This Axios headline is problematic: Summers: Automation is the middle class worst enemy.

The accompanying piece doesnt actually quote economist Larry Summers making that declaration. Rather it summarizes an interview in which Summers indeed points out the challenge automation poses for workers. Hes right. Of course thats been the case for the past 200 years and will likely be the case for the next 200. But in exchange for a degree of instability and disruption, technological progress has dramatically raised living standards for workers.

Automation is kind of like alcohol, which, as Homer Simpson putsit,is the cause of, and solution to, all of lifes problems. Its the job of policymakers to make sure workers are ready to climb to the next footholdor ledge as the waters of automation continue to rise. Its also their job to make sure policy is as supportive as possible of innovation. Indeed, we need more tech progress, not less. The U.S. economy currently suffers not from too much automation, but rather from too little investment in the sort of technology that would raise the countrys lackluster productivity, writesDerek Thompson in an excellent new piece at the Atlantic.

Technology will erase jobs but also create them. Unfortunately, as Kevin Kelly writes, we cant see those jobs from here because we cant yet see the machines and technologies that will make them.

The piece also includes this chart, which shows lower US labor force participation than other advanced economies:

But I doubt whether Summers blames automation vs. the lack of USpolicies that center-left economists see as supporting workers, such as paid leave, and high USincarceration rates. And here is economist David Autor on the net impact of automation on jobs this century:

A final observation is that while much contemporary economic pessimism attributes the labor market woes of the past decade to the adverse impacts of computerization, I remain skeptical of this inference. Clearly, computerization has shaped the structure of occupational change and the evolution of skill demands. But it is harder to see the channel through which computerization could have dramatically reduced labor demand after 1999. My suspicion is that the deceleration of the U.S. labor market after 2000, and further after 2007, is more closely associated with two other macroeconomic events. A first is the bursting of the dot-com bubble, followed by the collapse of the housing market and the ensuing financial crisis, both of which curtailed investment and innovative activity. A second is the employment dislocations in the U.S. labor market brought about by rapid globalization, particularly the sharp rise of import penetration from China following its accession to the World Trade Organization in 2001.

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Is Automation Really the Worst Enemy of the Middle Class? - Ricochet.com

BERKELEY Advances in artificial intelligence and robotics are powering a new wave of automation, with machines matching or outperforming humans in a fast-growing range of tasks, including some that require complex cognitive capabilities and advanced degrees. This process has outpaced the expectations of experts; not surprisingly, its possible adverse effects on both the quantity and quality of employment have raised serious concerns.

To listen to President Donald Trumps administration, one might think that trade remains the primary reason for the loss of manufacturing jobs in the United States. Trumps treasury secretary, Steven Mnuchin, has declared that the possible technological displacement of workers is not even on [the administrations] radar screen.

Among economists, however, the consensus is that about 80% of the loss in US manufacturing jobs over the last three decades was a result of labor-saving and productivity-enhancing technological change, with trade coming a distant second. The question, then, is whether we are headed toward a jobless future, in which technology leaves many unemployed, or a good-jobless future, in which a growing number of workers can no longer earn a middle-class income, regardless of their education and skills.

The answer may be some of both. The most recent major study on the topic found that, from 1990 to 2007, the penetration of industrial robots defined as autonomous, automatically controlled, reprogrammable, and multipurpose machines undermined both employment and wages.

Based on the studys simulations, robots probably cost about 400,000 US jobs each year, many of them middle-income manufacturing jobs, especially in industries like automobiles, plastics, and pharmaceuticals. Of course, as a recent Economic Policy Institute report points out, these are not large numbers, relative to the overall size of the US labor market. But local job losses have had an impact: many of the most affected communities were in the Midwestern and southern states that voted for Trump, largely because of his protectionist, anti-trade promises.

As automation substitutes for labor in a growing number of occupations, the impact on the quantity and quality of jobs will intensify. And, as a recent McKinsey Global Institute study shows, there is plenty more room for such substitution. The study, which encompassed 46 countries and 80% of the global labor force, found that relatively few occupations less than 5% could be fully automated. But some 60% of all occupations could have at least 30% of their constitutive tasks or activities automated, based on current demonstrated technologies.

The activities most susceptible to automation in the near term are routine cognitive tasks like data collection and data processing, as well as routine manual and physical activities in structured, predictable environments. Such activities now account for 51% of US wages, and are most prevalent in sectors that employ large numbers of workers, including hotel and food services, manufacturing, and retail trade.

The McKinsey report also found a negative correlation between tasks wages and required skill levels on the one hand, and the potential for their automation on the other. On balance, automation reduces demand for low- and middle-skill labor in lower-paying routine tasks, while increasing demand for high-skill, high-earning labor performing abstract tasks that require technical and problem-solving skills. Simply put, technological change is skill-biased.

Over the last 30 years or so, skill-biased technological change has fueled the polarization of both employment and wages, with median workers facing real wage stagnation and non-college-educated workers suffering a significant decline in their real earnings. Such polarization fuels rising inequality in the distribution of labor income, which in turn drives growth in overall income inequality a dynamic that many economists, from David Autor to Thomas Piketty, have emphasized.

As Michael Spence and I argue in a recent paper, skill-biased and labor-displacing intelligent machines and automation drive income inequality in several other ways, including winner-take-all effects that bring massive benefits to superstars and the luckiest few, as well as rents from imperfect competition and first-mover advantages in networked systems. Returns to digital capital tend to exceed the returns to physical capital and reflect power-law distributions, with an outsize share of returns again accruing to relatively few actors.

Technological change, Spence and I point out, has also had another inequality-enhancing consequence: it has turbo-charged globalization by enabling companies to source, monitor, and coordinate production processes at far-flung locations quickly and cheaply, in order to take advantage of lower labor costs. Given this, it is difficult to distinguish between the effects of technology and the effects of globalization on employment, wages, and income inequality in developed countries.

Our analysis concludes that the two forces reinforce each other, and have helped to fuel the rise in capitals share of national income a key variable in Pikettys theory of wealth inequality. The April 2017 IMF World Economic Outlook reaches a similar conclusion, attributing about 50% of the 30-year decline in labors share of national income in the developed economies to the impact of technology. Globalization, the IMF estimates, contributed about half that much to the decline.

Mounting anxiety about the potential effects of increasingly intelligent tools on employment, wages, and income inequality has led to calls for policies to slow the pace of automation, such as a tax on robots. Such policies, however, would undermine innovation and productivity growth, the primary force behind rising living standards.

Rather than cage the golden goose of technological progress, policymakers should focus on measures that help those who are displaced, such as education and training programs, and income support and social safety nets, including wage insurance, lifetime retraining loans, and portable health and pension benefits. More progressive tax and transfer policies will also be needed, in order to ensure that the income and wealth gains from automation are more equitably shared.

Three years ago, I argued that whether the benefits of smart machines are distributed broadly will depend not on their design, but on the design of the policies surrounding them. Since then, I have not been alone. Unfortunately, Trumps team hasnt gotten the message.

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Labor Markets in the Age of Automation by Laura Tyson - Project ... - Project Syndicate

Intelligence analysts are swimming in data pouring in from an array of vehicles and platforms a problem that isnt new, but for which government leaders still seek the right solutions.

To help stem the deluge and better position analysts and key mission-critical data, intelligence community officials are targeting automation as a high priority, with a futuristic vision for applications down the road as well.

A significant chunk of my analytic workforce today, I will send them to a dark room to look at TV monitors to do national security-essential work but boy, is it inefficient, Robert Cardillo, director of the National Geospatial-Intelligence Agency, told reporters at the annual GEOINT Symposium in San Antonio, Texas. The number of people needed to maintain awareness of, if not exploitation of, one sensor is really daunting. I suspect were going to get more of those sensors. I cant double my human population in those dark rooms.

The near-term goal particularly centers on analysis of full-motion video that streams in from unmanned aerial systemsthe wolf really close to the door, as Cardillo put it.But he and other officials also are looking toward future uses for different types of automation, including artificial intelligence. And Cardillo, among others, are looking to partner up for help.

As the commercial industry and academic think tanks and advanced science and engineering schools move to artificial intelligence and machine learning, theyre all desperate to get a hold of some data with [which] to train their algorithms and teach their machines to learn, Cardillo said. He added that intelligence community leaders, including those at the NGA, are looking at how to safely expose data sets to accelerate development in automated tools.

But the NGA isnt just looking outside for solutions. Internally, the agency has launched a new Office of Ventures and Innovation aimed at guiding emerging technology from incubation through the entire life cycle.

To get to this automated, augmented future that were talking about, we need to coordinate across a lot of different parts of the agency, Anthony Vinci, NGA director of plans and programs, told C4ISRNET. Its not just a technology issue, its bringing technology and [research and development] into the operational units, into analysis or into the business services units, human development or finance for business analytics.

Vinci said the NGA is working closely with other government agencies, including the Defense Department, to further automation and AI and get to a new level of intelligence and military operations.

How can we use automation to take some of that pressure off of the analysts who are putting together those products? How do we buy back some of their time by automating some of these processes so they can focus on the higher-end analysis that they need to be focusing on? Thats a fundamental thing were trying to do using all the tools at the agencys disposal, Vinci said.

On the higher endthe exquisite analysishow can we bring in modeling and AI and some of those tools that are on the high end of technology to support analysis and do missions that werent even possible until now because we didnt have the tools to analyze that amount of data, or because we couldnt analyze some complex phenomenonologies? How can we bring them those tools and those models?

In a future operational landscape where autonomous vehicles dominate and interact with adversaries, its easy to see where intelligence missions cross over to operational military missions, Vinci noted.

These tools are not just intelligence tools; theyre operational as well. Were not just an intelligence agency, were a combat support agency, so we help across the spectrum, he said. Were a support function to someone who has to make a decision.

"We have to be able to act faster and get as far left of boom as possible.

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Intel officials pin high hopes on automation, artificial intelligence - C4ISR & Networks

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Automation Controllers With MQTT and Analytics Onboard Enable Lean IIoT Architectures - Automation World

New technologies are transforming our profession, and theyre also transforming the skills well need to stave off extinction.

In a paper titled The Future of Employment: How Susceptible are Jobs to Computerisation? University of Oxford researchers Carl Benedikt Frey and Michael Osborne tried to gauge the odds that certain occupations will be completely automated within the next 20 years. Among their predictions:

In fact, only seven occupationscargo and freight agents, watch repairers, insurance underwriters, mathematical technicians, hand sewers, title examiners, and telemarketersfared worse in the study than tax preparers.

The researchers admit that these estimates are rough and likely to be wrong, writes National Public Radios Quoctrung Bui. But consider this a snapshot of what some smart people think the future might look like. If it says your job will likely be replaced by a machine, youve been warned.

Other studies offer similar predictions:

One way or anothercomplete automation or partialour jobs are about to change. This type of disruption is coming. In one notable example, in fact, it has already arrived.

Perhaps the biggest disruption bearing down on the CPA profession is coming from IBM Watson, a cognitive learning system that is capable of answering questions asked in natural language. From health care and education to law and finance to food preparation and satellite imagery, Watson is redefining how work gets done in stunning ways.

Heres what:

This stuff isnt science fiction anymore. Its here and its impacting our profession as we speak.

How will CPAs react? Will they scramble to keep up, as usual? Or will they work to position themselves to move beyond that disruption and create future-focused value for their clients?

If theyre smart, theyll do the latterand that means learning the new skills theyll need to remain relevant in an age of automation.

Numerous studies conducted over the past several years are nearly unanimous: Going forward, CPAs must become proficient at skills that have little to do with the professions traditional data-driven core. These skills include the following:

The most important skill of all, though, might also be the most ambiguous. Its anticipationthe ability to identify future trends early and position your organization and your clients to take advantage of those trends before they arrive. Renowned futurist and New York Times best-selling author Daniel Burrus calls it the key missing competency in business today.

He might be right. A 2014 report from The Sleeter Group found that the most often-cited reason why small and midsized businesses leave their CPA firms is because those firms provide reactive advice instead of proactive services. In essence, clients say they leave because their CPAs arent future-ready enough.

It seems the age of automation has also given birth to the age of anticipation. The good news is this: Were starting to see more and more resources being developed specifically to deliver these types of competencies for accounting and finance professionals.

One is Burruss own Anticipatory Organization. The Business Learning Institute worked with Burrus to create a version of his Anticipatory Organization learning platform specifically for accounting and finance professionals. Thats available now and is becoming extremely popular among CPAs throughout the country.

Another is IBMs Big Data University. Its an online curriculum designed to help accounting and finance professionals learn key skills in artificial intelligence, big data, and cognitive computingskills that will be huge differentiators going forward, and will help CPAs play a bigger role in guiding digital transformation within their organizations. The Maryland Association of CPAs and the Business Learning Institute have entered into an exclusive partnership with IBM to deliver these skills to accounting and finance professionals throughout the world.

As this age of automation progresses, accounting and finance professionals would be wise to ask themselves a few key questions:

What can I become quite good at thats really difficult for a computer to do one day soon? Seth Godin writes. How can I become so resilient, so human and such a linchpin that shifts in technology wont be able to catch up? It was always important, but now its urgent.

Put another way, to paraphrase Fast Company Editor Robert Safian, the most important skill going forward will be the ability to learn new skills.

The learning must begin now.

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Why Lifelong Learning is Our Competitive Advantage in the Automation Age - Accountingweb.com

By Vinnie Mirchandani on June 7, 2017

In this Strategy+Business article I summarized a century of automation from Silicon Collar like UPC scanners in groceries and ATM machines in banking and related impact of jobs. The summary: History shows that new technologies evolve faster than society adopts them.

There has been much hype and hysteria around the contemporary crop of automation technologies like AI and drones, but early results show they will get adopted just as gradually. Here are some recent findings:

Robotic Process Automation (RPA) McKinsey saysseveral robotics programs have been put on hold, or CIOs have flatly refused to install new botseven those vendors have worked on for monthstill solutions have been defined to scale the program effectively.

Autonomous cars Heather Knight an expert in human-robot interfaces has this to say about Teslas autopilot Id estimate that Autopilot classified ~30% of other cars, and 1% of bicyclists. Not being able to classify objects doesnt mean the Tesla doesnt see that something is there, but given the lives at stake, we recommend that people NEVER USE TESLA AUTOPILOT AROUND BICYCLISTS!. And car technology is evolving much faster than our road infrastructure, laws, ethics when it comes to driverless cars.

Industrial Robotics For six years since the tsunami wrecked the Fukushima nuclear reactor, robots have been sent in to try and help with the clean-up. Says TechCrunch Robots keep getting fried on their missions, literally from radiation damage, or stranded on-site wasting precious money and time. And this is Japan, the leading maker and consumer of robots, accounting for half of the worlds production and with the worlds largest concentration of robot engineers.

That will not stop vendors from saying its different this time or academics and analysts from screaming the sky is falling and that we will lose hundreds of millions of jobs. But most practitioners I talk to are well aware of the immaturity of many of these technologies and of their economics. These practitioners will gradually adopt these tools to change processes and work streams. The key word is gradual.

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(Cross-posted @ Deal Architect)

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More Slow-Motion Automation - Enterprise Irregulars (blog)

Chinas steady economic growthas opposed to hyper growthover the past five years has reset expectations and coincides with the countrys transition to more advanced manufacturing in multiple industries. Chinas manufacturers are investing in more equipment, such as robotics for their automotive industry, and on better asset management strategies for both legacy and new plants.

This optimization push centers on upgrading data acquisition platforms and crossing the bridge to true predictive maintenance approaches in many industries in China, including harsh plant floor environments.

Recently, China Steel upgraded the legacy monitoring system at its main facility in Kaohsiung, Taiwanand another steel production plant in Taichung Cityto provide better access to real-time equipment data, such as vibration levels for roller bearings and motors in their milling production area. This continuous process application includes molten metal being produced into blooms or slabs that eventually are rolled into finished products.

Due to the continuous nature of our production facility and the harsh environment+300 C temp, moisture and high vibrationit is very hard for us to maintain equipment once it gets deployed to the field, says Zhizhong Wang in China Steels R&D department.

The steel manufacturer used a legacy condition monitoring system, called the Facility Online Monitoring and Diagnosis System (FOMOS), to track mill equipment vibrations for multiple mill lines within facilities. However, the original monitoring system didnt provide real-time maintenance dataor efficiencybecause of a long lag time for this data to move from the plant floor to the database. Operators would typically collect raw data locally and upload condensed analysis results to a database, for example.

Also, this legacy system created volumes of vibration data as well as a large number of false alarms caused by rudimentary monitoring coming from the mill equipment. Its common to find that two pieces of the same equipment at similar locations and operating conditions exhibit different vibration levels after several years of operation, Wang says.

The original monitoring system relied on National Instruments PXI PC-based platform for the data acquisition. China Steel called on the automation supplier to upgrade this legacy system for all of its facilities in Chinaat least three steel production facilities. The upgraded monitoring platform, called FOMOS-AI, uses NIs LabView software to better define the vibration data coming from the milling equipment and leverages the companys PXI and CompactDAQ hardware for these plants.

As the monitoring systems name indicates, machine learning is a vital component. The system looks for useful condition indicators from the vibration signals and creates four different patternsin an operational contextfor the monitored equipment: constant speed and stable load; constant speed and variable load; variable speed and load; and reciprocating.

China Steel created these classifications and now they can apply a different algorithm for each asset type, says Brett Burger, principal marketing manager for National Instruments. A bump and shimmy on one type of motor might mean one thing, but the same thing on another motor might mean something completely different.

One of the features of the new system includes a baseline setup for equipment conditions that are determined by observation of diverse changes of the vibration signals through multiple indicators. The system also establishes a multidimensional baseline/alarm setting using statistical analysis based on operation regime and machine behavior, according to China Steel.

One example of this baseline setting in the field was a main motor cooling fan for a high-speed machining (HSM) finish mill. This cooling fan showed no signs of deterioration by overall trends, but a rise of acceleration in a high-frequency band in early July gave a preview to a functional failure of the motor bearings in early November.

Since the introduction of the monitoring system upgrade, unscheduled maintenance is down. One recent example of this is in steel forging, where abrasion wear on sliding liners can occur during the slab sliding process and can lead to broken main beams, seized synchronizer bearings and synchronous shaft fractures. Operators identified three sliding liners in failure mode via spectrum analysis in the FOMOS-AI and, after a field inspection, technicians replaced these liners during a scheduled maintenance period.

Before adopting the FOMOS-AI, the total number of unscheduled downtime hours was 250 hours per year. After three years with the new monitoring system, it's at 65 hours per year. In one year, the biggest steel maker in Taiwan saved about $230,000 in maintenance costs for all of its production facilities where FOMOS-AI was running.

The monitoring solution is more a gateway from the engineering side of the world, rather than the IT, Burger says. This system has sensors, processing and software that can run on it. And it has many networking capabilities, such as Modbus, serial and Ethernet.

Optimization for many legacy plants is the logical move and these step changes are proving to be profitable to both the company and its workforce.

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China Steel Forges a New Condition Monitoring Approach - Automation World