Whether describing your top sales performers or evaluating your portfolio against a benchmark, Quill identifies the facts that are foundational to your narrative. Since not every result from this data is interesting or important, Quill uses your business rules to identify thresholds, drivers, trends and relationships to determine what matters most to your business.
The software would presumably work best with data-loaded events which aren’t open to much interpretation, Some of their early work, for instance, was automatically writing summaries for baseball games. Baseball is an easily quantifiable sport which progress through an ordered time-scale. You can review the box score of a baseball game and reconstruct a fairly accurate history of what was happening—who was ahead, what major events occured in the game, etc.
What Narrative Science does is not write stories in advance, as much as it wrotes potential stories in advance. It identifies the ways in which stories might be written.
When Narrative Science inks a deal with a new client, their writers begin work customizing the existing platform within a configuration layer. House style—how to format names and dates, when to italicize, and so on—is the easy part. What takes more time is establishing the facts and inferences that will conceivably be drawn from client data, as well as a “constellation” of possible story angles through which the data might be presented. In the case of baseball, this means “all the scenarios that might be derived from the raw data of a box score": the slugfest, the shutout, the pitcher’s duel, the back-and-forth, postponed by rain, on and on.
For their part, they claim this isn’t an effort to put writers out of work:
[...] while Narrative Science will certainly replace some types of human-generated writing, the stories they’re most excited about are the ones journalists rarely cover. Because of readership expectations, no journalist would write a story with relevance to only one person, or a few—sports writers, for instance, don’t write about Little League games in the first place. That’s why the company’s putting special effort into what they call “audience of one” applications—narratives that bring professional-caliber prose insight where right now we only have confusing data.
The author the Atlantic article notes the following:
As a journalist and fiction writer, it of course struck me to think about the relevance of all of this to what I do. I arrived at the Chicago office prepared to have my own biases confirmed—that the human mind is a sacred mystery, that our relationship to words is unique and profound, that no automaton could ever replicate the writerly experience. But speaking with Hammond, I realized how much of the writing process—what I tend to think of as unpredictable, even baffling—can be quantified and modeled.
There’s the key: “how much of the writing process...can be quantified and modeled.” The quantification and modeling of a human-centered process is the key to automation. This last passage has me wondering how much of lives and jobs we might find surprisingly quantifiable.
Martin Ford’s book, Rise of the Robots was on many “best of” books for 2015, It’s a broad overview of the problems of automation and the potential effects on the economy and society.
Beyond the standard litany of automation horror stories, Ford offers some interesting analysis, primarily in seven reasons why the answer to the question “Is this time different?” is absolutely yes.
A Bear Market for labor’s share, and a Raging Bull for corporations
Declining labor force participation
Diminishing job creation, lengthening jobless recoveries, and soaring long-term unemployment
Declining incomes and underemployment for recent college graduates
Polarization and part-time jobs
He makes another obvious point which escapes many: workers are also consumers, He captures this in an anecdote:
There is an often-told story about Henry Ford II and Walter Reuther, the legendary head of the United Auto Workers union, jointly touring a recently automated car manufacturing plant. The Ford Motor Company CEO taunts Reuther by asking, “Walter, how are going to get these robots to pay union dues?” Reuther comes right back at Ford, asking, “Henry, how are you going to get them to buy your cars?”
Could companies collectively automate away their own markets?
The book ends predictably with a call for guaranteed basic income, but Ford commendably includes figures and analysis in which he claims a $10,000/year basic income would effectively pay for itself in growth and increased tax revenue.
Ford briefly detours into the technological ideas of the singularity, nanotechnology, and an Elysium style future where the wealthy are sequestered in idyllic communities, attended to by robots and protected form the teeming hordes of zombie-like masses.
The book is a good overview of the problem. Beyond call for basic income, it doesn’t provide a whole lot of solutions, but perhaps that just reflects that the solutions are much trickier than the problem?
Swiss Bank UBS has released a report (to which I am unable to find a direct link) saying that the move to automation is the “fourth industrial revolution,” and will drive considerable gains to the already wealthy, and increase income inequality.
The richest stand to gain more from the introduction of new technology than those in poorer sections of society, according to a report which warns that policymakers may be required to intervene to tackle the widening inequality.
The so-called fourth industrial revolution, following on from the introduction of steam power, electricity and electronics, will have less of an impact on developed economies, such as Switzerland, Singapore and the UK. Emerging markets – notably in parts of Latin America and India – will suffer when artificial intelligence and robots become widely used, reducing the competitive advantage of their cheap labour.
There is significant conservative spin on this article, clearly, but it summarizes what apparently happening on an earnings call for Panera Bread.
[...] the CEO of one large restaurant chain warns that customers are now comfortable enough with automated systems to allow another huge round of job cuts in the near future, To put it simply, the government is pricing labor out of the market, and machines are standing ready to fill the gap.
And, the conservative summary on the end:
This is a paradigm shift, and it will never be reversed — once the huge capital outlays for automation have been made, and customers have grown comfortable with it, those jobs will be gone for good, Contrary to left-wing rhetoric about “burger-flipper” jobs, young people in many demographic groups will miss these low-skill on-ramps to employment, And, unlike the banking industry and the automated teller machines President Obama once ignorantly blamed for the high unemployment rates of his presidency, these service industries won’t find new uses for the labor displaced by automation, They’re explicitly seeking ways to make do with fewer hours of high-cost, heavily burdened human labor… and, as the digital generation comes of age, such methods will be found.
Interesting information in this article about how automation drives the pace of a chicken assembly line.
Today, the maximum permissible rate is 140 birds per minute, and the industry recently pushed for a higher limit of 170.
[...] Line speeds are set by the Department of Agriculture with an eye to food safety, rather than worker well-being. The rate refers to the speed at which machines eviscerate each carcass, and that number naturally determines human production speed down the line. More than 75% of poultry workers in line jobs reported cumulative trauma disorders in their hands and wrists, according to a 2013 survey by the Southern Poverty Law Center.
This article claims that the Gig Economy has another casualty: middle management. With real-time rating systems, we’re turning into a world of managers.
The rating systems used by these companies have turned customers into unwitting and sometimes unwittingly ruthless middle managers, more efficient than any boss a company could hope to hire. They’re always there, working for free, hypersensitive to the smallest error. All the algorithm has to do is tally up their judgments and deactivate accordingly.
Companies don’t have to bother evaluating their contractors, because the customers base is essentially always doing it, in real-time.
At the same time, we are living in an era of maximum productivity. It has never been easier for employers to track the performance of workers and discard those who don’t meet their needs.
[...] When it comes to low-wage positions, companies like Amazon are now able to precisely calibrate the size of its workforce to meet consumer demand, week by week or even day by day.
[...] you are always disposable. You are at least one entity removed from the company where you work, and you are only as good as your last recorded input in a computerized performance monitoring system.
Technology is enabling employers to ensure workers are working harder and more efficiently, That combination reduces the need for manpower.
This article (a teaser for an upcoming book) argues that it’s not just middle class jobs at risk. Indeed, there’s apparently nothing special about white collar jobs either.
“The perception is that a lawyer drafts a unique legal document for each client and there’s something of the craftsman to it all,” Susskind says. “Architects are perceived as leaning over their easel with a pencil, scratching away and designing buildings.”
But in fact, he explains, when you divide the work of each profession into distinct tasks, many actions can be “routinized” into standard operating procedures that can be carried out by computers.
Politicians are working with start-ups to understand the so-called “gig economy.” It’s driving conversations about worker protections, lack of health care, income inequality, etc.
David Kochel, the chief strategist for Mr. Bush, said the candidate was reaching out to start-ups because “the gig economy is transforming the way we think of work.”
Many policy makers are only now catching up to the implications of the trend. Even though many start-ups that have pioneered more flexible work arrangements began operating in 2009 and 2010, lawmakers did not start to pay attention to them until Uber ran into legal trouble over whether its drivers should be defined as employees [...]