Tag Archives: beyond
#438014 Meet Blueswarm, a Smart School of ...
Anyone who’s seen an undersea nature documentary has marveled at the complex choreography that schooling fish display, a darting, synchronized ballet with a cast of thousands.
Those instinctive movements have inspired researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Wyss Institute for Biologically Inspired Engineering. The results could improve the performance and dependability of not just underwater robots, but other vehicles that require decentralized locomotion and organization, such as self-driving cars and robotic space exploration.
The fish collective called Blueswarm was created by a team led by Radhika Nagpal, whose lab is a pioneer in self-organizing systems. The oddly adorable robots can sync their movements like biological fish, taking cues from their plastic-bodied neighbors with no external controls required. Nagpal told IEEE Spectrum that this marks a milestone, demonstrating complex 3D behaviors with implicit coordination in underwater robots.
“Insights from this research will help us develop future miniature underwater swarms that can perform environmental monitoring and search in visually-rich but fragile environments like coral reefs,” Nagpal said. “This research also paves a way to better understand fish schools, by synthetically recreating their behavior.”
The research is published in Science Robotics, with Florian Berlinger as first author. Berlinger said the “Bluedot” robots integrate a trio of blue LED lights, a lithium-polymer battery, a pair of cameras, a Raspberry Pi computer and four controllable fins within a 3D-printed hull. The fish-lens cameras detect LED’s of their fellow swimmers, and apply a custom algorithm to calculate distance, direction and heading.
Based on that simple production and detection of LED light, the team proved that Blueswarm could self-organize behaviors, including aggregation, dispersal and circle formation—basically, swimming in a clockwise synchronization. Researchers also simulated a successful search mission, an autonomous Finding Nemo. Using their dispersion algorithm, the robot school spread out until one could detect a red light in the tank. Its blue LEDs then flashed, triggering the aggregation algorithm to gather the school around it. Such a robot swarm might prove valuable in search-and-rescue missions at sea, covering miles of open water and reporting back to its mates.
“Each Bluebot implicitly reacts to its neighbors’ positions,” Berlinger said. The fish—RoboCod, perhaps?—also integrate a Wifi module to allow uploading new behaviors remotely. The lab’s previous efforts include a 1,000-strong army of “Kilobots,” and a robotic construction crew inspired by termites. Both projects operated in two-dimensional space. But a 3D environment like air or water posed a tougher challenge for sensing and movement.
In nature, Berlinger notes, there’s no scaly CEO to direct the school’s movements. Nor do fish communicate their intentions. Instead, so-called “implicit coordination” guides the school’s collective behavior, with individual members executing high-speed moves based on what they see their neighbors doing. That decentralized, autonomous organization has long fascinated scientists, including in robotics.
“In these situations, it really benefits you to have a highly autonomous robot swarm that is self-sufficient. By using implicit rules and 3D visual perception, we were able to create a system with a high degree of autonomy and flexibility underwater where things like GPS and WiFi are not accessible.”
Berlinger adds the research could one day translate to anything that requires decentralized robots, from self-driving cars and Amazon warehouse vehicles to exploration of faraway planets, where poor latency makes it impossible to transmit commands quickly. Today’s semi-autonomous cars face their own technical hurdles in reliably sensing and responding to their complex environments, including when foul weather obscures onboard sensors or road markers, or when they can’t fix position via GPS. An entire subset of autonomous-car research involves vehicle-to-vehicle (V2V) communications that could give cars a hive mind to guide individual or collective decisions— avoiding snarled traffic, driving safely in tight convoys, or taking group evasive action during a crash that’s beyond their sensory range.
“Once we have millions of cars on the road, there can’t be one computer orchestrating all the traffic, making decisions that work for all the cars,” Berlinger said.
The miniature robots could also work long hours in places that are inaccessible to humans and divers, or even large tethered robots. Nagpal said the synthetic swimmers could monitor and collect data on reefs or underwater infrastructure 24/7, and work into tiny places without disturbing fragile equipment or ecosystems.
“If we could be as good as fish in that environment, we could collect information and be non-invasive, in cluttered environments where everything is an obstacle,” Nagpal said. Continue reading
Posted in Human Robots
#437974 China Wants to Be the World’s AI ...
China’s star has been steadily rising for decades. Besides slashing extreme poverty rates from 88 percent to under 2 percent in just 30 years, the country has become a global powerhouse in manufacturing and technology. Its pace of growth may slow due to an aging population, but China is nonetheless one of the world’s biggest players in multiple cutting-edge tech fields.
One of these fields, and perhaps the most significant, is artificial intelligence. The Chinese government announced a plan in 2017 to become the world leader in AI by 2030, and has since poured billions of dollars into AI projects and research across academia, government, and private industry. The government’s venture capital fund is investing over $30 billion in AI; the northeastern city of Tianjin budgeted $16 billion for advancing AI; and a $2 billion AI research park is being built in Beijing.
On top of these huge investments, the government and private companies in China have access to an unprecedented quantity of data, on everything from citizens’ health to their smartphone use. WeChat, a multi-functional app where people can chat, date, send payments, hail rides, read news, and more, gives the CCP full access to user data upon request; as one BBC journalist put it, WeChat “was ahead of the game on the global stage and it has found its way into all corners of people’s existence. It could deliver to the Communist Party a life map of pretty much everybody in this country, citizens and foreigners alike.” And that’s just one (albeit big) source of data.
Many believe these factors are giving China a serious leg up in AI development, even providing enough of a boost that its progress will surpass that of the US.
But there’s more to AI than data, and there’s more to progress than investing billions of dollars. Analyzing China’s potential to become a world leader in AI—or in any technology that requires consistent innovation—from multiple angles provides a more nuanced picture of its strengths and limitations. In a June 2020 article in Foreign Affairs, Oxford fellows Carl Benedikt Frey and Michael Osborne argued that China’s big advantages may not actually be that advantageous in the long run—and its limitations may be very limiting.
Moving the AI Needle
To get an idea of who’s likely to take the lead in AI, it could help to first consider how the technology will advance beyond its current state.
To put it plainly, AI is somewhat stuck at the moment. Algorithms and neural networks continue to achieve new and impressive feats—like DeepMind’s AlphaFold accurately predicting protein structures or OpenAI’s GPT-3 writing convincing articles based on short prompts—but for the most part these systems’ capabilities are still defined as narrow intelligence: completing a specific task for which the system was painstakingly trained on loads of data.
(It’s worth noting here that some have speculated OpenAI’s GPT-3 may be an exception, the first example of machine intelligence that, while not “general,” has surpassed the definition of “narrow”; the algorithm was trained to write text, but ended up being able to translate between languages, write code, autocomplete images, do math, and perform other language-related tasks it wasn’t specifically trained for. However, all of GPT-3’s capabilities are limited to skills it learned in the language domain, whether spoken, written, or programming language).
Both AlphaFold’s and GPT-3’s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm—more input data yields increased capability—China could well have an advantage.
But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology’s fast approach to the limits of current computing power and its immense energy consumption.
Thus, while China’s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It’s useful for building products that incorporate or rely on today’s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users’ spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.
But data quantity isn’t what’s going to advance AI. As Frey and Osborne put it, “Data efficiency is the holy grail of further progress in artificial intelligence.”
To that end, research teams in academia and private industry are working on ways to make AI less data-hungry. New training methods like one-shot learning and less-than-one-shot learning have begun to emerge, along with myriad efforts to make AI that learns more like the human brain.
While not insignificant, these advancements still fall into the “baby steps” category. No one knows how AI is going to progress beyond these small steps—and that uncertainty, in Frey and Osborne’s opinion, is a major speed bump on China’s fast-track to AI dominance.
How Innovation Happens
A lot of great inventions have happened by accident, and some of the world’s most successful companies started in garages, dorm rooms, or similarly low-budget, nondescript circumstances (including Google, Facebook, Amazon, and Apple, to name a few). Innovation, the authors point out, often happens “through serendipity and recombination, as inventors and entrepreneurs interact and exchange ideas.”
Frey and Osborne argue that although China has great reserves of talent and a history of building on technologies conceived elsewhere, it doesn’t yet have a glowing track record in terms of innovation. They note that of the 100 most-cited patents from 2003 to present, none came from China. Giants Tencent, Alibaba, and Baidu are all wildly successful in the Chinese market, but they’re rooted in technologies or business models that came out of the US and were tweaked for the Chinese population.
“The most innovative societies have always been those that allowed people to pursue controversial ideas,” Frey and Osborne write. China’s heavy censorship of the internet and surveillance of citizens don’t quite encourage the pursuit of controversial ideas. The country’s social credit system rewards people who follow the rules and punishes those who step out of line. Frey adds that top-down execution of problem-solving is effective when the problem at hand is clearly defined—and the next big leaps in AI are not.
It’s debatable how strongly a culture of social conformism can impact technological innovation, and of course there can be exceptions. But a relevant historical example is the Soviet Union, which, despite heavy investment in science and technology that briefly rivaled the US in fields like nuclear energy and space exploration, ended up lagging far behind primarily due to political and cultural factors.
Similarly, China’s focus on computer science in its education system could give it an edge—but, as Frey told me in an email, “The best students are not necessarily the best researchers. Being a good researcher also requires coming up with new ideas.”
Winner Take All?
Beyond the question of whether China will achieve AI dominance is the issue of how it will use the powerful technology. Several of the ways China has already implemented AI could be considered morally questionable, from facial recognition systems used aggressively against ethnic minorities to smart glasses for policemen that can pull up information about whoever the wearer looks at.
This isn’t to say the US would use AI for purely ethical purposes. The military’s Project Maven, for example, used artificially intelligent algorithms to identify insurgent targets in Iraq and Syria, and American law enforcement agencies are also using (mostly unregulated) facial recognition systems.
It’s conceivable that “dominance” in AI won’t go to one country; each nation could meet milestones in different ways, or meet different milestones. Researchers from both countries, at least in the academic sphere, could (and likely will) continue to collaborate and share their work, as they’ve done on many projects to date.
If one country does take the lead, it will certainly see some major advantages as a result. Brookings Institute fellow Indermit Gill goes so far as to say that whoever leads in AI in 2030 will “rule the world” until 2100. But Gill points out that in addition to considering each country’s strengths, we should consider how willing they are to improve upon their weaknesses.
While China leads in investment and the US in innovation, both nations are grappling with huge economic inequalities that could negatively impact technological uptake. “Attitudes toward the social change that accompanies new technologies matter as much as the technologies, pointing to the need for complementary policies that shape the economy and society,” Gill writes.
Will China’s leadership be willing to relax its grip to foster innovation? Will the US business environment be enough to compete with China’s data, investment, and education advantages? And can both countries find a way to distribute technology’s economic benefits more equitably?
Time will tell, but it seems we’ve got our work cut out for us—and China does too.
Image Credit: Adam Birkett on Unsplash Continue reading
Posted in Human Robots
#437924 How a Software Map of the Entire Planet ...
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“3D map data is the scaffolding of the 21st century.”
–Edward Miller, Founder, Scape Technologies, UK
Covered in cameras, sensors, and a distinctly spaceship looking laser system, Google’s autonomous vehicles were easy to spot when they first hit public roads in 2015. The key hardware ingredient is a spinning laser fixed to the roof, called lidar, which provides the car with a pair of eyes to see the world. Lidar works by sending out beams of light and measuring the time it takes to bounce off objects back to the source. By timing the light’s journey, these depth-sensing systems construct fully 3D maps of their surroundings.
3D maps like these are essentially software copies of the real world. They will be crucial to the development of a wide range of emerging technologies including autonomous driving, drone delivery, robotics, and a fast-approaching future filled with augmented reality.
Like other rapidly improving technologies, lidar is moving quickly through its development cycle. What was an expensive technology on the roof of a well-funded research project is now becoming cheaper, more capable, and readily available to consumers. At some point, lidar will come standard on most mobile devices and is now available to early-adopting owners of the iPhone 12 Pro.
Consumer lidar represents the inevitable shift from wealthy tech companies generating our world’s map data, to a more scalable crowd-sourced approach. To develop the repository for their Street View Maps product, Google reportedly spent $1-2 billion sending cars across continents photographing every street. Compare that to a live-mapping service like Waze, which uses crowd-sourced user data from its millions of users to generate accurate and real-time traffic conditions. Though these maps serve different functions, one is a static, expensive, unchanging map of the world while the other is dynamic, real-time, and constructed by users themselves.
Soon millions of people may be scanning everything from bedrooms to neighborhoods, resulting in 3D maps of significant quality. An online search for lidar room scans demonstrates just how richly textured these three-dimensional maps are compared to anything we’ve had before. With lidar and other depth-sensing systems, we now have the tools to create exact software copies of everywhere and everything on earth.
At some point, likely aided by crowdsourcing initiatives, these maps will become living breathing, real-time representations of the world. Some refer to this idea as a “digital twin” of the planet. In a feature cover story, Kevin Kelly, the cofounder of Wired magazine, calls this concept the “mirrorworld,” a one-to-one software map of everything.
So why is that such a big deal? Take augmented reality as an example.
Of all the emerging industries dependent on such a map, none are more invested in seeing this concept emerge than those within the AR landscape. Apple, for example, is not-so-secretly developing a pair of AR glasses, which they hope will deliver a mainstream turning point for the technology.
For Apple’s AR devices to work as anticipated, they will require virtual maps of the world, a concept AR insiders call the “AR cloud,” which is synonymous with the “mirrorworld” concept. These maps will be two things. First, they will be a tool that creators use to place AR content in very specific locations; like a world canvas to paint on. Second, they will help AR devices both locate and understand the world around them so they can render content in a believable way.
Imagine walking down a street wanting to check the trading hours of a local business. Instead of pulling out your phone to do a tedious search online, you conduct the equivalent of a visual google search simply by gazing at the store. Albeit a trivial example, the AR cloud represents an entirely non-trivial new way of managing how we organize the world’s information. Access to knowledge can be shifted away from the faraway monitors in our pocket, to its relevant real-world location.
Ultimately this describes a blurring of physical and digital infrastructure. Our public and private spaces will thus be comprised equally of both.
No example demonstrates this idea better than Pokémon Go. The game is straightforward enough; users capture virtual characters scattered around the real world. Today, the game relies on traditional GPS technology to place its characters, but GPS is accurate only to within a few meters of a location. For a car navigating on a highway or locating Pikachus in the world, that level of precision is sufficient. For drone deliveries, driverless cars, or placing a Pikachu in a specific location, say on a tree branch in a park, GPS isn’t accurate enough. As astonishing as it may seem, many experimental AR cloud concepts, even entirely mapped cities, are location specific down to the centimeter.
Niantic, the $4 billion publisher behind Pokémon Go, is aggressively working on developing a crowd-sourced approach to building better AR Cloud maps by encouraging their users to scan the world for them. Their recent acquisition of 6D.ai, a mapping software company developed by the University of Oxford’s Victor Prisacariu through his work at Oxford’s Active Vision Lab, indicates Niantic’s ambition to compete with the tech giants in this space.
With 6D.ai’s technology, Niantic is developing the in-house ability to generate their own 3D maps while gaining better semantic understanding of the world. By going beyond just knowing there’s a temporary collection of orange cones in a certain location, for example, the game may one day understand the meaning behind this; that a temporary construction zone means no Pokémon should spawn here to avoid drawing players to this location.
Niantic is not the only company working on this. Many of the big tech firms you would expect have entire teams focused on map data. Facebook, for example, recently acquired the UK-based Scape technologies, a computer vision startup mapping entire cities with centimeter precision.
As our digital maps of the world improve, expect a relentless and justified discussion of privacy concerns as well. How will society react to the idea of a real-time 3D map of their bedroom living on a Facebook or Amazon server? Those horrified by the use of facial recognition AI being used in public spaces are unlikely to find comfort in the idea of a machine-readable world subject to infinite monitoring.
The ability to build high-precision maps of the world could reshape the way we engage with our planet and promises to be one of the biggest technology developments of the next decade. While these maps may stay hidden as behind-the-scenes infrastructure powering much flashier technologies that capture the world’s attention, they will soon prop up large portions of our technological future.
Keep that in mind when a car with no driver is sharing your road.
Image credit: sergio souza / Pexels Continue reading
Posted in Human Robots
#437905 New Deep Learning Method Helps Robots ...
One of the biggest things standing in the way of the robot revolution is their inability to adapt. That may be about to change though, thanks to a new approach that blends pre-learned skills on the fly to tackle new challenges.
Put a robot in a tightly-controlled environment and it can quickly surpass human performance at complex tasks, from building cars to playing table tennis. But throw these machines a curve ball and they’re in trouble—just check out this compilation of some of the world’s most advanced robots coming unstuck in the face of notoriously challenging obstacles like sand, steps, and doorways.
The reason robots tend to be so fragile is that the algorithms that control them are often manually designed. If they encounter a situation the designer didn’t think of, which is almost inevitable in the chaotic real world, then they simply don’t have the tools to react.
Rapid advances in AI have provided a potential workaround by letting robots learn how to carry out tasks instead of relying on hand-coded instructions. A particularly promising approach is deep reinforcement learning, where the robot interacts with its environment through a process of trial-and-error and is rewarded for carrying out the correct actions. Over many repetitions it can use this feedback to learn how to accomplish the task at hand.
But the approach requires huge amounts of data to solve even simple tasks. And most of the things we would want a robot to do are actually comprised of many smaller tasks—for instance, delivering a parcel involves learning how to pick an object up, how to walk, how to navigate, and how to pass an object to someone else, among other things.
Training all these sub-tasks simultaneously is hugely complex and far beyond the capabilities of most current AI systems, so many experiments so far have focused on narrow skills. Some have tried to train AI on multiple skills separately and then use an overarching system to flip between these expert sub-systems, but these approaches still can’t adapt to completely new challenges.
Building off this research, though, scientists have now created a new AI system that can blend together expert sub-systems specialized for a specific task. In a paper in Science Robotics, they explain how this allows a four-legged robot to improvise new skills and adapt to unfamiliar challenges in real time.
The technique, dubbed multi-expert learning architecture (MELA), relies on a two-stage training approach. First the researchers used a computer simulation to train two neural networks to carry out two separate tasks: trotting and recovering from a fall.
They then used the models these two networks learned as seeds for eight other neural networks specialized for more specific motor skills, like rolling over or turning left or right. The eight “expert networks” were trained simultaneously along with a “gating network,” which learns how to combine these experts to solve challenges.
Because the gating network synthesizes the expert networks rather than switching them on sequentially, MELA is able to come up with blends of different experts that allow it to tackle problems none could solve alone.
The authors liken the approach to training people in how to play soccer. You start out by getting them to do drills on individual skills like dribbling, passing, or shooting. Once they’ve mastered those, they can then intelligently combine them to deal with more dynamic situations in a real game.
After training the algorithm in simulation, the researchers uploaded it to a four-legged robot and subjected it to a battery of tests, both indoors and outdoors. The robot was able to adapt quickly to tricky surfaces like gravel or pebbles, and could quickly recover from being repeatedly pushed over before continuing on its way.
There’s still some way to go before the approach could be adapted for real-world commercially useful robots. For a start, MELA currently isn’t able to integrate visual perception or a sense of touch; it simply relies on feedback from the robot’s joints to tell it what’s going on around it. The more tasks you ask the robot to master, the more complex and time-consuming the training will get.
Nonetheless, the new approach points towards a promising way to make multi-skilled robots become more than the sum of their parts. As much fun as it is, it seems like laughing at compilations of clumsy robots may soon be a thing of the past.
Image Credit: Yang et al., Science Robotics Continue reading
Posted in Human Robots