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Category: robotics/AI – Page 1529

For many years now, China has been the world’s factory. Even in 2,020 as other economies struggled with the effects of the pandemic, China’s manufacturing output was $3.854 trillion, up from the previous year, accounting for nearly a third of the global market.

But if you are still thinking of China’s factories as sweatshops, it’s probably time to change your perception. The Chinese economic recovery from its short-lived pandemic blip has been boosted by its world-beating adoption of artificial intelligence (AI). After overtaking the U.S. in 2,014 China now has a significant lead over the rest of the world in AI patent applications. In academia, China recently surpassed the U.S. in the number of both AI research publications and journal citations. Commercial applications are flourishing: a new wave of automation and AI infusion is crashing across a swath of sectors, combining software, hardware and robotics.

As a society, we have experienced three distinct industrial revolutions: steam power, electricity and information technology. I believe AI is the engine fueling the fourth industrial revolution globally, digitizing and automating everywhere. China is at the forefront in manifesting this unprecedented change.

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We’ve seen a lot of electric vehicle growth and success stories in the past several years, but one area that’s been a bit of a letdown has been the semi truck market. Unfortunately, we still don’t have the Tesla Semi, and it was recently delayed until 2,022 and a big side area of that market that “futurists” have long been excited about is potential self-driving trucks. Platoons of self-driving semi trucks are especially exciting since tight, train-like caravans of semi trucks would use far less energy than the current system, and those trucks could much more easily be cost-competitive electric trucks with zero tailpipe emissions. Anyway, though, we’re getting ahead of ourselves again.

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Doubtful. But, i hope so, it will convince them to spend more money here to move AI research faster.

TOKYO — China is overtaking the U.S. in artificial intelligence research, setting off alarm bells on the other side of the Pacific as the world’s two largest economies jockey for AI supremacy.

In 2,020 China topped the U.S. for the first time in terms of the number of times an academic article on AI is cited by others, a measure of the quality of a study. Until recently, the U.S. had been far ahead of other countries in AI research.

One reason China is coming on strong in AI is the ample data it generates. By 2,030 an estimated 8 billion devices in China will be connected via the Internet of Things — a vast network of physical objects linked via the internet. These devices, mounted on cars, infrastructure, robots and other instruments, generate a huge amount of data.

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In other words, the mix of positives and negatives puts this potent new suite of technologies on a knife-edge. Do we have confidence that a handful of companies that have already lost public trust can take AI in the right direction? We should have ample reason for worry considering the business models driving their motivations. To advertising-driven companies like Google and Facebook, it’s clearly beneficial to elevate content that travels faster and draws more attention—and misinformation usually does —while micro-targeting that content by harvesting user data. Consumer product companies, such as Apple, will be motivated to prioritize AI applications that help differentiate and sell their most profitable products—hardly a way to maximize the beneficial impact of AI.

Yet another challenge is the prioritization of innovation resources. The shift online during the pandemic has led to outsized profits for these companies, and concentrated even more power in their hands. They can be expected to try to maintain that momentum by prioritizing those AI investments that are most aligned with their narrow commercial objectives while ignoring the myriad other possibilities. In addition, Big Tech operates in markets with economies of scale, so there is a tendency towards big bets that can waste tremendous resources. Who remembers IBM’s Watson initiative? It aspired to become the universal, go-to digital decision tool, especially in healthcare—and failed to live up to the hype, as did the trendy driverless car initiatives of Amazon and Google parent Alphabet. While failures, false starts, and pivots are a natural part of innovation, expensive big failures driven by a few enormously wealthy companies divert resources away from more diversified investments across a range of socially productive applications.

Despite AI’s growing importance, U.S. policy on how to manage the technology is fragmented and lacks a unified vision. It also appears to be an afterthought, with lawmakers more focused on Big Tech’s anti-competitive behavior in its main markets—from search to social media to app stores. This is a missed opportunity, because AI has the potential for much deeper societal impacts than search, social media, and apps.

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Tesla has started updating its Autopark feature with its new Tesla Vision computer vision system, which now powers Autopilot and its Full Self-Driving Beta.

Like many other premium (and even non-premium) vehicles, Tesla vehicles have been equipped with an autonomous parking feature called ‘ Autopark.

Tesla’s Autopark has been relying on ultrasonic sensors around the vehicles.

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Circa 2016

Scientists and engineers since the 1940s have been toying with the idea of building self-replicating machines, or von Neumann machines, named for John von Neumann. With recent advances in 3D printing (including in zero gravity) and machine learning AI, it seems like self-replicating machines are much more feasible today. In the 21st century, a tantalizing possibility for this technology has emerged: sending a space probe out to a different star system, having it mine resources to make a copy of itself, and then launching that one to yet another star system, and on and on and on.

As a wild new episode of PBS’s YouTube series Space Time suggests, if we could send a von Neumann probe to another star system—likely Alpha Centauri, the closest to us at about 4.4 light years away—then that autonomous spaceship could land on a rocky planet, asteroid, or moon and start building a factory. (Of course, it’d probably need a nuclear fusion drive, something we still need to develop.)

That factory of autonomous machines could then construct solar panels, strip mine the world for resources, extract fuels from planetary atmospheres, build smaller probes to explore the system, and eventually build a copy of the entire von Neumann spacecraft to send off to a new star system and repeat the process. It has even been suggested that such self-replicating machines could build a Dyson sphere to harness energy from a star or terraform a planet for the eventual arrival of humans.

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Artificial camouflage is the functional mimicry of the natural camouflage that can be observed in a wide range of species1,2,3. Especially, since the 1800s, there were a lot of interesting studies on camouflage technology for military purposes which increases survivability and identification of an anonymous object as belonging to a specific military force4,5. Along with previous studies on camouflage technology and natural camouflage, artificial camouflage is becoming an important subject for recently evolving technologies such as advanced soft robotics1,6,7,8 electronic skin in particular9,10,11,12. Background matching and disruptive coloration are generally claimed to be the underlying principles of camouflage covering many detailed subprinciples13, and these necessitate not only simple coloration but also a selective expression of various disruptive patterns according to the background. While the active camouflage found in nature mostly relies on the mechanical action of the muscle cells14,15,16, artificial camouflage is free from matching the actual anatomies of the color-changing animals and therefore incorporates much more diverse strategies17,18,19,20,21,22, but the dominant technology for the practical artificial camouflage at visible regime (400–700 nm wavelength), especially RGB domain, is not fully established so far. Since the most familiar and direct camouflage strategy is to exhibit a similar color to the background23,24,25, a prerequisite of an artificial camouflage at a unit device level is to convey a wide range of the visible spectrum that can be controlled and changed as occasion demands26,27,28. At the same time, the corresponding unit should be flexible and mechanically robust, especially for wearable purposes, to easily cover the target body as attachable patches without interrupting the internal structures, while being compatible with the ambient conditions and the associated movements of the wearer29,30.

System integration of the unit device into a complete artificial camouflage device, on the other hand, brings several additional issues to consider apart from the preceding requirements. Firstly, the complexity of the unit device is anticipated to be increased as the sensor and the control circuit, which are required for the autonomous retrieval and implementation of the adjacent color, are integrated into a multiplexed configuration. Simultaneously, for nontrivial body size, the concealment will be no longer effective with a single unit unless the background consists of a monotone. As a simple solution to this problem, unit devices are often laterally pixelated12,18 to achieve spatial variation in the coloration. Since its resolution is determined by the numbers of the pixelated units and their sizes, the conception of a high-resolution artificial camouflage device that incorporates densely packed arrays of individually addressable multiplexed units leads to an explosive increase in the system complexity. While on the other hand, solely from the perspective of camouflage performance, the delivery of high spatial frequency information is important for more natural concealment by articulating the texture and the patterns of the surface to mimic the microhabitats of the living environments31,32. As a result, the development of autonomous and adaptive artificial camouflage at a complete device level with natural camouflage characteristics becomes an exceptionally challenging task.

Our strategy is to combine thermochromic liquid crystal (TLC) ink with the vertically stacked multilayer silver (Ag) nanowire (NW) heaters to tackle the obstacles raised from the earlier concept and develop more practical, scalable, and high-performance artificial camouflage at a complete device level. The tunable coloration of TLC, whose reflective spectrum can be controlled over a wide range of the visible spectrum within the narrow range of temperature33,34, has been acknowledged as a potential candidate for artificial camouflage applications before21,34, but its usage has been more focused on temperature measurement35,36,37,38 owing to its high sensitivity to the temperature change. The susceptible response towards temperature is indeed an unfavorable feature for the thermal stability against changes in the external environment, but also enables compact input range and low power consumption during the operation once the temperature is accurately controlled.

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