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Scientists have linked up two silicon-based artificial neurons with a biological one across multiple countries into a fully-functional network. Using standard internet protocols, they established a chain of communication whereby an artificial neuron controls a living, biological one, and passes on the info to another artificial one.
We’ve talked plenty about brain-computer interfaces and novel computer chips that resemble the brain. We’ve covered how those “neuromorphic” chips could link up into tremendously powerful computing entities, using engineered communication nodes called artificial synapses.
As Moore’s law is dying, we even said that neuromorphic computing is one path towards the future of extremely powerful, low energy consumption artificial neural network-based computing—in hardware—that could in theory better link up with the brain. Because the chips “speak” the brain’s language, in theory they could become neuroprosthesis hubs far more advanced and “natural” than anything currently possible.
This month, an international team put all of those ingredients together, turning theory into reality.
The three labs, scattered across Padova, Italy, Zurich, Switzerland, and Southampton, England, collaborated to create a fully self-controlled, hybrid artificial-biological neural network that communicated using biological principles, but over the internet.
The three-neuron network, linked through artificial synapses that emulate the real thing, was able to reproduce a classic neuroscience experiment that’s considered the basis of learning and memory in the brain. In other words, artificial neuron and synapse “chips” have progressed to the point where they can actually use a biological neuron intermediary to form a circuit that, at least partially, behaves like the real thing.
That’s not to say cyborg brains are coming soon. The simulation only recreated a small network that supports excitatory transmission in the hippocampus—a critical region that supports memory—and most brain functions require enormous cross-talk between numerous neurons and circuits. Nevertheless, the study is a jaw-dropping demonstration of how far we’ve come in recreating biological neurons and synapses in artificial hardware.
And perhaps one day, the currently “experimental” neuromorphic hardware will be integrated into broken biological neural circuits as bridges to restore movement, memory, personality, and even a sense of self.
The Artificial Brain Boom
One important thing: this study relies heavily on a decade of research into neuromorphic computing, or the implementation of brain functions inside computer chips.
The best-known example is perhaps IBM’s TrueNorth, which leveraged the brain’s computational principles to build a completely different computer than what we have today. Today’s computers run on a von Neumann architecture, in which memory and processing modules are physically separate. In contrast, the brain’s computing and memory are simultaneously achieved at synapses, small “hubs” on individual neurons that talk to adjacent ones.
Because memory and processing occur on the same site, biological neurons don’t have to shuttle data back and forth between processing and storage compartments, massively reducing processing time and energy use. What’s more, a neuron’s history will also influence how it behaves in the future, increasing flexibility and adaptability compared to computers. With the rise of deep learning, which loosely mimics neural processing as the prima donna of AI, the need to reduce power while boosting speed and flexible learning is becoming ever more tantamount in the AI community.
Neuromorphic computing was partially born out of this need. Most chips utilize special ingredients that change their resistance (or other physical characteristics) to mimic how a neuron might adapt to stimulation. Some chips emulate a whole neuron, that is, how it responds to a history of stimulation—does it get easier or harder to fire? Others imitate synapses themselves, that is, how easily they will pass on the information to another neuron.
Although single neuromorphic chips have proven to be far more efficient and powerful than current computer chips running machine learning algorithms in toy problems, so far few people have tried putting the artificial components together with biological ones in the ultimate test.
That’s what this study did.
A Hybrid Network
Still with me? Let’s talk network.
It’s gonna sound complicated, but remember: learning is the formation of neural networks, and neurons that fire together wire together. To rephrase: when learning, neurons will spontaneously organize into networks so that future instances will re-trigger the entire network. To “wire” together, downstream neurons will become more responsive to their upstream neural partners, so that even a whisper will cause them to activate. In contrast, some types of stimulation will cause the downstream neuron to “chill out” so that only an upstream “shout” will trigger downstream activation.
Both these properties—easier or harder to activate downstream neurons—are essentially how the brain forms connections. The “amping up,” in neuroscience jargon, is long-term potentiation (LTP), whereas the down-tuning is LTD (long-term depression). These two phenomena were first discovered in the rodent hippocampus more than half a century ago, and ever since have been considered as the biological basis of how the brain learns and remembers, and implicated in neurological problems such as addition (seriously, you can’t pass Neuro 101 without learning about LTP and LTD!).
So it’s perhaps especially salient that one of the first artificial-brain hybrid networks recapitulated this classic result.
To visualize: the three-neuron network began in Switzerland, with an artificial neuron with the badass name of “silicon spiking neuron.” That neuron is linked to an artificial synapse, a “memristor” located in the UK, which is then linked to a biological rat neuron cultured in Italy. The rat neuron has a “smart” microelectrode, controlled by the artificial synapse, to stimulate it. This is the artificial-to-biological pathway.
Meanwhile, the rat neuron in Italy also has electrodes that listen in on its electrical signaling. This signaling is passed back to another artificial synapse in the UK, which is then used to control a second artificial neuron back in Switzerland. This is the biological-to-artificial pathway back. As a testimony in how far we’ve come in digitizing neural signaling, all of the biological neural responses are digitized and sent over the internet to control its far-out artificial partner.
Here’s the crux: to demonstrate a functional neural network, just having the biological neuron passively “pass on” electrical stimulation isn’t enough. It has to show the capacity to learn, that is, to be able to mimic the amping up and down-tuning that are LTP and LTD, respectively.
You’ve probably guessed the results: certain stimulation patterns to the first artificial neuron in Switzerland changed how the artificial synapse in the UK operated. This, in turn, changed the stimulation to the biological neuron, so that it either amped up or toned down depending on the input.
Similarly, the response of the biological neuron altered the second artificial synapse, which then controlled the output of the second artificial neuron. Altogether, the biological and artificial components seamlessly linked up, over thousands of miles, into a functional neural circuit.
So…I’m still picking my jaw up off the floor.
It’s utterly insane seeing a classic neuroscience learning experiment repeated with an integrated network with artificial components. That said, a three-neuron network is far from the thousands of synapses (if not more) needed to truly re-establish a broken neural circuit in the hippocampus, which DARPA has been aiming to do. And LTP/LTD has come under fire recently as the de facto brain mechanism for learning, though so far they remain cemented as neuroscience dogma.
However, this is one of the few studies where you see fields coming together. As Richard Feynman famously said, “What I cannot recreate, I cannot understand.” Even though neuromorphic chips were built on a high-level rather than molecular-level understanding of how neurons work, the study shows that artificial versions can still synapse with their biological counterparts. We’re not just on the right path towards understanding the brain, we’re recreating it, in hardware—if just a little.
While the study doesn’t have immediate use cases, practically it does boost both the neuromorphic computing and neuroprosthetic fields.
“We are very excited with this new development,” said study author Dr. Themis Prodromakis at the University of Southampton. “On one side it sets the basis for a novel scenario that was never encountered during natural evolution, where biological and artificial neurons are linked together and communicate across global networks; laying the foundations for the Internet of Neuro-electronics. On the other hand, it brings new prospects to neuroprosthetic technologies, paving the way towards research into replacing dysfunctional parts of the brain with AI chips.”
Image Credit: Gerd Altmann from Pixabay Continue reading
Will getting full bars on your 5G connection mean getting caught out by sudden weather changes?
The question may strike you as hypothetical, nonsensical even, but it is at the core of ongoing disputes between meteorologists and telecommunications companies. Everyone else, including you and I, are caught in the middle, wanting both 5G’s faster connection speeds and precise information about our increasingly unpredictable weather. So why can’t we have both?
Perhaps we can, but because of the way 5G networks function, it may take some special technology—specifically, artificial intelligence.
The Bandwidth Worries
Around the world, the first 5G networks are already being rolled out. The networks use a variety of frequencies to transmit data to and from devices at speeds up to 100 times faster than existing 4G networks.
One of the bandwidths used is between 24.25 and 24.45 gigahertz (GHz). In a recent FCC auction, telecommunications companies paid a combined $2 billion for the 5G usage rights for this spectrum in the US.
However, meteorologists are concerned that transmissions near the lower end of that range can interfere with their ability to accurately measure water vapor in the atmosphere. Wired reported that acting chief of the National Oceanic and Atmospheric Administration (NOAA), Neil Jacobs, told the US House Subcommittee on the Environment that 5G interference could substantially cut the amount of weather data satellites can gather. As a result, forecast accuracy could drop by as much as 30 percent.
Among the consequences could be less time to prepare for hurricanes, and it may become harder to predict storms’ paths. Due to the interconnectedness of weather patterns, measurement issues in one location can affect other areas too. Lack of accurate atmospheric data from the US could, for example, lead to less accurate forecasts for weather patterns over Europe.
The Numbers Game
Water vapor emits a faint signal at 23.8 GHz. Weather satellites measure the signals, and the data is used to gauge atmospheric humidity levels. Meteorologists have expressed concern that 5G signals in the same range can disturb those readings. The issue is that it would be nigh on impossible to tell whether a signal is water vapor or an errant 5G signal.
Furthermore, 5G disturbances in other frequency bands could make forecasting even more difficult. Rain and snow emit frequencies around 36-37 GHz. 50.2-50.4 GHz is used to measure atmospheric temperatures, and 86-92 GHz clouds and ice. All of the above are under consideration for international 5G signals. Some have warned that the wider consequences could set weather forecasts back to the 1980s.
Telecommunications companies and interest organizations have argued back, saying that weather sensors aren’t as susceptible to interference as meteorologists fear. Furthermore, 5G devices and signals will produce much less interference with weather forecasts than organizations like NOAA predict. Since very little scientific research has been carried out to examine the claims of either party, we seem stuck in a ‘wait and see’ situation.
To offset some of the possible effects, the two groups have tried to reach a consensus on a noise buffer between the 5G transmissions and water-vapor signals. It could be likened to limiting the noise from busy roads or loud sound systems to avoid bothering neighboring buildings.
The World Meteorological Organization was looking to establish a -55 decibel watts buffer. In Europe, regulators are locked in on a -42 decibel watts buffer for 5G base stations. For comparison, the US Federal Communications Commission has advocated for a -20 decibel watts buffer, which would, in reality, allow more than 150 times more noise than the European proposal.
How AI Could Help
Much of the conversation about 5G’s possible influence on future weather predictions is centered around mobile phones. However, the phones are far from the only systems that will be receiving and transmitting signals on 5G. Self-driving cars and the Internet of Things are two other technologies that could soon be heavily reliant on faster wireless signals.
Densely populated areas are likely going to be the biggest emitters of 5G signals, leading to a suggestion to only gather water-vapor data over oceans.
Another option is to develop artificial intelligence (AI) approaches to clean or process weather data. AI is playing an increasing role in weather forecasting. For example, in 2016 IBM bought The Weather Company for $2 billion. The goal was to combine the two companies’ models and data in IBM’s Watson to create more accurate forecasts. AI would also be able to predict increases or drops in business revenues due to weather changes. Monsanto has also been investing in AI for forecasting, in this case to provide agriculturally-related weather predictions.
Smartphones may also provide a piece of the weather forecasting puzzle. Studies have shown how data from thousands of smartphones can help to increase the accuracy of storm predictions, as well as the force of storms.
“Weather stations cost a lot of money,” Cliff Mass, an atmospheric scientist at the University of Washington in Seattle, told Inside Science, adding, “If there are already 20 million smartphones, you might as well take advantage of the observation system that’s already in place.”
Smartphones may not be the solution when it comes to finding new ways of gathering the atmospheric data on water vapor that 5G could disrupt. But it does go to show that some technologies open new doors, while at the same time, others shut them.
Image Credit: Image by Free-Photos from Pixabay Continue reading
Robots excel at carrying out specialized tasks in controlled environments, but put them in your average office and they’d be lost. Alphabet wants to change that by developing what they call the Everyday Robot, which could learn to help us out with our daily chores.
For a long time most robots were painstakingly hand-coded to carry out their functions, but since the deep learning revolution earlier this decade there’s been a growing effort to imbue them with AI that lets them learn new tasks through experience.
That’s led to some impressive breakthroughs, like a robotic hand nimble enough to solve a Rubik’s cube and a robotic arm that can accurately toss bananas across a room.
And it turns out Alphabet’s early-stage research and development division, Alphabet X, has also secretly been using similar machine learning techniques to develop robots adaptable enough to carry out a range of tasks in cluttered and unpredictable human environments like homes and offices.
The robots they’ve built combine a wheeled base with a single arm and a head full of sensors (including LIDAR) for 3D scanning, borrowed from Alphabet’s self-driving car division, Waymo.
At the minute, though, they’re largely restricted to sorting trash for recycling, project leader Hans Peter Brondmo writes in a blog post. While that might sound mundane, identifying different kinds of trash, grasping it, and moving it to the correct bin is still a difficult thing for a robot to do consistently. Some of the robots also have to navigate around the office to sort trash at various recycling stations.
Alphabet says even its human staff were getting it wrong 20 percent of the time, but after several months of training the robots have managed to get that down to 3.5 percent.
Every day, 30 robots toil away in what’s been dubbed the “playpen” sorting trash, and then every night thousands of virtual robots continue to practice in a simulation. This experience is then used to update the robots’ control algorithms each night. All the robots also share their experiences with the others through a process called collaborative learning.
The process isn’t flawless, though. Simonite notes that while the robots exhibit some uncannily smart behaviors, like stirring piles of rubbish to make it easier to grab specific items, they also frequently miss or fumble the objects they’re trying to grasp.
Nonetheless, the project’s leaders are happy with their progress so far. And the hope is that creating robots that are able to learn from little more than experience in complex environments like an office should be a first step towards general-purpose robots that can pick up a variety of useful skills to assist humans.
Taking that next step will be the major test of the project. So far there’s been limited evidence that experience gained by robots in one task can be transferred to learning another. That’s something the group hopes to demonstrate next year.
And it seems there may be more robot news coming out of Alphabet X soon. The group has several other robotics “moonshots” in the pipeline, built on technology and talent transferred over in 2016 from the remains of a broadly unsuccessful splurge on robotics startups by former Google executive Andy Rubin.
Whether this robotics renaissance at Alphabet will finally help robots break into our homes and offices remains to be seen, but with the resources they have at hand, they just may be able to make it happen.
Image Credit: Everyday Robot, Alphabet X Continue reading
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!):
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
DJI’s new Mavic Mini looks like a pretty great drone for US $400 ($500 for a combo with more accessories): It’s tiny, flies for 30 minutes, and will do what you need as far as pictures and video (although not a whole lot more).
DJI seems to have put a bunch of effort into making the drone 249 grams, 1 gram under what’s required for FAA registration. That means you save $5 and a few minutes of your time, but that does not mean you don’t have to follow the FAA’s rules and regulations governing drone use.
[ DJI ]
Don’t panic, but Clearpath and HEBI Robotics have armed the Jackal:
After locking eyes across a crowded room at ICRA 2019, Clearpath Robotics and HEBI Robotics basked in that warm and fuzzy feeling that comes with starting a new and exciting relationship. Over a conference hall coffee, they learned that the two companies have many overlapping interests. The most compelling was the realization that customers across a variety of industries are hunting for an elusive true love of their own – a robust but compact robotic platform combined with a long reach manipulator for remote inspection tasks.
After ICRA concluded, Arron Griffiths, Application Engineer at Clearpath, and Matthew Tesch, Software Engineer at HEBI, kept in touch and decided there had been enough magic in the air to warrant further exploration. A couple of months later, Matthew arrived at Clearpath to formally introduce the HEBI’s X-Series Arm to Clearpath’s Jackal UGV. It was love.
[ Clearpath ]
I’m really not a fan of the people-carrying drones, but heavy lift cargo drones seem like a more okay idea.
Volocopter, the pioneer in Urban Air Mobility, presented the demonstrator of its VoloDrone. This marks Volocopters expansion into the logistics, agriculture, infrastructure and public services industry. The VoloDrone is an unmanned, fully electric, heavy-lift utility drone capable of carrying a payload of 200 kg (440 lbs) up to 40 km (25 miles). With a standardized payload attachment, VoloDrone can serve a great variety of purposes from transporting boxes, to liquids, to equipment and beyond. It can be remotely piloted or flown in automated mode on pre-set routes.
[ Volocopter ]
JAY is a mobile service robot that projects a display on the floor and plays sound with its speaker. By playing sounds and videos, it provides visual and audio entertainment in various places such as exhibition halls, airports, hotels, department stores and more.
[ Rainbow Robotics ]
The DARPA Subterranean Challenge Virtual Tunnel Circuit concluded this week—it was the same idea as the physical challenge that took place in August, just with a lot less IRL dirt.
The awards ceremony and team presentations are in this next video, and we’ll have more on this once we get back from IROS.
[ DARPA SubT ]
NASA is sending a mobile robot to the south pole of the Moon to get a close-up view of the location and concentration of water ice in the region and for the first time ever, actually sample the water ice at the same pole where the first woman and next man will land in 2024 under the Artemis program.
About the size of a golf cart, the Volatiles Investigating Polar Exploration Rover, or VIPER, will roam several miles, using its four science instruments — including a 1-meter drill — to sample various soil environments. Planned for delivery in December 2022, VIPER will collect about 100 days of data that will be used to inform development of the first global water resource maps of the Moon.
[ NASA ]
Happy Halloween from HEBI Robotics!
[ HEBI ]
Happy Halloween from Soft Robotics!
[ Soft Robotics ]
Halloween must be really, really confusing for autonomous cars.
[ Waymo ]
Once a year at Halloween, hardworking JPL engineers put their skills to the test in a highly competitive pumpkin carving contest. The result: A pumpkin gently landed on the Moon, its retrorockets smoldering, while across the room a Nemo-inspired pumpkin explored the sub-surface ocean of Jupiter moon Europa. Suffice to say that when the scientists and engineers at NASA’s Jet Propulsion Laboratory compete in a pumpkin-carving contest, the solar system’s the limit. Take a look at some of the masterpieces from 2019.
Now in its ninth year, the contest gives teams only one hour to carve and decorate their pumpkin though they can prepare non-pumpkin materials – like backgrounds, sound effects and motorized parts – ahead of time.
[ JPL ]
The online autonomous navigation and semantic mapping experiment presented [below] is conducted with the Cassie Blue bipedal robot at the University of Michigan. The sensors attached to the robot include an IMU, a 32-beam LiDAR and an RGB-D camera. The whole online process runs in real-time on a Jetson Xavier and a laptop with an i7 processor.
[ BPL ]
Misty II is now available to anyone who wants one, and she’s on sale for a mere $2900.
[ Misty ]
We leveraged LIDAR-based slam, in conjunction with our specialized relative localization sensor UVDAR to perform a de-centralized, communication-free swarm flight without the units knowing their absolute locations. The swarming and obstacle avoidance control is based on a modified Boids-like algorithm, while the whole swarm is controlled by directing a selected leader unit.
[ MRS ]
The MallARD robot is an autonomous surface vehicle (ASV), designed for the monitoring and inspection of wet storage facilities for example spent fuel pools or wet silos. The MallARD is holonomic, uses a LiDAR for localisation and features a robust trajectory tracking controller.
The University of Manchester’s researcher Dr Keir Groves designed and built the autonomous surface vehicle (ASV) for the challenge which came in the top three of the second round in Nov 2017. The MallARD went on to compete in a final 3rd round where it was deployed in a spent fuel pond at a nuclear power plant in Finland by the IAEA, along with two other entries. The MallARD came second overall, in November 2018.
[ RNE ]
I sometimes get the sense that in the robotic grasping and manipulation world, suction cups are kinda seen as cheating at times. But, their nature allows you to do some pretty interesting things.
More clever octopus footage please.
[ CMU ]
A Personal, At-Home Teacher For Playful Learning: From academic topics to child-friendly news bulletins, fun facts and more, Miko 2 is packed with relevant and freshly updated content specially designed by educationists and child-specialists. Your little one won’t even realize they’re learning.
As we point out pretty much every time we post a video like this, keep in mind that you’re seeing a heavily edited version of a hypothetical best case scenario for how this robot can function. And things like “creating a relationship that they can then learn how to form with their peers” is almost certainly overselling things. But at $300 (shipping included), this may be a decent robot as long as your expectations are appropriately calibrated.
[ Miko ]
ICRA 2018 plenary talk by Rodney Brooks: “Robots and People: the Research Challenge.”
[ IEEE RAS ]
ICRA-X 2018 talk by Ron Arkin: “Lethal Autonomous Robots and the Plight of the Noncombatant.”
[ IEEE RAS ]
On the most recent episode of the AI Podcast, Lex Fridman interviews Garry Kasparov.
[ AI Podcast ] Continue reading