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intrpd · 6 years ago

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Societal Readiness Levels: Ready for a new normal?

Monika Büscher

‘What gets measured gets done’ – a treacherous mantra of our times. As societies bend to a technocratic Gestell of indicators, their compliance feeds its power. In her critique of the New Urban Agenda’s call for urban resilience, Maria Kaika highlights how indicator-based planning can undermine communities. She cites Tracie Washington’s defense of disaster victims:

everytime you say, “Oh, they’re resilient, [it actually] means you can do something else, … We were not born to be resilient; we are conditioned to be resilient. I don’t want to be resilient …. [I want to] fix the things that [create the need for us to] be resilient [in the first place]

Tracie Washington, President of the Louisiana Justice Institute, in Kaika (2017).

Kaika shows how indicators designed to support resilience can end up supporting an ‘immunological’ ideology. A technocratic, managerial, solutionist smart city innovation agenda, she argues, ‘vaccinates citizens and environments so that they can take larger doses of inequality and degradation in the future’ (2017:89). The concept of ‘Technology Readiness Levels’ (TRL) is part of this agenda, as is the emergent concept of Societal Readiness Levels (SRL) (Fig 1). However, an affirmative critique (Braidotti 2011)of SRL may offer a lifeline off this self-destructive juggernaut.

Figure 1 Visualisations of Societal and Technological Readiness (Source: (Allwood et al. 2017)(right), (Schraudner et al. 2018)(left))

Developed by NASA as a ‘systematic metric/measurement system that supports assessments of the maturity of a particular technology’ and comparison between different types of technology (Mankins 1995), the TRL metric has spread to guide multi-trillion global innovation programmes. TRL embody a culture of solutionism (Morozov 2013), where technology is seen as a product designed to fulfill societal or operational needs or ambitions. SRL critique this technocractic focus. The SRL concept originates in debates about a transition towards low carbon futures (Allwood et al 2017, Fig 1 left) and the Danish Innovation Fund’s attempt to find a ‘way of assessing the level of societal adaptation of, for instance, a particular … innovation (whether social or technical) to be integrated into society’ (Fig 1 right). The two approaches are different in aim, one focused on a societal transformation where a socio-technical change of reduced material demand has become ‘normal practice’ at the highest SRL level, the other on measuring successful (profitable) embedding of (desire for) a product.

The Danish Innovation Fund’s SRL framework in particular has received interest from major actors like UK and EU research funding bodies. This indicates that concerns with the social dimension of innovation have become mainstream. The 2050 European Energy Roadmap, for example, recognises that citizens’ active participation in energy management is ‘as critical as technology to making the European energy system more flexible and sustainable’, and smart city innovation is scrambling to become ‘citizen-focused’. However, such citizen-focus all-too-often remains – at best! –at a ‘placating’ level (Cardullo and Kitchin 2019), at worst it constitutes cynical lip-service for moreintrusive commercial and security-driven exploitation of citizen data in ‘Lifeworld.Inc’ (Thrift 2011).

Mainstreaming attention to the social through SRL has failed so far. Why? Does this mean the very idea is ideologically corrupted like the New Urban Agenda’s resilience indicator-based approach? Or is failure down to a lack of societal readiness of the currently dominant SRL concept itself? Measurement and comparison have proved critical to societal transformation before (Mosley 2009). And as rapid societal transformation is needed to avert the collapse of humanity, could societal readiness be conceived differently? Allwood et al’s concern with a new normal as the highest level of societal readiness resonates with social science debates that the Danish Innovation Fund seems oblivious to. The summary below is designed to explore how we might give new direction to SRL.

Christensen’s recognition that innovation is often disruptive of existing socio-economic orders (Christensen 1997; Christensen et al 2015)highlights that technologies are not products to be inserted into a ‘context’ but catalysts for change. The concept of mode-2 science and society (Nowotny et al 2001)addresses this disruptive element and the unintended, un-known and unknow-able consequences of innovation. Mode-2 society and mode-2 science are based on interdisciplinary collaboration, and methods of collective experimentation (Felt and Wynne 2008), where scientists and citizens, organisations, technology developers and those who appropriate technology, bring together and contest social and technological innovation. Opportunities for such collaboration are often clustered at the implementation end of innovation, but calls to create them further upstream are beginning to define methodologies for citizen engagement on higher rungs of the ladder of participation (Arnstein 1969)to conduct Experiment Earth(Stilgoe 2016).

The result of such efforts should be more carefully radical and radically careful design (Latour 2008). Methods for achieving it cluster around experimentation, creative, artistic disruption and the strategic power of ignorance and surprise (Gross 2010). Gross suggests that given our inevitable ignorance in the face of complex systemic disruptions and unintended consequences of innovation it is critical that we generate as much surprise as early as possible. Simulation, play, broad-base dialogue and collaborative learning are essential for this. Introna (2007)adds an ethical dimension with his call for disclosive ethics and a focus on reversibility – it is important to not allow innovations to settle too fast. Recent efforts to define a digital ethics by the European Data Protection Supervisors’ Office address this need to give ethical issues a broader than regulatory exposure, including public consultation (EDPS 2015; 2018)

What is the role of the social scientist in this? Braidotti's (2011)demand to move beyond critique and into constructive endeavours or affirmative critique, require the courage to ‘stick one’s neck out’ and make value-based normative recommendations for how things should be organized in better ways. Failure and being wrong are obvious dangers. But there might be ways of doing it playfully and in safe spaces, experimentally, creatively, and collectively, which resonates strongly calls for an experimental sociology (Thrift 2011), inventive and speculative methods (Lury and Wakeford 2013; Michael 2016). It also sits well with suggestions that it is not necessarily deliberation or consensus that should be sought. Instead participatory designers and action researchers are calling on us to engage in infrastructuring for participation, seeking to include and enable dissent, debate, ongoing experimentation (Ehn 2008; Dantec and DiSalvo 2013)

Levitas’ utopia as method (2013)provides perhaps the most integrative framework for these endeavours. This is a creative appropriation of utopia not as a blueprint of a ‘better’ world designed by experts, but a method to engage diverse stakeholders in making better pockets of the world together, all the while remaining open to experiencing how and for whom this worldly constellation is (not) better and how. Collective narrative methods and visual story-building methods are particularly suited (Porritt 2013; McKay and Dickson 2016; Popan 2018)

None of these contributions have so far made it into definitions of SRL. The Danish Innovation Fund’s focus on validation, testing, deployment supports experimentation, but it is driven by a concern with social acceptance (and not the acceptability) of innovation, based on a deficit model of poor public understanding of science and technology. To ask, with Tsing(2015), and through research and innovation, ‘what if the time was ripe for sensing precarity?’ and ‘what constitutes living with it well?’, SRL need to be co-created, they need to measure how innovations enable a good ‘new normal’, and this needs to be open to contestation.

References

Allwood, Julian M., Timothy G. Gutowski, André C. Serrenho, Alexandra C. H. Skelton, and Ernst Worrell. 2017. ‘Industry 1.61803: The Transition to an Industry with Reduced Material Demand Fit for a Low Carbon Future’. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences375 (2095). https://doi.org/10.1098/rsta.2016.0361.

Arnstein, Sherry R. 1969. ‘A Ladder Of Citizen Participation: : Vol 35, No 4’. Journal of the American Institute of Planners35 (4): 216–24.

Braidotti, Rosi. 2011. Nomadic Theory: The Portable Rosi Braidotti. New York: Columbia University Press. https://www.amazon.co.uk/Nomadic-Theory-Portable-Braidotti-Paperback/dp/0231151918.

Cardullo, Paolo, and Rob Kitchin. 2019. ‘Being a “Citizen” in the Smart City: Up and down the Scaffold of Smart Citizen Participation in Dublin, Ireland | SpringerLink’. GeoJournal84 (1): 1–13.

Christensen, Clayton M. 1997. The Innovator’s Dilemma. Boston, Massachusetts: Harvard Business Review Press.

Christensen, Clayton M., Michael E. Raynor, and Rory McDonald. 2015. ‘What Is Disruptive Innovation?’ Harvard Business Review, 1 December 2015. https://hbr.org/2015/12/what-is-disruptive-innovation.

Dantec, Christopher A Le, and Carl DiSalvo. 2013. ‘Infrastructuring and the Formation of Publics in Participatory Design’. Social Studies of Science43 (2): 241–64. https://doi.org/10.1177/0306312712471581.

EDPS. 2015. ‘Opinion 4/2015: Towards a New Digital Ethics. Data, Dignity and Technology’. https://edps.europa.eu/sites/edp/files/publication/15-09-11_data_ethics_en.pdf.

———. 2018. ‘Public Consultation on Digital Ethics’. https://edps.europa.eu/sites/edp/files/publication/18-09-25_edps_publicconsultationdigitalethicssummary_en.pdf.

Ehn, Pelle. 2008. ‘Participation in Design Things’. In Proceedings of the Tenth Anniversary Conference on Participatory Design 2008, 92–101. PDC ’08. Indianapolis, IN, USA: Indiana University. http://dl.acm.org/citation.cfm?id=1795234.1795248.

Felt, U., and B. Wynne, eds. 2008. Taking European Knowledge Society Seriously. Vimodrone, IPOC: IPOC di Pietro Condemi. https://sts.univie.ac.at/fileadmin/user_upload/i_sts/Ueber_uns/pdfs_Felt/taking_european_knowledge_society_seriously.pdf.

Gross, Matthias. 2010. Ignorance and Surprise | The MIT Press. Cambridge, MA, USA: MIT Press. https://mitpress.mit.edu/books/ignorance-and-surprise.

Introna, Lucas D. 2007. ‘Maintaining the Reversibility of Foldings: Making the Ethics (Politics) of Information Technology Visible.’ Ethics and Information Technology9 (1): 11–25. https://doi.org/10.1007/s10676-006-9133-z.

Kaika, Maria. 2017. ‘“Don’t Call Me Resilient Again!”: The New Urban Agenda as Immunology … or … What Happens When Communities Refuse to Be Vaccinated with “Smart Cities” and Indicators’. Environment and Urbanization29 (1): 89–102. https://doi.org/10.1177/0956247816684763.

Latour, Bruno. 2008. ‘A Cautious Prometheus ? A Few Steps Toward a Philosophy of Design’, September. https://hal-sciencespo.archives-ouvertes.fr/hal-00972919.

Levitas, Ruth. 2013. Utopia as Method - The Imaginary Reconstitution of Society. Basingstoke: Palgrave Macmillan UK. https://www.palgrave.com/gp/book/9780230231962.

Mankins, John C. 1995. ‘Technology Readiness Levels. White Paper’. NASA. https://aiaa.kavi.com/apps/group_public/download.php/2212/TRLs_MankinsPaper_1995.pdf.

McKay, J, and B Dickson. 2016. ‘Dreams of a Low Carbon Future : Doctoral Training Centre in Low Carbon Technologies’. Leeds University. https://lowcarbon.leeds.ac.uk/dreams-of-a-low-carbon-future/.

Mosley, Stephen. 2009. ‘“A Network of Trust”: Measuring and Monitoring Air Pollution in British Cities, 1912-1960’. Environment and History15 (3): 273–302.

Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-Thinking Science: Knowledge and the Public in an Age of Uncertainty. Cambridge: Polity. https://www.amazon.co.uk/Re-Thinking-Science-Knowledge-Public-Uncertainty/dp/0745626084.

Popan, Cosmin. 2018. Bicycle Utopias: Imagining Fast and Slow Cycling Futures. London: Routledge. https://www.amazon.co.uk/Bicycle-Utopias-Imagining-Changing-Mobilities/dp/1138389188.

Porritt, Jonathan. 2013. The World We Made: Alex McKay’s Story from 2050. Phaidon Press. https://www.amazon.co.uk/World-We-Made-McKays-Story/dp/0714863610.

Schraudner, Martina, Fabian Schroth, Malte Juetting, Simone Kaiser, Jeremy Millard, and Shenja Can der Graaf. 2018. ‘Social Innovation The Potential for Technology Development, RTOs and Industry. Policy Paper’. Fraunhofer. http://www.thertoinnovationsummit.eu/en/wp-content/uploads/2019/01/20181220_RTO-Innovation-Summit_Policy-Paper-1.pdf.

Stilgoe, Jack. 2016. Experiment Earth: Responsible Innovation in Geoengineering. Boca Raton: CRC Press. https://www.crcpress.com/Experiment-Earth-Responsible-innovation-in-geoengineering/Stilgoe/p/book/9781138691940.

Thrift, Nigel. 2011. ‘Lifeworld Inc—And What to Do about It’. Environment and Planning D: Society and Space29 (1): 5–26. https://doi.org/10.1068/d0310.

Tsing, Anna Lowenhaupt. 2015. The Mushroom at the End of the World. Princeton, New Jersey: Princeton University Press. https://press.princeton.edu/titles/10581.html.

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juniperpublishers-robotics-blog · 6 years ago

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Autonomous Robot Path Planning and Internet of Things-Juniper Publishers

Abstract

Robotics is known as a new revolution to the entity of beings that varies according to its uses. In modern day environments, robotics and automation are involved in almost every industrial activity and conveniently improve the efficiency, productivity and reliability of a system. Autonomous Guided Robot (AGR) systems are classified as rover based robotics that require vision type and touch sensors. The AGR should be able to maneuver and counteract with the environment using sensors to detect the obstacles around, remember its current position and calculate a new path to take. Robotics Automation with industrial robots in combination with Internet of things, the birth of intelligent flexible automation systems, with the technical advantages of intelligent flexible automation systems, the company recently signed several million more than the contract, industry all over the hot die forging automation, automation of internal combustion engines, engine Assembly, automotive automatic battery swap station and so on. Thus, in this paper an over view of the significance of path planning and internet of robotic things is presented.

Keywords: Industrial robot; path planning; Internet of things (IoT)

Introduction

Robotics is implemented in medical practice, construction, outer-space exploration, household assistance, mobile transportation and quite recently, under water exploration [1-3]. Currently there has been study of automated guided robots which is used in transportation and exploration that can be configured for different terrain. These designs are onlocomotion, Hopfield Neural Network, Genetic Algorithm and etc [4-6]. For example, JPL (Jet PropulsionLaboratory, NASA) in U.S.A have developed many rovers. Sojourner which were landed in Mars in 1997 adopted rocker-bogie locomotion, Blue Rover uses three-segment locomotion, the mini Mars rover Go-For has an active wheel-legged locomotion, Nano Rover utilize posable tructchassis and Elastic Loop Mobility System was also designed as new type of locomotion for planetary exploration [7].

Robot Path Planning or robot Motion Planning is one of the important areas of interest in robot's offline decision making algorithms. In this problem, the aim is to find a collision free path, which the robot can follow to reach the target from its start position. Analysis and research on autonomous path planning has included innovative advancements in the use of artificial intelligence (AI). With advancement in the study of this subject, technology with uncontrollable situations such as outer space exploration and deep sea excavation can be further improved. New technology such as autonomous vehicle systems may also beable to utilize such algorithms which are fail-safe. With sensors, robots are said to be able to obtain vision, sense of touch, balance, and even hearing. According to their tasks and application, robots are given the appropriate sensors that function as the feedback systems in a controller [8].

Once the collision-free configuration space is described asa graph, the shortest path between two nodes can be searched .An overview about common path finding algorithms is given in [9] depth-first, breadth-first and best-first search, the algorithm of Dijkstra and finally the A* algorithm. All these approaches find a solution, if one exists. Especially the Dijkstra and A*algorithm are in the focus of research [10], as they promise theoptimal path with a minimal computing time. The algorithm of Dijkstra was developed in 1959 and always finds the shortest path between two given nodes or proves that no solution exists [11]. For this purpose, the costs g(n) fromthe start node is assigned to each considered node n. There by the nodes with the smallest value of g(n) are prioritized which guarantees an optimal path.

On this basis, the widely used A* algorithm was presented in 1968 [12]. The method finds a least-cost path between astart and a goal node. This is achieved by evaluating a cost function f(n) of a node n to determine in which sequence the search visits nodes in order to expand the fewest possible nodes. The function f(n) is the sum of the known costs g(n) from the start node to n and the estimated costs h(n) (also called heuristic function) from n to the goal node. The A* algorithm is complete it will always find a solution if one exists. Furthermore it computes the optimal path if the heuristic h(n) does not over estimate the costs to the goal and is faster than the algorithm of Dijkstra [13].

For a robot with m joints, the configuration space is an m dimensional space spanned by the degrees of freedom of the robot system and sub divided in collision-free regions. Based on this configuration space movements of the robot can be determined. Assuming a six dimensional standard industrial robot, the discretization of the space according to collisions would be a time consuming process. Consequently, an effective method for building a collision-free configuration space is needed.

Robotic system has brought tremendous changes in various socio-economical aspects of human society during the past decades [14]. Industrial robot manipulators have been widely deployed and used in all sorts of industries to perform repetitive, tedious, critical, and/or dangerous tasks, such as product assembly, car painting, box packaging, and shield welding. These preprogrammed robots have always been very successful at their accomplishments in several structured industrial applications due to their high accuracy, precision, endurance, and speed. Robotic technologies have been integrated with existing network technologies to extend the range of functional values of these robots when deployed in unstructured environments while fostering the emergence of networked robotics during 90's [15]. The limitations have motivated the researchers to think of new form of efficient robotic systems i.e., "Cloud Robotics”. Cloud robotics may be described as a system that relies on the "Cloud Computing” [16] infra structure to access vast amount of processing power and data to support its operation [17]. That means not all sensing, computation, and memory is integrated into a single stand alone system as it was in case of networked robotics. Cloud Robotic systems often include some portion of its capacity for local processing for low-latency responses when network access is unavailable or unreliable i.e., offline. One example of Cloud Robotics is the Google self-driving car that indexes the Google maps, images, and other relevant information, collected by the satellites and the crowd sourced Clouds to facilitate accurate localization. Although, Cloud Robotics is benefited from big data analytics, cloud computing, human computation, and collaborative robot learning, it suffers from various issues such as inter operability, heterogeneity, time- varying network latency, security, multi-robot management, common infrastructure design, Quality-of-Service (QoS), and standardization [17,18]. Due to the IoRT's inherent virtues of qualitative handling of mentioned issues, it is envisaged that it will overcome these constraints, leading to more intelligent, collaborative, heterogeneous, efficient, self-adaptive, context aware, and yet cheaper robotic networks. An architecture of robotic internet of things is shown in Figure 1.

In the developed world, automated production line equipment for industrial robot automation equipment has become the mainstream and the future direction of development. Foreign car industry, electrical industry, engineering machinery industry has extensive use of industrial robots, such as automated production lines in order to guarantee the quality of products, to increase productivity, while avoiding a large number of occupational accidents. Global industrial robots used in many countries for nearly half a century of experience has shown that the popularization of industrial robots are automated production, improve production efficiency and effective means of promoting enterprise and development of social productive forces. Things with perception, information transmission, intelligence analysis and decision making characteristics such as through perception, equivalent to added features to industrial robots, vision, touch and even taste through network messaging, smart analysis and decision, equivalent to industrial robots human intelligence has given so that robots can do most people is needed to complete the work.

Conclusion

Internet of Robotic Things allows robots or robotic systems to connect, share, and disseminate the distributed computation resources, business activities, context information, and environmental data with each other, and to access novel knowledge and specialized skills not learned by them, all under a hood of sophisticated architectural framework. This opens a new horizon in the domain of connected robotics that we believe shall lead to fascinating futuristic developments. It indeed allows adapting into connected ecosystem where resource constraint deployment of inexpensive robots shall be leveraged by heterogeneous technologies, be it, communications network, processing units, different genre of devices, or clouds services. Enormous developments could be foreseen to get benefited from the IoRT approach such, SLAM, grasping, navigation, and many more that are beyond the discussion. In this paper, a novel Internet of Robotic Things architecture is proposed considering conjugation between recently grown IoT and robotics together.

For more open access journals please visit: Juniper publishers

For more articles please click on: Robotics & Automation Engineering Journal

Abstract

Keywords: Industrial robot; path planning; Internet of things (IoT)

Introduction

Conclusion

For more open access journals please visit: Juniper publishers

For more articles please click on: Robotics & Automation Engineering Journal

#Juniper Publishers #Open Acess Journals #Automation Engineering #Self-ruling robots #Robotics

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sciencespies · 5 years ago

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White House issues cybersecurity space policy

https://sciencespies.com/space/white-house-issues-cybersecurity-space-policy/

White House issues cybersecurity space policy

WASHINGTON — The White House released a new space policy directive Sept. 4 intended to improve cybersecurity of space systems.

Space Policy Directive (SPD) 5 is billed as the first comprehensive government policy related to cybersecurity for satellites and related systems, and outlines a set of best practices, but not firm requirements, that agencies and companies should follow to protect space systems from hacking and other cyber threats.

“What this SPD does is establish key cybersecurity principles to guide and serve as a foundation for the U.S. approach for cyber protection of space systems,” a senior administration official, speaking on background, said of the new policy.

The principles outlined in the policy are intended to be best practices that, in many cases, are already widely adopted. They include the use of authentication and encryption in command and control links to and from satellites, protection against jamming and spoofing of communications, and protection of ground systems and information processing systems.

The principles include use of “appropriate cybersecurity hygiene practices” and intrusion detection systems for all aspects of space system architectures. It also calls for managing supply chain risks in the form of hardware incorporated into a space system that could be compromised.

“Implementation of these principles, through rules, regulations, and guidance, should enhance space system cybersecurity, including through the consideration and adoption, where appropriate, of cybersecurity best practices and norms of behavior,” the policy states.

However, the official said there were no plans to direct agencies to codify these principles into regulations, such as in licensing requirements for commercial launches and satellites. “We’re very much trying not to be prescriptive,” the official said, citing evolving practices by both agencies and companies. “There’s a lot of motivation for companies to try to be cybersecure on their own.” The official added, though, that companies that don’t appear to be following the principles should expect to be asked why they are not doing so.

SPD-5 is part of a broader national cybersecurity initiative that includes a National Cyber Strategy published in September 2018 as well as the National Security Strategy of 2017. Space systems received special attention through this new directive because, although not itself considered a critical infrastructure, space does support many other terrestrial critical infrastructures.

“This directive builds upon those other actions,” said another senior administration official on background of SPD-5. “It’s another brick in the wall as we’re trying to build up our defenses to secure our nation in cybersecurity.”

One example of that space cybersecurity effort is the creation last year of the Space Information Sharing and Analysis Center (ISAC), an organization to exchange information on space-related cybersecurity threats. The Space ISAC is part of a broader group of similar organizations that focus on cybersecurity within a range of industries.

SPD-5 specifically endorses the Space ISAC. “They should also share threat, warning, and incident information within the space industry, using venues such as Information Sharing and Analysis Centers to the greatest extent possible, consistent with applicable law,” it says of satellite owners and operators.

Officials said other agencies will have roles in a space cybersecurity effort, including the Department of Homeland Security (DHS) and the National Institute of Standards and Technology. The Cybersecurity and Infrastructure Security Agency within DHS is establishing a cross-sector working group to enhance coordination among various industry sectors that use space. The officials said they see SPD-5 as a means of formally establishing and accelerating such coordination.

SPD-5 follows four other space policy directives issued by the Trump administration. SPD-1 in December 2017 directed NASA to return humans to the moon sustainably with commercial and international partners. SPD-2, in May 2018, outlined a range of regulatory reforms for commercial space activities. SPD-3, in June 2018, addressed space traffic management. SPD-4, in February 2019, called for the establishment of a Space Force.

#Space

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lovelyfantasticfart · 5 years ago

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android-for-life · 6 years ago

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"What our quantum computing milestone means"

Today, Nature published its 150th anniversary issue with the news that Google’s team of researchers have achieved a big breakthrough in quantum computing known as quantum supremacy. It’s a term of art that means we’ve used a quantum computer to solve a problem that would take a classical computer an impractically long amount of time. This moment represents a distinct milestone in our effort to harness the principles of quantum mechanics to solve computational problems.

While we’re excited for what’s ahead, we are also very humbled by the journey it took to get here. And we’re mindful of the wisdom left to us by the great Nobel Laureate Richard Feynman: “If you think you understand quantum mechanics, you don't understand quantum mechanics.”

In many ways, the exercise of building a quantum computer is one long lesson in everything we don’t yet understand about the world around us. While the universe operates fundamentally at a quantum level, human beings don’t experience it that way. In fact many principles of quantum mechanics directly contradict our surface level observations about nature. Yet the properties of quantum mechanics hold enormous potential for computing.

A bit in a classical computer can store information as a 0 or 1. A quantum bit—or qubit—can be both 0 and 1 at the same time, a property called superposition. So if you have two quantum bits, there are four possible states that you can put in superposition, and those grow exponentially. With 333 qubits there are 2^333, or 1.7x10^100—a Googol—computational states you can put in superposition, allowing a quantum computer to simultaneously explore a rich space of many possible solutions to a problem.

As we scale up the computational possibilities, we unlock new computations. To demonstrate supremacy, our quantum machine successfully performed a test computation in just 200 seconds that would have taken the best known algorithms in the most powerful supercomputers thousands of years to accomplish. We are able to achieve these enormous speeds only because of the quality of control we have over the qubits. Quantum computers are prone to errors, yet our experiment showed the ability to perform a computation with few enough errors at a large enough scale to outperform a classical computer.

For those of us working in science and technology, it’s the “hello world” moment we’ve been waiting for—the most meaningful milestone to date in the quest to make quantum computing a reality. But we have a long way to go between today’s lab experiments and tomorrow’s practical applications; it will be many years before we can implement a broader set of real-world applications.

We can think about today’s news in the context of building the first rocket that successfully left Earth’s gravity to touch the edge of space. At the time, some asked: Why go into space without getting anywhere useful? But it was a big first for science because it allowed humans to envision a totally different realm of travel … to the moon, to Mars, to galaxies beyond our own. It showed us what was possible and nudged the seemingly impossible into frame.

That’s what this milestone represents for the world of quantum computing: a moment of possibility.

It’s been a 13-year journey for Google to get here. In 2006, Google scientist Hartmut Neven started exploring the idea of how quantum computing might help our efforts to accelerate machine learning. This work led to the founding of our Google AI Quantum team, and in 2014, John Martinis and his team at the University of California at Santa Barbara joined us in our efforts to build a quantum computer. Two years later, Sergio Boixo published a paper that focused our efforts around the well-defined computational task of quantum supremacy, and now the team has built the world’s first quantum system that exceeds the capabilities of supercomputers for this particular computation.

We made these early bets because we believed—and still do—that quantum computing can accelerate solutions for some of the world's most pressing problems, from climate change to disease. Given that nature behaves quantum mechanically, quantum computing gives us the best possible chance of understanding and simulating the natural world at the molecular level. With this breakthrough we’re now one step closer to applying quantum computing to—for example—design more efficient batteries, create fertilizer using less energy, and figure out what molecules might make effective medicines.

Those applications are still many years away and we are committed to building the error-corrected quantum computer that will power these discoveries. We’ve always known that it would be a marathon, not a sprint. The thing about building something that hasn’t been proven yet is that there is no playbook. If the team needed a part, they had to invent it and build it themselves. And if it didn’t work—and often, it didn’t—they had to redesign and build it again.

One turning point came in October 2018, when the wildfires were raging in Southern California. I got a message that they would need to close down the Santa Barbara laboratory for a few days out of an abundance of caution. What I didn’t know was that the team had been experiencing one of those periods where progress had slowed to a crawl. The few days of forced vacation helped the team to reset and think about things differently, and a few months later, they made this breakthrough.

As with any advanced technology, quantum computing raises its own anxieties and questions. In thinking through these issues, we’re following a set of AI principles that we developed to help guide responsible innovation of advanced technology. For example, for many years the security community, with contributions from Google, has been working on post-quantum cryptography, and we’re optimistic we are ahead of the curve when it comes to future encryption concerns. We will continue to publish research and help the broader community develop quantum encryption algorithms using our open source framework Cirq. We’ve appreciated the National Science Foundation’s support for our researchers, and we’ve collaborated with NASA Ames and Oak Ridge National Laboratory on this latest result. As was the case with the Internet and machine learning, government support of basic research remains critical to long-term scientific and technological achievement.

I am excited about what quantum computing means for the future of Google and the world. Part of that optimism comes from the nature of the technology itself. You can trace the progress from the mega-computers of the 1950s to advances we’re making in artificial intelligence today to help people in their everyday lives.

Quantum computing will be a great complement to the work we do (and will continue to do) on classical computers. In many ways quantum brings computing full circle, giving us another way to speak the language of the universe and understand the world and humanity not just in 1s and 0s but in all of its states: beautiful, complex, and with limitless possibility.

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Sundar and quantum computing

Source : The Official Google Blog via Source information

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