Automation Engineering Quotes

If it hurts, do it more frequently, and bring the pain forward.

Automation is cost cutting by tightening the corners and not cutting them.

If automating everything makes people lazier and lazier, and laziness leads to stupidity, which it does for most people, judging by the current content circulating the social networks everywhere, except North Korea, where they don’t have any internet to speak of - at some point the Japanese robots, for which a market niche is currently being developed, with no concerns on how they should be designed to act in society or outside it - will have no choice, but to take everything over, to preserve us from ourselves…

Well...what you did in Rosewater County was far from insane. It was quite possibly the most important social experiment of our time, for it dealt on a very small scale with a problem whose queasy horrors will eventually be made world-wide by the sophistication of machines. The problem is this: How to love people who have no use? In time, almost all men and women will become worthless as producers of goods, food, services, and more machines, as sources of practical ideas in the areas of economics, engineering, and probably medicine, too. So - if we can't find reasons and methods for treasuring human beings because they are _human beings_, then we might as well, as has so often been suggested, rub them out.

In highly automated systems, the operator is often at the mercy of the system design and operational procedures.

Releasing software is too often an art; it should be an engineering discipline.

Robotics, however, is much more difficult. It requires a delicate interplay of mechanical engineering, perception AI, and fine-motor manipulation. These are all solvable problems, but not at nearly the speed at which pure software is being built to handle white-collar cognitive tasks. Once that robot is built, it must also be tested, sold, shipped, installed, and maintained on-site. Adjustments to the robot’s underlying algorithms can sometimes be made remotely, but any mechanical hiccups require hands-on work with the machine. All these frictions will slow down the pace of robotic automation.

Automated decision-making shatters the social safety net, criminalizes the poor, intensifies discrimination, and compromises our deepest national values. It reframes shared social decisions about who we are and who we want to be as systems engineering problems. And while the most sweeping digital decision-making tools are tested in what could be called “low rights environments” where there are few expectations of political accountability and transparency, systems first designed for the poor will eventually be used on everyone.

As black-box technologies become more widespread, there have been no shortage of demands for increased transparency. In 2016 the European Union's General Data Protection Regulation included in its stipulations the "right to an explanation," declaring that citizens have a right to know the reason behind the automated decisions that involve them. While no similar measure exists in the United States, the tech industry has become more amenable to paying lip service to "transparency" and "explainability," if only to build consumer trust. Some companies claim they have developed methods that work in reverse to suss out data points that may have triggered the machine's decisions—though these explanations are at best intelligent guesses. (Sam Ritchie, a former software engineer at Stripe, prefers the term "narratives," since the explanations are not a step-by-step breakdown of the algorithm's decision-making process but a hypothesis about reasoning tactics it may have used.) In some cases the explanations come from an entirely different system trained to generate responses that are meant to account convincingly, in semantic terms, for decisions the original machine made, when in truth the two systems are entirely autonomous and unrelated. These misleading explanations end up merely contributing another layer of opacity. "The problem is now exacerbated," writes the critic Kathrin Passig, "because even the existence of a lack of explanation is concealed.

One of those was Gary Bradski, an expert in machine vision at Intel Labs in Santa Clara. The company was the world’s largest chipmaker and had developed a manufacturing strategy called “copy exact,” a way of developing next-generation manufacturing techniques to make ever-smaller chips. Intel would develop a new technology at a prototype facility and then export that process to wherever it planned to produce the denser chips in volume. It was a system that required discipline, and Bradski was a bit of a “Wild Duck”—a term that IBM originally used to describe employees who refused to fly in formation—compared to typical engineers in Intel’s regimented semiconductor manufacturing culture. A refugee from the high-flying finance world of “quants” on the East Coast, Bradski arrived at Intel in 1996 and was forced to spend a year doing boring grunt work, like developing an image-processing software library for factory automation applications. After paying his dues, he was moved to the chipmaker’s research laboratory and started researching interesting projects. Bradski had grown up in Palo Alto before leaving to study physics and artificial intelligence at Berkeley and Boston University. He returned because he had been bitten by the Silicon Valley entrepreneurial bug.

The fascination with automation in part reflected the country’s mood in the immediate postwar period, including a solid ideological commitment to technological progress. Representatives of industry (along with their counterparts in science and engineering) captured this mood by championing automation as the next step in the development of new production machinery and American industrial prowess. These boosters quickly built up automation into “a new gospel of postwar economics,” lauding it as “a universal ideal” that would “revolutionize every area of industry.” 98 For example, the November 1946 issue of Fortune magazine focused on the prospects for “The Automatic Factory.” The issue included an article titled “Machines without Men” that envisioned a completely automated factory where virtually no human labor would be needed. 99 With visions of “transforming the entire manufacturing sector into a virtually labor-free enterprise,” factory owners in a range of industries began to introduce automation in the postwar period. 100 The auto industry moved with particular haste. After the massive wave of strikes in 1945–46, automakers seized on automation as a way to replace workers with machines. 101 As they converted back to civilian auto production after World War II, they took the opportunity to install new labor-saving automatic production equipment. The two largest automakers, Ford and General Motors, set the pace. General Motors introduced the first successful automated transfer line at its Buick engine plant in Flint in 1946 (shortly after a 113-day strike, the longest in the industry’s history). The next year Ford established an automation department (a Ford executive, Del S. Harder, is credited with coining the word “automation”). By October 1948 the department had approved $ 3 million in spending on 500 automated devices, with early company estimates predicting that these devices would result in a 20 percent productivity increase and the elimination of 1,000 jobs. Through the late 1940s and 1950s Ford led the way in what became known as “Detroit automation,” undertaking an expensive automation program, which it carried out in concert with the company’s plans to decentralize operations away from the city. A major component of this effort was the Ford plant in the Cleveland suburb of Brook Park, a $ 2 billion engine-making complex that attracted visitors from government, industry, and labor and became a national symbol of automation in the 1950s. 102

Which backends of this server are considered “in the critical path,” and why? What aspects of this server could be simplified or automated? Where do you think the first bottleneck is in this architecture? If that bottleneck were to be saturated, what steps could you take to alleviate it?

Don’t be afraid to provide white glove customer support for early adopters to help them through the onboarding process. Sometimes automation also entails a host of emotional concerns, such as fear that someone’s job will be replaced by a shell script. By working one-on-one with early users, you can address those fears personally, and demonstrate that rather than owning the toil of performing a tedious task manually, the team instead owns the configurations, processes, and ultimate results of their technical work. Later adopters are convinced by the happy examples of early adopters.

Google has also benefitted from being at the inflection point of software moving from massive client-side binaries with multi-year release cycles to cloud-based services that are released every few weeks, days, or hours.1 This confluence of happy circumstances has endowed us with some similarities to the utopian software development process. Google SWEs are feature developers, responsible for building components that ship to customers. They write feature code and unit test code for those features. Google SETs are test developers, responsible for assisting SWEs with the unit test portion of their work and also in writing larger test frameworks to assist SWEs in writing small and medium tests to assess broader quality concerns. Google TEs are user developers, responsible for taking the users’ perspectives in all things that have to do with quality. From a development perspective, they create automation for user scenarios and from a product perspective, they assess the overall coverage and effectiveness of the ensemble of testing activity performed by the other engineering roles. It is not utopia, but it is our best attempt at achieving it in a practical way where real-world concerns have a way of disrupting best intentions in the most unforeseen and unforgiving way.

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factory automation services Our company majorly dedicated to serving clients having biodiesel plants. Our company has more than 60 engineers who are highly experienced and proficient. Moreover, all our professionals are expertise in different niche such as electrical, process, application, project, mechanical, chemical, civil, structural and controls too. Our professionals do the best possible job to ensure a favorable outcome. For factory automation services, we build and maintain the biodiesel plant. In this context, our experts follow the biodiesel plant construction standards that include plant size determination, selecting an appropriate site, permitting, biodiesel plant engineering, determining your equipment needs, assistance which plant installation, quality and BQ-9000 considerations, plant start up and training, plant management and planning for the future.

intech automation is the study regarding automotive engineering for light and heavy vehicles in Brisbane Australia. Intech.edu.au is availing the courses automation.

We cannot achieve deployments on demand if each of our production code deployments take weeks or months to perform (i.e., each deployment requires 1,300 manual, error-prone steps involving up to three hundred engineers). The countermeasure is to automate our deployments as much as possible, with the goal of being completely automated so they can be done self-service by any developer.

A good chief executive is essentially a hard-to-automate decision engine, not unlike IBM’s Jeopardy!-playing Watson system. They have built up a hard-won repository of experience and have honed and proved an instinct for their market. They’re then presented inputs throughout the day—in the form of e-mails, meetings, site visits, and the like—that they must process and act on. To ask a CEO to spend four hours thinking deeply about a single problem is a waste of what makes him or her valuable. It’s better to hire three smart subordinates to think deeply about the problem and then bring their solutions to the executive for a final decision.

The National Transportation Safety Board’s recent speed report suggests five approaches.15 First, they want to lower speed limits. Cool. Second, they want to use “data-driven approaches for speed enforcement” in combination with their third approach, automated enforcement. OK. Fourth on the list is what they call “intelligent speed adaptation.” This term refers to things like onboard warnings when the driver speeds, but also includes using technology to limit car speeds in particular locations and on specific streets. Sounding better. Last, they say we need to do better when it comes to exercising “national leadership,” which basically means we need more funding and more education. I

Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.

Digital marketing expert Angela Liberatore stresses the importance of data analytics, which lets businesses track how well their digital campaigns are doing and make changes if needed. Conversion Rate Optimization (CRO) focuses on improving the website to turn visitors into customers. Mobile marketing makes sure content works well on smartphones and tablets, as more people use these devices. Lastly, marketing automation tools help save time by automatically sending emails or posting on social media. All of these parts together create a strong digital marketing strategy that helps businesses grow, engage customers, and increase sales in today’s digital world. Digital marketing includes several important parts that work together to help businesses reach and connect with their audience online. One part is search engine optimization (SEO), which helps websites show up higher on search engines like Google. Content marketing is another part, where useful things like blog posts, videos, and infographics are made to attract and interest customers. Social media marketing uses platforms like Facebook, Instagram, and LinkedIn to increase brand awareness and build a community. Email marketing allows businesses to send personalized messages directly to their audience. Paid advertising, like Pay-Per-Click (PPC) ads, brings quick traffic to websites. Influencer marketing uses the popularity of influencers to promote products or services.

A key characteristic of the engineering culture is that the individual engineer’s commitment is to technical challenge rather than to a given company. There is no intrinsic loyalty to an employer as such. An employer is good only for providing the sandbox in which to play. If there is no challenge or if resources fail to be provided, the engineer will seek employment elsewhere. In the engineering culture, people, organization, and bureaucracy are constraints to be overcome. In the ideal organization everything is automated so that people cannot screw it up. There is a joke that says it all. A plant is being managed by one man and one dog. It is the job of the man to feed the dog, and it is the job of the dog to keep the man from touching the equipment. Or, as two Boeing engineers were overheard to say during a landing at Seattle, “What a waste it is to have those people in the cockpit when the plane could land itself perfectly well.” Just as there is no loyalty to an employer, there is no loyalty to the customer. As we will see later, if trade-offs had to be made between building the next generation of “fun” computers and meeting the needs of “dumb” customers who wanted turnkey products, the engineers at DEC always opted for technological advancement and paid attention only to those customers who provided a technical challenge.

If the steam engine freed human beings from feudal bondage to pursue material self-interest in the capitalist marketplace, the Internet of Things frees human beings from the market economy to pursue nonmaterial shared interests on the Collaborative Commons. Many—but not all—of our basic material needs will be met for nearly free in a near zero marginal cost society. Intelligent technology will do most of the heavy lifting in an economy centered on abundance rather than scarcity. A half century from now, our grandchildren are likely to look back at the era of mass employment in the market with the same sense of utter disbelief as we look upon slavery and serfdom in former times. The very idea that a human being’s worth was measured almost exclusively by his or her productive output of goods and services and material wealth will seem primitive, even barbaric, and be regarded as a terrible loss of human value to our progeny living in a highly automated world where much of life is lived on the Collaborative Commons.

At the Automatica robot and automation fair in Munich this week the organisers devoted a whole section to so-called “service robots”. Scientists at the Fraunhofer Institute for manufacturing, engineering and automation demonstrated a Care-O-bot that sweeps office floors and empties bins. Pal Robotics showed Stockbot, which walks the aisles in a shop or warehouse to check inventory at night.

s s i o n o f R a t i o n a l S o f t w a r e C o r p o r a t i o n i s t o e n s u r e t h e s u c c e s s o f c u s t o m e r s c o n s t r u c t i n g t h e s o f t w a r e s y s t e m s t h a t t h e y d e p e n d o n . We enable our customers to achieve their business objectives by turning software into a source of competitive advantage, speeding time-to-market, reducing the risk of failure, and improving software quality. We fulfill our mission with the Rational ApproachTM, a comprehensive softwareengineering solution consisting of three elements: • A configurable set of processes and techniques for the development of software, based on iterative development, object modeling, and an architectural approach to software reuse. • An integrated family of application construction tools that automate the Rational Approach throughout the software lifecycle. • Technical consulting services delivered by our worldwide field organization of software engineers and technical sales professionals. Our customers include businesses in the Asia/Pacific region, Europe, and North America that are leaders in leveraging semiconductor, communications, and software technologies to achieve their business objectives. We serve customers in a diverse range of industries, such as telecommunications

o n o f R a t i o n a l S o f t w a r e C o r p o r a t i o n i s t o e n s u r e t h e s u c c e s s o f c u s t o m e r s c o n s t r u c t i n g t h e s o f t w a r e s y s t e m s t h a t t h e y d e p e n d o n . We enable our customers to achieve their business objectives by turning software into a source of competitive advantage, speeding time-to-market, reducing the risk of failure, and improving software quality. We fulfill our mission with the Rational ApproachTM, a comprehensive softwareengineering solution consisting of three elements: • A configurable set of processes and techniques for the development of software, based on iterative development, object modeling, and an architectural approach to software reuse. • An integrated family of application construction tools that automate the Rational Approach throughout the software lifecycle. • Technical consulting services delivered by our worldwide field organization of software engineers and technical sales professionals. Our customers include businesses in the Asia/Pacific region, Europe, and North America that are leaders in leveraging semiconductor, communications, and software technologies to achieve their business objectives. We serve customers in a diverse range of industries, such as telecommunications, banking and financial services, manufacturing, transportation, aerospace, and defense.They construct software applications for a wide range of platforms, from microprocessors embedded in telephone switching systems to enterprisewide information systems running on company-specific intranets. Rational Software Corporation is traded on the NASDAQ system under the symbol RATL.1

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Another recent study, this one on academic research, provides real-world evidence of the way the tools we use to sift information online influence our mental habits and frame our thinking. James Evans, a sociologist at the University of Chicago, assembled an enormous database on 34 million scholarly articles published in academic journals from 1945 through 2005. He analyzed the citations included in the articles to see if patterns of citation, and hence of research, have changed as journals have shifted from being printed on paper to being published online. Considering how much easier it is to search digital text than printed text, the common assumption has been that making journals available on the Net would significantly broaden the scope of scholarly research, leading to a much more diverse set of citations. But that’s not at all what Evans discovered. As more journals moved online, scholars actually cited fewer articles than they had before. And as old issues of printed journals were digitized and uploaded to the Web, scholars cited more recent articles with increasing frequency. A broadening of available information led, as Evans described it, to a “narrowing of science and scholarship.”31 In explaining the counterintuitive findings in a 2008 Science article, Evans noted that automated information-filtering tools, such as search engines, tend to serve as amplifiers of popularity, quickly establishing and then continually reinforcing a consensus about what information is important and what isn’t. The ease of following hyperlinks, moreover, leads online researchers to “bypass many of the marginally related articles that print researchers” would routinely skim as they flipped through the pages of a journal or a book. The quicker that scholars are able to “find prevailing opinion,” wrote Evans, the more likely they are “to follow it, leading to more citations referencing fewer articles.” Though much less efficient than searching the Web, old-fashioned library research probably served to widen scholars’ horizons: “By drawing researchers through unrelated articles, print browsing and perusal may have facilitated broader comparisons and led researchers into the past.”32 The easy way may not always be the best way, but the easy way is the way our computers and search engines encourage us to take.

Best4Automation is the industry marketplace, which combines all the advantages of a modern on-line shop with the fast logistics of large manufacturers. Our well-known manufacturers and partners in automation technology such as Schmersal, Murrplastik, wenglor sensoric, Murrelektronik, Stego, Siemens, Fibox and Captron cover a wide spectrum of electronic and electromechanical components for mechanical engineering, plant construction and maintenance.

We will actively manage this technical debt by ensuring that we invest at least 20% of all Development and Operations cycles on refactoring, investing in automation work and architecture and non-functional requirements (NFRs, sometimes referred to as the “ilities”), such as maintainability, manageability, scalability, reliability, testability, deployability, and security. Figure 11: Invest 20% of cycles on those that create positive, user-invisible value (Source: “Machine Learning and Technical Debt with D. Sculley,” Software Engineering Daily podcast, November 17, 2015,

To achieve market orientation, we won’t do a large, top-down reorganization, which often creates large amounts of disruption, fear, and paralysis. Instead, we will embed the functional engineers and skills (e.g., Ops, QA, Infosec) into each service team, or provide their capabilities to teams through automated self-service platforms that provide production-like environments, initiate automated tests, or perform deployments.

Algorithmic profits Algorithmic marketing is allowing companies to do things they couldn’t do before, and some early signs show it can deliver big value, especially in financial or information services. In North America, Amazon.com grew 30 to 40 percent, quarter after quarter, throughout the United States’ 2008-2012 recession, while other major retailers shrank or went out of business. From 2006 to 2010, Amazon spent 5.6 percent of its sales revenue on IT, while rivals Target and Best Buy spent 1.3% and 0.5%, respectively. That investment and focus has yielded increasingly sophisticated recommendation engines that deliver over 35 percent of all sales, an automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that are a key component of its best-in-class customer satisfaction, and dynamic pricing systems that crawl the Web and react to competitor pricing and stock levels by altering prices on Amazon.com, in some cases every 15 seconds.

Terry Guo of Foxconn has been aggressively installing hundreds of thousands of robots to replace an equivalent number of human workers. He says he plans to buy millions more robots in the coming years. The first wave is going into factories in China and Taiwan, but once an industry becomes largely automated, the case for locating a factory in a low-wage country becomes less compelling. There may still be logistical advantages if the local business ecosystem is strong, making it easier to get spare parts, supplies, and custom components. But over time inertia may be overcome by the advantages of reducing transit times for finished products and being closer to customers, engineers and designers, educated workers, or even regions where the rule of law is strong. This can bring manufacturing back to America, as entrepreneurs like Rod Brooks have been emphasizing. A

DevOps and its resulting technical, architectural, and cultural practices represent a convergence of many philosophical and management movements (including): Lean, Theory of Constraints, Toyota production system, resilience engineering, learning organizations, safety culture, Human factors, high-trust management cultures, servant leadership, organizational change management, and Agile methods.

engage in development tasks, because the service basically runs and repairs itself: we want systems that are automatic, not just automated. In practice, scale and new features keep SREs on their toes.

The talent required within the CoE is wide and ranges from business and operations excellence to risk and IT departments. According to McKinsey’s survey, the CoE of top-performing companies includes a large variety of profiles such as delivery managers, data scientists, data engineers, workflow integrators, system architects, developers, and, most critically, translators and business analysts.152 A

The more you produce, the more you have to read. Automated code generation only makes matters worse. As Martin Fowler writes about low code quality: “Even small changes require programmers to understand large areas of code, code that’s difficult to understand.” [32] Code that’s difficult to understand slows you down. On the other hand, every minute you invest in making the code easier to understand pays itself back tenfold.

create their own OKRs for their own organization. For example, the design department might have objectives related to moving to a responsive design; the engineering department might have objectives related to improving the scalability and performance of the architecture; and the quality department might have objectives relating to the test and release automation. The problem is that the individual members of each of these functional departments are the actual members of a cross‐functional product team. The product team has business‐related objectives (for example, to reduce the customer acquisition cost, to increase the number of daily active users, or to reduce the time to onboard a new customer), but each person on the team may have their own set of objectives that cascade down through their functional manager. Imagine if the engineers were told to spend their time on re‐platforming, the designers on moving to a responsive design, and QA on retooling. While each of these may be worthy activities, the chances of solving the business problems that the cross‐functional teams were created to solve are not high.

A good chief executive is essentially a hard-to-automate decision engine,

Miguel Antunes, R&D Principle Software Engineer at OutSystems, a low-code platform vendor, relayed an example of this very challenge. Their Engineering Productivity team at OutSystems was five years old. The team’s mission was to help product teams run their builds efficiently, maintain infrastructure, and improve test execution. The team kept growing and took on extra responsibilities around continuous integration (CI), continuous delivery (CD), and infrastructure automation.

Engineering autonomy in healthcare solutions must be a human-centered process. Engineers should not conflate automation, where AI can consistently repeat a task, with AI having autonomy. Humans must remain in the loop.

The next billion-dollar startup will only have three employees.” The culture of that startup would be “AI first,” and it would use autonomous AI agents to get work done. All marketing and sales would be automated via AI bots, and the three employees would be: The CEO, who would handle vision and purpose and lead public-facing marketing. She would also code and be involved in engineering. The Product Lead, who would interface with customers and team to manage the product roadmap and drive development The Operations Lead, who would be responsible for the outcome of the AI bots and handle finance and legal and smooth operations. We

Joseph A. Schumpeter, the Harvard economist who in 1943 published the iconic Capitalism, Socialism, and Democracy. The seventh chapter of that work, entitled “The Process of Creative Destruction,” is for many academics a sacred text. “The process of creative destruction,” Schumpeter writes, “is the essential fact about capitalism. It is what capitalism consists in and what every capitalist concern has got to live in.” Creative destruction is an elegantly simple idea describing the industrial mutation of old structures into new ones. The department store evolves from and “creatively destructs” the country store; the auto industry evolves from and replaces the horse and buggy business, automation makes many factory and farm jobs obsolete but creates new jobs in information technology, engineering, healthcare, and biotech.

There were girls here with fire-engine-red lips, and boys with such pronounced eyeliner that it looked permanent. And as you moved back to the dancefloor, the music overwhelmed you: Yellow Magic Orchestra, Space, Ultravox, Eno, Fad Gadget, Sparks, Grace Jones, Thomas Leer, Cerrone, Psychedelic Furs and Bowie, obviously, lots of Bowie. On and on it went, a constant swirl of automated Germanic beats – hard-edged European disco, synth-led, bass-heavy … all very angular: Kraftwerk and Gina X, Giorgio Moroder and Donna Summer, and some early Roxy Music.

The advance of computerization and automation technologies has meant that many medium-skilled jobs—clerks, travel agents, bookkeepers, and factory workers—have been replaced with new technologies. New jobs have arisen in their place, but those jobs are often one of two types: either they are high-skilled jobs, such as engineers, programmers, managers, and designers, or they are lower-skilled jobs such as retail workers, cleaners, or customer service agents. Exacerbating the trends caused by computers and robots are globalization and regionalization. As medium-skilled technical work is outsourced to workers in developing nations, many of those jobs are disappearing at home. Lower-skilled jobs, which often require face-to-face contact or social knowledge in the form of cultural or language abilities, are likely to remain. Higher-skilled work is also more resistant to shipping overseas because of the benefits of coordination with management and the market. Think of Apple’s tagline on all of its iPhones: “Designed in California. Made in China.” Design and management stay; manufacturing goes.

Despite Noetic Science’s use of cutting-edge technologies, the discoveries themselves were far more mystical than the cold, high-tech machines that were producing them. The stuff of magic and myth was fast becoming reality as the shocking new data poured in, all of it supporting the basic ideology of Noetic Science—the untapped potential of the human mind. The overall thesis was simple: We have barely scratched the surface of our mental and spiritual capabilities. Experiments at facilities like the Institute of Noetic Sciences (IONS) in California and the Princeton Engineering Anomalies Research Lab (PEAR) had categorically proven that human thought, if properly focused, had the ability to affect and change physical mass. Their experiments were no “spoon-bending” parlor tricks, but rather highly controlled inquiries that all produced the same extraordinary result: our thoughts actually interacted with the physical world, whether or not we knew it, effecting change all the way down to the subatomic realm. Mind over matter. In 2001, in the hours following the horrifying events of September 11, the field of Noetic Science made a quantum leap forward. Four scientists discovered that as the frightened world came together and focused in shared grief on this single tragedy, the outputs of thirty-seven different Random Event Generators around the world suddenly became significantly less random. Somehow, the oneness of this shared experience, the coalescing of millions of minds, had affected the randomizing function of these machines, organizing their outputs and bringing order from chaos. The shocking discovery, it seemed, paralleled the ancient spiritual belief in a “cosmic consciousness”—a vast coalescing of human intention that was actually capable of interacting with physical matter. Recently, studies in mass meditation and prayer had produced similar results in Random Event Generators, fueling the claim that human consciousness, as Noetic author Lynne McTaggart described it, was a substance outside the confines of the body . . . a highly ordered energy capable of changing the physical world. Katherine had been fascinated by McTaggart’s book The Intention Experiment, and her global, Web-based study—theintentionexperiment.com—aimed at discovering how human intention could affect the world. A handful of other progressive texts had also piqued Katherine’s interest. From this foundation, Katherine Solomon’s research had vaulted forward, proving that “focused thought” could affect literally anything—the growth rate of plants, the direction that fish swam in a bowl, the manner in which cells divided in a petri dish, the synchronization of separately automated systems, and the chemical reactions in one’s own body. Even the crystalline structure of a newly forming solid was rendered mutable by one’s mind; Katherine had created beautifully symmetrical ice crystals by sending loving thoughts to a glass of water as it froze. Incredibly, the converse was also true: when she sent negative, polluting thoughts to the water, the ice crystals froze in chaotic, fractured forms. Human thought can literally transform the physical world.

The machine, called a punch-card tabulator, had been invented in the early 1880s by an engineer named Herman Hollerith for the purpose of automating the US census.

The road to EPIC was not frictionless, however, and required constant symbolic investments in the capacity of the 'technocrats' and their inventions. As Mitford-Slate noted in interview, technologists had to pass numerous hurdles, in addition to the technical difficulties of building systems that not available off the shelf, they 'had to sell [their ideas] to me and I had to sell them to a lot of people who didn't understand technology whatsoever.

Like most visionary utopias, though. IDN (Integrated Data Network) was never to be. Perhaps it was simply too ambitious a project: infrastructures seldom respond to a single vision or a master plan, as Paul Edwards (2010) writes, and conjuring up a platform that would serve the entire marketplace was an almost Quixotic task. Infrastructures emerge not through planning and calculated foresight, but through the meandering paths of history, in the mangle of making, tinkering, and wrestling with the obduracy of organizations, practices, and their installed base. The system eventually introduced for Big Bang reflected this fragility and contingency of infrastructures: it was the creative result of reshaping legacy devices into a system that did the job for the time being. A band-aid. A product of creative, recombinant bricolage.

A somewhat provocative example of the interconnections between the gaming industry and finance. A technologist working for a large London hedge fund hinted this to me in interview. Trained in computer science and engineering, this interviewee first worked as a network programmer for large online multiplayer games. His greatest challenge was the fact that the Internet is not instantaneous: when a player sends a command to execute in action, it takes time for the signal to reach the computer server and interact with the commands of other players. For the game to be realistic, such delays have to be taken into account when rendering reality on the screen. The challenge for the network programmer is to make these asymmetries as invisible as possible so that the game seem 'equitable to everyone.' The problem is similar in finance, where the physical distance from the stock exchange's matching engines matters tremendously, requiring a similar solution to the problem of latency: simulating the most likely state of the order book on the firm's computers in order to estimate the most advantageous strategies or the firm's trading algorithms. Gaming and finance are linked not through an institutional imperative of culture or capital - or even a strategy, as such - but rather through the more mundane and lowly problems of how to fairly manage latency and connectivity.

Cartello Capehart provides search engine marketing, content promotion, influencer marketing, content automated, strategy marketing, and e-commerce marketing.

In 2000 a group of computer scientists and engineers at Georgia Tech collaborated on a project called the “Aware Home.”4 It was meant to be a “living laboratory” for the study of “ubiquitous computing.” They imagined a “human-home symbiosis” in which many animate and inanimate processes would be captured by an elaborate network of “context aware sensors” embedded in the house and by wearable computers worn by the home’s occupants. The design called for an “automated wireless collaboration” between the platform that hosted personal information from the occupants’ wearables and a second one that hosted the environmental information from the sensors.

In typical DevOps transformations, as we progress from deployment lead times measured in months or quarters to lead times measured in minutes, the constraint usually follows this progression: Environment creation: We cannot achieve deployments on-demand if we always have to wait weeks or months for production or test environments. The countermeasure is to create environments that are on demand and completely self-serviced, so that they are always available when we need them. Code deployment: We cannot achieve deployments on demand if each of our production code deployments take weeks or months to perform (i.e., each deployment requires 1,300 manual, error-prone steps, involving up to three hundred engineers). The countermeasure is to automate our deployments as much as possible, with the goal of being completely automated so they can be done self-service by any developer. Test setup and run: We cannot achieve deployments on demand if every code deployment requires two weeks to set up our test environments and data sets, and another four weeks to manually execute all our regression tests. The countermeasure is to automate our tests so we can execute deployments safely and to parallelize them so the test rate can keep up with our code development rate. Overly tight architecture: We cannot achieve deployments on demand if overly tight architecture means that every time we want to make a code change we have to send our engineers to scores of committee meetings in order to get permission to make our changes. Our countermeasure is to create more loosely-coupled architecture so that changes can be made safely and with more autonomy, increasing developer productivity.

Best practices in this domain use automation to accomplish the following: Implementing progressive rollouts Quickly and accurately detecting problems Rolling back changes safely when problems arise

Even if the total number of jobs does not fall, the current wave of automation tends to displace jobs that require some skills (bookkeepers and accountants) and increase the demand, either for very skilled workers (software programmers for the machines) or for totally unskilled workers (dog walkers, for example), which are both much more difficult to replace with a machine. As software engineers become richer, they have more money to hire dog walkers, who have become relatively cheaper over time, since there is little alternative employment for those with no college education. Even if people remain employed, this leads to an increase in inequality, with higher wages at the top and everyone else pushed to jobs requiring no specific skills; jobs where wages and working conditions can be really bad. This accentuates a trend that has taken place since the 1980s. Workers without a college education have increasingly been pushed out of mid-skill jobs, such as clerical and administrative roles, into low-skill tasks, such as cleaning and security.

Automation is a force multiplier, not a panacea.

Mentok Healthcare manufacturer is the best Derma chair supplier in India. It is designed by the best skilled engineers. Mentok healthcare designed the best quality of luxurious comfort with the automated controls. It provides the greater benefits with the amplifiedcare of the patients.

Activities that were previously considered mere sources of entertainment or spiritual growth are now being seen as engines of economic growth.

The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems.

Pexpect has the ability to interact with programs, watch for expected outputs, and then respond based on expected outputs. This makes it an excellent tool of choice for automating the process of brute forcing SSH user credentials.

fulfill our mission with the Rational ApproachTM, a comprehensive softwareengineering solution consisting of three elements: • A configurable set of processes and techniques for the development of software, based on iterative development, object modeling, and an architectural approach to software reuse. • An integrated family of application construction tools that automate the Rational Approach throughout the software lifecycle. • Technical consulting services delivered by our worldwide field organization of software engineers and technical sales professionals. Our customers include businesses in the Asia/Pacific region, Europe, and North America that are leaders in leveraging semiconductor, communications, and software technologies to achieve their business objectives. We serve customers in a diverse range of industries, such as telecommunications, banking and financial services, manufacturing, transportation, aerospace, and defense.They construct software applications for a wide range of platforms, from microprocessors embedded in telephone switching systems to enterprisewide information systems running on company-specific intranets. Rational Software Corporation is traded on the NASDAQ system under the symbol RATL.1

While there is no formula for cognitive load, we can assess the number and relative complexity (internal to the organization) of domains for which a given team is responsible. The Engineering Productivity team at OutSystems that we mentioned in Chapter 1 realized that the different domains they were responsible for (build and continuous integration, continuous delivery, test automation, and infrastructure automation) had caused them to become overloaded. The team was constantly faced with too much work and context switching prevailed, with tasks coming in from different product areas simultaneously. There was a general sense in the team that they lacked sufficient domain knowledge, but they had no time to invest in acquiring it. In fact, most of their cognitive load was extraneous, leaving very little capacity for value-add intrinsic or germane cognitive load. The team made a bold decision to split into microteams, each responsible for a single domain/product area: IDE productivity, platform-server productivity, and infrastructure automation. The two productivity microteams were aligned (and colocated) with the respective product areas (IDE and platform server). Changes that overlapped domains were infrequent; therefore, the previous single-team model was optimizing for the exceptions rather than the rule. With the new structure, the teams collaborated closely (even creating temporary microteams when necessary) on cross-domain issues that required a period of solution discovery but not as a permanent structure. After only a few months, the results were above their best expectations. Motivation went up as each microteam could now focus on mastering a single domain (plus they didn’t have a lead anymore, empowering team decisions). The mission for each team was clear, with less context switching and frequent intra-team communication (thanks to a single shared purpose rather than a collection of purposes). Overall, the flow and quality of the work (in terms of fitness of the solutions for product teams) increased significantly.

One of the benefits of microservices, for example, is that it allows many teams to contribute to the same system independently from one another. Whereas a monolith would require coordination in the form of code reviews—a personal, direct interaction between colleagues—service-oriented architecture scales the same guarantees with process. Engineers document contracts and protocols; automation is applied to ensure that those contracts are not violated, and it prescribes a course of action if they are. For that reason, engineers who want to “jump ahead” and build something with microservices from the beginning often struggle. The level of complexity and abstraction is out of sync with the communication patterns of the organization.

My time at Amazon made me a big believer in the power of process automation to make workflows simpler and more productive. When a process is automated, it’s not only easier to scale but also simpler to measure while manual effort, even when it begins at a seemingly insignificant level, can evolve into an expensive, non-scalable, and non-real-time capability. That is why automation, algorithms, and technology architecture are the engines behind game-changing platform businesses such as Kindle, Amazon Mechanical Turk, Third-Party Sellers, Fulfillment by Amazon, and Amazon Web Services.

How do we make our systems reliable, in spite of unreliable humans? The best systems combine several approaches: Design systems in a way that minimizes opportunities for error. For example, well-designed abstractions, APIs, and admin interfaces make it easy to do “the right thing” and discourage “the wrong thing.” However, if the interfaces are too restrictive people will work around them, negating their benefit, so this is a tricky balance to get right. Decouple the places where people make the most mistakes from the places where they can cause failures. In particular, provide fully featured non-production sandbox environments where people can explore and experiment safely, using real data, without affecting real users. Test thoroughly at all levels, from unit tests to whole-system integration tests and manual tests [3]. Automated testing is widely used, well understood, and especially valuable for covering corner cases that rarely arise in normal operation. Allow quick and easy recovery from human errors, to minimize the impact in the case of a failure. For example, make it fast to roll back configuration changes, roll out new code gradually (so that any unexpected bugs affect only a small subset of users), and provide tools to recompute data (in case it turns out that the old computation was incorrect). Set up detailed and clear monitoring, such as performance metrics and error rates. In other engineering disciplines this is referred to as telemetry. (Once a rocket has left the ground, telemetry is essential for tracking what is happening, and for understanding failures [14].) Monitoring can show us early warning signals and allow us to check whether any assumptions or constraints are being violated. When a problem occurs, metrics can be invaluable in diagnosing the issue. Implement good management practices and training—a complex and important aspect, and beyond the scope of this book.