Implementation Proposal And Spending Plan For A Data Warehouse: Fill & Download for Free

The past fifteen years have seen extensive investments in business infrastructure, which have improved the ability to collect data throughout the enterprise. Virtually every aspect of business is now open to data collection and often even instrumented for data collection: operations, manufacturing, supply-chain management, customer behavior, marketing campaign performance, workflow procedures, and so on. At the same time, information is now widely available on external events such as market trends, industry news, and competitors’ movements. This broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from data—the realm of data science.The Ubiquity of Data OpportunitiesWith vast amounts of data now available, companies in almost every industry are focused on exploiting data for competitive advantage. In the past, firms could employ teams of statisticians, modelers, and analysts to explore datasets manually, but the volume and variety of data have far outstripped the capacity of manual analysis. At the same time, computers have become far more powerful, networking has become ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible. The convergence of these phenomena has given rise to the increasingly widespread business application of data science principles and data-mining techniques.Probably the widest applications of data-mining techniques are in marketing for tasks such as targeted marketing, online advertising, and recommendations for cross-selling. Data mining is used for general customer relationship management to analyze customer behavior in order to manage attrition and maximize expected customer value. The finance industry uses data mining for credit scoring and trading, and in operations via fraud detection and workforce management. Major retailers from Walmart to Amazon apply data mining throughout their businesses, from marketing to supply-chain management. Many firms have differentiated themselves strategically with data science, sometimes to the point of evolving into data mining companies.The primary goals of this book are to help you view business problems from a data perspective and understand principles of extracting useful knowledge from data. There is a fundamental structure to data-analytic thinking, and basic principles that should be understood. There are also particular areas where intuition, creativity, common sense, and domain knowledge must be brought to bear. A data perspective will provide you with structure and principles, and this will give you a framework to systematically analyze such problems. As you get better at data-analytic thinking you will develop intuition as to how and where to apply creativity and domain knowledge.Throughout the first two chapters of this book, we will discuss in detail various topics and techniques related to data science and data mining. The terms “data science” and “data mining” often are used interchangeably, and the former has taken a life of its own as various individuals and organizations try to capitalize on the current hype surrounding it. At a high level, data science is a set of fundamental principles that guide the extraction of knowledge from data. Data mining is the extraction of knowledge from data, via technologies that incorporate these principles. As a term, “data science” often is applied more broadly than the traditional use of “data mining,” but data mining techniques provide some of the clearest illustrations of the principles of data science.NOTEIt is important to understand data science even if you never intend to apply it yourself. Data-analytic thinking enables you to evaluate proposals for data mining projects. For example, if an employee, a consultant, or a potential investment target proposes to improve a particular business application by extracting knowledge from data, you should be able to assess the proposal systematically and decide whether it is sound or flawed. This does not mean that you will be able to tell whether it will actually succeed—for data mining projects, that often requires trying—but you should be able to spot obvious flaws, unrealistic assumptions, and missing pieces.Throughout the book we will describe a number of fundamental data science principles, and will illustrate each with at least one data mining technique that embodies the principle. For each principle there are usually many specific techniques that embody it, so in this book we have chosen to emphasize the basic principles in preference to specific techniques. That said, we will not make a big deal about the difference between data science and data mining, except where it will have a substantial effect on understanding the actual concepts.Let’s examine two brief case studies of analyzing data to extract predictive patterns.Example: Hurricane FrancesConsider an example from a New York Times story from 2004:Hurricane Frances was on its way, barreling across the Caribbean, threatening a direct hit on Florida’s Atlantic coast. Residents made for higher ground, but far away, in Bentonville, Ark., executives at Wal-Mart Stores decided that the situation offered a great opportunity for one of their newest data-driven weapons … predictive technology.A week ahead of the storm’s landfall, Linda M. Dillman, Wal-Mart’s chief information officer, pressed her staff to come up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes’ worth of shopper history that is stored in Wal-Mart’s data warehouse, she felt that the company could ‘start predicting what’s going to happen, instead of waiting for it to happen,’ as she put it. (Hays, 2004)Consider why data-driven prediction might be useful in this scenario. It might be useful to predict that people in the path of the hurricane would buy more bottled water. Maybe, but this point seems a bit obvious, and why would we need data science to discover it? It might be useful to project the amount of increase in sales due to the hurricane, to ensure that local Wal-Marts are properly stocked. Perhaps mining the data could reveal that a particular DVD sold out in the hurricane’s path—but maybe it sold out that week at Wal-Marts across the country, not just where the hurricane landing was imminent. The prediction could be somewhat useful, but is probably more general than Ms. Dillman was intending.It would be more valuable to discover patterns due to the hurricane that were not obvious. To do this, analysts might examine the huge volume of Wal-Mart data from prior, similar situations (such as Hurricane Charley) to identify unusual local demand for products. From such patterns, the company might be able to anticipate unusual demand for products and rush stock to the stores ahead of the hurricane’s landfall.Indeed, that is what happened. The New York Times (Hays, 2004) reported that: “… the experts mined the data and found that the stores would indeed need certain products—and not just the usual flashlights. ‘We didn’t know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane,’ Ms. Dillman said in a recent interview. ‘And the pre-hurricane top-selling item was beer.’”[2]Example: Predicting Customer ChurnHow are such data analyses performed? Consider a second, more typical business scenario and how it might be treated from a data perspective. This problem will serve as a running example that will illuminate many of the issues raised in this book and provide a common frame of reference.Assume you just landed a great analytical job with MegaTelCo, one of the largest telecommunication firms in the United States. They are having a major problem with customer retention in their wireless business. In the mid-Atlantic region, 20% of cell phone customers leave when their contracts expire, and it is getting increasingly difficult to acquire new customers. Since the cell phone market is now saturated, the huge growth in the wireless market has tapered off. Communications companies are now engaged in battles to attract each other’s customers while retaining their own. Customers switching from one company to another is called churn, and it is expensive all around: one company must spend on incentives to attract a customer while another company loses revenue when the customer departs.You have been called in to help understand the problem and to devise a solution. Attracting new customers is much more expensive than retaining existing ones, so a good deal of marketing budget is allocated to prevent churn. Marketing has already designed a special retention offer. Your task is to devise a precise, step-by-step plan for how the data science team should use MegaTelCo’s vast data resources to decide which customers should be offered the special retention deal prior to the expiration of their contracts.Think carefully about what data you might use and how they would be used. Specifically, how should MegaTelCo choose a set of customers to receive their offer in order to best reduce churn for a particular incentive budget? Answering this question is much more complicated than it may seem initially. We will return to this problem repeatedly through the book, adding sophistication to our solution as we develop an understanding of the fundamental data science concepts.NOTEIn reality, customer retention has been a major use of data mining technologies—especially in telecommunications and finance businesses. These more generally were some of the earliest and widest adopters of data mining technologies, for reasons discussed later.Figure 1-1. Data science in the context of various data-related processes in the organization.Data Science, Engineering, and Data-Driven Decision MakingData science involves principles, processes, and techniques for understanding phenomena via the (automated) analysis of data. In this book, we will view the ultimate goal of data science as improving decision making, as this generally is of direct interest to business.Figure 1-1 places data science in the context of various other closely related and data-related processes in the organization. It distinguishes data science from other aspects of data processing that are gaining increasing attention in business. Let’s start at the top.Data-driven decision-making (DDD) refers to the practice of basing decisions on the analysis of data, rather than purely on intuition. For example, a marketer could select advertisements based purely on her long experience in the field and her eye for what will work. Or, she could base her selection on the analysis of data regarding how consumers react to different ads. She could also use a combination of these approaches. DDD is not an all-or-nothing practice, and different firms engage in DDD to greater or lesser degrees.The benefits of data-driven decision-making have been demonstrated conclusively. Economist Erik Brynjolfsson and his colleagues from MIT and Penn’s Wharton School conducted a study of how DDD affects firm performance (Brynjolfsson, Hitt, & Kim, 2011). They developed a measure of DDD that rates firms as to how strongly they use data to make decisions across the company. They show that statistically, the more data-driven a firm is, the more productive it is—even controlling for a wide range of possible confounding factors. And the differences are not small. One standard deviation higher on the DDD scale is associated with a 4%–6% increase in productivity. DDD also is correlated with higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal.The sort of decisions we will be interested in in this book mainly fall into two types: (1) decisions for which “discoveries” need to be made within data, and (2) decisions that repeat, especially at massive scale, and so decision-making can benefit from even small increases in decision-making accuracy based on data analysis. The Walmart example above illustrates a type 1 problem: Linda Dillman would like to discover knowledge that will help Walmart prepare for Hurricane Frances’s imminent arrival.In 2012, Walmart’s competitor Target was in the news for a data-driven decision-making case of its own, also a type 1 problem (Duhigg, 2012). Like most retailers, Target cares about consumers’ shopping habits, what drives them, and what can influence them. Consumers tend to have inertia in their habits and getting them to change is very difficult. Decision makers at Target knew, however, that the arrival of a new baby in a family is one point where people do change their shopping habits significantly. In the Target analyst’s words, “As soon as we get them buying diapers from us, they’re going to start buying everything else too.” Most retailers know this and so they compete with each other trying to sell baby-related products to new parents. Since most birth records are public, retailers obtain information on births and send out special offers to the new parents.However, Target wanted to get a jump on their competition. They were interested in whether they could predict that people are expecting a baby. If they could, they would gain an advantage by making offers before their competitors. Using techniques of data science, Target analyzed historical data on customers who later were revealed to have been pregnant, and were able to extract information that could predict which consumers were pregnant. For example, pregnant mothers often change their diets, their wardrobes, their vitamin regimens, and so on. These indicators could be extracted from historical data, assembled into predictive models, and then deployed in marketing campaigns. We will discuss predictive models in much detail as we go through the book. For the time being, it is sufficient to understand that a predictive model abstracts away most of the complexity of the world, focusing in on a particular set of indicators that correlate in some way with a quantity of interest (who will churn, or who will purchase, who is pregnant, etc.). Importantly, in both the Walmart and the Target examples, the data analysis was not testing a simple hypothesis. Instead, the data were explored with the hope that something useful would be discovered.[3]Our churn example illustrates a type 2 DDD problem. MegaTelCo has hundreds of millions of customers, each a candidate for defection. Tens of millions of customers have contracts expiring each month, so each one of them has an increased likelihood of defection in the near future. If we can improve our ability to estimate, for a given customer, how profitable it would be for us to focus on her, we can potentially reap large benefits by applying this ability to the millions of customers in the population. This same logic applies to many of the areas where we have seen the most intense application of data science and data mining: direct marketing, online advertising, credit scoring, financial trading, help-desk management, fraud detection, search ranking, product recommendation, and so on.The diagram in Figure 1-1 shows data science supporting data-driven decision-making, but also overlapping with data-driven decision-making. This highlights the often overlooked fact that, increasingly, business decisions are being made automatically by computer systems. Different industries have adopted automatic decision-making at different rates. The finance and telecommunications industries were early adopters, largely because of their precocious development of data networks and implementation of massive-scale computing, which allowed the aggregation and modeling of data at a large scale, as well as the application of the resultant models to decision-making.

I write this for those that are willing to think past the silly headlines.The reality is real wages after adjusting for inflation the median USA worker has been going backward indefinitely for decades. This will continue indefinitely.Wages aren’t growing when adjusted for inflation, new data findsThis short video describes the future.We are in a new economic realm where real economic growth means and requires less jobs for humans not more jobs as ever cheaper ever more intelligent automated machines are simply more economically efficient than human workers.So in my opinion what happens is we need to realize as a society and global culture that working for a living is going to end eventually for most humans. Probably sooner as opposed to later.It is just as a question of when not if.Humans one day in the future will marvel that most humans in ancient times (e.g. the present time) were actually more or less enslaved to a certain extent trading a good portion of their lives for the mundane task of working for a living to scratch out an existence. Some humans survived better than others but the vast majority of humans had no choice other than to trade a good portion of their lives for work. The fortunate humans had more choices than the less fortunate but the similarities among humans outweighed the differences.In the future once the vast majority of humans are freed from the artificial construct of working, humans may realize their true potential.So here we are on the human evolutionary path where the current economic system of Capitalism - Wikipedia is just yet another transitory economic waypoint in the list of many prior ones to the next destination. Who knows what the final destination is as there will always be another step.Yes this time it is different yet again which is no surprise since history is filled with revolutionary waypoints. For instance the institutions of Government - Wikipedia and Democracy - Wikipedia are recent events of the geological timescale. Ever wonder when was human language created? Now that is real milestone waypoint.Ever cheaper ever more intelligent automated machines will replace the vast majority of humans workers providing an ever increasing bounty of ever cheaper economic goods and services. These machines will be self replicating, self maintaining and eventually be able to create the next generation of machines. This process is well underway at the present time despite the fact that most humans are unaware of it.Scarcity will continue to fade as these machines will work ceaselessly without complaint or issue. At that point the notion of Capitalism - Wikipedia will likely fade into oblivion as the supply of all goods and services without exception exceeds demand permanently.The key point is that in the past machines automated human muscles. We are now well underway in automating the human brain and the human senses. Ever cheaper ever more intelligent automated machines have senses that are vastly superior to humans, as they can see, touch and hear in multiple directions simultaneously - as well as detect infrared radiation (heat), ultrasonic energy, etc … These machines are and will be superior to human workers in every aspect. When was the last time any human answered more questions correctly than Google - Wikipedia?Intelligent automated machines are now essentially automating the human brain as these machines learn faster and more precisely than most humans with regard to required job skills for performing work in a growing number of fields. Furthermore, these intelligent automated machines are less prone to error and far cheaper than humans. These intelligent automated machines simply represent more efficient and economic workers with regard to producing less costly economic goods and services compared to the vast majority of human workers. The incremental cost of scaling up the business is very low as the incremental cost of replicating a machine is very low. Adding an additional human worker is very high by comparison.While many incorrectly argue that automation has been going on for centuries and humans have and will always find other means of making a living by creating new jobs in new fields, these same people also incorrectly argue that nothing ever changes. This is known as the Luddite Fallacy which is now proven false. The STEM Fallacy is a sound refutation of the Luddite Fallacy.For instance, automated systems will self diagnose, self correct and self repair at least 90% of the time trending well above that over time. It is a fairytale myth that human workers in any substantial number will be required to run these systems. The design and implementation goal of these automated systems is to remove as many error-prone costly human workers from the process. The goal is always fewer costly human workers not more.So we have a permanent growing excess of displaced human workers that are increasingly not economically competitive. This means fewer jobs and declining wages for the median human worker as time progresses.As a person that has made a very good living in Silicon Valley - Wikipedia by permanently eliminating the jobs of others for 32 years, this issue is real for me. Silicon Valley - Wikipedia employs and will always employ a small minority of humans. The specific intellectual demands required only suit a minority of humans. It is analogous to the issue of why the majority of humans will never win a Nobel Prize - Wikipedia like my physics professor Hugh David Politzer - Wikipedia did. All humans are equal in value but have vastly differing skills and intrinsic abilities.So here we are as ever increasing levels of economic goods and services are produced by ever cheaper ever more capable intelligent automated machines coupled with permanently declining human jobs and declining wages.Machines have no healthcare costs, no sick leave, no vacations, no employee turnover, no employee dissatisfaction, the elimination of human errors, declining costs over time as these machines become cheaper, etc … I could trivially list over 100 reasons why ever cheaper ever more intelligent automated machines will always be vastly superior to human workers for the vast majority of jobs.What is likely required is that universal Basic income - Wikipedia needs to be phased in gradually over the coming decades at ever increasing levels as an ever increasing share of economic goods and services are produced by ever cheaper ever more capable intelligent automated machines. This creates a permanent growing excess of human workers with a sustained permanent deflationary trend in the median wage for all human workers. As intelligent automated machines get more proficient and cheaper by the year, businesses that replace human workers with ever cheaper ever more capable intelligent automated machines are the inevitable long term economic winners.The naive wishful thinking that humans can economically compete with these ever cheaper ever more capable intelligent automated machines is just that wishful thinking not grounded in reality. Any new jobs created will be also automated at an ever increasing rate.The upside in the future is that in that humans are not predestined to work for a living as that is precisely why we have ever cheaper ever more capable intelligent automated machines. Technology will continue to advance at an increasing rate but the real change is that an increasing fraction of humans will no longer need and / or be able to work for a decent living.In terms of framing the problem I prefer the term ever cheaper ever more intelligent automated machines over “robots” which connotes a physical entity like a human worker. In the realm of Artificial intelligence - Wikipedia these “robots” are merely software entities which are some of the most economically productive entities on the planet. Google search is one such ever cheaper ever more intelligent automated machine that is virtual not physical in nature.There is and will continue to be fewer well paying jobs and rising wealth inequality as positive GDP growth occurs with declining employment as the rate of worker productivity increases exceeds the underlying rates of business and GDP growth.The existing economic framework of exponentially rising worker productivity creates rising wealth inequality as ever cheaper, ever more capable intelligent automated machines continue to inevitably replace the majority of low and medium skill workers.The majority of remaining jobs are ever lower paying service jobs which may keep unemployment temporarily low but only increases the issue of rising wealth inequality and underemployment.Capital Investment seeks the greatest Return on investment. The greatest return on capital investment is achieved by investing in ever cheaper, ever more capable intelligent automated machines as opposed to creating jobs for ever more expensive humans. As time passes human workers have rising costs whereas these intelligent automated machines that replace them have decreasing costs.What Silicon Valley has been, is, and will be doing in the future is reducing the cost of and increasing the supply of an ever increasing array of economic goods and services. The most efficient use of Venture capital is to use Capital Investment to replace human jobs with ever cheaper ever more intelligent automated machines. Society should learn to accept this inevitable economic process as the vast majority of jobs in the end will be automated over time. This automation process has been well underway for over 3 decades.This is the business model of Silicon Valley, the home of the largest most profitable corporations on Earth, where the goal is more profits, more sales, more productive employees and less employees. Amazon now has 45,000 robots in its warehouses. Wal-Mart's new robots scan shelves to restock items faster as "Walmart Replaced Me with a Robot".This scenario of permanently replacing human workers plays out on Sand Hill Road on a daily basis where Venture capital is deployed to fund Startup companies with the goal of replacing workers with intelligent automated machines to increase economic efficiency. There is a growing ocean of Venture capital which seeks this destination as the future earnings are safely protected by United States patent law which reduces investment risk. Higher rewards coupled with reduced capital risk is optimal.Estimating potential future cash flow is always uncertain to a degree, but the United States patent law is deliberately constructed to precisely mitigate this economic obstacle by protecting Intellectual property rights which are the sole property of the Startup company. As a Silicon Valley worker at many early stage startups, I am quite familiar with this process. The venture capitalists understand this as well so the risk is quite manageable now and indefinitely into the future which is why I am positive that many diverse industries will be increasingly automated within a few decades.The first jobs to be replaced are the jobs with the greatest Return on investment that are low skilled, reasonably paid and repetitive. For instance manufacturing and transportation (Autonomous car) will be fully automated within a few decades. As time progresses, all higher paying jobs with a lower degree of difficulty will be partially automated and then fully automated. The last jobs to be automated in the end will be the jobs that create the automation process.As far as whether Universal Basic income is adopted it is only a question of when - not if - for the USA. The advent of ever cheaper ever more capable intelligent machines will likely force the world to adopt a universal Basic income in the coming decades as many of the existing assumptions of Capitalism will no longer hold such as the vast majority of people can no longer work for a living without government assistance.In a global economy there is no way to stop the increasing use of ever cheaper, ever more capable intelligent automated machines replacing a growing number of human workers. On a global basis, corporations that use humans lose while corporations that use ever cheaper, ever more capable intelligent automated machines win. In the end, increasing economic efficiency is unstoppable.Universal Basic income is a free market idea where citizens and consumers can decide how to spend their money in the Free Market of economic goods and services. It is far better than the government picking economic winners and losers, or worse yet, directly providing economic goods and services. Economic liberalism is a better model than government directed Socialism.Universal Basic income will allow consumers to purchase ever cheaper goods and services as most humans will be either unemployed or working in jobs with ever decreasing wages. Again raising the minimum wage is a spectacularly bad idea as it only accelerates the rate at which ever cheaper automated intelligent machines permanently replace human workers as investment capital is focused on replacing the ever more expensive human workers.We need to transition to a society where work is optional as most people likely do not work to sustain themselves and instead rely on universal Basic income. Europe is waking up to this new reality and the rest of the world needs to do the same.Universal Basic income is unconditional in that it is received by all citizens at the same amount regardless of income, wealth or assets. It does not depend on employment, lack thereof or age. The level / amount of Universal Basic income will likely be phased in over many decades increasing as time passes. The initial level will be minimal at best.Universal Basic income replaces all existing public assistance programs such as the existing regrettable welfare program that reduces benefits as income rises (an absurd negative economic incentive).Let the free market decide what a fair wage is by eliminating all Minimum wage / living wage laws. If someone wants to work for $5 / hour let them. Higher wages only accelerates the rate at which Capital Investment is used to replace human jobs with ever cheaper ever more intelligent automated machines. A higher minimum wage and an expected future minimum higher wage increases the expected return for the Capital Investment making it more profitable and lower risk. The economic goal for Venture capital in Silicon Valley is to use Capital Investment to replace human jobs.Universal Basic income is funded by taxing corporations which indirectly taxes the work of the ever cheaper ever more capable intelligent automated machines that work autonomously 24 x 7. Humans workers will be the exception going forward as the vast majority of economic goods and services will be produced by ever cheaper ever more capable intelligent automated machines that work autonomously 24 x 7. These robotic workers do not care if their work is taxed by the way.As time marches on, an ever decreasing fraction of human workers will be highly economically productive. This fact is independent of whether or not universal Basic income is implemented. This is pretty obvious from observing Silicon Valley where the most highly valued successful companies focus on employee productivity, intentionally not hiring more workers and instead only hiring the absolute best workers. The median human worker simply will never be valuable enough to contribute meaningfully to the required innovation that is Silicon Valley.Furthermore while many people can recognize the problem of growing global wealth inequality they incorrectly assess blame and propose counterproductive solutions such as living wages and an ever higher Minimum wage which in fact only increases the growing global wealth inequality.I have worked over 30 years at over 20 companies making a great living permanently destroying the jobs of others thru automation and productivity software. Often times it comes in the form of productivity software which allows 1 worker to do the job of what previously would require N workers to do, reducing the number of jobs by (N-1). It's time for most to wake up and intellectually digest what has been, is and will be happening in Silicon Valley. Positive economic growth requires fewer human workers not more human workers as these ever cheaper, automated intelligent machines are essentially unpaid slave labor.Unfortunately there will be a growing excess of human labor which will cause the wages for the remaining jobs (nannies, painters, musicians, artists, etc …) to plummet as the supply of workers greatly exceeds demand.This makes the idea of Universal Basic income an attractive solution to this issue because the possibility of working for a decent living is disappearing rapidly for the vast majority of human workers. This is the direct result of the unstoppable inevitable rise of ever cheaper, ever more capable intelligent automated machines.The companies using robots to produce economic goods and services will win in the marketplace while the companies using human workers will lose. This is an optimal desirable economic result. These ever cheaper automated intelligent machines are more economically productive than the average low-to-medium skilled human worker. The long-term economic race has already been lost forever for all these low to medium skill workers despite the fact that most humans are in denial, for obvious reasons, as losing to a machine is embarrassing.The key issue to resolve is the political economic transition as machines, not humans, increasingly generate the majority of economic output. The economic gains will not be distributed evenly causing a growing wealth disparity. Intellectual property holders will reap a growing share of wealth as is happening today. On the whole, the world will get richer, but the average worker will likely get poorer as the wage deflation rate will exceed the deflation rate of produced economic goods and services.Traditionally the wages of human workers have been taxed to fund governments. Going forward, the ever cheaper ever more intelligent automated machines will need to be increasingly taxed somehow to account for this revenue shortfall. So much for the wisdom of reducing corporate tax rates.What is happening, and will happen, is a massive deflationary continuous event both in terms of human wages and produced economic goods and services. Ironically any minimum wage, or even worse, a rising minimum wage, will ensure that the vast majority of human workers will lose at a faster rate as the intelligent automated machine becomes ever cheaper while the human worker becomes more expensive.A sample of current articles documents that this process has been well under way for years. This is not news to anyone who has been paying attention in the last several decades.Can universal basic income counter the ill-effects of the gig economy?While aid donors embrace transformation, India mulls a universal basic incomeWhat universal basic income could mean for the future of workBasic Income Is No Silver Bullet, But It May Still Save UsiPhone manufacturer Foxconn plans to replace almost every human worker with robotsChinese factory replaces 90% of human workers with robots. Production rises by 250%, defects drop by 80%Bill Gates says it's too early for basic income, but over time 'countries will be rich enough'Bill Gates says robots that take your job should pay taxesElon Musk doubles down on universal basic income: 'It's going to be necessary'Where It’s Made: A Ford Car in ChinaA Robot Revolution, This Time in China

None really as Silicon Valley - Wikipedia is the best place on the planet for a highly proficient engineer.The opportunity cost of working else is simply too high.I worked at 22 companies in 32 years with no problems, mainly startups.There is a chance that your first companies are sub-optimal but after a few years of productive work you will be contacted relentlessly by other companies seeking your talents.The initial cost of living is high but very doable especially with two incomes in the long term.If you are a highly proficient engineer jobs will find you as Silicon Valley - Wikipedia is built on references and connections as engineers move freely from company to company. That is why H-1B visa - Wikipedia workers have been highly prized by the big technology giants as these guys are locked in for a few years before their chains are released. Even they are treated great because the big companies hope to retain some of them longterm.Data science - Wikipedia is a great field and highly valued by innovative companies.Silicon Valley - Wikipedia simply is and will remain the most economically productive place on the planet. These are the reasons why.Silicon Valley - Wikipedia is and will irreversibly change the world ending the horrid requirement that most humans work their entire lives for a living whether they want to or not.Silicon Valley - Wikipedia seeks out, creates and embraces change as this time it is truly different. Living in the past as most of the world does is not economically productive.Silicon Valley - Wikipedia will continue to prosper enormously as everyone in the world comes here to make their fortune because previously everyone else in the world has come to Silicon Valley - Wikipedia for decades to make their fortune. Silicon Valley - Wikipedia is the economic epicenter of the world for this reason.Silicon Valley - Wikipedia unquestionably has and will continue to have the greatest density of creative intellectual technical talent on the planet. This is precisely the reason why everyone comes here to make their fortune.Silicon Valley - Wikipedia is and will eliminate the need for most humans to work for a living which is the most profitable business on the planet.What is key is that automation killing all jobs is only a matter of when not if.The core issue is an ever-increasing majority of human workers will become permanently unemployed as ever cheaper ever more intelligent automated machines inevitably replace more expensive human workers.Economic growth now and in the future requires that fewer jobs are created for human workers. This is the desirable inevitable consequence of the capitalist systemSo as a global society we need to have plan to deal with this inevitable change.Automation has been, is and will always be a friend to human society if human society on a global basis is prepared to deal with the inevitable consequences.I spent my working career increasing automation and increasing efficiency. This consequently reduces the hours worked by humans while increasing the output economic goods and services at an ever decreasing price.Why not have ever cheaper ever more intelligent automated machines create the vast majority of required economic goods and services as opposed to human workers?Why not have most human workers working less and retiring earlier?There is much more to life than working forever.Fortunately, automation has been going on for decades and will continue as an irreversible process as ever cheaper ever more intelligent automated machines permanently replace more costly human workers.I write this answer for the people that have an open mind and are not living in the past this is what I feel most effectively deas with the tractable issues that automation creates.The question at hand is how we deal with automation going forward as a society.Automation is only a threat because society has yet to acknowledge automation is happening let alone effectively a having a plan to address it.This short video describes the future.Investment capital is deployed to reduce the number of increasingly expensive human workers with ever cheaper ever more intelligent automated machines to permanently replace these workers. The most profitable businesses succeed in the marketplace which are those businesses with ever cheaper ever more intelligent automated machines and fewer increasingly expensive human workers. The contradiction is that rising wages leads to widespread pervasive job destruction. Most jobs if not all jobs in the end will become automated. This is only a question of when not if.Furthermore while many people can recognize the problem of growing global wealth inequality they incorrectly assess blame and propose counterproductive solutions such as living wages and an ever higher Minimum wage which in fact only increases the growing global wealth inequality. Higher wages only accelerates the rate at which Capital Investment is used to replace human jobs with ever cheaper ever more intelligent automated machines. A higher minimum wage and an expected future minimum higher wage increases the expected return for the Capital Investment making it more profitable and lower risk. The economic goal for Venture capital in Silicon Valley is to use Capital Investment to replace human jobs.I greatly prefer the following which I will extensively argue is inevitablethe elimination of most government means-tested Welfare - Wikipediathe implementation of a substantial level of universal Basic income - Wikipedia that starts at a level that equals existing welfare benefits and minimum wage levelsEver cheaper ever more intelligent automated machines have no healthcare costs, no sick leave, no vacations, no employee turnover, no employee dissatisfaction, far fewer errors than humans, declining costs over time as these machines become cheaper, etc …I could easily list over 100 reasons why ever cheaper ever more intelligent automated machines will always be vastly superior to human workers for the vast majority of jobs.The key point is that in the past machines automated human muscles. We are now well underway in the process of automating the human brain and the human senses. Ever cheaper ever more intelligent automated machines have senses that are superior to humans in many respects, as they can see, touch and hear in multiple directions simultaneously - as well as detect infrared radiation (heat), ultrasonic energy, etc … These machines are and will be superior to human workers in the long run in almost every aspect. When was the last time any human answered more questions correctly than Google - Wikipedia?Intelligent automated machines are now essentially automating the human brain as these machines learn faster and more precisely than most humans with regard to required job skills for performing work in a growing number of fields. Furthermore, these intelligent automated machines are less prone to error and far cheaper than their human counterparts. These intelligent automated machines simply represent more efficient and economic workers with regards to producing less costly economic goods and services compared to the vast majority of human workers. The incremental cost of growing a business is very low as the incremental cost of replicating a machine is very low. Adding an additional human worker is very high in comparison involving training costs and the uncertainty of employee retention.While many incorrectly argue that automation has been going on for centuries and humans have and will always find other means of making a living by creating new jobs in new fields, these same people also incorrectly argue that nothing ever changes. This is known as the Luddite Fallacy which has now been proven false. The STEM Fallacy is a sound refutation of the Luddite Fallacy.For instance, automated systems will self diagnose, self-correct and self-repair at least 90% of the time trending well above that over time. It is a fairytale myth that human workers in any substantial number will be required to run these systems. The design and implementation goal of these automated systems is to remove as many error-prone costly human workers from the process. The end goal is always fewer human workers not more human workers.So we have a permanent growing excess of displaced human workers that are increasingly not economically competitive. This means fewer jobs and declining wages for the median human worker as time progresses.As a person that has made a very good living in Silicon Valley - Wikipedia by permanently eliminating the jobs of others for 32 years, this issue is real for me. Silicon Valley - Wikipedia employs and will always employ a small minority of humans. The specific intellectual demands required only suit a minority of humans. It is analogous to the issue of why the majority of humans will never win a Nobel Prize - Wikipedia like my physics professor Hugh David Politzer - Wikipedia did. All humans are equal in value but have vastly different skills and intrinsic abilities.So here we are as ever-increasing levels of economic goods and services are produced by ever cheaper ever more capable intelligent automated machines coupled with permanently declining human jobs and declining wages.What is likely required is that universal Basic income - Wikipedia needs to be phased in gradually over the coming decades at ever increasing levels as an ever-increasing share of economic goods and services are produced by ever cheaper ever more capable intelligent automated machines. This creates a permanent growing excess of human workers with a sustained permanent deflationary trend in the inflation-adjusted median wage for all human workers. As intelligent automated machines get more proficient and cheaper by the year, businesses that replace human workers with ever cheaper ever more capable intelligent automated machines are the inevitable longterm economic winners.The naive wishful thinking that humans can economically compete with these ever cheaper ever more capable intelligent automated machines is just that wishful thinking not grounded in reality. Any new jobs created will be also automated at an ever-increasing rate.The upside in the future is that in that humans are not predestined to work for a living as that is precisely why we have ever cheaper ever more capable intelligent automated machines. Technology will continue to advance at an increasing rate but the real change is that an increasing fraction of humans will no longer need and/or be able to work for a decent living.The economic details and rationale of why this will happen is as follows:There is and will continue to be fewer well paying jobs and rising wealth inequality as positive GDP growth occurs with declining employment as the rate of worker productivity increases exceeds the underlying rates of business and GDP growth.The existing economic framework of exponentially rising worker productivity creates rising wealth inequality as ever cheaper, ever more capable intelligent automated machines continue to inevitably replace the majority of low and medium skill workers.The majority of remaining jobs are lower paying service jobs which may keep unemployment temporarily low but only increases the issue of rising wealth inequality and underemployment.Capital Investment seeks the greatest Return on investment. The greatest return on capital investment is achieved by investing in ever cheaper, ever more capable intelligent automated machines as opposed to creating jobs for ever more expensive humans. As time passes human workers have rising costs whereas these intelligent automated machines that replace them have decreasing costs.What Silicon Valley has been, is, and will be doing in the future is reducing the cost of and increasing the supply of an ever increasing array of economic goods and services. The most efficient use of Venture capital is to use Capital Investment to replace human jobs with ever cheaper ever more intelligent automated machines. Society should learn to accept this inevitable economic process as the vast majority of jobs in the end will be automated over time. This automation process has been well underway for over 3 decades.This is the business model of Silicon Valley, the home of the largest most profitable corporations on Earth, where the goal is more profits, more sales, more productive employees and less employees. Amazon now has 45,000 robots in its warehouses. Wal-Mart's new robots scan shelves to restock items faster as "Walmart Replaced Me with a Robot".This scenario of permanently replacing human workers plays out on Sand Hill Road on a daily basis where Venture capital is deployed to fund Startup companies with the goal of replacing workers with intelligent automated machines to increase economic efficiency. There is a growing ocean of Venture capital which seeks this destination as the future earnings are safely protected by United States patent law which reduces investment risk. Higher rewards coupled with reduced capital risk is optimal.Estimating potential future cash flow is always uncertain to a degree, but the United States patent law is deliberately constructed to precisely mitigate this economic obstacle by protecting Intellectual property rights which are the sole property of the Startup company. As a Silicon Valley worker at many early stage startups, I am quite familiar with this process. The venture capitalists understand this as well so the risk is quite manageable now and indefinitely into the future which is why I am positive that many diverse industries will be increasingly automated within a few decades.The first jobs to be replaced are the jobs with the greatest Return on investment that are low skilled, reasonably paid and repetitive. For instance, manufacturing and transportation (Autonomous car) will likely be fully automated over the next few decades. As time progresses, all higher paying jobs with a lower degree of difficulty will be partially automated and then fully automated. The last jobs to be automated in the end will be the jobs that create the automation process.As far as whether Universal Basic income is adopted it is only a question of when - not if - for the USA. The advent of ever cheaper ever more capable intelligent automated machines will likely force the world to adopt a universal Basic income in the coming decades as many of the existing assumptions of Capitalism will no longer hold as the vast majority of people can no longer work for a living without government assistance.In a global economy there is no way to stop the increasing use of ever cheaper, ever more capable intelligent automated machines replacing a growing number of human workers. On a global basis, corporations that use humans lose while corporations that use ever cheaper, ever more capable intelligent automated machines win. In the end, increasing economic efficiency is unstoppable.Universal Basic income is a free market idea where citizens and consumers can decide how to spend their money in the Free Market of economic goods and services. It is far better than the government picking economic winners and losers, or worse yet, directly providing economic goods and services. Economic liberalism is a better model than government directed Socialism.Universal Basic income will allow consumers to purchase ever cheaper goods and services as most humans will be either unemployed or working in jobs with ever decreasing wages. Again raising the minimum wage is a spectacularly bad idea as it only accelerates the rate at whichever cheaper intelligent automated machines permanently replace human workers as investment capital is focused on replacing the ever more expensive human workers.We need to transition to a society where work is optional as most people likely do not work to sustain themselves and instead rely on universal Basic income. Europe is waking up to this new reality and the rest of the world needs to do the same.Universal Basic income is unconditional in that it is received by all citizens at the same amount regardless of income, wealth or assets. It does not depend on employment, lack thereof or age. The level/amount of Universal Basic income will likely be phased in over many decades increasing as time passes. The initial level will be minimal at best.Universal Basic income replaces all existing public assistance programs such as the existing regrettable welfare program that reduces benefits as income rises (an absurd negative economic incentive).Universal Basic income is funded by taxing corporations which indirectly taxes the work of the ever cheaper ever more capable intelligent automated machines that work autonomously 24 x 7. Humans workers will be the exception going forward as the vast majority of economic goods and services will be produced by ever cheaper ever more capable intelligent automated machines that work autonomously 24 x 7. These robotic workers do not care if their work is taxed by the way.As time marches on, an ever decreasing fraction of human workers will be highly economically productive. This fact is independent of whether or not universal Basic income is implemented. This is pretty obvious from observing Silicon Valley where the most highly valued successful companies focus on employee productivity, intentionally not hiring more workers and instead only hiring the absolute best workers. The median human worker simply will never be valuable enough to contribute meaningfully to the required innovation that is Silicon Valley.I have worked over 30 years at over 20 companies making a great living permanently destroying the jobs of others thru automation and productivity software. Often times it comes in the form of productivity software which allows 1 worker to do the job of what previously would require N workers to do, reducing the number of jobs by (N-1). It's time for most to wake up and intellectually digest what has been, is and will be happening in Silicon Valley. Positive economic growth requires fewer human workers not more human workers as these ever cheaper, intelligent automated machines are essentially unpaid slave labor.Unfortunately, there will be a growing excess of human labor which will cause the wages for the remaining jobs (nannies, painters, musicians, artists, etc …) to plummet as the supply of workers greatly exceeds demand.This makes the idea of Universal Basic income an attractive solution to this issue because the possibility of working for a decent living is disappearing rapidly for the vast majority of human workers. This is the direct result of the unstoppable inevitable rise of ever cheaper, ever more capable intelligent automated machines.The companies using robots to produce economic goods and services will win in the marketplace while the companies using human workers will lose. This is an optimal desirable economic result. These ever cheaper intelligent automated machines are more economically productive than the average low-to-medium skilled human worker. The long-term economic race has already been lost forever for all these low to medium skill workers despite the fact that most humans are in denial, for obvious reasons, as losing to a machine is embarrassing.The key issue to resolve is the political-economic transition as machines, not humans, increasingly generate the majority of economic output. The economic gains will not be distributed evenly causing a growing wealth disparity. Intellectual property holders will reap a growing share of wealth as is happening today. On the whole, the world will get richer, but the average worker will likely get poorer as the wage deflation rate will exceed the deflation rate of produced economic goods and services.Traditionally the wages of human workers have been taxed to fund governments. Going forward, the robotic workers will need to be increasingly taxed somehow to account for this revenue shortfall. So much for the wisdom of reducing corporate tax rates since taxing corporations is likely the only way to tax robotic workers.What is happening, and will happen, is a massive deflationary continuous event both in terms of human wages and produced economic goods and services. Ironically any minimum wage, or even worse, a rising minimum wage, will ensure that the vast majority of human workers will lose at a faster rate as the intelligent automated machine becomes ever cheaper while the human worker becomes more expensive.A sample of current articles documents that this process has been well under way for years. This is not news to anyone who has been paying attention in the last several decades.Can universal basic income counter the ill-effects of the gig economy?While aid donors embrace transformation, India mulls a universal basic incomeWhat universal basic income could mean for the future of workBasic Income Is No Silver Bullet, But It May Still Save UsiPhone manufacturer Foxconn plans to replace almost every human worker with robotsAmazon now has more than 100,000 warehouse robots on its payrollChinese factory replaces 90% of human workers with robots. Production rises by 250%, defects drop by 80%Bill Gates says it's too early for basic income, but over time 'countries will be rich enough'Bill Gates says robots that take your job should pay taxesElon Musk doubles down on universal basic income: 'It's going to be necessary'Where It’s Made: A Ford Car in ChinaA Robot Revolution, This Time in China