Automation Related Quotes

The human being is a very poorly designed machine tool. The human being excels in coordination. He excels in relating perception to action. He works best if the entire human being, muscles, senses, and mind, is engaged in the work.

(one study found that 88 percent of the loss of US manufacturing jobs between 2006 and 2013 was due to automation and related factors),69 it is easy to blame trade with other countries for hollowing out industrial towns and throwing workers onto the unemployment line.

The longer someone ignores an email before finally responding, the more relative social power that person has. Map these response times across an entire organization and you get a remarkably accurate chart of the actual social standing. The boss leaves emails unanswered for hours or days; those lower down respond within minutes. There’s an algorithm for this, a data mining method called “automated social hierarchy detection,” developed at Columbia University.8 When applied to the archive of email traffic at Enron Corporation before it folded, the method correctly identified the roles of top-level managers and their subordinates just by how long it took them to answer a given person’s emails. Intelligence agencies have been applying the same metric to suspected terrorist gangs, piecing together the chain of influence to spot the central figures.

Good riding techniques result in immediate automated control forces, resulting in controlling scary situations automatically. Sliding changes seat, peg and grip positions relative to the rider. Good riding techniques use these changes to your advantage. Good body posture, weight distribution and muscle tension then result in the desired immediate automated control forces.

And I will read part, as much as time allows in the second half, of the new issue of Computer and Automation, the November 1971 issue. And the statement above the article is on the assassination of President Kennedy. It’s the pattern of coup d’état and public deception. And it begins with this quotation of author Edmund Berkeley.            We must begin to recognize history as it is happening to us. We can no longer toy with illusion. Our war adventures in Asia are not related to national security in any rational sense. A coup d’état took place in the United States on November 22, 1963, when President John F. Kennedy was assassinated. That came eight years after I began my research. People talk about never having fascism in this country or never being overthrown – they have already been overthrown, and they’re not aware of it.

But you're stuck filming crap now." Hal snorted. "Chased by monsters? Better be damn good at running." "And exactly how do you get hurt filming a landscaping show?" Taggart retorted. "If it can't kill us, we don't film it," Jane said, to stop the fighting before it could start. "There's a lot of dangerous flora and fauna in Pittsburgh and it doesn't stay beyond the Rim. It comes into people's backyards and sets up shop. We teach our viewers how to deal with it, but it means we have to actually get close enough to get hurt." "Deal with, as in kill?" Nigel seemed flabbergasted. "This isn't Earth. These aren't endangered species. This morning we were dealing with a very large strangler vine in a neighborhood with lots of children. There's no way to 'move' it to someplace where it isn’t a danger, especially while it's actively trying to kill anything that stumbles into its path. Pets. Children. Automated lawnmowers." "That one is always amusing to watch but it always ends badly for the lawnmower," Hal said.

It is then simplest to think of the problem as follows: the purpose of commodity production is to convert the surplus value extracted from living labour into capital. But accumulation – the reproduction and expansion of capital – does not happen unless a sufficient magnitude of surplus value is produced. If the surplus value generated is insufficient then it only reproduces the part of capital that it is equal in value to – the rest becomes surplus capital. Capital is only fully “valorised” if it is reproduced and expanded. Grossman therefore says overaccumulation is produced by “imperfect valorisation”. This abstraction can be applied to ‘individual capital’, the capital owned by each individual capitalist, and total capital. Imperfect valorisation therefore explains cyclical crises. The total investment in production tends to grow faster relative to the growth of profits returned, because constant capital has to grow relative to variable capital. The mass of capital has continued to rise but at a declining rate. This is expressed as a falling rate of profit. There is a lack of surplus value relative to total capital  – an underproduction of surplus value is at once an overaccumulation of capital.

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]

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.

Therefore, when labour-saving technology reduces total socially necessary labour time (per commodity – for an increase in the number of commodities made may increase socially necessary labour time in absolute terms), there tends to be a relative fall in the surplus value contained in the total value of commodities, ie less surplus value per commodity, despite the fact that the rate of exploitation has increased, ie that each worker is now giving the capitalist more surplus labour time and therefore producing more surplus value relative to their necessary labour. As Grossman says: “Technological progress means that since commodities are created with a smaller expenditure of labour their value falls. This is not only true of the newly produced commodities. The fall in value reacts back on the commodities that are still on the market but which were produced under the older methods, involving a greater expenditure of labour time. These commodities are devalued.”[67] The very possibility of crisis is contained in the contradictory nature of the commodity. It is at once an object of use, or use-value, and something that can be exchanged for another thing, an exchange-value. Since different commodities contain different magnitudes of value and therefore cannot be directly exchanged, the creation of money proceeds logically and historically from the contradiction. It is not the exchange of commodities which regulates the magnitude of their value, but the magnitude of their value which controls their exchange value. Exchange-value is the only form in which the value of commodities can be expressed. Someone will buy a use-value because they need or want it, but only if they can exchange it for something else, ie money. If they do not have enough money, they cannot buy it, and profit goes unrealised. But to focus on this final ‘surface level’ aspect is what produces the mistaken underconsumptionist theory, for it forgets or ignores where it arose from – the dual character of the commodity.

In the past decade, the historically consistent division in the United States between the share of total national income going to labor and that going to physical capital seems to have changed significantly. As the economists Susan Fleck, John Glaser, and Shawn Sprague noted in the U.S. Bureau of Labor Statistics’ Monthly Labor Review in 2011, “Labor share averaged 64.3 percent from 1947 to 2000. Labor share has declined over the past decade, falling to its lowest point in the third quarter of 2010, 57.8 percent.” Recent moves to “re-shore” production from overseas, including Apple’s decision to produce its new Mac Pro computer in Texas, will do little to reverse this trend. For in order to be economically viable, these new domestic manufacturing facilities will need to be highly automated. Other countries are witnessing similar trends. The economists Loukas Karabarbounis and Brent Neiman have documented significant declines in labor’s share of GDP in 42 of the 59 countries they studied, including China, India, and Mexico. In describing their findings, Karabarbounis and Neiman are explicit that progress in digital technologies is an important driver of this phenomenon: “The decrease in the relative price of investment goods, often attributed to advances in information technology and the computer age, induced firms to shift away from labor and toward capital. The lower price of investment goods explains roughly half of the observed decline in the labor share.

I’ll then play it down a number of times with the automation in “trim” or “relative” mode, usually carefully riding the vocal to keep it properly focused and featuring melodic moments from various

research suggests that in areas near the U.S.-Mexico border, only one-tenth of paid domestic labor is on the books. The BLS puts the yearly average housekeeping wage at $19,570, which is both below the poverty line for a family of three and no doubt inflated by underreporting. Domestic workers without immigration papers not only lack the so-called protection of the law; they’re constantly vulnerable to deportation. So much for Lyotard’s “doing away with all privileges of place.” As Evan Calder Williams writes, “the days and bodies of humans are still far cheaper than any automation, provided money knows where to look. And it always has.” White supremacy and the gender division aren’t archaisms that capital will puree into a flow of neutered beige singularities; they’re labor relations, and integral ones.

A widely quoted study from the Oxford Martin School predicts that technology threatens to replace 47 percent of all US jobs within 20 years. One of Pew experts even foresees the advent of “robotic sex partners.’’ The world’s oldest profession may be no more. When all this happens, what, exactly, will people do? Half of those in the Pew report are relatively unconcerned, believing — as has happened in the past — that even as technology destroys jobs, it creates more new ones. But half are deeply worried, fearing burgeoning unemployment, a growing schism between the highly educated and everyone else, and potentially massive social dislocation. (The fact that Pew’s experts are evenly split also exposes one of the truths of prognostication: A coin flip might work just as well.) Much of this debate over more or fewer jobs misses a key element, one brought up by some of those surveyed by Pew: These are primarily political issues; what happens is up to us. If lower-skilled jobs are no more, the solution, quite obviously, is training and education. Moreover, the coming world of increasingly ubiquitous robotics has the potential for significant increases in productivity. Picture, for instance, an entirely automated farm, with self-replicating and self-repairing machines planting, fertilizing, harvesting, and delivering. Food wouldn’t be free, but it could become so cheap that, like water (Detroit excepted), it’s essentially available to everyone for an almost nominal cost. It’s a welfare state, of course, but at some point, with machines able to produce the basic necessities of life, why not? We’d have a world of less drudgery and more leisure. People would spend more time doing what they want to do rather than what they have to do. It might even cause us to rethink what it means to be human. Robots will allow us to use our “intelligence in new ways, freeing us up from menial tasks,’’ says Tiffany Shlain, host of AOL’s “The Future Starts Here.’’ Just as Lennon hoped and Star Trek predicted.

The document, which would later be published by Monthly Review Press, first as a special summer issue of the magazine and then as a book,19 began by describing the death of the union because of its failure to grapple with the question of automation. It went on to say that the rapid development of the productive forces by capitalism and the diminishing number of workers resulting from high technology were forcing us to go beyond Marx because Marx’s analyses and projections had been made in the springtime of capitalism, a period of scarcity rather than of abundance. The document projected blacks replacing workers as the revolutionary social force in the 1960s. It concluded by insisting that no group is automatically revolutionary: People in every stratum [must] clash not only with the agents of the silent police state but with their own prejudices, their own outmoded ideas, their own fears which keep them from grappling with the new realities of our age. The American people must find a way to insist upon their own right and responsibility to make political decisions and to determine policy in all spheres of social existence—whether it is foreign policy, the work process, education, race relations, community life. The coming struggle is a political struggle to take political power out of the hands of the few and put it into the hands of the many. But in order to get this power into the hands of the many, it will be necessary for the many not only to fight the powerful few but to fight and clash among themselves as well.20

If, if we get our heads straight about money, I predict that by ad 2000, or sooner, no one will pay taxes, no one will carry cash, utilities will be free, and everyone will carry a general credit card. This card will be valid up to each individual’s share in a guaranteed basic income or national dividend, issued free, beyond which he may still earn anything more that he desires by an art or craft, profession or trade that has not been displaced by automation. (For detailed information on the mechanics of such an economy, the reader should refer to Robert Theobald’s Challenge of Abundance and Free Men and Free Markets, and also to a series of essays that he has edited, The Guaranteed Income. Theobald is an avant–garde economist on the faculty of Columbia University.)

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.

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

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)

As a result, tax revenues and state budgets shrink, at least in relative terms per capita. National debt inevitably grows in order to at least partially cover the shortfall. Of course, it grew enormously after governments bailed out the banks in the wake of the financial crash. The British government did so to the tune of 136.6bn and has admitted that it will never recoup at least £27bn of that amount. In the US the bailout cost at least $14.4 trillion.[56] At the start of of 2019, the US’s national debt stood at nearly $22 trillion, having increased by 10% since Trump took office two years earlier. Under his predecessor Barack Obama, the national debt increased 100%, from $10 trillion to $20 trillion. National debt has to be repaid to the government’s creditors: bondholders, ie people, companies and foreign governments; international organisations such as the World Bank; and private financial institutions. If debt is not or cannot be repaid it becomes increasingly difficult to attract creditors. US national debt when the Great Depression kicked off stood at 16% of GDP and rose to 44% when the depression ended at the end of World War Two. Before the The Great Recession it stood at 65% and by 2013 had exploded to over 100%.[57] Gross national debt and household debt have been at record highs at the same time for the first time ever. Austerity, the socialisation of national debt, therefore becomes an economic necessity, not simply an unfair and immoral ‘political choice’, as is claimed by democratic socialists. That public spending as a share of national income in Britain in 2017 (39.6%) was at the same level as in 2007 (39.6%) after seven years of debt servicing via savage cuts to state welfare and public services suggests national income must have fallen per capita. Indeed, official forecasts suggest that GDP per adult in 2022 will be 18% lower than it would have been had it grown by 2% a year since 2008 – it has averaged 1.1% – broadly the expected rate of growth at that time.

Human labour power is a unique commodity in that it produces surplus value – the amount of value that goes to the capitalist after the worker has been ‘paid’ for the part of the day that equates to the amount they and their family need to live on. The working day is split into two parts: necessary labour time and surplus labour time. Necessary labour time is the time it takes the worker (on average) to produce enough value to buy the commodities they need to reproduce themselves, ie to stay what is socially considered healthy enough to continue working. Surplus labour time is the time the worker works beyond necessary labour time. Since the going rate for labour power is necessary labour time, surplus labour time is surplus value that goes to the capitalist, realised through the sale of the commodities workers produce. For example: a worker in a toy factory is paid £10 a day to work 10 hours; she produces 10 toys a day, and a toy is worth £10 each. The capitalist is only paying the worker for her ability to work one hour each day to produce enough value to reproduce herself (one toy = one hour’s labour = £10). Her necessary labour time is one hour, and her surplus labour time that goes to the capitalist is nine hours. If the worker needs £10 a day to reproduce herself, then that £10 is the value of her labour power. If the capitalist cuts the daily wage below £10, he has pushed the wage below the value of labour power. (Indeed, struggles for better wages are usually struggles to push them back up to the proper value of labour power.) The price of labour power is determined like the price of any other commodity – on average, the cost of its production, ie necessary labour time. But if commodities are sold for the cost of their production then how does the capitalist make any profit? The capitalist purchases the worker’s human labour power  – the ability to work –  but, uniquely, always ends up with more than the amount it cost to purchase the commodity. The wage obscures the fact that the capitalist has only paid for necessary labour time. (Marx calls the social relations that are concealed by economic relations commodity fetishism.) Profit then is essentially unpaid labour. Wage labour is – especially for the poorest workers whose daily subsistence depends exclusively on the sale of their labour-power – wage-slavery. Marx’s investigations led him to realise that his analysis of capitalism must start with the commodity, since capitalism “presents itself as an immense accumulation of commodities”.[66] What all commodities have in common is that they are all exchangeable  –  they all possess exchange-value. And as they are all products of labour, what they all have in common which gives them this exchange-value is general human labour in the abstract. Therefore, the total value of all commodities is determined by total socially necessary labour time – how long they took to produce. (The socially necessary labour time of each finished product includes that of each component that goes into it.)

But the contradiction remains and the cycle repeats itself: the capital investment needed to raise productivity through innovation means constant capital grows relative to variable capital and also, therefore, the surplus value produced by variable capital. Surplus value is converted into capital faster than it is produced and so capital once again over-accumulates. And because the overall mass of capital is now even greater than before, an even greater magnitude of surplus value is required alongside an even greater devaluation of capital in order to reproduce and expand it yet further. Crisis is therefore inherent to the system, as increasing magnitudes of capital become dormant while waiting for profitable conditions to return, and cannot be put down merely to ‘greed’, hoarding or the ‘bad’ or ‘erroneous choices’ of capitalists, politicians, economists and civil servants. Private and public debt rises not because of arbitrary overspending but in order to make up for the insufficient production of surplus value.

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

Senile imperialism What we are seeing then is this: the highest stage of capitalism has gone past its own high point and is elapsing as a historical epoch – automation is undoing the economic relations that underpin imperialism. The productive forces now demand a higher mode of production altogether. Monopoly capitalism had a chance of surviving despite the turmoil it wrought 100 years ago because it was still in its infancy, when the law of value still had plenty of life left in it given that full automation was a distant reality. Today imperialism is old and senile with nowhere left to go but ‘home’, and highly developed automation has brought the expiration of the law of value into view. This is being expressed, even as the world economy becomes increasingly integrated technologically, through the weakening of ‘globalisation’, which, contrary to neoliberal propaganda, was in retreat before the emergence of Britain’s ‘Brexit’ from the EU and the election of Trump. In 2015-16, the G20 economies introduced a record number of trade-restrictive measures, at 21 per month.[236] More precisely, the rising organic composition of capital in developing countries is undermining imperialist economic relations. Over-accumulations of capital are now so great that it is becoming more and more unprofitable to invest at home or overseas.

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

Step Four: Ideal-Week Planning Now you need to take your “only I can do” list and actually plot out how you will get all these things done. I hope your to-do list is shorter than when you picked up this book. If so, that reduction is a massive win in itself. The goal is to schedule all these things out. Literally, go through the list, plot each item into your calendar, and create an automated repeating appointment so it shows up in your calendar on a weekly basis. For example, if only you can write a weekly blog post and you know you need about three hours to write and publish a post, create a three-hour appointment in your calendar from ten to one o’clock on Mondays, for example, and then make it a recurring appointment. The same process can be followed for child-related activities. If you are the person who primarily picks up your kids from school, put an appointment in your calendar for the amount of time it takes to drive or walk to the school, pick them up, and return home. Repeat this task for all the activities you have on the only-you list. Once you’ve entered these activities, you may be thinking, Okay, Lisa, that’s great, but I have now run out of time. So what happens if you actually block everything in and you run out of hours in the week? If I were sitting across from you in a private coaching session, this is what I would ask: •Are all the activities in your calendar truly things only you can do? Is there anything that could be delegated to someone else? •Can any of these activities be batched with something else? For example, could you do research for a blog post on your phone while you run on the treadmill? Can you do phone calls on your commute home or while grocery shopping for your family? •Is everything in your calendar actually aligned with your ideal life plan? Is there anything on the list that is no longer supporting this plan? Be honest with yourself about things that need to go—even if you are having a hard time letting go. •Can you reduce the amount of time it takes to do an activity? This might seem like an incredibly overwhelming exercise, but trust me, it is an incredibly worthwhile exercise. It might seem rigid to schedule everything in your life, but scheduling brings the freedom not to worry about how you are spending your time. You have thought it through, and you know that every worthwhile activity has been accounted for. This system, my friend, is the cure to mom guilt. When you know you have appropriately scheduled dedicated time for your children, your spouse, yourself, and your work, what do you have to feel guilty about?

Corporate interests raised a nearly unified voice heralding automation as a certain and universal beneficial advancement. However, some observers saw the new technology as a cause for concern and cautioned that the final word on automation would depend on the choices that industry and the nation made in the face of difficult questions regarding the pace of automation’s implementation, the uses of the new productivity, and the fate of displaced workers as well as depleted or eliminated job classifications, communities, and even industries. Norbert Wiener, for example, a prominent MIT mathematician and pioneer in the science of cybernetics, emphasized the potentially calamitous economic and social consequences of the new production technology. Wiener had begun to express concerns about the impacts of automation on labor and the entire society during World War II, and he authored two books in the immediate Cold War years warning that potentially disastrous unemployment and related social problems may come from industry’s drive toward automation. He characterized automation and computer controls in the production process as the “modern” or “second” industrial revolution, which even more than the first held “unbounded possibilities for good and evil.” 104 In particular, Wiener feared that the larger impact of the changes caused by automation would be a massive displacement of workers, compounded by the profit-driven indifference of industry. “The automatic machine … will produce an unemployment situation, in comparison with which the present recession and even the depression of the thirties will seem a pleasant joke.” 105

Yes, yes. Everybody wants something. I’m glad my job’s gotten less demanding, in this modern age. Don’t have to tolerate as many prayers. Though, some gods see that as a negative. But I see prayers as giving man false power. If you want to empower a human—to give him real potential—you teach him the Arts. You give him power he can control. Like Alchemy. People’ve forgotten my hand in Alchemy. Now they always relate me to volcanos and blacksmiths. I’m more.” He took the towels from Dorian, to toss them. “Am I not an Arch-chemic, Dori?

As emphasized in the critical success factors related to “Management support, vision, governance, and structure” earlier in this section, a leading practice to secure support from management is to provide a high-level estimation of the benefits expected from the transformation. This is often referred to as a “high-level automation assessment” or a “top-down automation assessment”. For example, such an estimation might show that, by leveraging IA, the organization has the potential to increase revenue by 20% while reducing costs by 30% in the coming 18 months.

Event Rental Systems provides software for party rental and bounce house companies. ERS offers web-based software packages that allow bounce house rental businesses to run on near auto-pilot. The goal of the software is to boost sales while automating and digitizing all tasks related to a party rental business.

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 . . .

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

Volume is key. Twitter now estimates that Russia used more than fifty thousand automated accounts or bots to Tweet election-related content during the 2016 presidential campaign. Twitter and Facebook are the best-known disinformation superhighways, but there are many others. Russian officers have infiltrated everything from 4chan to Pinterest.

Nick Lightfoot holds the position of Vice President in Atlanta, GA. His expertise in automation, coupled with his exceptional business development and client relations skills, has contributed to the company's success. Having been a part of strategic acquisitions and executive management.

This created an opportunity for plastics makers such as Nomacorc to step into the breech. Nomacorc’s value chain made it relatively easy for it to undertake research into the chemistry of wine taint, and to solve the problem. While the traditional cork makers were stuck in an older mind-set (“we’re in the cork business”), the plastics makers could see how to become part of a larger value-creating process. By 2009, Nomacorc’s automated North Carolina factory was churning out close to 160 million plastic stoppers a month, and synthetic corks had captured 20 percent of the market.

More fundamentally, productivity gains from automation may always be somewhat limited, especially compared to the introduction of new products and tasks that transform the production process, such as those in the early Ford factories. Automation is about substituting cheaper machines or algorithms for human labor, and reducing production costs by 10 or even 20 percent in a few tasks will have relatively small consequences for TFP or the efficiency of the production process. In contrast, introducing new technologies, such as electrification, novel designs, or new production tasks, has been at the root of transformative TFP gains throughout much of the twentieth century.

Phase 1: Discovery 1. Define the problem statement What is the challenge that will be solved? The problem statement is defined at this step and becomes the foundation of the project. Here is a sample problem statement: The company has more than one hundred thousand email addresses and has sent more than one million emails in the last twelve months, but open rates remain low at 8 percent, and sales attributed to email have remained flat since 2018. Based on current averages, a 2 percentage-point lift in email open rates could produce a $50,000 increase in sales over the next twelve months. It’s important to note that a strong and valid problem statement should include the value of solving the problem. This helps ensure that the project is worth the investment of resources and keeps everyone focused on the goal. 2. Build and prioritize the issues list What are the primary issues causing the problem? The issues are categorized into three to five primary groups and built into an issues tree. Sample issues could be: •​Low open rates •​Low click rates •​Low sales conversion rates 3. Identify and prioritize the key drivers. What factors are driving the issues and problem? Sample key drivers could include: •​List fatigue •​Email creatives •​Highly manual, human-driven processes •​Underutilized or missing marketing technology solutions •​Lack of list segmentation •​Lack of reporting and performance management •​Lack of personalization 4. Develop an initial hypothesis What is the preliminary road map to solving the problem? Here is a sample initial hypothesis: AI-powered technologies can be integrated to intelligently automate priority use cases that will drive email efficiency and performance. 5. Conduct discovery research What information can we gain about the problem, and potential solutions, from primary and secondary research? •​How are talent, technology, and strategy gaps impacting performance? •​What can be learned from interviews with stakeholders and secondary research related to the problem? Ask questions such as the following: •​What is the current understanding of AI within the organization? •​Does the executive team understand and support the goal of AI pilot projects?

The fund’s computers told the banks which stocks to place in the basket and how they should be traded. Brown himself helped create the code to make it all happen. All day, Medallion’s computers sent automated instructions to the banks, sometimes an order a minute or even a second. After a year or so, Medallion exercised its options, claiming whatever returns the shares generated, less some related costs.

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.

Something amazing happens when we learn a new skill. Our brain starts to form new neural pathways so that something you first had to consciously focus on can become automated. That’s pretty powerful stuff. Now let’s use this principle on your anxiety. If you’ve suffered from any anxiety-related issue for some time, that anxious or at least negative way of thinking has now become an automatism. That’s why you probably seem to get way more negative thoughts than positive ones. For the moment, it’s still just a habit of your brain, one that we can change.

When robots and automation do our most basic work, making it relatively easy for us to be fed, clothed, and sheltered, then we are free to ask, “What are humans for?” Industrialization did more than just extend the average human lifespan. It led a greater percentage of the population to decide that humans were meant to be ballerinas, full-time musicians, mathematicians, athletes, fashion designers, yoga masters, fan-fiction authors, and folks with one-of-a-kind titles on their business cards. With the help of our machines, we could take up these roles—but, of course, over time the machines will do these as well. We’ll then be empowered to dream up yet more answers to the question “What should we do?” It will be many generations before a robot can answer that.

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.

In a world that’s getting ever richer, where cows produce more milk and robots produce more stuff, there’s more room for friends, family, community service, science, art, sports, and all the other things that make life worthwhile. But there’s also more room for bullshit. As long as we continue to be obsessed with work, work, and more work (even as useful activities are further automated or outsourced), the number of superfluous jobs will only continue to grow. Much like the number of managers in the developed world, which has grown over the last thirty years without making us a dime richer. On the contrary, studies show that countries with more managers are actually less productive and innovative.15 In a survey of 12,000 professionals by the Harvard Business Review, half said they felt their job had no “meaning and significance,” and an equal number were unable to relate to their company’s mission.16 Another recent poll revealed that as many as 37% of British workers think they have a bullshit job.17

Fiscal Numbers (the latter uniquely identifies a particular hospitalization for patients who might have been admitted multiple times), which allowed us to merge information from many different hospital sources. The data were finally organized into a comprehensive relational database. More information on database merger, in particular, how database integrity was ensured, is available at the MIMIC-II web site [1]. The database user guide is also online [2]. An additional task was to convert the patient waveform data from Philips’ proprietary format into an open-source format. With assistance from the medical equipment vendor, the waveforms, trends, and alarms were translated into WFDB, an open data format that is used for publicly available databases on the National Institutes of Health-sponsored PhysioNet web site [3]. All data that were integrated into the MIMIC-II database were de-identified in compliance with Health Insurance Portability and Accountability Act standards to facilitate public access to MIMIC-II. Deletion of protected health information from structured data sources was straightforward (e.g., database fields that provide the patient name, date of birth, etc.). We also removed protected health information from the discharge summaries, diagnostic reports, and the approximately 700,000 free-text nursing and respiratory notes in MIMIC-II using an automated algorithm that has been shown to have superior performance in comparison to clinicians in detecting protected health information [4]. This algorithm accommodates the broad spectrum of writing styles in our data set, including personal variations in syntax, abbreviations, and spelling. We have posted the algorithm in open-source form as a general tool to be used by others for de-identification of free-text notes [5].

Man: Don't these precedents suggest that there is something inherently pre-industrial about the applicability of libertarian ideas—that they necessarily presuppose a rather rural society in which technology and production are fairly simple, and in which the economic organization tends to be small-scale and localized? Well, let me separate that into two questions: one, how anarchists have felt about it, and two, what I think is the case. As far as anarchist reactions are concerned, there are two. There has been one anarchist tradition—and one might think, say, of Kropotkin as a representative—which had much of the character you describe. On the other hand there's another anarchist tradition that develops into anarcho-syndicalism which simply regarded anarchist ideas as the proper mode of organization for a highly complex advanced industrial society. And that tendency in anarchism merges, or at least inter-relates very closely with a variety of left-wing Marxism, the kind that one finds in, say, the Council Communists that grew up in the Luxemburgian tradition, and that is later represented by Marxist theorists like Anton Pannekoek, who developed a whole theory of workers' councils in industry and who is himself a scientist and astronomer, very much part of the industrial world. So which of these two views is correct? I mean, is it necessary that anarchist concepts belong to the pre-industrial phase of human society, or is anarchism the rational mode of organization for a highly advanced industrial society? Well, I myself believe the latter, that is, I think that industrialization and the advance of technology raise possibilities for self-management over a broad scale that simply didn't exist in an earlier period. And that in fact this is precisely the rational mode for an advanced and complex industrial society, one in which workers can very well become masters of their own immediate affairs, that is, in direction and control of the shop, but also can be in a position to make the major substantive decisions concerning the structure of the economy, concerning social institutions, concerning planning regionally and beyond. At present, institutions do not permit them to have control over the requisite information, and the relevant training to understand these matters. A good deal could be automated. Much of the necessary work that is required to keep a decent level of social life going can be consigned to machines—at least in principle—which means humans can be free to undertake the kind of creative work which may not have been possible, objectively, in the early stages of the industrial revolution.

One possible benefit of new workforce trends is that people will have more leisure time than in the past. This can happen in one of two ways. Some people will not be needed in the new digital economy, so they will find other ways to construct meaning in their lives outside the workplace. Alternatively, even those who work may find themselves with time for other kinds of pursuits. Rather than most waking hours being spent on work-related tasks, the society of the future may have time for nonwork activities, including art, culture, music, sports, and theater.

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 nodes 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.

Solar-panel manufacturing is relatively simple (it’s less complex than, say, making a car), and a lot of it can be done using automated methods or low-skilled labor, of which China has plenty.

When you launch an AWS resource like an Amazon EC2 instance or Amazon Relational Database (Amazon RDS) DB instance, you start with a default configuration. You can then execute automated bootstrapping actions. That is, scripts that install software or copy data to bring that resource to a particular state.

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The scale shift of labour composition from the nineteenth to the twentieth centuries affected also the logic of automation, that is, the scientific paradigms involved in this transformation. The relatively simple industrial division of labour and its seemingly rectilinear assembly lines could easily be compared to a simple algorithm, a rulebased procedure with an ‘if/then’ structure which has its equivalent in the logical form of deduction. Deduction, not by coincidence, is the logical form that via Leibniz, Babbage, Shannon, and Turing innervated into electromechanical computation and eventually symbolic AI. Deductive logic is useful for modelling simple processes, but not systems with a multitude of autonomous agents, such as society, the market, or the brain.