Automation Process Quotes

When you repeat a behavior over time, your brain learns to automate the process. It's more energy efficient to automatically do something than to manually weigh your options and decide to act the same way every time. When you make a decision very quickly, it is probably from habit, even if you think you're actively deciding. In a way, you made the decision a while ago.

As a new entrepreneur, you're probably gonna have to hustle hard to get things going at first. But as the business grows and becomes more established, that unrefined hustle should be replaced by automated profit-producing processes and systems. Hustle is good as a temporary mode of operating, but it's unsustainable long term and unprofitable long term.

However, one intriguing shift that suggests there are limits to automation was the recent decision by Toyota to systematically put working humans back into the manufacturing process. In quality and manufacturing on a mass scale, Toyota has been a global leader in automation technologies based on the corporate philosophy of kaizen (Japanese for “good change”) or continuous improvement. After pushing its automation processes toward lights-out manufacturing, the company realized that automated factories do not improve themselves. Once Toyota had extraordinary craftsmen that were known as Kami-sama, or “gods” who had the ability to make anything, according to Toyota president Akio Toyoda.49 The craftsmen also had the human ability to act creatively and thus improve the manufacturing process. Now, to add flexibility and creativity back into their factories, Toyota chose to restore a hundred “manual-intensive” workspaces.

1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

He was thinking about automated teller machines. The term was aged and burdened by its own historical memory. It worked at cross-purposes, unable to escape the inferences of fuddled human personnel and jerky moving parts. The term was part of the process that the device was meant to replace. It was anti-futuristic, so cumbrous and mechanical that even the acronym seemed dated.

A Checklist is an Externalized, predefined Standard Operating Procedure for completing a specific task. Creating a Checklist is enormously valuable for two reasons. First, Checklisting will help you define a System for a process that hasn’t yet been formalized—once the Checklist has been created, it’s easier to see how to improve or Automate the system. Second, using Checklists as a normal part of working can help ensure that you don’t forget to handle important steps that are easily overlooked when things get busy.

We’ve now established three things. First, we don’t need willpower when we don’t desire to do something, and it isn’t a thing some of us have in excess and some of us don’t have at all. It’s a cognitive function, like deciding what to eat or solving a math equation or remembering your dad’s birthday. Willpower is also a limited resource; we have more of it at the beginning of the day and lose it throughout the day as we use it to write emails or not eat cookies. When you automate some decisions or processes (through forming habits), you free up more brain power. Second, for us to make and change a habit, we need a cue, a routine, and a reward, and enough repetition must occur for the process to move from something we have to think about consciously (“I need to brush my teeth,” “I don’t want to drink wine”) to something we do naturally, automatically. Third, throughout the day, we must manage our energy so that we don’t blow out and end up in the place of no return—a hyperaroused state where the only thing that can bring us down is a glass (or a bottle) of wine. Maybe

chickens are grown to standardized sizes so as to make them compatible with automated slaughtering and processing.

Always wait until the end of designing a process—after you have questioned all the requirements and deleted unnecessary parts—before you introduce automation.

The more automated our experience becomes, the less involved we are in the art of living.

You may not be able to automate the whole process, but you can make the first action mindless. Make it easy to start and the rest will follow.

I began looking for these four: Smart. It doesn’t mean high IQ (although that’s great), it means disposed toward learning. If there’s a best practice anywhere, adopt it. We want to turn as much as possible into a routine so we can focus on the few things that require human intelligence and creativity. A good interview question for this is: “Tell me about the last significant thing you learned about how to do your job better.” Or you might ask a candidate: “What’s something that you’ve automated? What’s a process you’ve had to tear down at a company?” Humble. I don’t mean meek or unambitious, I mean being humble in the way that Steph Curry is humble. If you’re humble, people want you to succeed. If you’re selfish, they want you to fail. It also gives you the capacity for self-awareness, so you can actually learn and be smart. Humility is foundational like that. It is also essential for the kind of collaboration we want at Slack. Hardworking. It does not mean long hours. You can go home and take care of your family, but when you’re here, you’re disciplined, professional, and focused. You should also be competitive, determined, resourceful, resilient, and gritty. Take this job as an opportunity to do the best work of your life. Collaborative. It’s not submissive, not deferential—in fact it’s kind of the opposite. In our culture, being collaborative means providing leadership from everywhere. I’m taking responsibility for the health of this meeting. If there’s a lack of trust, I’m going to address that. If the goals are unclear, I’m going to deal with that. We’re all interested in getting better and everyone should take responsibility for that. If everyone’s collaborative in that sense, the responsibility for team performance is shared. Collaborative people know that success is limited by the worst performers, so they are either going to elevate them or have a serious conversation. This one is easy to corroborate with references, and in an interview you can ask, “Tell me about a situation in your last company where something was substandard and you helped to fix it.

Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.

The optimists claim that through the course of evolution the nervous system has become adept at “chunking” bits of information so that processing capacity is constantly expanded. Simple functions like adding a column of numbers or driving a car grow to be automated, leaving the mind free to deal with more data. We

A use case is a description of the way that an automated system is used. It specifies the input to be provided by the user, the output to be returned to the user, and the processing steps involved in producing that output. A use case describes application-specific business rules as opposed to the Critical Business Rules within the Entities.

So, when should you think about automating a process? The simplest answer is, “When you have to do it a second time.” The third time you do something, it should be done using an automated process. This fine-grained incremental approach rapidly creates a system for automating the repeated parts of your development, build, test, and deployment process.

Every change that is made to an application’s configuration, source code, environment, or data, triggers the creation of a new instance of the pipeline. One of the first steps in the pipeline is to create binaries and installers. The rest of the pipeline runs a series of tests on the binaries to prove that they can be released. Each test that the release candidate passes gives us more confidence that this particular combination of binary code, configuration information, environment, and data will work. If the release candidate passes all the tests, it can be released. The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process.

On one weekend, they marched through the factory painting marks on machinery to be jettisoned. “We put a hole in the side of the building just to remove all that equipment,” Musk says. The experience became a lesson that would become part of Musk’s production algorithm. Always wait until the end of designing a process—after you have questioned all the requirements and deleted unnecessary parts—before you introduce automation.

Kensi Gounden, Highly innovative new technologies can be both disruptive and transformative, but technology adoption can also be incremental, such as simply automating a manual process. So introducing business technology innovations, either incremental or step-change, may embrace increasing online connectivity across the business, strategic technology acquisition and use or using time-saving technologies to improve internal communication.

I am the person who spends a great deal of time on the intricacies of interstellar communication through jumpgates—a process, Your Excellency, which is so very automated and regular that my time is absolutely most valuably spent on informing you about it. Would you like to come into a conference room?” One Cyclamen was amazingly obsequious, and in such a way that Eight Antidote felt more flattered than annoyed. He wished he could learn that skill.

advances in AI are poised to drive dramatic productivity increases and perhaps eventually full automation. Radiologists, for example, are trained to interpret the images that result from various medical scans. Image processing and recognition technology is advancing rapidly and may soon be able to usurp the radiologist’s traditional role. Software can already recognize people in photos posted on Facebook and even help identify potential terrorists in airports.

The coming wave of automation will move way beyond the factory or public infrastructure and into our very biological processes such as aging and even giving birth. Used as we are to the gradual societal shifts brought about by previous change waves, often allowing decades to adjust and respond, I ask if we as a tribe are ready to abdicate our human sovereignty to the faceless forces of technology? Are you ready for the biggest loss of free will and individual human control in history?

Whenever you face a problem repeatedly, your brain begins to automate the process of solving it. Your habits are just a series of automatic solutions that solve the problems and stresses you face regularly. As behavioral scientist Jason Hreha writes, “Habits are, simply, reliable solutions to recurring problems in our environment.” As habits are created, the level of activity in the brain decreases. You learn to lock in on the cues that predict success and tune out everything else. When

The process of decision making and projecting a future in which one future among many can be selected depends less and less on human will. We may call it the paradox of the decider: as the circulation of information becomes faster and more complex, the time available for the elaboration of relevant information becomes shorter. The more space taken by the available information, the less time there is for understanding and conscious choice. This is why the interdependence between data and decisions is more and more embedded in infomachinery, in technolinguistic interfaces. This is why the execution of the program is entrusted to automated procedures that human operators can neither change nor ignore. The machine pretends to be neutral, purely mathematical, but we know that its procedures are only the technical reification of social interests: profit, accumulation, competition—these are the criteria underlying the automatic procedures embedded in the machine. Human volition is reduced to a procedural pretense.

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.

2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation

While poorhouses have been physically demolished, their legacy remains alive and well in the automated decision-making systems that encage and entrap today's poor. For all their high-tech polish, our modern systems of poverty management - automated decision-making, data mining, and predictive analysis - retain a remarkable kinship with the poorhouses of the past. Our new digital tools spring from punitive, moralistic views of poverty and create a system of high-tech containment and investigation. The digital poorhouse deters the poor from accessing public resources; polices their labor, spending, sexuality, and parenting; tries to predict their future behavior; and punishes and criminalizes those who do not comply with its dictates. In the process, it creates ever-finer moral distinctions between the 'deserving' and 'undeserving' poor, categorizations that rationalize our national failure to care for one another.

Work must be refused and reduced, building our synthetic freedom in the process.136 As we have set out in this chapter, achieving this will require the realisation of four minimal demands: 1.Full automation 2.The reduction of the working week 3.The provision of a basic income 4.The diminishment of the work ethic While each of these proposals can be taken as an individual goal in itself, their real power is expressed when they are advanced as an integrated programme. This is not a simple, marginal reform, but an entirely new hegemonic formation to compete against the neoliberal and social democratic options. The demand for full automation amplifies the possibility of reducing the working week and heightens the need for a universal basic income. A reduction in the working week helps produce a sustainable economy and leverage class power. And a universal basic income amplifies the potential to reduce the working week and expand class power.

The specific nature of capitalist commodity production shows itself in the fact that it is not simply a labour process in which products are created by the elements of production M and L. Rather the capitalistic form of commodity production is constructed dualistically – it is simultaneously a labour process for the creation of products and a valorisation process. The elements of production M and L figure not only in their natural form, but at the same time as values c and v respectively. They are used for the production of a sum of values, w, and indeed only on condition that over and above the used up value magnitudes c and v there is a surplus s (that is, s = w - c + v). The capitalist expansion of production, or accumulation of capital, is defined by the fact that the expansion of M relative to L occurs on the basis of the law of value; it takes the specific form of a constantly expanding capital c relative to the sum of wages v, such that both components of capital are necessarily valorised. It follows that the reproduction process can only be continued and expanded further if the advanced, constantly growing capital c + v can secure a profit, s. The problem can then be defined as follows – is a process of this sort possible in the long run?”[68]

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.

five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience.

If we’re not careful, the automation of mental labor, by changing the nature and focus of intellectual endeavor, may end up eroding one of the foundations of culture itself: our desire to understand the world. Predictive algorithms may be supernaturally skilled at discovering correlations, but they’re indifferent to the underlying causes of traits and phenomena. Yet it’s the deciphering of causation—the meticulous untangling of how and why things work the way they do—that extends the reach of human understanding and ultimately gives meaning to our search for knowledge. If we come to see automated calculations of probability as sufficient for our professional and social purposes, we risk losing or at least weakening our desire and motivation to seek explanations, to venture down the circuitous paths that lead toward wisdom and wonder. Why bother, if a computer can spit out “the answer” in a millisecond or two? In his 1947 essay “Rationalism in Politics,” the British philosopher Michael Oakeshott provided a vivid description of the modern rationalist: “His mind has no atmosphere, no changes of season and temperature; his intellectual processes, so far as possible, are insulated from all external influence and go on in the void.” The rationalist has no concern for culture or history; he neither cultivates nor displays a personal perspective. His thinking is notable only for “the rapidity with which he reduces the tangle and variety of experience” into “a formula.”54 Oakeshott’s words also provide us with a perfect description of computer intelligence: eminently practical and productive and entirely lacking in curiosity,

Gadgetry will continue to relieve mankind of tedious jobs. Kitchen units will be devised that will prepare ‘automeals,’ heating water and converting it to coffee; toasting bread; frying, poaching or scrambling eggs, grilling bacon, and so on. Breakfasts will be ‘ordered’ the night before to be ready by a specified hour the next morning. Communications will become sight-sound and you will see as well as hear the person you telephone. The screen can be used not only to see the people you call but also for studying documents and photographs and reading passages from books. Synchronous satellites, hovering in space will make it possible for you to direct-dial any spot on earth, including the weather stations in Antarctica. [M]en will continue to withdraw from nature in order to create an environment that will suit them better. By 2014, electroluminescent panels will be in common use. Ceilings and walls will glow softly, and in a variety of colors that will change at the touch of a push button. Robots will neither be common nor very good in 2014, but they will be in existence. The appliances of 2014 will have no electric cords, of course, for they will be powered by long- lived batteries running on radioisotopes. “[H]ighways … in the more advanced sections of the world will have passed their peak in 2014; there will be increasing emphasis on transportation that makes the least possible contact with the surface. There will be aircraft, of course, but even ground travel will increasingly take to the air a foot or two off the ground. [V]ehicles with ‘Robot-brains’ … can be set for particular destinations … that will then proceed there without interference by the slow reflexes of a human driver. [W]all screens will have replaced the ordinary set; but transparent cubes will be making their appearance in which three-dimensional viewing will be possible. [T]he world population will be 6,500,000,000 and the population of the United States will be 350,000,000. All earth will be a single choked Manhattan by A.D. 2450 and society will collapse long before that! There will, therefore, be a worldwide propaganda drive in favor of birth control by rational and humane methods and, by 2014, it will undoubtedly have taken serious effect. Ordinary agriculture will keep up with great difficulty and there will be ‘farms’ turning to the more efficient micro-organisms. Processed yeast and algae products will be available in a variety of flavors. The world of A.D. 2014 will have few routine jobs that cannot be done better by some machine than by any human being. Mankind will therefore have become largely a race of machine tenders. Schools will have to be oriented in this direction…. All the high-school students will be taught the fundamentals of computer technology will become proficient in binary arithmetic and will be trained to perfection in the use of the computer languages that will have developed out of those like the contemporary “Fortran". [M]ankind will suffer badly from the disease of boredom, a disease spreading more widely each year and growing in intensity. This will have serious mental, emotional and sociological consequences, and I dare say that psychiatry will be far and away the most important medical specialty in 2014. [T]he most glorious single word in the vocabulary will have become work! in our a society of enforced leisure.

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THE SK8 MAKER VS. GLOBAL INDUSTRIALIZATION This new era of global industrialization is where my personal analogy with the history of the skateboard maker diverges. It’s no longer cost-effective to run a small skateboard company in the U.S., and the handful of startups that pull it off are few and far between. The mega manufacturers who can churn out millions of decks at low cost and record speed each year in Chinese factories employ proprietary equipment and techniques that you and I can barely imagine. Drills that can cut all eight truck holes in a stack of skateboard decks in a single pull. CNC machinery to create CAD-perfect molds used by giant two-sided hydraulic presses that can press dozens of boards in a few hours. Computer-operated cutting bits that can stamp out a deck to within 1⁄64 in. of its specified shape. And industrial grade machines that apply multicolored heat-transfer graphics in minutes. In a way, this factory automation has propelled skateboarding to become a multinational, multi-billion dollar industry. The best skateboarders require this level of precision in each deck. Otherwise, they could end up on their tails after a failed trick. Or much worse. As the commercial deck relies more and more on a process that is out of reach for mere mortals, there is great value in the handmade and one of a kind. Making things from scratch is a dying art on the brink of extinction. It was pushed to the edge when public schools dismissed woodworking classes and turned the school woodshop into a computer lab. And when you separate society from how things are made—even a skateboard—you lose touch with the labor and the materials and processes that contributed to its existence in the first place. It’s not long before you take for granted the value of an object. The result is a world where cheap labor produces cheap goods consumed by careless customers who don’t even value the things they own.

five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation.

I became a broken record on the algorithm,” Musk says. “But I think it’s helpful to say it to an annoying degree.” It had five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation.

The most important qualification of a process analyst is not a facility for sending out information, but a facility for receiving it. Good process analysts naturally want to understand everything thoroughly.

Social networks like Facebook seem impelled by a similar aspiration. Through the statistical "discovery" of potential friends, the provision of "Like" buttons and other clickable tokens of affection, and the automated management of many of the time-consuming aspects of personal relations, they seek to streamline the messy process of affiliation. Facebook's founder, Mark Zuckerberg, celebrates all of this as "frictionless sharing"--the removal of conscious effort from socializing. But there's something repugnant about applying the bureaucratic ideals of speed, productivity, and standardization to our relations with others. The most meaningful bonds aren't forged through transactions in a marketplace or other routinized exchanges of data. People aren't notes on a network grid. The bonds require trust and courtesy and sacrifice, all of which, at least to a technocrat's mind, are sources of inefficiency and inconvenience. Removing the friction from social attachments doesn't strengthen them; it weakens them. It makes them more like the attachments between consumers and products--easily formed and just as easily broken. Like meddlesome parents who never let their kids do anything on their own, Google, Facebook, and other makers of personal software end up demeaning and diminishing qualities of character that, at least in the past, have been seen as essential to a full and vigorous life: ingenuity, curiosity, independence, perseverance, daring. It may be that in the future we'll only experience such virtues vicariously, though the exploits of action figures like John Marston in the fantasy worlds we enter through screens.

The human touch is there, it has worked for long, sitting by the patient’s bedside and trying to lift their spirits. Although with the advances of human genomics, parts of the process have become automated, medical professionals cannot practice the way they used to, it is no longer the same human contact that we had before. Patients usually get interested in new technologies first; therefore there is a constant request that physicians start using them. Medical professionals don’t have to get detailed training about how magnetic resonance imaging works, they just need to know why it works.

The history of the development of any urban center will be incomplete without a mention of the role of mass transit in the whole process. In deed the role of mass transit as a catalyst of industrial revolution and urbanization cannot be over emphasized. In the case of the New

But the test happens, whether we make it formal or not. We ask and you answer. We seek a human response. But more than that—you are my test, Elefsis. Every minute I fail and imagine in my private thoughts the process for deleting you from my body and running this place with a simple automation routine which would never cover itself with flowers. Every minute I pass it, and teach you something new instead. Every minute I fail and hide things from you. Every minute I pass and show you how close we can be, with your light passing into me in a lake out of time. So close there might be no difference at all between us. Our test never ends.

Executives need to understand the basic challenges of their current architecture and work to improve it over time. The build process needs to support managing different artifacts in the system as independent entities. Additionally, a solid, maintainable test automation framework needs to be in place so developers can trust the ability to quickly localize defects in their code when it fails. Until these fundamentals are in place, you will have limited success effectively transforming your processes.

Determine Keywords Use Compete PRO to find out which keywords drive traffic in an industry category; then create a search with them in Traackr to find the influencers. Validate Use the automated tools as a stepping-stone in the influencer discovery process, read their blogs, and validate the data to build relationships with influencers. Show Commitment Influencer identification is not a one-time thing. You need to do it regularly. It is a marriage, not a date. Welcome Surprises Don’t discount the “little” guy. Some of their one-to-one interactions are stronger than those of broadcasters. It takes only a pebble to start an avalanche. Combine Tools Use Google to find the most relevant keywords, Traackr to gain insights, and Klout to validate Twitter. Value Relevance Over Popularity Choose relevance over popularity. The most popular influencers on social media are often the least helpful in a social media campaign. Study Understand the influencer’s core audience. Combine Human and Machine Tools and technology are a good place to start, but we always need the human element.

technologies like payroll processing software, factory automation, computer-controlled machines, automated inventory control, and word processing have been deployed for routine work, substituting for workers in clerical tasks, on the factory floor, and doing rote information processing.

This principle is really a statement of our aim in writing this book: Releasing software should be easy. It should be easy because you have tested every single part of the release process hundreds of times before. It should be as simple as pressing a button. The repeatability and reliability derive from two principles: automate almost everything, and keep everything you need to build, deploy, test, and release your application in version control.

Amazingly, the transformations are not primarily based on automation. Instead, the incredible improvements come from modifying policies around the system of work and the policies that control work in process, ensuring that there are effective cross-functional teams, subordinating everything to the constraint, and managing handoffs well.

Drinks DUI expert group to help guide However, the best men s and women s drunken food you like it petty crimes, other traffic violations on the wrong goal that seems to be the direction. If you see that the light sentences and fines to get website traffic is violated, the citizen towards crime. When under the influence of a great interest behind the violation was due to more significant impact. Prison term effects were stuck down the back of people who are well, these licenses is likely that you want to deal with nutrition break and automated attacks can be, that s why. Yes it is expensive insurance, and other options in the outcome of the order of DUI, in everyday life, it affects people and the need to process, I love you. An experienced legal drunk driving charges, and it was presented to a lawyer immediately after the contract has announced that although his own. You are trying to remember the legal rights towards the maximum is very cool, you must be straight. The alternative thinking in any direction, does not encourage conservation officials as a record on suspicion of drunken driving after turning self, yourself simplest explanation, it may be possible to do so until is. His car really only answer whether the director should start by asking, encourages statement. A judgment is impaired, you probably have a file, you can use your account to say that the elements can get. When he finished, completely, their legal rights, and in a quiet warehouse to check their own direction and I will speak, and the optimal route is being used against itself. Most use a positive direction, you might think it accuses because your self, and also to examine the consequences of drinking have been able to rule out the presence of blood. Of course, as long as you do not accept the claims are by drinking in the area, they are deprived of a lawyer. Additional measures will not fix it claims that his lawyer, the Czech-out you can. Therefore, it is also within the laws of their country to be aware of your car. Owned independent certification system will be canceled. It can record their own and as an alternative to the paper license, driving license, was arrested for drunken driving, the licensee, are confiscated in accordance with the direction. License, for how long, but canceling function is based on the severity of their crime. But even apart from some a license, you completely lose its supply is proposed well motivated are not sure. Your sins, so not only is it important for your car can pass only confiscated. DUI price of any of the reception towards obtaining a driving license, DMV hearing is removed again, but the case was registered, although this aspect of themselves independently as a condition of. The court file, however, take care of yourself, as well as independent experts was chosen to listen to their constitution.

You could automate a purge process to delete old logs, but you might find that it’s more expensive to set up such a process than it is to keep the logs.

It is essential for the smooth running of the delivery process to fly people back and forth periodically, so that each local group has personal contact with members from other groups.

Business Process Management (BPM) is a systemic approach for capturing, designing, executing, documenting, measuring, monitoring, and controlling both automated and non-automated processes to meet the objectives and business strategies of a company. BPM embraces the conscious, comprehensive, and increasingly technology-enabled definition, improvement, innovation, and maintenance of end-to-end processes. Through this systemic and conscious management of processes, companies achieve better results faster and more flexibly.

Predictive modeling generates the entire model from scratch. All the model’s math or weights or rules are created automatically by the computer. The machine learning process is designed to accomplish this task, to mechanically develop new capabilities from data. This automation is the means by which PA builds its predictive power.

Though Cannon's study serves to sanction on biological grounds the principle of automation, it also exposes the limitation of an economy that seeks to translate man's higher functions into an automatic system that will finally be capable of making decisions and plans of action without calling upon anticipatory mental processes or memories except those that can be programmed on a computer. The path of human advance is not toward such collective automation but toward the increase of personal and communal autonomy; and any system that reverses this direction not merely turns man's most highly developed organ, his brain, into a virtual non-entity, but cuts itself off from the most precious products of the human mind: that vast storehouse and powerhouse of images, forms, ideas, institutions, and structures, through which man rises above the conditions of his immediate environment. To reduce or destroy this heritage is to inflict brain damage on the human race.

This immense, still impending total human sacrifice cannot be appraised in the rational or scientific terms that those who have created this system favor: it is, I stress again, an essentially religious phenomenon. As such it offers a close parallel with the original doctrines of Buddhism, even down to the fact that it shares Prince Gautama's atheism. What, indeed, is the elimination of man himself from the process he in fact has discovered and perfected, with its promised end of all striving and seeking, but the Buddha's final escape from the Wheel of Life? Once complete and universal, total automation means total renunciation of life and eventually total extinction: that very retreat into Nirvana that Prince Gautama pictured as man's only way to free himself from sorrow and pain and misfortune. When the life-impulse is depressed, this doctrine, we know, exerts an immense attraction upon masses of disappointed and disheartened souls: for a few centuries Buddhism became dominant in India and swept over China. For similar reasons it is reviving again today. But note: those who originally accepted this view of man's ultimate destiny, and sought to meet death halfway, did not go to the trouble of creating an elaborate technology to accomplish this end: in that direction they went no farther, significantly enough, than the invention of a water-driven prayer wheel. Instead they practiced concentrated meditation and inner detachment, acts as free from technological intervention as the air they breathed. And they earned an unexpected reward for this mode of withdrawal, a reward that the worshippers of the machine will never know. Instead of extinguishing forever their capacity to feel pleasure or pain, they intensified it, creating poems, philosophies, paintings, sculptures, monuments, ceremonies that restored their hope, their organic animation, their creative zeal: revealing once more in the erotic exuberance an impassioned and exalted sense of man's own potential destiny. Our latter-day technocratic Buddhism can make no such promises

Consider this incomplete list of things that can be made better with data: Answering a factual question Telling a story Exploring a relationship Discovering a pattern Making a case for a decision Automating a process Judging an experiment

Countrywide was an early adopter of information technology to process applications. By the mid-1990s, fully 70 percent of loans passing through its automated underwriting system required no human intervention.

We recommend creating four types of folders: • Archives (any email that contains information that might be needed) • Automated (any email newsletter that relates to a strategy you’d like to pursue in the future) • Follow-Up (any email relating to a specific action that needs to be completed) • Send (if you use an assistant to process email, then have this person filter messages that require your final approval into this folder)

It was 5:30 in the morning, and Wally’s coffee maker was just completing its automated process, yielding its much appreciated nectar for Richard’s consumption. He filled the biggest cup he could find two-thirds of the way up, and then opened the cabinet, selecting an espresso shot from the shelf. It proclaimed in bright red letters: “WARNING HIGH CAFFIENE. LIMIT 2 PER DAY”. Richard laughed a little as he dumped four of them into his coffee

Selling is crucial to your success because without the sale, you do not make any money. The great thing about writing a book to position yourself is that the book does a lot of the selling for you. People read the book and come to you for more answers. If you have products created to match the theme of your book, your platform (website) will do the selling for you. Automate as much of the process as you can with opt-in boxes, video sales landing pages and special offers. Make it as easy as you can for your fans and followers. Once your products are created, simply write about them, talk about them, and create articles from the content and say, “Yes” to interviews. The buzz created will point people back to your site where your automatic sales team is ready to take orders 24 hours a day.

Other problems: the repetition of some central ideas, such as the aforementioned, almost ritualized bows to the blessings of automation and the way automation frequently begins to dictate human actions and thought processes rather than the other way around. And there is what I thought might be an unnecessary overabundance of quoted sources — 375 or so footnotes for 232 pages of text. But that apparatus also demonstrates the

For example, there’s an uncharacteristic explosion of creativity among accountants. Yes, accountants: Groups like the Thriveal C.P.A. Network and the VeraSage Institute are leading that profession from its roots in near-total risk aversion to something approaching the opposite. Computing may have commoditized much of the industry’s everyday work, but some enterprising accountants are learning how to use some of their biggest assets — the trust of their clients and access to financial data — to provide deep insights into a company’s business. They’re identifying which activities are most profitable, which ones are wasteful and when the former become the latter. Accounting once was entirely backward-looking and, because no one would pay for an audit for fun, dependent on government regulation. It was a cost. Now real-time networked software can make it forward-looking and a source of profit. It’s worth remembering, though, that this process never ends: As soon as accountants discover a new sort of service to provide their customers, some software innovator will be seeking ways to automate it, which means those accountants will work to constantly come up with even newer ideas. The failure loop will

The goal of Continuous Delivery is to create a constant flow of changes to production: an automated software production line. The core concept that makes this happen is the Continuous Delivery pipeline. The pipeline breaks the software delivery process down into a number of stages. Each stage is aimed at verifying quality of new features from a different angle in order to prevent errors from affecting your users.

Instead of having a lengthy integration and qualification cycle, an agile process makes it part of the ongoing development process. This shift is accomplished through approaches like continuous integration/delivery, sprints with complete requirements, test-driven design, and automated testing. All this is put in place so that when customers think they have enough of the capabilities ready, the code is close to being ready to deploy.

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

Maintaining an automated process is estimated to be about 20% of the initial implementation cost per year.

The long tail of lower-value automation opportunities should involve less focus on the redesign. The value will be captured through the systematic use of the lean methodology to improve the processes incrementally (e.g., simplify, reduce the number of actions).

IA not only makes transaction processes more efficient, but it also generates log files for every action, creating transparency and ease of compliance. Machine learning leverages the digital information created by these programs to recognize predictive patterns and project trends.

4. When to use RPA versus smart workflow platforms? These technologies can be used interchangeably. The leading practice is to use smart workflow and low-code platforms as a foundation of the overall automation platform. RPA is used when IA needs to be integrated with legacy systems or automation of bespoke processes.

For a well-defined, standard, and stable process involving hand-offs between people and systems, it is preferable to use a smart workflow platform. Such platforms offer pre-developed modules. These are ready-to-use automation programs customized by industry and by business function (e.g., onboarding of clients in retail banking). In addition, they are modular. For example, a module might include a form for client data collection, and another module might support an approval workflow. In addition, these modules can be linked to external systems and databases using connectors, such as application programming interfaces (APIs), which enable resilient data connectivity. Hence, with smart workflows, there is no need to develop bespoke internal and external data bridges. This integration creates a system with high resiliency and integrity. In addition, the standardization by industry and function of these platforms, combined with the low-code functionality, helps to accelerate the implementation.

There are no ready-to-use modules with RPA. Most of the development is bespoke, and all process flows need to be built almost from scratch. The connections also need to be constructed. This results in a more flexible design and implementation of the programs developed, which can fit with more specific business requirements. The key advantage of RPA is that it allows the creation of automation programs that can involve legacy systems (e.g., those which can’t use APIs) or address non-standard requirements (e.g., onboarding of clients for a broker insurance company under Singapore regulations). However, with RPA, the lack of native integration amongst the components has weaknesses. For example, it involves less robustness, weaker data integrity, and lower resilience to process changes. If one part of an RPA program fails, the whole end-to-end process is stopped. As an outcome, based on our experience, the leading practice is to use low-code and smart workflow platforms as a foundation of the overall automation platform. In contrast, RPA is used for any integration of the overall platform with legacy systems or for automation of bespoke processes.

As an outcome, based on our experience, the leading practice is to use low-code and smart workflow platforms as a foundation of the overall automation platform. In contrast, RPA is used for any integration of the overall platform with legacy systems or for automation of bespoke processes.

Deep learning techniques are used when data features are numerous and there is a lack of domain understanding to identify and understand them. This data is typically complex, such as images, videos, or voice recordings. For example, an image contains an indefinite amount of data features (e.g., points, edges, shapes, or objects), and some are relevant to the problem being solved, but others are not. Deep learning is used to solve problems such as image classification, natural language processing, and speech recognition.

Another common issue is the lack of interdisciplinarity in the transformation. In our experience, the highest impact is the result of multi-lever end-to-end process automation – not small, siloed implementations, focused on one single technology lever. To achieve this, management should advocate for getting the right talents from across the different parts of an organization to work together (e.g., data scientists, developers, business analysts). Interdisciplinarity is also about avoiding limiting the transformation to the implementation of one single technology lever (e.g., RPA), and about implementing IA on end-to-end processes instead of only a few process tasks. By combining talents and technology levers and targeting end-to-end processes, the organization will create synergies, build economies of scale, and remove potential bottlenecks. Organizations failing to achieve this are not able to scale their IA transformation.

asked them two key questions. With your current data and processes, what do you need help with? That helped identify inefficiencies and automations that we could put in place to deliver those quick wins. I also asked a broader question: what are you not able to do that we can help with? This helped identify new, additional data sources or functionality not in place that formed our more longer-term plans.

Whenever you face a problem repeatedly, your brain begins to automate the process of solving it. Your habits are just a series of automatic solutions that solve the problems and stresses you face regularly.

In 2018, Arowana is working with RPA (robotic process automation) specialists to introduce robotic process automation software and tools into our companies. RPA will help them process menial, repetitive tasks faster, with zero errors and at much lower costs. In one case, an accounts payable robot can process in 1 hour what a human being can process in 1 week.

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.

AI can be a great tool to automate repetitive and time-consuming work where human-level decision-making is involved.

The most common options include: Ebook apps, which often allow you to export your highlights or annotations all at once. Read later apps that allow you to bookmark content you find online for later reading (or in the case of podcasts or videos, listening or watching). Basic notes apps that often come preinstalled on mobile devices and are designed for easily capturing short snippets of text. Social media apps, which usually allow you to “favorite” content and export it to a notes app. Web clippers, which allow you to save parts of web pages (often included as a built-in feature of notes apps). Audio/voice transcription apps that create text transcripts from spoken words. Other third-party services, integrations, and plug-ins that automate the process of exporting content from one app to another.

Pharmaceutical Product Exporters in Ahmedabad India’s Leading Exporters of Quality Pharmaceutical Formulations in India. We offer Pharmaceutical Drug Formulation and Pharmaceutical Marketers. Research Pvt. Ltd. is top manufacturing the anti-infective Tablets. We have a wide range of pharmaceutical product exporters in Ahmedabad India. ERPL is one of the fastest growing pharmaceutical manufacturing company in India. Spread over 1,47,500 sq. ft. acres, this modernized manufacturing plant with ultra-modern facility is set up in North Indian State of Uttarakhand. The facilities are approved by cGMP, WHO-GMP, GLP, ISO 9001:2000 and other global regulatory bodies. ERPL has carved out a distinct place of its own in manufacturing of best quality pharmaceutical products. Our success is the mirror of the hard work and team spirit put up the excellence of the professionalism by our dedicated Directors to excel and concurred new horizons with the motive of “Commit to Quality and Customer Satisfaction” Manufacturing Facility Installed with latest technology, automated manufacturing and packing operation and best practices, ensuring the highest quality of healthcare. • Commitment and zero compromise on quality • Follow up WHO-GMP, CGMP and GLP • Instrument Lab • Primary Area • Analytical Lab • Production process • Quality Management system • Process and products design, Evolution & Validation of process.

Any change to environments or the software that runs on them should be applied using an automated process from version control

The Most Widely Known Path If you're like most people, you believe landing an interview is limited to these three steps: 1.) Applying online, 2.) HR reviewing your application, and 3.) If your application is selected, the hiring manager reviewing it. You believe this because almost everything you’ve read comes from current or former HR folks. This process has significant flaws. Because the Internet made applying for positions easy, HR was drowning in applications. As a result, the HR Elimination system was born. That’s not its official name, but the name fits. The official name is Applicant Tracking System or ATS. ATS systems reject, on average, 75% of all applicants. Sometimes the rejection rate can be as high as 90%. J. P. Medved, content director at Capterra, a firm that helps companies find the right software for their business, said, Reducing the number of candidates might seem good if we're weeding out irrelevant resumes...In reality, many of these rejected candidates were knocked out of the running for bad reasons. An automated system, like an ATS, will sometimes reject people for very minor reasons, like incorrect resume formatting. Bersin & Associates, an Oakland-based firm specializing in talent management, tested an ATS system. They created the perfect resume for an ideal candidate for a clinical scientist position. Matching the resume to the job description from a leading manufacturer, they submitted the resume to an applicant tracking system. The ATS lost one of the candidate's work experiences. It also failed to read several educational degrees. As a result, the perfect resume for a clinical scientist position earned a score of 43, because the applicant tracking system misread it. Similarly, a Vice-President of Human Resources decided to test his company's ATS system. He applied for a job at his own company and received an automated rejection letter from the ATS.

For example, if you are building a software business, you can visit Makerpad.co and learn how to connect Gumroad and Carrd to accept orders on your website without writing a single line of code. And when you are ready to automate your manual fulfillment process, it will teach you how to add Airtable and Google Forms and Mailchimp. There are products like Notion, which we use to run our entire company. And there are services like Zapier, which allow you to automate the connections between all the software you use. Seriously, check out Makerpad. You’ll be surprised how much you can build without writing a single line of code.

Why people churn Most churn occurs at the time of the sale. In 2017, my churn was over 60%. I signed up customers who were a poor fit for my solution. Many customers thought Connex was an inventory management tool and others thought we built custom software. We had no onboarding process and we expected users to figure out Connex on their own. Many users failed to choose the right settings, since they are small business owners and not accountants. Since the software failed to work as expected, they quickly cancelled. From experience, most users churn in the first 30 days. It is critical that you reach out to them and ensure the software works correctly. My staff performs an onboarding and ensures Connex works to the customer’s satisfaction. Users churned because my software lacked features that it has today. We noticed a dramatic shift in churn, after implementing a sales and marketing process. In the first quarter of 2021, we had only a handful of refunds out of 100 purchases. People churn because they fail to achieve their desired result or experience. People buy Connex because they want accurate financial information, better order fulfillment, or protection from overselling. If the sync were inaccurate and unreliable then we would lose customers. In other cases, your software may become superfluous. For example, I used the excellent meeting automation tool Calendly. When I migrated to HubSpot, however, I no longer needed Calendly because HubSpot offered meeting automation as part of its suite of offerings. Even if your tool works, your customer’s desired situation or desired outcomes may change. I churned from my ticketing system because I was unhappy with the customer service and experienced technical issues with their chat and phone system. Companies often tack on features that are nowhere near as usable as their core offering.

2. MIGRATE YOUR PRODUCT LEK had to move away from ‘standard’ strategy towards analysis of competitors. This led to ‘relative cost position’ and ‘acquisition analysis’. Your task is to find a unique product or service, one not offered in that form by anyone else. Your raw material is, of course, what you and the rest of your industry do already. Tweak it in ways that could generate an attractive new product. The ideal product is: ★ close to something you already do very well, or could do very well; ★ something customers are already groping towards or you know they will like; ★ capable of being ‘automated’ or otherwise done at low cost, by using a new process (cutting out costly steps, such as self-service), a new channel (the phone or Internet), new lower-cost employees (LEK’s ‘kids’, highly educated people in India), new raw materials (cheap resins, free data from the Internet), excess capacity from a related industry (especially manufacturing capacity), new technology or simply new ideas; ★ able to be ‘orchestrated’ by your firm while you yourself are doing as little as possible; ★ really valuable or appealing to a clearly defined customer group - therefore commanding fatter margins; ★ difficult for any rival to provide as well or as cheaply - ideally something they cannot or would not want to do. Because you are already in business, you can experiment with new products in a way that someone thinking of starting a venture cannot do. Sometimes the answer is breathtakingly simple. The Filofax system didn’t start to take off until David Collischon provided ‘filled organisers’ - a wallet with a standard set of papers installed. What could you do that is simple, costs you little or nothing and yet is hugely attractive to customers? Ask customers if they would like something different. Mock up a prototype; show it around. Brainstorm new ideas. Evolution needs false starts. If an idea isn’t working, don’t push it uphill. If a possible new product resonates at all, keep tweaking it until you have a winner. At the same time . . .

a deployment pipeline. That’s your entire value stream from code check-in to production. That’s not an art. That’s production. You need to get everything in version control. Everything. Not just the code, but everything required to build the environment. Then you need to automate the entire environment creation process. You need a deployment pipeline where you can create test and production environments, and then deploy code into them, entirely on-demand.

Server Automation This is very specific to a tech start-up, but server stability is a very important part of the product. Our customers relied on WebMerge in their business every day, and it could have a domino effect on their day if something went wrong. The easiest automation for server tracking is simple up-time tracking. This checks to make sure the app is loading every minute, every day. I set up alerts that if any downtime was detected, it would send a text message to my phone and also send me an email every minute. The text message was the most helpful, and I could often jump online in minutes to fix any issues. Over time, I started to run into server issues in the middle of the night. I had to set the alert tone on my phone to the emergency tone so it would wake me up. Well, often it took a few alerts to wake me or an elbow from my wife! I was waking up at 3:00 a.m. a few times per week to address issues. This couldn’t continue. To fix this, I created an internal system that would check the app uptime, and if there were issues, it would automatically restart services in the app that were most likely causing the problem. This auto-healing process worked like a charm, and I rarely had to wake up in the middle of the night again (or deal with many issues during the day). Is your product or service critical to your customers? If so, try to implement as many automated processes as you can to keep the service running at all hours. Your customers (and your sanity) will thank you.

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

By now, though, it had been a steep learning curve, he was fairly well versed on the basics of how clearing worked: When a customer bought shares in a stock on Robinhood — say, GameStop — at a specific price, the order was first sent to Robinhood's in-house clearing brokerage, who in turn bundled the trade to a market maker for execution. The trade was then brought to a clearinghouse, who oversaw the trade all the way to the settlement. During this time period, the trade itself needed to be 'insured' against anything that might go wrong, such as some sort of systemic collapse or a default by either party — although in reality, in regulated markets, this seemed extremely unlikely. While the customer's money was temporarily put aside, essentially in an untouchable safe, for the two days it took for the clearing agency to verify that both parties were able to provide what they had agreed upon — the brokerage house, Robinhood — had to insure the deal with a deposit; money of its own, separate from the money that the customer had provided, that could be used to guarantee the value of the trade. In financial parlance, this 'collateral' was known as VAR — or value at risk. For a single trade of a simple asset, it would have been relatively easy to know how much the brokerage would need to deposit to insure the situation; the risk of something going wrong would be small, and the total value would be simple to calculate. If GME was trading at $400 a share and a customer wanted ten shares, there was $4000 at risk, plus or minus some nominal amount due to minute vagaries in market fluctuations during the two-day period before settlement. In such a simple situation, Robinhood might be asked to put up $4000 and change — in addition to the $4000 of the customer's buy order, which remained locked in the safe. The deposit requirement calculation grew more complicated as layers were added onto the trading situation. A single trade had low inherent risk; multiplied to millions of trades, the risk profile began to change. The more volatile the stock — in price and/or volume — the riskier a buy or sell became. Of course, the NSCC did not make these calculations by hand; they used sophisticated algorithms to digest the numerous inputs coming in from the trade — type of equity, volume, current volatility, where it fit into a brokerage's portfolio as a whole — and spit out a 'recommendation' of what sort of deposit would protect the trade. And this process was entirely automated; the brokerage house would continually run its trading activity through the federal clearing system and would receive its updated deposit requirements as often as every fifteen minutes while the market was open. Premarket during a trading week, that number would come in at 5:11 a.m. East Coast time, usually right as Jim, in Orlando, was finishing his morning coffee. Robinhood would then have until 10:00 a.m. to satisfy the deposit requirement for the upcoming day of trading — or risk being in default, which could lead to an immediate shutdown of all operations. Usually, the deposit requirement was tied closely to the actual dollars being 'spent' on the trades; a near equal number of buys and sells in a brokerage house's trading profile lowered its overall risk, and though volatility was common, especially in the past half-decade, even a two-day settlement period came with an acceptable level of confidence that nobody would fail to deliver on their trades.

To explain how a dApp works, we’ll use an example from the company Etherisc, which created a dApp for flight insurance to a well-known Ethereum conference. This flight insurance was purchased by 31 of the attendees.23 Figure 5.1 shows a simplified diagram. Using Ethereum, developers can mimic insurance pools with strings of conditional transactions. Open sourcing this process and running it on top of Ethereum’s world computer allows everyday investors to put their capital in an insurance pool to earn returns from the purchasers of insurance premiums that are looking for coverage from certain events. Everyone trusts the system because it runs in the open and is automated by code.

Think big, start small, and scale fast”: Initiate the transformation with the definition of a multi-year, company-wide vision, roadmap, and business case. These plans need to be flexible and adaptable. Then, “start small” with the implementation of a pilot, and take the time to learn from this first experience. Finally, implement the broader scope in stages to manage the risks. Gradually increase the speed and scale of the transformation, and as a result, generate high impact. “IA is a business transformation, not a technology project”: The perspective of business benefits should guide the transformation. This transformation involves not only technology, but more importantly, people – with change management, and retraining – and processes – with redesigns. “IA is a journey, not a destination”: IA is not a one-off exercise; it is a never-ending transformation journey. It continually brings additional benefits to the organization by applying evolving concepts, methods, and technologies. Hence, building teams with the right skills to guide the company in this transformation is critical. “Infusing IA into the culture of the company”: Implementing IA with siloed, isolated teams does not work. Automation needs to be infused into the company. Change management, education, empowerment, and incentivization of everyone in the company is vital. Every employee should know what IA is and what its benefits are, and be empowered and incentivized to identify use cases and build automation.

Bitcoin is not a smart network. Bitcoin is a dumb network. It really is a dumb network. It is a dumb transaction-processing network. It’s a dumb network for verifying a very simple scripting language. It doesn’t offer a complete range of financial services and products. It doesn’t have automation and incredible features built in. Bitcoin is simply a dumb network, and that is one of its strongest and most important features. When you design networks, when you architect network systems, one of the most fundamental choices is this: do you make a dumb network that supports smart devices, or do you make a smart network that supports dumb devices? 5.1.1. The Smart Network - Phones The phone network was a very smart network. The telephone at the end of that network was a very dumb device. If you had a pulse-dialing phone, that thing had maybe four electronic components inside it. It was basically a switch on a wire with a speaker attached to it. You could dial by flicking the hook up and down fast enough. 
The phone was a dumb device; it had no intelligence whatsoever. Everything the phone network did was in the network. Caller ID was a network feature. Call waiting was a network feature. And if you wanted to make the experience better, you had to upgrade the network but you didn’t need to upgrade the device. That was a critical design decision because, at that time, the belief was that smart networks were better because you could deliver these incredible services just by upgrading the network for everyone. There is one small disadvantage with smart networks. They have to be upgraded from the center out. And that means innovation occurs at the center, by one player, and requires permission. As a result of smart network design, innovation only happens when a feature is needed by all of the subscribers of the network, when it is compelling enough to disrupt the function of the entire network to upgrade it. 5.1.2. The Dumb Network - Internet The internet is a dumb network. It’s dumb as rocks. All it can do is move data from point A to point B. It doesn’t know what that data is. It can’t tell the difference between a Skype call and a web page. It doesn’t know if the device on the end is a desktop computer or a mobile phone, a vacuum cleaner, a refrigerator, or a car. It doesn’t know if that device is powerful or not. If it can do multimedia or not. It doesn’t know, it doesn’t care. In order to run a new application or innovate on a dumb network, all you have to do is add innovation at the edge. Because a dumb network can support smart devices, you don’t need to change anything in the network. If you push intelligence to the edge of the network, an application that only has five users can be implemented so long as those five users upgrade their devices to implement that application. The dumb network will transport their data because it doesn’t know the difference and it doesn’t care. 5.1.3. Bitcoin’s Dumb Network Bitcoin is a dumb network supporting really smart devices, and that is an incredibly powerful concept because bitcoin pushes all of the intelligence to the edge. It doesn’t care if the bitcoin address is the address of a multimillionaire, the address of a central bank, the address of a smart contract, the address of a device, or the address of a human. It doesn’t know. It doesn’t care if the transaction is carrying lots of money or not much money at all. It doesn’t care if the address is in Kuala Lumpur or downtown New York. It doesn’t know, it doesn’t care. It moves money from one address to another based on a simple locking script. And that means that if you want to build a new application on top of bitcoin, you can upgrade the

the reality is that the best outsourcing is not about getting rid of work you don’t want to do. If it’s truly dumb work, maybe it doesn’t have to be done at all. Maybe processes can be automated.