn’t have a name yet, but you can give it one! As long as you’re not too old! Which you probably are!

[ Mars 2020 ]

I desperately wish that we could watch this next video at normal speed, not just slowed down, but it’s quite impressive anyway.

Here’s one more video from the Namiki Lab showing some high speed tracking with a pair of very enthusiastic robotic cameras:

[ Namiki Lab ]

Normally, tedious modeling of mechanics, electronics, and information science is required to understand how insects’ or robots’ moving parts coordinate smoothly to take them places. But in a new study, biomechanics researchers at the Georgia Institute of Technology boiled down the sprints of cockroaches to handy principles and equations they then used to make a test robot amble about better.

[ Georgia Tech ]

More magical obstacle-dodging footage from Skydio’s still secret new drone.

We’ve been hard at work extending the capabilities of our upcoming drone, giving you ways to get the control you want without the stress of crashing. The result is you can fly in ways, and get shots, that would simply be impossible any other way. How about flying through obstacles at full speed, backwards?

[ Skydio ]

This is a cute demo with Misty:

[ Misty Robotics ]

We’ve seen pieces of hardware like this before, but always made out of hard materials—a soft version is certainly something new.

Utilizing vacuum power and soft material actuators, we have developed a soft reconfigurable surface (SRS) with multi-modal control and performance capabilities. The SRS is comprised of a square grid array of linear vacuum-powered soft pneumatic actuators (linear V-SPAs), built into plug-and-play modules which enable the arrangement, consolidation, and control of many DoF.

[ RRL ]

The EksoVest is not really a robot, but it’ll make you a cyborg! With super strength!

“This is NOT intended to give you super strength but instead give you super endurance and reduce fatigue so that you have more energy and less soreness at the end of your shift.”

Drat!

[ EksoVest ]

We have created a solution for parents, grandparents, and their children who are living separated. This is an amazing tool to stay connected from a distance through the intimacy that comes through interactive play with a child. For parents who travel for work, deployed military, and families spread across the country, the Cushybot One is much more than a toy; it is the opportunity for maintaining a deep connection with your young child from a distance.

Hmm.

I think the concept here is great, but it’s going to be a serious challenge to successfully commercialize.

[ Indiegogo ]

What happens when you equip RVR with a parachute and send it off a cliff? Watch this episode of RVR Launchpad to find out – then go Behind the Build to see how we (eventually) accomplished this high-flying feat.

[ Sphero ]

These omnidirectional crawler robots aren’t new, but that doesn’t keep them from being fun to watch.

[ NEDO ] via [ Impress ]

We’ll finish up the week with a couple of past ICRA and IROS keynote talks—one by Gill Pratt on The Reliability Challenges of Autonomous Driving, and the other from Peter Hart, on Making Shakey.

[ IEEE RAS ] Continue reading

Posted in Human Robots

#435522 Harvard’s Smart Exo-Shorts Talk to the ...

Exosuits don’t generally scream “fashionable” or “svelte.” Take the mind-controlled robotic exoskeleton that allowed a paraplegic man to kick off the World Cup back in 2014. Is it cool? Hell yeah. Is it practical? Not so much.

Yapping about wearability might seem childish when the technology already helps people with impaired mobility move around dexterously. But the lesson of the ill-fated Google Glassholes, which includes an awkward dorky head tilt and an assuming voice command, clearly shows that wearable computer assistants can’t just work technologically—they have to look natural and allow the user to behave like as usual. They have to, in a sense, disappear.

To Dr. Jose Pons at the Legs + Walking Ability Lab in Chicago, exosuits need three main selling points to make it in the real world. One, they have to physically interact with their wearer and seamlessly deliver assistance when needed. Two, they should cognitively interact with the host to guide and control the robot at all times. Finally, they need to feel like a second skin—move with the user without adding too much extra mass or reducing mobility.

This week, a US-Korean collaboration delivered the whole shebang in a Lululemon-style skin-hugging package combined with a retro waist pack. The portable exosuit, weighing only 11 pounds, looks like a pair of spandex shorts but can support the wearer’s hip movement when needed. Unlike their predecessors, the shorts are embedded with sensors that let them know when the wearer is walking versus running by analyzing gait.

Switching between the two movement modes may not seem like much, but what naturally comes to our brains doesn’t translate directly to smart exosuits. “Walking and running have fundamentally different biomechanics, which makes developing devices that assist both gaits challenging,” the team said. Their algorithm, computed in the cloud, allows the wearer to easily switch between both, with the shorts providing appropriate hip support that makes the movement experience seamless.

To Pons, who was not involved in the research but wrote a perspective piece, the study is an exciting step towards future exosuits that will eventually disappear under the skin—that is, implanted neural interfaces to control robotic assistance or activate the user’s own muscles.

“It is realistic to think that we will witness, in the next several years…robust human-robot interfaces to command wearable robotics based on…the neural code of movement in humans,” he said.

A “Smart” Exosuit Hack
There are a few ways you can hack a human body to move with an exosuit. One is using implanted electrodes inside the brain or muscles to decipher movement intent. With heavy practice, a neural implant can help paralyzed people walk again or dexterously move external robotic arms. But because the technique requires surgery, it’s not an immediate sell for people who experience low mobility because of aging or low muscle tone.

The other approach is to look to biophysics. Rather than decoding neural signals that control movement, here the idea is to measure gait and other physical positions in space to decipher intent. As you can probably guess, accurately deciphering user intent isn’t easy, especially when the wearable tries to accommodate multiple gaits. But the gains are many: there’s no surgery involved, and the wearable is low in energy consumption.

Double Trouble
The authors decided to tackle an everyday situation. You’re walking to catch the train to work, realize you’re late, and immediately start sprinting.

That seemingly easy conversion hides a complex switch in biomechanics. When you walk, your legs act like an inverted pendulum that swing towards a dedicated center in a predictable way. When you run, however, the legs move more like a spring-loaded system, and the joints involved in the motion differ from a casual stroll. Engineering an assistive wearable for each is relatively simple; making one for both is exceedingly hard.

Led by Dr. Conor Walsh at Harvard University, the team started with an intuitive idea: assisted walking and running requires specialized “actuation” profiles tailored to both. When the user is moving in a way that doesn’t require assistance, the wearable needs to be out of the way so that it doesn’t restrict mobility. A quick analysis found that assisting hip extension has the largest impact, because it’s important to both gaits and doesn’t add mass to the lower legs.

Building on that insight, the team made a waist belt connected to two thigh wraps, similar to a climbing harness. Two electrical motors embedded inside the device connect the waist belt to other components through a pulley system to help the hip joints move. The whole contraption weighed about 11 lbs and didn’t obstruct natural movement.

Next, the team programmed two separate supporting profiles for walking and running. The goal was to reduce the “metabolic cost” for both movements, so that the wearer expends as little energy as needed. To switch between the two programs, they used a cloud-based classification algorithm to measure changes in energy fluctuation to figure out what mode—running or walking—the user is in.

Smart Booster
Initial trials on treadmills were highly positive. Six male volunteers with similar age and build donned the exosuit and either ran or walked on the treadmill at varying inclines. The algorithm performed perfectly at distinguishing between the two gaits in all conditions, even at steep angles.

An outdoor test with eight volunteers also proved the algorithm nearly perfect. Even on uneven terrain, only two steps out of all test trials were misclassified. In an additional trial on mud or snow, the algorithm performed just as well.

“The system allows the wearer to use their preferred gait for each speed,” the team said.

Software excellence translated to performance. A test found that the exosuit reduced the energy for walking by over nine percent and running by four percent. It may not sound like much, but the range of improvement is meaningful in athletic performance. Putting things into perspective, the team said, the metabolic rate reduction during walking is similar to taking 16 pounds off at the waist.

The Wearable Exosuit Revolution
The study’s lightweight exoshorts are hardly the only players in town. Back in 2017, SRI International’s spin-off, Superflex, engineered an Aura suit to support mobility in the elderly. The Aura used a different mechanism: rather than a pulley system, it incorporated a type of smart material that contracts in a manner similar to human muscles when zapped with electricity.

Embedded with a myriad of sensors for motion, accelerometers and gyroscopes, Aura’s smartness came from mini-computers that measure how fast the wearer is moving and track the user’s posture. The data were integrated and processed locally inside hexagon-shaped computing pods near the thighs and upper back. The pods also acted as the control center for sending electrical zaps to give the wearer a boost when needed.

Around the same time, a collaboration between Harvard’s Wyss Institute and ReWalk Robotics introduced a fabric-based wearable robot to assist a wearer’s legs for balance and movement. Meanwhile, a Swiss team coated normal fabric with electroactive material to weave soft, pliable artificial “muscles” that move with the skin.

Although health support is the current goal, the military is obviously interested in similar technologies to enhance soldiers’ physicality. Superflex’s Aura, for example, was originally inspired by technology born from DARPA’s Warrior Web Program, which aimed to reduce a soldier’s mechanical load.

That said, military gear has had a long history of trickling down to consumer use. Similar to the way camouflage, cargo pants, and GORE-TEX trickled down into the consumer ecosphere, it’s not hard to imagine your local Target eventually stocking intelligent exowear.

Image and Video Credit: Wyss Institute at Harvard University. Continue reading

Posted in Human Robots

#435474 Watch China’s New Hybrid AI Chip Power ...

When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.

No kidding.

The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”

Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.

However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.

As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.

For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.

But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.

Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.

First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.

Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.

Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.

In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.

The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.

Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.

General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.

However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.

Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.

*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.

Image Credit: Alexander Ryabintsev / Shutterstock.com Continue reading

Posted in Human Robots