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#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
#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
#437896 Solar-based Electronic Skin Generates ...
Replicating the human sense of touch is complicated—electronic skins need to be flexible, stretchable, and sensitive to temperature, pressure and texture; they need to be able to read biological data and provide electronic readouts. Therefore, how to power electronic skin for continuous, real-time use is a big challenge.
To address this, researchers from Glasgow University have developed an energy-generating e-skin made out of miniaturized solar cells, without dedicated touch sensors. The solar cells not only generate their own power—and some surplus—but also provide tactile capabilities for touch and proximity sensing. An early-view paper of their findings was published in IEEE Transactions on Robotics.
When exposed to a light source, the solar cells on the s-skin generate energy. If a cell is shadowed by an approaching object, the intensity of the light, and therefore the energy generated, reduces, dropping to zero when the cell makes contact with the object, confirming touch. In proximity mode, the light intensity tells you how far the object is with respect to the cell. “In real time, you can then compare the light intensity…and after calibration find out the distances,” says Ravinder Dahiya of the Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, where the study was carried out. The team used infra-red LEDs with the solar cells for proximity sensing for better results.
To demonstrate their concept, the researchers wrapped a generic 3D-printed robotic hand in their solar skin, which was then recorded interacting with its environment. The proof-of-concept tests showed an energy surplus of 383.3 mW from the palm of the robotic arm. “The eSkin could generate more than 100 W if present over the whole body area,” they reported in their paper.
“If you look at autonomous, battery-powered robots, putting an electronic skin [that] is consuming energy is a big problem because then it leads to reduced operational time,” says Dahiya. “On the other hand, if you have a skin which generates energy, then…it improves the operational time because you can continue to charge [during operation].” In essence, he says, they turned a challenge—how to power the large surface area of the skin—into an opportunity—by turning it into an energy-generating resource.
Dahiya envisages numerous applications for BEST’s innovative e-skin, given its material-integrated sensing capabilities, apart from the obvious use in robotics. For instance, in prosthetics: “[As] we are using [a] solar cell as a touch sensor itself…we are also [making it] less bulkier than other electronic skins.” This, he adds, will help create prosthetics that are of optimal weight and size, thus making it easier for prosthetics users. “If you look at electronic skin research, the the real action starts after it makes contact… Solar skin is a step ahead, because it will start to work when the object is approaching…[and] have more time to prepare for action.” This could effectively reduce the time lag that is often seen in brain–computer interfaces.
There are also possibilities in the automation sector, particularly in electrical and interactive vehicles. A car covered with solar e-skin, because of its proximity-sensing capabilities, would be able to “see” an approaching obstacle or a person. It isn’t “seeing” in the biological sense, Dahiya clarifies, but from the point of view of a machine. This can be integrated with other objects, not just cars, for a variety of uses. “Gestures can be recognized as well…[which] could be used for gesture-based control…in gaming or in other sectors.”
In the lab, tests were conducted with a single source of white light at 650 lux, but Dahiya feels there are interesting possibilities if they could work with multiple light sources that the e-skin could differentiate between. “We are exploring different AI techniques [for that],” he says, “processing the data in an innovative way [so] that we can identify the the directions of the light sources as well as the object.”
The BEST team’s achievement brings us closer to a flexible, self-powered, cost-effective electronic skin that can touch as well as “see.” At the moment, however, there are still some challenges. One of them is flexibility. In their prototype, they used commercial solar cells made of amorphous silicon, each 1cm x 1cm. “They are not flexible, but they are integrated on a flexible substrate,” Dahiya says. “We are currently exploring nanowire-based solar cells…[with which] we we hope to achieve good performance in terms of energy as well as sensing functionality.” Another shortcoming is what Dahiya calls “the integration challenge”—how to make the solar skin work with different materials. Continue reading
Posted in Human Robots
#437869 Video Friday: Japan’s Gundam Robot ...
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!):
ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today’s videos.
Another BIG step for Japan’s Gundam project.
[ Gundam Factory ]
We present an interactive design system that allows users to create sculpting styles and fabricate clay models using a standard 6-axis robot arm. Given a general mesh as input, the user iteratively selects sub-areas of the mesh through decomposition and embeds the design expression into an initial set of toolpaths by modifying key parameters that affect the visual appearance of the sculpted surface finish. We demonstrate the versatility of our approach by designing and fabricating different sculpting styles over a wide range of clay models.
[ Disney Research ]
China’s Chang’e-5 completed the drilling, sampling and sealing of lunar soil at 04:53 BJT on Wednesday, marking the first automatic sampling on the Moon, the China National Space Administration (CNSA) announced Wednesday.
[ CCTV ]
Red Hat’s been putting together an excellent documentary on Willow Garage and ROS, and all five parts have just been released. We posted Part 1 a little while ago, so here’s Part 2 and Part 3.
Parts 4 and 5 are at the link below!
[ Red Hat ]
Congratulations to ANYbotics on a well-deserved raise!
ANYbotics has origins in the Robotic Systems Lab at ETH Zurich, and ANYmal’s heritage can be traced back at least as far as StarlETH, which we first met at ICRA 2013.
[ ANYbotics ]
Most conventional robots are working with 0.05-0.1mm accuracy. Such accuracy requires high-end components like low-backlash gears, high-resolution encoders, complicated CNC parts, powerful motor drives, etc. Those in combination end up an expensive solution, which is either unaffordable or unnecessary for many applications. As a result, we found the Apicoo Robotics to provide our customers solutions with a much lower cost and higher stability.
[ Apicoo Robotics ]
The Skydio 2 is an incredible drone that can take incredible footage fully autonomously, but it definitely helps if you do incredible things in incredible places.
[ Skydio ]
Jueying is the first domestic sensitive quadruped robot for industry applications and scenarios. It can coordinate (replace) humans to reach any place that can be reached. It has superior environmental adaptability, excellent dynamic balance capabilities and precise Environmental perception capabilities. By carrying functional modules for different application scenarios in the safe load area, the mobile superiority of the quadruped robot can be organically integrated with the commercialization of functional modules, providing smart factories, smart parks, scene display and public safety application solutions.
[ DeepRobotics ]
We have developed semi-autonomous quadruped robot, called LASER-D (Legged-Agile-Smart-Efficient Robot for Disinfection) for performing disinfection in cluttered environments. The robot is equipped with a spray-based disinfection system and leverages the body motion to controlling the spray action without the need for an extra stabilization mechanism. The system includes an image processing capability to verify disinfected regions with high accuracy. This system allows the robot to successfully carry out effective disinfection tasks while safely traversing through cluttered environments, climb stairs/slopes, and navigate on slippery surfaces.
[ USC Viterbi ]
We propose the “multi-vision hand”, in which a number of small high-speed cameras are mounted on the robot hand of a common 7 degrees-of-freedom robot. Also, we propose visual-servoing control by using a multi-vision system that combines the multi-vision hand and external fixed high-speed cameras. The target task was ball catching motion, which requires high-speed operation. In the proposed catching control, the catch position of the ball, which is estimated by the external fixed high-speed cameras, is corrected by the multi-vision hand in real-time.
More details available through IROS on-demand.
[ Namiki Laboratory ]
Shunichi Kurumaya wrote in to share his work on PneuFinger, a pneumatically actuated compliant robotic gripping system.
[ Nakamura Lab ]
Thanks Shunichi!
Motivated by insights into the human teaching process, we introduce a method for incorporating unstructured natural language into imitation learning. At training time, the expert can provide demonstrations along with verbal descriptions in order to describe the underlying intent, e.g., “Go to the large green bowl’’. The training process, then, interrelates the different modalities to encode the correlations between language, perception, and motion. The resulting language-conditioned visuomotor policies can be conditioned at run time on new human commands and instructions, which allows for more fine-grained control over the trained policies while also reducing situational ambiguity.
[ ASU ]
Thanks Heni!
Gita is on sale for the holidays for only $2,000.
[ Gita ]
This video introduces a computational approach for routing thin artificial muscle actuators through hyperelastic soft robots, in order to achieve a desired deformation behavior. Provided with a robot design, and a set of example deformations, we continuously co-optimize the routing of actuators, and their actuation, to approximate example deformations as closely as possible.
[ Disney Research ]
Researchers and mountain rescuers in Switzerland are making huge progress in the field of autonomous drones as the technology becomes more in-demand for global search-and-rescue operations.
[ SWI ]
This short clip of the Ghost Robotics V60 features an interesting, if awkward looking, righting behavior at the end.
[ Ghost Robotics ]
Europe’s Rosalind Franklin ExoMars rover has a younger ’sibling’, ExoMy. The blueprints and software for this mini-version of the full-size Mars explorer are available for free so that anyone can 3D print, assemble and program their own ExoMy.
[ ESA ]
The holiday season is here, and with the added impact of Covid-19 consumer demand is at an all-time high. Berkshire Grey is the partner that today’s leading organizations turn to when it comes to fulfillment automation.
[ Berkshire Grey ]
Until very recently, the vast majority of studies and reports on the use of cargo drones for public health were almost exclusively focused on the technology. The driving interest from was on the range that these drones could travel, how much they could carry and how they worked. Little to no attention was placed on the human side of these projects. Community perception, community engagement, consent and stakeholder feedback were rarely if ever addressed. This webinar presents the findings from a very recent study that finally sheds some light on the human side of drone delivery projects.
[ WeRobotics ] Continue reading
Posted in Human Robots