Automated Systems Quotes

Life in this society being, at best, an utter bore and no aspect of society being at all relevant to women, there remains to civic-minded, responsible, thrill-seeking females only to overthrow the government, eliminate the money system, institute complete automation and destroy the male sex.

As advances in AI, machine learning, and neural networks evolve, incomprehensibility will reach even higher levels - exposing these complex systems to both human and machine errors.

Man is to technology what the bee is to the flower. It’s man’s intervention that allows technology to expand and evolve itself and in return, technology offers man convenience, wealth and the lessening burden of physical labor via its automated systems.

Find a printer paper and imagine a full-grown bird shaped something like a football with legs standing on it. Imagine 33,000 of these rectangles in a grid. (Broilers are never in cages, and never on multiple levels.) Now enclose the grid with windowless walls and put a ceiling on top. Run in automated (drug-laced) feed, water, heating, and ventilation systems. This is a farm.

Paradox of Automation: the more efficient the Automated system, the more crucial the contribution of the human operators of that system.

AI won’t replace humans, but people who can use it will.” This sounds reassuring, but it oversimplifies the complex future of work and AI integration. Experts predict a surge in opportunities, but the intricate interplay between cognification, mass automation, and how we work remains uncharted. The net effect of AI on employment is unknown - we have no data on the future.

In the end, managing your finances well is a lot like developing a strong personal productivity system: You keep track of everything without making it your full-time job; you set goals; you break them down into small bite-size tasks; you save yourself time by automating manual work; and you spend your time and brainpower focusing on the big picture. That’s what I try to do with my time and money.

We always thought the robot apocalypse would be fleets of killer drones and war mecha the size of apartment blocks and terminators with red eyes. Not a row of mechanised checkouts in the local Extra and the alco station; online banking; self-driving taxis; an automated triage system in the hospital. One by one, the bots came and replaced us.

in 1983, the psychologist Lisanne Bainbridge wrote a seminal essay on the hidden dangers of relying too heavily on automated systems.48 Build a machine to improve human performance, she explained, and it will lead – ironically – to a reduction in human ability.

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.

The more automated society gets and the more powerful the attacking AI becomes, the more devastating cyberwarfare can be. If you can hack and crash your enemy’s self-driving cars, auto-piloted planes, nuclear reactors, industrial robots, communication systems, financial systems and power grids, then you can effectively crash his economy and cripple his defenses. If you can hack some of his weapons systems as well, even better.

The two pillars of the Toyota production system are just-in-time and automation with a human touch, or autonomation.

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

The container is at the core of a highly automated system for moving goods from anywhere, to anywhere, with a minimum of cost and complication on the way. The container made shipping cheap, and by doing so changed the shape of the world economy.

I find these comparisons particularly poignant: life versus death, hope versus fear. Space exploration and the highly mechanized destruction of people use similar technology and manufacturers, and similar human qualities of organization and daring. Can we not make the transition from automated aerospace killing to automated aerospace exploration of the solar system in which we live?

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.

In 1953, Allen Dulles, then director of the USA Central Intelligence Agency (CIA), named Dr Sidney Gottlieb to direct the CIA's MKULTRA programme, which included experiments conducted by psychiatrists to create amnesia, new dissociated identities, new memories, and responses to hypnotic access codes. In 1972, then-CIA director Richard Helms and Gottlieb ordered the destruction of all MKULTRA records. A clerical error spared seven boxes, containing 1738 documents, over 17,000 pages. This archive was declassified through a Freedom of Information Act Request in 1977, though the names of most people, universities, and hospitals are redacted. The CIA assigned each document a number preceded by "MORI", for "Managament of Officially Released Information", the CIA's automated electronic system at the time of document release. These documents, to be referenced throughout this chapter, are accessible on the Internet (see: abuse-of-power (dot) org/modules/content/index.php?id=31). The United States Senate held a hearing exposing the abuses of MKULTRA, entitled "Project MKULTRA, the CIA's program of research into behavioral modification" (1977).

I put myself away. I came up with the great vanishing system, in which I could retreat so deep within myself that, though I might still appear the same creature, actually I was very different. I thrust all thoughts and feelings into the depths of me, where they were safe, but in an outward way I became something like an automation.

Marginalized groups face higher levels of data collections when they access public benefits, walk through highly policed neighborhoods, enter the health-care system, or cross national borders. That data acts to reinforce their marginality when it is used to target them for suspicion and extra scrutiny. Those groups seen as undeserving are singled out for punitive public policy and more intense surveillance, and the cycle begins again. It is a kind of collective red-flagging, a feedback loop of injustice.

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.

Hello, Jackass Dirtbag,” says a perky computer voice. “Welcome to your divisional experience! I am your fully automated Unwinding Intelli-System, but you can call me UNIS.

Automation weakens the bond between tool and user not because computer-controlled systems are complex but because they ask so little of us.

human traders who are not psychologically prepared will often override their automated trading systems' decisions, especially when there is a position or day with abnormal profit or loss.

All descriptions of how near certainty is to be achieved are based primarily on emerging technologies. A Global Information Grid of “persistent surveillance” will gather information and share that information in a networked “collaborative information environment.” Automated systems will fuse that intelligence and make possible “virtual collaboration among geographically dispersed” analysts who will generate intelligence and, ultimately, knowledge. Some even assume that this “robust intelligence” will deliver not only a clear appreciation for the current situation, but also generate “predictive intelligence” that will allow US forces to “anticipate the unexpected." Despite its enthusiastic embrace, the assumption of near-certainty in future war is a dangerous fallacy.

Let us fool ourselves no longer. At the very moment Western nations, threw off the ancient regime of absolute government, operating under a once-divine king, they were restoring this same system in a far more effective form in their technology, reintroducing coercions of a military character no less strict in the organization of a factory than in that of the new drilled, uniformed, and regimented army. During the transitional stages of the last two centuries, the ultimate tendency of this system might b e in doubt, for in many areas there were strong democratic reactions; but with the knitting together of a scientific ideology, itself liberated from theological restrictions or humanistic purposes, authoritarian technics found an instrument at hand that h as now given it absolute command of physical energies of cosmic dimensions. The inventors of nuclear bombs, space rockets, and computers are the pyramid builders of our own age: psychologically inflated by a similar myth of unqualified power, boasting through their science of their increasing omnipotence, if not omniscience, moved by obsessions and compulsions no less irrational than those of earlier absolute systems: particularly the notion that the system itself must be expanded, at whatever eventual co st to life. Through mechanization, automation, cybernetic direction, this authoritarian technics has at last successfully overcome its most serious weakness: its original dependence upon resistant, sometimes actively disobedient servomechanisms, still human enough to harbor purposes that do not always coincide with those of the system. Like the earliest form of authoritarian technics, this new technology is marvellously dynamic and productive: its power in every form tends to increase without limits, in quantities that defy assimilation and defeat control, whether we are thinking of the output of scientific knowledge or of industrial assembly lines. To maximize energy, speed, or automation, without reference to the complex conditions that sustain organic life, have become ends in themselves. As with the earliest forms of authoritarian technics, the weight of effort, if one is to judge by national budgets, is toward absolute instruments of destruction, designed for absolutely irrational purposes whose chief by-product would be the mutilation or extermination of the human race. Even Ashurbanipal and Genghis Khan performed their gory operations under normal human limits. The center of authority in this new system is no longer a visible personality, an all-powerful king: even in totalitarian dictatorships the center now lies in the system itself, invisible but omnipresent: all its human components, even the technical and managerial elite, even the sacred priesthood of science, who alone have access to the secret knowledge by means of which total control is now swiftly being effected, are themselves trapped by the very perfection of the organization they have invented. Like the Pharoahs of the Pyramid Age, these servants of the system identify its goods with their own kind of well-being: as with the divine king, their praise of the system is an act of self-worship; and again like the king, they are in the grip of an irrational compulsion to extend their means of control and expand the scope of their authority. In this new systems-centered collective, this Pentagon of power, there is no visible presence who issues commands: unlike job's God, the new deities cannot be confronted, still less defied. Under the pretext of saving labor, the ultimate end of this technics is to displace life, or rather, to transfer the attributes of life to the machine and the mechanical collective, allowing only so much of the organism to remain as may be controlled and manipulated.

Whereas automation is the use of technology to support this traditional model, innovation enables ways of making practical expertise available that simply were not possible (or even imaginable) without the systems in question.

The digital poorhouse is persistent. Once they scale up, digital systems can be remarkably hard to decommission. Think, for example, about what might happen if the world learned about a gross violation of trust at a large data company like Google.

He invested heavily in an automated general store in which customers would put a coin in a slot and a moment later a bag of coal, potatoes, onions, nails, hairpins, or other desired commodity would come sliding down a chute to them. The system never worked. It never came close to working.

That went well," Christina murmured to him. "Pffaww," he agreed. "They're a pair! They don't like anything. They don't even like the dachshund. Who doesn't like dachshunds? They're little parcels of dog-shaped goodness. I've known Jalabite Hegemon ships give up conquest and start little farmsteads just so they can have happy dachshunds. Everyone likes dachshunds, everywhere in the universe. Well, except on Bithomorency. People there got into a war with a refugee column of evolutionarily advanced dachshund supersoldiers fleeing the destruction of their homeworld. The wire-haired marines took out an entire town - two hundred thousand dead. And it was a tragic misunderstanding. The dachshunds only stopped to ask for some biscuits, automated defence systems fired on them. There's a lesson: never give control of your space weapons to an unsupervised machine." He shrugged, and she found herself nodding: schoolboy error.

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

When you feel the need to escape your problems, to escape from this world, don't make the mistake of resorting to suicide Don't do it! You will hear the empty advice of many scholars in the matter of life and death, who will tell you, "just do it" there is nothing after this, you will only extinguish the light that surrounds you and become part of nothingness itself, so when you hear these words remember this brief review of suicide: When you leave this body after committing one of the worst acts of cowardice that a human being can carry out, you turn off the light, the sound and the sense of reality, you become nothing waiting for the programmers of this game to pick you up from the darkness, subtly erase your memories and enable your return and I emphasize the word subtle because sometimes the intelligence behind this maneuver or automated mechanism is wrong and send human beings wrongly reset to such an extent, that when they fall to earth and are born again, they begin to experience memories of previous lives, in many cases they perceive themselves of the opposite sex, and science attributes this unexplainable phenomenon to genetic and hormonal factors, but you and I know better! And we quickly identified this trigger as a glitch in the Matrix. Then we said! That a higher intelligence or more advanced civilization throws you back into this game for the purpose of experimenting, growing and developing as an advanced consciousness and due to your toxic and destructive behavior you come back again but in another body and another life, but you are still you, then you will carry with you that mark of suicide and cowardice, until you learn not to leave this experience without having learned the lesson of life, without having experienced and surprised by death naturally or by design of destiny. About this first experience you will find very little material associated with this event on the internet, it seems that the public is more reserved, because they perceive themselves and call themselves "awakened" And that is because the system has total control over the algorithm of fame and fortune even over life and death. Now, according to religion and childish fears, which are part of the system's business to keep you asleep, eyes glued to the cellular device all day, it says the following: If you commit this act of sin, you turn off light, sound and sense of reality, and from that moment you begin to experience pain, fear and suffering on alarming scales, and that means they will come for you, a couple of demons and take you to the center of the earth where the weeping and gnashing of teeth is forever, and in that hell tormented by demons you will spend eternity. About this last experience we will find hundreds of millions of people who claim to have escaped from there! And let me tell you that all were captivated by the same deity, one of dubious origin, that feeds on prayers and energetic events, because it is not of our nature, because it knows very well that we are beings of energy, then this deity or empire of darkness receives from the system its food and the system receives from them power, to rule, to administer, to control, to control, to kill, to exclude, to inhibit, to classify, to imprison, to silence, to infect, to contaminate, to depersonalize. So now that you know the two sides of the same coin, which one will your intelligence lean towards! You decide... Heads or tails? From the book Avatars, the system's masterpiece.

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.

Purchase Price $250,000 Down Payment $ 25,000 Mortgage Amount $225,000 At 7% Interest Rate 30 Years $1,349 $485,636 15 Years $1,899 $341,762 Difference $550 $143,874 Five hundred fifty dollars more per month, and you will save almost $150,000 and fifteen years of bondage. The really interesting thing I have observed is that fifteen-year mortgages always pay off in fifteen years. Again, part of a Total Money Makeover is putting in place systems that automate smart moves, which is what a fifteen-year mortgage is. Thirty-year mortgages are for people who enjoy slavery so much they want to extend it for fifteen more years and pay thousands of dollars more for the privilege. If you must take out a mortgage, pretend only fifteen-year mortgages exist. If you have a great interest rate, it is not necessary to refinance to pay a mortgage off in fifteen years or earlier. Simply make payments as if you have a fifteen-year mortgage, and your mortgage will pay off in fifteen years. If you want to pay any mortgage off in twelve years or any number you want, visit my website or get a calculator and calculate the proper payment at your interest rate on your balance for a twelve-year mortgage (or the number you want). Once you have that payment amount, add to your monthly mortgage payment the difference between the new principal and interest payment and your current principal and interest payment, and you will pay off your home in twelve years.

Many duct-taper jobs are the result of a glitch in the system that no one has bothered to correct—tasks that could easily be automated, for instance, but haven’t been either because no one has gotten around to it, or because the manager wants to maintain as many subordinates as possible, or because of some structural confusion, or because of some combination of the three.

Why does Joe Normie think it’s a litmus test for morality if one returns one’s shopping cart? Big-box stores put out of business local retailers, they automated their systems to reduce employees, and they got customers to be their own cashiers without getting paid for their labor, and yet to prove I’m a good person, I’m supposed to do more unpaid work for them to streamline their operation?

Pham Nuwen spent years learning to program/explore. Programming went back to the beginning of time. It was a little like the midden out back of his father’s castle. Where the creek had worn that away, ten meters down, there were the crumpled hulks of machines—flying machines, the peasants said—from the great days of Canberra’s original colonial era. But the castle midden was clean and fresh compared to what lay within the Reprise’s local net. There were programs here that had been written five thousand years ago, before Humankind ever left Earth. The wonder of it—the horror of it, Sura said—was that unlike the useless wrecks of Canberra’s past, these programs still worked! And via a million million circuitous threads of inheritance, many of the oldest programs still ran in the bowels of the Qeng Ho system. Take the Traders’ method of timekeeping. The frame corrections were incredibly complex—and down at the very bottom of it was a little program that ran a counter. Second by second, the Qeng Ho counted from the instant that a human had first set foot on Old Earth’s moon. But if you looked at it still more closely. . .the starting instant was actually some hundred million seconds later, the 0-second of one of Humankind’s first computer operating systems. So behind all the top-level interfaces was layer under layer of support. Some of that software had been designed for wildly different situations. Every so often, the inconsistencies caused fatal accidents. Despite the romance of spaceflight, the most common accidents were simply caused by ancient, misused programs finally getting their revenge. “We should rewrite it all,” said Pham. “It’s been done,” said Sura, not looking up. She was preparing to go off-Watch, and had spent the last four days trying to root a problem out of the coldsleep automation. “It’s been tried,” corrected Bret, just back from the freezers. “But even the top levels of fleet system code are enormous. You and a thousand of your friends would have to work for a century or so to reproduce it.” Trinli grinned evilly. “And guess what—even if you did, by the time you finished, you’d have your own set of inconsistencies. And you still wouldn’t be consistent with all the applications that might be needed now and then.” Sura gave up on her debugging for the moment. “The word for all this is ‘mature programming environment.’ Basically, when hardware performance has been pushed to its final limit, and programmers have had several centuries to code, you reach a point where there is far more signicant code than can be rationalized. The best you can do is understand the overall layering, and know how to search for the oddball tool that may come in handy—take the situation I have here.” She waved at the dependency chart she had been working on. “We are low on working fluid for the coffins. Like a million other things, there was none for sale on dear old Canberra. Well, the obvious thing is to move the coffins near the aft hull, and cool by direct radiation. We don’t have the proper equipment to support this—so lately, I’ve been doing my share of archeology. It seems that five hundred years ago, a similar thing happened after an in-system war at Torma. They hacked together a temperature maintenance package that is precisely what we need.” “Almost precisely.

In the morning I walked to the bank. I went to the automated teller machine to check my balance. I inserted my card, entered my secret code, tapped out my request. The figure on the screen roughly corresponded to my independent estimate, feebly arrived at after long searches through documents, tormented arithmetic. Waves of relief and gratitude flowed over me. The system had blessed my life. I felt its support and approval. The system hardware, the mainframe sitting in a locked room in some distant city. What a pleasing interaction. I sensed that something of deep personal value, but not money, not that at all, had been authenticated and confirmed. A deranged person was escorted from the bank by two armed guards. The system was invisible, which made it all the more impressive, all the more disquieting to deal with. But we were in accord, at least for now.

Seemed like a fact of the universe that the closer you got to anything, the worse it looked. Take the most beautiful person in the solar system, zoom in on them at the right magnification and they were an apocalyptic cratered landscape crawling with horrors. That’s what the Earth was. A shining jewel from space, up close a blasted landscape covered with mites living by devouring the dying. “One ticket to New York,” he said to the automated kiosk.

If You Only Track Five Metrics… Track as many of these as you can in your sales force automation system’s dashboards: New leads created per month (also, from what source). Conversion rate of leads to opportunities. Number of, and pipeline dollar value of, qualified opportunities created per month. This is the most important leading indicator of revenue! Conversion rates of opportunities to closed deals. Booked revenues in three categories: New Business, Add-On Business, Renewal Business.

Oath of Non-Harm for an Age of Big Data I swear to fulfill, to the best of my ability, the following covenant: I will respect all people for their integrity and wisdom, understanding that they are experts in their own lives, and will gladly share with them all the benefits of my knowledge. I will use my skills and resources to create bridges for human potential, not barriers. I will create tools that remove obstacles between resources and the people who need them. I will not use my technical knowledge to compound the disadvantage created by historic patterns of racism, classism, able-ism, sexism, homophobia, xenophobia, transphobia, religious intolerance, and other forms of oppression. I will design with history in mind. To ignore a four-century-long pattern of punishing the poor is to be complicit in the “unintended” but terribly predictable consequences that arise when equity and good intentions are assumed as initial conditions. I will integrate systems for the needs of people, not data. I will choose system integration as a mechanism to attain human needs, not to facilitate ubiquitous surveillance. I will not collect data for data’s sake, nor keep it just because I can. When informed consent and design convenience come into conflict, informed consent will always prevail. I will design no data-based system that overturns an established legal right of the poor. I will remember that the technologies I design are not aimed at data points, probabilities, or patterns, but at human beings.

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

This, I think, is the most tragic loss brought on by the modern health care system. The loving interconnectedness of doctor, family, and community is being destroyed. These ties that bind us to each other are the bonds that define our humanity, and yet they are being systematically severed in the service of automated bookkeeping. The entire health care system is now being organized around machines instead of human beings. Not prioritized to reduce human suffering, but rather to optimize a computerized recordkeeping system. This is a tragedy.

Imagine a scenario where we can develop AI to a point where AI largely runs the logistical aspects of everyone’s lives: transportation, clothing, personal care, health—everything is automated. In that world, our brain is now freed from doing what it does for 80% of the day. It’s free to pursue higher-order complexities. The question now is, what will we do? For example, what if studying physics and quantum theory produced the same reward system that watching the Kardashians does today? What if we found out that our brains could extend to four, five, or ten dimensions? What would we create? What would we do?

In other words, however much devaluation of capital may devastate the individual capitalist in periods of crisis, they are a safety valve for the capitalist class as a whole. For the system devaluation of capital is a means of prolonging its life span, of defusing the dangers that threaten to explode the entire mechanism. The individual is thus sacrificed in the interest of the species.” However, since the share of the value-creating variable capital has shrunk, the same tendency that has staved off breakdown goes on to reproduce it. “A capital that fails to fulfil its function of valorisation ceases to be capital; hence its devaluation.”[117

New high-tech tools allow for more precise measuring and tracking, better sharing of information, and increased visibility of targeted populations. In a system dedicated to supporting poor and working-class people's self-determination, such diligence would guarantee that they attain all the benefits they are entitled to by law. In that context, integrated data and modernized administration would not necessarily result in bad outcomes for poor communities. But automated decision-making in our current welfare system acts a lot like older, atavistic forms of punishment and containment. It filters and diverts. It is a gatekeeper, not a facilitator.

-the bank recently introduced a new system that automated many tasks in my role as a risk analyst and therefore my position has been eliminated; I’m an exemplary employee and this is in no way a reflection of my performance; the company will provide me with ample support during the “transition.” I might be the only person in the history of mankind to eat an entire banana while losing her job. The “transition” would begin immediately. As in, I wasn’t allowed to go back to my desk, to collect my things, or to say goodbye to my coworkers. I was to be walked down to the security desk like a criminal and handed my belongings in a box, then shown the curb.

As automation threatens to shake the capitalist system to its foundation, one might suppose that communism could make a comeback. But communism was not built to exploit that kind of crisis. Twentieth-century communism assumed that the working class was vital for the economy, and communist thinkers tried to teach the proletariat how to translate its immense economic power into political clout. The communist political plan called for a working-class revolution. How relevant will these teachings be if the masses lose their economic value, and therefore need to struggle against irrelevance rather than against exploitation? How do you start a working-class revolution without a working class?

His early research wasn’t especially original. Ax identified slight upward trends in a number of investments and tested if their average price over the previous ten, fifteen, twenty, or fifty days was predictive of future moves. It was similar to the work of other traders, often called trenders, who examine moving averages and jump on market trends, riding them until they peter out. Ax’s predictive models had potential, but they were quite crude. The trove of data Simons and others had collected proved of little use, mostly because it was riddled with errors and faulty prices. Also, Ax’s trading system wasn’t in any way automated—his trades were made by phone, twice a day, in the morning and at the end of the trading day.

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.

The fugitive species learned that to survive at all they had to hide, and hide expertly. There were pockets of space where intelligence had not arisen in recent times—sterilised by supernova explosions, or neutron star mergers—and these cleansed zones made the best hiding places. But there were dangers. Intelligence was always waiting to emerge; new cultures were always evolving and spilling into space. It was these outbreaks of life which drew the predatory machines. They placed automated watching devices and traps around promising solar systems, ready to be triggered as soon as new spacefaring cultures stumbled upon them. So the grubs and their allies—the few that remained—grew intensely paranoid and watchful for the signs of new life.

THE RIVE BROTHERS used to be like a technology gang. In the late 1990s, they would jump on skateboards and zip around the streets of Santa Cruz, knocking on the doors of businesses and asking if they needed any help managing their computing systems. The young men, who had all grown up in South Africa with their cousin Elon Musk, soon decided there must be an easier way to hawk their technology smarts than going door-to-door. They wrote some software that allowed them to take control of their clients’ systems from afar and to automate many of the standard tasks that companies required, such as installing updates for applications. The software became the basis of a new company called Everdream, and the brothers promoted their technology in some compelling ways.

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.

There's a clear link between this cultural pattern and Germany's place in history as one of the first countries in the world to become heavily industrialized. Imagine being a factory worker in the German automative industry. If you arrive at work four minutes late, the machine for which you are responsible gets started late, which exacts a real, measurable financial cost. To this day, the perception of time in Germany is partially rooted in the early impact of the industrial revolution, where factory work required the labor force to be on hand and in place at a precisely appointed moment. In other societies -particularly in developing world- life centers around the fact of constant change. As political systems shift and financial systems alter, as traffic surges and wanes, as monsoons or water shortages raise unforeseeable challenges, the successful managers are those who have developed the ability to ride out the changes with ease and flexibility.

If Jim was back at the imaginary dinner party, trying to explain what he did for a living, he'd have tried to keep it simple: clearing involved everything that took place between the moment someone started at trade — buying or selling a stock, for instance — and the moment that trade was settled — meaning the stock had officially and legally changed hands. Most people who used online brokerages thought of that transaction as happening instantly; you wanted 10 shares of GME, you hit a button and bought 10 shares of GME, and suddenly 10 shares of GME were in your account. But that's not actually what happened. You hit the Buy button, and Robinhood might find you your shares immediately and put them into your account; but the actual trade took two days to complete, known, for that reason, in financial parlance as 'T+2 clearing.' By this point in the dinner conversation, Jim would have fully expected the other diners' eyes to glaze over; but he would only be just beginning. Once the trade was initiated — once you hit that Buy button on your phone — it was Jim's job to handle everything that happened in that in-between world. First, he had to facilitate finding the opposite partner for the trade — which was where payment for order flow came in, as Robinhood bundled its trades and 'sold' them to a market maker like Citadel. And next, it was the clearing brokerage's job to make sure that transaction was safe and secure. In practice, the way this worked was by 10:00 a.m. each market day, Robinhood had to insure its trade, by making a cash deposit to a federally regulated clearinghouse — something called the Depository Trust & Clearing Corporation, or DTCC. That deposit was based on the volume, type, risk profile, and value of the equities being traded. The riskier the equities — the more likely something might go wrong between the buy and the sell — the higher that deposit might be. Of course, most all of this took place via computers — in 2021, and especially at a place like Robinhood, it was an almost entirely automated system; when customers bought and sold stocks, Jim's computers gave him a recommendation of the sort of deposits he could expect to need to make based on the requirements set down by the SEC and the banking regulators — all simple and tidy, and at the push of a button.

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

The cheerleaders of the new data regime rarely acknowledge the impacts of digital decision-making on poor and working-class people. This myopia is not shared by those lower on the economic hierarchy, who often see themselves as targets rather than beneficiaries of these systems. For example, one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.” Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today. Digital tracking and decision-making systems have become routine in policing, political forecasting, marketing, credit reporting, criminal sentencing, business management, finance, and the administration of public programs. As these systems developed in sophistication and reach, I started to hear them described as forces for control, manipulation, and punishment

Space Rockets as Power Symbols The moon rocket is the climactic expression of the power system: the maximum utilization of the resources of science and technics for the achievement of a relatively miniscule result: the hasty exploration of a barren satellite. Space exploration by manned rockets enlarges and intensifies all the main components of the power system: increased energy, accelerated motion, automation, cyber-nation, instant communication, remote control. Though it has been promoted mainly under military pressure, the most vital result of moon visitation so far turns out to be an unsought and unplanned one-a full view of the beautiful planet we live on, an inviting home for man and for all forms of life. This distant view on television evoked for the first time an active, loving response from many people who had hitherto supposed that modern technics would soon replace Mother Earth with a more perfect, scientifically organized, electronically controlled habitat, and who took for granted that this would be an improvement. Note that the moon rocket is itself necessarily a megastructure: so it naturally calls forth such vulgar imitations as the accompanying bureaucratic obelisk (office building) of similar dimensions, shown here (left). Both forms exhibit the essentially archaic and regressive nature of the science-fiction mind.

Look,” she told me one day in a Millsport coffeehouse. “Shopping—actual, physical shopping—could have been phased out centuries ago if they’d wanted it that way.” “They who?” “People. Society.” She waved a hand impatiently. “Whoever. They had the capacity back then. Mail order, virtual supermarkets, automated debiting systems. It could have been done and it never happened. What does that tell you?” At twenty-two years old, a Marine Corps grunt via the street gangs of Newpest, it told me nothing. Carlyle took in my blank look and sighed. “It tells you that people like shopping. That it satisfies a basic, acquisitive need at a genetic level. Something we inherited from our hunter-gatherer ancestors. Oh, you’ve got automated convenience shopping for basic household items, mechanical food distribution systems for the marginalized poor. But you’ve also got a massive proliferation of commercial hives and speciality markets in food and crafts that people physically have to go to. Now why would they do that, if they didn’t enjoy it?” I probably shrugged, maintaining my youthful cool. “Shopping is physical interaction, exercise of decision-making capacity, sating of the desire to acquire, and an impulse to more acquisition, a scouting urge. It’s so basically fucking human when you think about it. You’ve got to learn to love it, Tak. I mean you can cross the whole archipelago on a hover; you never even need to get wet. But that doesn’t take the basic pleasure out of swimming, does it? Learn to shop well, Tak. Get flexible. Enjoy the uncertainty.

It felt like fate when I first encountered the automated trading system that promised to transform small investments into substantial wealth over time. The marketing was aggressive, bombarding my social media feeds with images of people lounging on exotic beaches, driving fancy cars, and celebrating their newfound financial freedom. WhatsApp info:+12723328343 As a recent college graduate struggling to make ends meet, I was desperate for a way out of my financial rut, and the allure of easy money was too tempting to ignore. On a whim, I decided to take the plunge. I borrowed from my meager savings and even took out a small loan to fund my excitement. The rush I felt when signing up was like nothing I had ever experienced—an intoxicating thrill, like hopping onto a rollercoaster at full speed. At first, everything seemed to be going exactly as promised. My investment seemed to grow almost overnight, doubling and tripling in value.  My skepticism began to fade, replaced by a sense of confidence and hope for the future. I even shared my success with friends and family, excitedly telling them about the platform that was going to change my life. I imagined a future free from financial worries, a life of luxury and freedom, all thanks to this “revolutionary” trading system. But soon, a familiar sense of unease began to settle in. What had been an impressive surge in profits suddenly plateaued, and I found myself facing unexpected hurdles when trying to withdraw my funds. Pop-up messages about my “account needing an upgrade” and “market tightening” explained away the issues, but the discomfort grew. Still, I convinced myself that success required patience and continued to hold out hope that the system would recover. As weeks turned into months, my investment continued to dwindle. The once-promising account balance plummeted, and each attempt to reach customer support went unanswered. The promises of easy wealth had turned into an unsettling nightmare. Email info: Adwarerecoveryspecialist@auctioneer. net Desperate for answers, I began scouring the internet for any information or advice. That’s when I stumbled across reviews of ADWARE RECOVERY SPECIALIST , a service that seemed to specialize in helping people like me recover lost funds from fraudulent platforms. I felt a glimmer of hope as I read about others who had managed to retrieve their investments with the help of ADWARE RECOVERY SPECIALIST. Perhaps, after all, there was still a way out of this mess. I reached out to their team, and to my relief, they were able to assist me in recovering a portion of the money I thought I had lost for good. ADWARE RECOVERY SPECIALIST gave me the guidance and support I needed to navigate this complicated process, helping me regain control of a situation that had seemed hopeless. Their professionalism and expertise allowed me to salvage what I could, and for that, I am incredibly grateful.

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.

If you’re feeling down about the world, the book, “Resource Revolution: How to Capture the Biggest Business Opportunity in a Century,” is an antidote. Mr. Rogers and Mr. Heck outline how emerging advances — among them 3-D printing, autonomous vehicles, modular construction systems and home automation — might in time alter some of the world’s largest industries and bring prosperity to billions of people.

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

RPA is software that emulates human execution of repetitive work. It's built on technologies designed to orchestrate, execute and enhance business workflows. Cognitive RPA adds intelligence the ability to perform human like decision making to RPA. CRPA can do things like read documents to identify specific information to pass to RPA to update another system. dayhuffgroup.com

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

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Across the country, poor and working-class people are targeted by new tools of digital poverty management and face life-threatening consequences as a result. Automated eligibility systems discourage them from claiming public resources that they need to survive and thrive. Complex integrated databases collect their most personal information, with few safeguards for privacy or data security, while offering almost nothing in return. Predictive models and algorithms tag them as risky investments and problematic parents. Vast complexes of social service, law enforcement, and neighborhood surveillance make their every move visible and offer up their behavior for government, commercial, and public scrutiny

Millions of copies of drivers’ licenses, social security cards, and other supporting documents were faxed to a centralized document processing center in Grant County; so many of them disappeared that advocates started calling it “the black hole in Marion.” Each month the number of verification documents that vanished—were not attached properly to digital case files in a process called “indexing”—rose exponentially. According to court documents, in December 2007 just over 11,000 documents were unindexed. By February 2009, nearly 283,000 documents had disappeared, an increase of 2,473 percent. The rise in technical errors far outpaced increased system use. The consequences are staggering if you consider that any single missing document could cause an applicant to be denied benefits.

The digital poorhouse is massively scalable. High-tech tools like automated decision-making systems, matching algorithms, and predictive risk models have the potential to spread very quickly.

So who’s right: those who say automated jobs will be replaced by better ones or those who say most humans will end up unemployable? If AI progress continues unabated, then both sides might be right: one in the short term and the other in the long term. But although people often discuss the disappearance of jobs with doom-and-gloom connotations, it doesn’t have to be a bad thing! Luddites obsessed about particular jobs, neglecting the possibility that other jobs might provide the same social value. Analogously, perhaps those who obsess about jobs today are being too narrow-minded: we want jobs because they can provide us with income and purpose, but given the opulence of resources produced by machines, it should be possible to find alternative ways of providing both the income and the purpose without jobs. Something similar ended up happening in the equine story, which didn’t end with all horses going extinct. Instead, the number of horses has more than tripled since 1960, as they were protected by an equine social-welfare system of sorts: even though they couldn’t pay their own bills, people decided to take care of horses, keeping them around for fun, sport and companionship. Can we similarly take care of our fellow humans in need?

Airbnb’s strategy for photographing its hosts’ rooms offers an instructive example. Early on, Airbnb’s founders discovered that one of the key factors that increased the chances of renting a room on Airbnb was the quality of the photographs of that room. It turns out that most of us aren’t professional photographers, and our poorly composed, poorly shot cell phone pictures don’t do a good job of conveying the awesomeness of our living spaces. So the founders took to the road, visiting hosts and taking photographs for them. Obviously, personally visiting every host was hardly a scalable solution, so the the task was soon outsourced to freelance photographers. As Airbnb grew, the strategy shifted from the founders managing a short list of photographers, to an employee managing a large group of photographers, to an automated system managing a global network of photographers. Founder Brian Chesky describes this strategy succinctly: “Do everything by hand until it’s too painful, then automate it.

With the smart grid for energy and the smart grid for water—what’s technically called resource consumption optimization—we’re seeing the second wave. Next up is the automation and control of far more complex autonomous systems—such as self-driving cars.

5.5 Specific Signs You Should Avoid A Van Rental Supplier! Here are 5.5 specific sign that you should avoid a van rental supplier: 1. Automated answering services: If you cannot get access to a human on the phone when you call to make a van reservation, where are they going to be when you have a mechanical breakdown? If the company cannot afford to provide a live person to receive your call, how will they afford to take care of your group when you have broken down on the side of the road or have been in an accident! 2. Rude or incompetent rental agents: If the rental company’s agents do not answer the phone cheerfully and sound like they are less than ecstatic to hear from you, they have set a negative tone for the entire van rental experience. If they place you on hold until you grow old, or refuse to acknowledge you immediately when you walk through the door of their office, get out of there! 3. Charging for mileage: Any van rental firm worth doing business with will offer you unlimited miles going anywhere in the USA. Anything else does not allow you the peace of mind needed when you are required to maximize your budget and do not need any unaccounted variables. 4. Encouraging drop-offs after business hours: This practice gives the rental company an unwritten power of attorney to charge you for any damages they find until the next business day! This leaves you or your organization wide open to paying for damages you did not cause or create! 5. Yield management systems: When a van rental firm employs this system, it skyrockets the van rental rates through the roof as demand gets tight and supply gets low. This system has been designed to squeeze every last dollar out of the client’s pocket and takes serious advantage of those groups that are forced to reserve later due to budget constraints or lack of commitments! 5.5 Accidents handled by a third party vendor: If you have an accident in a van, and the rental firm outsources this function to an outside agency, you will lose all power of negotiation and pay much more on the damage claim because the rental firm has to give that agency a substantial percentage. In addition, the agency employees have nothing to lose by treating you horribly.

the autonomous-driving side of things, Alphabet (formerly Google), which has logged several million self-driving-car test miles, continues to lead the pack. At the end of 2016, it created a new business division, called Waymo, for its autonomous driving technology. In May 2017, Waymo and Lyft announced that they would work together on developing the technology, and later in the year, Alphabet invested $1 billion in the start-up. Others, like Cruise Automation (which GM acquired for $1 billion) and Comma.ai, which offers open-source autonomous driving technology in the same vein as Google’s Android mobile operating system, are chasing hard. Baidu, China’s leading Internet search company, has an autonomous-driving research center in Sunnyvale. Byton—backed by China’s Tencent, Foxconn, and the China Harmony New Energy auto retailer group—has an office in Mountain View, as does Didi Chuxing, the Chinese ride-sharing company in which Apple invested $1 billion. Many of these companies have taken not just inspiration but also talent from Tesla. Part of the value of an innovation cluster like Silicon Valley lies in the dispersal of intellectual labor from one node to the next. For instance, PayPal is well known in the Valley for producing a number of high performers who left the company to start, join, or invest in others. The so-called PayPal Mafia includes Reid Hoffman, who founded LinkedIn; Max Levchin, whose most recent of several start-ups is the financial services company Affirm; Peter Thiel, a Facebook board member and President Trump–supporting venture capitalist who cofounded “big data” company Palantir; Jeremy Stoppelman, who started reviews site Yelp; Keith Rabois, who was chief operating officer at Square and then joined Khosla Ventures; David Sacks, who sold Yammer to Microsoft for $1.2 billion and later became CEO at Zenefits; Jawed Karim, who cofounded YouTube; and one Elon Musk.

The bank knew exactly what it was looking for and how to go about it. There was consensus on two critical aspects: the system had to be centralized and had to be based on UNIX, even if that meant spending tonnes of money. MicroBanker, a fully integrated online banking automation system, developed by Citicorp Information Technology Industries Ltd (Citil), a Citibank subsidiary, fit the bill, but Citil was not willing to deal with HDFC Bank. A small outfit, Citil thought, would not be able to afford the system. Citil was expanding operations in Africa and Europe and was not too keen to sell the software to a start-up Indian bank. While Citil reluctantly made a presentation on the system, Citibank intervened before a deal could be signed, saying that selling MicroBanker to HDFC Bank could give the Indian bank more muscle as a competitor. Aditya had to call Rajesh Hukku, Citil head, to play ball and he relented. Citil later became i-Flex Solutions Ltd (now Oracle Financial Services Software Ltd).

We invented a computer program capable of thinking non-numerically, and thereby solved and venerable mind/body problem, explaining how a system composed of matter can have the properties of mind. Opening the way to automate tasks that had previously required human intelligence.

There is one are of work that should be mentioned here, referred to as 'automatic theorem proving'. One set of procedures that would come under this heading consists of fixing some formal system H, and trying to derive theorems within this system. We recall, from 2.9, that it would be an entirely computational matter to provide proofs of all the theorems of H one after the other. This kind of thing can be automated, but if done without further thought or insight, such an operation would be likely to be immensely inefficient. However, with the employment of such insight in the setting up of the computational procedures, some quite impressive results have been obtained. In one of these schemes (Chou 1988), the rules of Euclidean geometry have been translated into a very effective system for proving (and sometimes discovering) geometrical theorems. As an example of one of these, a geometrical proposition known as V. Thebault's conjecture, which had been proposed in 1938 (and only rather recently proved, by K.B. Taylor in 1983), was presented to the system and solved in 44 hours' computing time. More closely analogous to the procedures discussed in the previous sections are attempts by various people over the past 10 years or so to provide 'artificial intelligence' procedures for mathematical 'understanding'. I hope it is clear from the arguments that I have given, that whatever these systems do achieve, what they do not do is obtain any actual mathematical understanding! Somewhat related to this are attempts to find automatic theorem-generating systems, where the system is set up to find theorems that are regarded as 'interesting'-according to certain criteria that the computational system is provided with. I do think that it would be generally accepted that nothing of very great actual mathematical interest has yet come out of these attempts. Of course, it would be argued that these are early days yet, and perhaps one may expect something much more exciting to come out of them in the future. However, it should be clear to anyone who has read this far, that I myself regard the entire enterprise as unlikely to lead to much that is genuinely positive, except to emphasize what such systems do not achieve.

If your main interest is in the business uses of machine learning, this book can help you in at least six ways: to become a savvier consumer of analytics; to make the most of your data scientists; to avoid the pitfalls that kill so many data-mining projects; to discover what you can automate without the expense of hand-coded software; to reduce the rigidity of your information systems; and to anticipate some of the new technology that’s coming your way. I’ve seen too much time and money wasted trying to solve a problem with the wrong learning algorithm, or misinterpreting what the algorithm said. It doesn’t take much to avoid these fiascoes. In fact, all it takes is to read this book.

They wanted the benefits of direct response copy in their business: they wanted to leverage their time, energy and money, while marketing one-to-many and automating their sales and marketing to free up their time and allow them to reach more people than they could without it… But they hated how it made them feel. Inauthentic. Hype-y. Sales-y. Slime-y. Like a used car salesman.

Working on a typewriter by touch, like riding a bicycle or strolling on a path, is best done by not giving it a glancing thought. Once you do, your fingers fumble and hit the wrong keys. To do things involving practiced skills, you need to turn loose the systems of muscles and nerves responsible for each maneuver, place them on their own, and stay out of it. There is no real loss of authority in this, since you get to decide whether to do the thing or not, and you can intervene and embellish the technique any time you like; if you want to ride a bicycle backward, or walk with an eccentric loping gait giving a little skip every fourth step, whistling at the same time, you can do that. But if you concentrate your attention on the details, keeping in touch with each muscle, thrusting yourself into a free fall with each step and catching yourself at the last moment by sticking out the other foot in time to break the fall, you will end up immobilized, vibrating with fatigue. It is a blessing to have options for choice and change in the learning of such unconsciously coordinated acts. If we were born with all these knacks inbuilt, automated like ants, we would surely miss the variety. It would be a less interesting world if we all walked and skipped alike, and never fell from bicycles. If we were all genetically programmed to play the piano deftly from birth, we might never learn to understand music.

Indeed, in many agricultural regions — including northern China, southern India (as well as the Punjab), Mexico, the western United States, parts of the Middle East, and elsewhere — water may be much more of a constraint to future food production than land, crop yield potential, or most other factors. Developing and distributing technologies and practices that improve water management is critical to sustaining the food production capability we now have, much less increasing it for the future. Water-short Israel is a front-runner in making its agricultural economy more water-efficient. Its current agricultural output could probably not have been achieved without steady advances in water management — including highly efficient drip irrigation, automated systems that apply water only when crops need it, and the setting of water allocations based on predetermined optimum water applications for each crop. The nation’s success is notable: between 1951 and 1990, Israeli farmers reduced the amount of water applied to each hectare of cropland by 36 percent. This allowed the irrigated area to more than triple with only a doubling of irrigation water use.37 Whether

risks that our attention spans will fail have risen. Studies from Yale, UCLA, Harvard, Berkeley, NASA, the National Institutes of Health, and elsewhere show errors are particularly likely when people are forced to toggle between automaticity and focus, and are unusually dangerous as automatic systems infiltrate airplanes, cars, and other environments where a misstep can be tragic. In the age of automation, knowing how to manage your focus is more critical than ever before.

What these organizations have in common is a high-trust culture that enables all departments to work together effectively, where all work is transparently prioritized and there is sufficient slack in the system to allow high-priority work to be completed quickly. This is, in part, enabled by automated self-service platforms that build quality into the products everyone is building.

Especially in Europe, the introduction of GDPR has undeniably added an additional level of complexity to effective automation and, equally crucial, is the problem of human error during data entry: If the information entered into the systems is incomplete, inaccurate or inconsistent, in fact, it becomes useless for automation purposes. Or, even worse, dangerous, as hoteliers would end up making assumptions that have no basis.

Some areas of opportunity: •   First, stop saying, “Well, this is just the way it is in our industry.” •   Have your available cash reported DAILY, with a short explanation of why it changed in the last 24 hours, and chart it against accounts receivable (AR) and accounts payable (AP) weekly. You’ll learn so much more about your business when you see how the cash is flowing on a daily basis. •   If you want to be paid sooner, ask. Small firms are finding that large companies (and governments!!) will pay considerably faster or even prepay if they simply ask, ask, ask, ask, and ask some more. •   Give value back to customers who pay on time or in advance. •   Get your invoices out more quickly. Hire one more person in accounting to do nothing but make sure invoicing is timely and follow up on payments. •   Send friendly reminders five days before the deadline that payments are due. Many customers are disorganized and will appreciate the reminders, resulting in faster payment. •   If invoices are recurring, obtain recurring credit card authorization from your customers to automate on-time payments. •   Understand why your clients are paying late. They might be unhappy with your product or service. Or perhaps an invoice has recurring mistakes, or it is not structured to flow through the customer’s automated invoicing system. •   Understand each customer’s payment cycles, and time your billings to coincide. •   Pay many of your own expenses with a credit card so you can play the float. Get your own customers to pay by credit card, so they can pay you quickly even if their cash flow is slow. •   Help your customers improve their cash flow so they can pay you on time. Offer them leasing options, for instance. •   Shorten cycles for delivery of your product or service. All of you have some kind of “work in progress.” The faster you complete projects, the faster you get paid. •   Offer a product or service so valuable that you have some leverage with your customers to get them to pay sooner. • Remember, improving margins and profit improves cash.

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.

A system of automated subroutines that reaches a certain level of complexity (and human brains certainly qualify) requires a high-level mechanism to allow the parts to communicate, dispense resources, and allocate control.

With the cannabis legalization boom, more and more cannabis organizations are looking for solutions to help automate their cannabis packaging processes. Dura-Pack has been helping cannabis organizations streamline their packaging operations for decades. Dura-Pack’s automated cannabis packaging filling and weighing systems are the perfect packaging solutions for cannabis flower, edibles, gummies and CBD products. If you have questions about cannabis packaging and would like to speak with a cannabis packaging expert, please contact us. We look forward to hearing from you.

Our UK company was established in 2012 after identifying the need for a specialist business to provide the service of “Contract Gasketing” in the UK. The decision to start this venture was based on the founders 30+ years in the industrial & automotive adhesive and sealant sector. Contract Gasketing is the use of high precision 6 axis robot systems, to automatically apply complex foam seals or adhesives directly to customers parts. This service being flexible enough to produce individual prototype parts, through the development phases to full, high-volume quantities. The benefit for the end customer, is that this robotic seal application can be adopted without the usual and significant capital investment in specialist automation and sealing technology.

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.

1) Only check email once, twice a day maximum. You need slow time to think, reflect and come up with new ideas. The switching costs of going back and forth between email and other tasks can affect focus in very negative ways. Focus on one thing at a time. Studies show that multi-tasking is actually counter productive. 2) Only do what only YOU can do. Everything else? Delegate! It's the only way you'll truly be able to grow your business. 3) Automate and come up with systems as much as you can. Always think: Is there a way to automate what I'm doing right now? 4) Have a Digital Detox, tech free day. It will help you be more productive in every possible way. You need time to recharge.

Simple Fast Funnels may be the new kid on the block when it comes to a complete bumper to bumper CRM system, but it’s a force to be reckoned with! Business owners are switching over right and left and I’m going to outline 10 of the best features of Simple Fast Funnels so you can see what all the buzz is about! Funnel builder: Simple Fast Funnels has easy intuitive software so you can build your own landing pages, funnels, websites, sales pages etc. No developer needed, everything included and simple to use Email Software: Instead of paying hundreds or thousands per month to send emails, this software does it for you! You can have your entire email list automated or send emails on the fly, whatever fits the bill for you, they’ve got you covered and it’s so easy to track your email results so you can modify and make improvements as you go. Online Membership Area: Now, for no additional fees that lot’s of CRM software likes to charge, you can build glorious membership areas for your clients. You can control timing on video releases, give access for certain time periods upset packages… whatever your business looks like, if you can dream it, you can build it in the membership area. Survey and quiz generator: Ramp up your lead capture game to grow your customer list! One of the best ways to get leads is to get your customers talking about themselves. Not only do people love to take surveys and quizzes, but it can help you gather information about your clients to serve them better and grow your sales! SMS Marketing Software: If you’re not messaging your customers, you’re missing out, and if you are messaging your customers you’re probably over paying. Amazing automated intuitive SMS marketing can make your life much easier and allow you to reach your customers in more ways. Being where your customers are more present is always good for business. Simple Fast Funnels helps you get the cheapest SMS rates around and it automatically integrates into the system for your unified messages. Appointment booking: Another expensive thing you used to have to pay for and try to get to work properly with your website AND look decent is also built right in. Now, without leaving Simple Fast Funnels, you’re able to capture the lead, follow up with the lead all over the place, engage with them, build trust, book appointments, schedule calls and even send them automated text reminders. E com Purchases: Directly on your website, you’ll be able to take payments. No more invoices sent from other platforms, everything buttoned up nice and clean. Unified messaging: From now on, whether a client emails, texts, calls etc, it all shows up in one place at your end. This might not seem like a big deal, but it’s a HUGE pain to have to follow customers about and keep track of conversations. Now you see all your communication with customers in a neat little area. Blogs: Blogs these days can really help your marketing efforts across the board, and of course your blogs will be a perfect fit in your simple fast funnel account. Analytics: Data tracking when you’re dealing with features on various platforms is a nightmare. If you capture a lead on a Word press landing page, send it an email software like Keep, mail chimp or whatever, send them to a new website to schedule calls and another to make purchases… How could you possibly expect to get good customer data? Hosting all of your “business” in one location makes tracking flawless. The more customers you have the more data you need to be efficient. Cheers to making it easy. All that software and that’s just the top 10, guys there’s more. Simplefastfunnels.com also lets you have a 2 week free trial. Don’t take anyone word for anything. Go try it for yourself.

A wildly successful business is a business where: Customers chase you, and not the other way around. Predictability and consistency generate new leads, clients, and revenue. You speak only to highly-qualified prospects you can actually help. You have an automated lead-generation system that delivers new customers on demand with minimal human effort. You focus only on the Highly Leveraged Activities that produce revenue.

Which company is best for using construction Project work? The Shree Siva Balaaji Steels project is a significant endeavor that encompasses the establishment and operation of a modern and advanced steel manufacturing facility. This project represents a fusion of innovation, cutting-edge technology, and industrial expertise, aimed at delivering high-quality steel products to meet the growing demands of various sectors. Key Features: State-of-the-Art Manufacturing Plant: The project involves the construction and operation of a state-of-the-art manufacturing plant equipped with the latest machinery, automation systems, and environmentally friendly processes. This allows for efficient production and reduced environmental impact. Diverse Product Range: Shree Siva Balaaji Steels aims to offer a diverse range of steel products to cater to different industries such as construction, automotive, infrastructure, and manufacturing. This versatility enables the company to meet the varying needs of clients and partners. Quality Assurance: A cornerstone of the project is its commitment to delivering high-quality steel products. The facility adheres to strict quality control measures and follows international standards to ensure that the end products are durable, reliable, and meet or exceed industry specifications. Sustainability Focus: The project places a strong emphasis on sustainability and environmentally conscious practices. Energy-efficient processes, recycling initiatives, and waste reduction strategies are integrated into the manufacturing process to minimize the ecological footprint. Employment Opportunities: Shree Siva Balaaji Steels contributes to local economies by creating employment opportunities across various skill levels, from skilled labor to technical experts. This helps stimulate economic growth in the region surrounding the manufacturing facility. Collaboration and Partnerships: The project fosters collaborations with suppliers, distributors, and clients, establishing strong relationships within the steel industry. This network facilitates efficient supply chain management and enables the company to provide tailored solutions to its customers. Innovation and Research: The project invests in research and development to constantly improve manufacturing processes, product quality, and the development of new steel products. This dedication to innovation positions the company at the forefront of the steel industry. Community Engagement: Shree Siva Balaaji Steels is committed to engaging with local communities and implementing corporate social responsibility initiatives. These efforts include supporting education, healthcare, and other community-centric projects, fostering goodwill and positive impact. Vision: The Shree Siva Balaaji Steels project envisions becoming a leading name in the steel manufacturing sector, renowned for its exceptional quality, technological innovation, and sustainability practices. By adhering to its core values of integrity, excellence, and environmental responsibility, the project strives to contribute positively to the industry and the communities it operates within.

The archipelagic nature of the Philippines posed a number of difficulties in the monitoring and implementation of Build, Build, Build projects. How do we monitor 20,000 projects simultaneously in a country composed of roughly 7,640 islands? How do we get rid of ghost projects? How do we minimize discretion at DPWH? Secretary Mark Villar was adamant to find a solution, one that was progressive, forward thinking, and feasible. First, he introduced an automated monitoring system called the Infra-Track App, which utilizes geo-tagging, satellite technology, and drone monitoring. “ - Anna Mae Yu Lamentillo , Night Owl: A Nationbuilder’s Manual 2nd Edition (p. 174, Build, Build, Build Projects MIMAROPA)

Dahil isang arkipelagong bansa ang Pilipinas, mas nagiging mahirap ang pagsubaybay at pagpapatupad ng mga proyektong Build, Build, Build. Paano nga ba epektibong masusubaybayan ang 20,000 proyekto nang sabay-sabay sa isang bansang binubuo ng humigit-kumulang 7,640 isla? Paano natin maaalis ang mga ghost project at mga pagpapasyang lihis sa mga itinakdang alituntunin sa ahensiya? Desidido si Secretary Mark Villar na humanap ng mga solusyon na progresibo at posibleng maisakatuparan. Ipinakilala niya ang isang automated monitoring system na tinatawag na Infra-Track App, na gumagamit ng geo-tagging, satellite technology, at drone monitoring.” - Night Owl: Edisyong Filipino (p. 174, Proyektong Build, Build, Build MIMAROPA)

the technical ignorance of elected politicians who are hardly credible in providing oversight or contemplating regulation; profound disagreements about what we value and how trade-offs should be made, whether in regard to data privacy, free speech, and content moderation or automation and the future of work; the slow, painstaking consideration of legislation that seems to generate competing bills—so that everyone has his or her name on one—without generating significant progress, especially in a highly polarized political environment; and the strong status quo bias of democratic institutions, which means that policy change is slow and sticky, making it difficult for regulators to respond flexibly and adaptably to new developments in technology.

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

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

Natural Language Generation - An Overview | Yellowfin Natural Language Generation (NLG) is a branch of artificial intelligence that focuses on transforming structured data into human-readable text. By using algorithms and linguistic rules, NLG systems can produce coherent narratives, summaries, and reports, enhancing communication in various applications like chatbots, automated journalism, and personalized content creation. This technology improves efficiency and accessibility in data interpretation. For more information, visit Yellowfin blog.

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