Daily Archives: June 19, 2017

Forbes

When Combating Crew Fatigue, Automation Has Its Rewards And Risks Forbes In 2013 the US Federal Aviation Administration introduced new regulations for aviation crew flight time and duty time, known as FAR 117. While those changes incorporated the latest in fatigue science up to that point, similar rules have not been ...

Originally posted here:

When Combating Crew Fatigue, Automation Has Its Rewards And Risks - Forbes

When Amazon completes its acquisition of the grocery chain Whole Foods, announced on Friday, the e-commerce giant plans to cut prices at the premium grocer while maintaining its reputation for high-quality boutique foods. The push for lower prices could be fueled by automation, staff reductions, and inventory changes.

The plans were first reported by Bloomberg and attributed to a source familiar with them. According to that person, Amazon also plans to add automated checkout systems at Whole Foods, which may include the technology under evaluation at the AmazonGo convenience store in Seattle.

That would mean fewer workers running checkout lanes. An Amazon representative quoted in the report denied that any layoffs or automation initiatives were planned for Whole Foods.

Get Data Sheet , Fortunes technology newsletter.

How will Amazon reduce Whole Foods' legendary "Whole Paycheck" reputation? Its plans could also include inventory changes that would eliminate the most expensive items from shelves and introduce more private-label goods.

Whole Foods' reputation has become a major pain point for the grocer, which has steadily lost sales to lower-priced competitionincluding Amazon. In February, after six straight quarters of falling sales, Whole Foods closed nine stores . It has already been lowering prices and experimenting with a lower-priced store format with fewer employees, 365 by Whole Foods.

True or not, the rumor of job cuts and automation points to a potential sticking point in the pending acquisition. Whole Foods has been recognized as one of Fortunes 100 Best Companies to Work For every year since the list was created in 1998. It's described by employees as a workplace offering fair pay and a welcoming environment.

Amazon, on the other hand, has been described as having an intense workplace culture marked by infighting and high turnover among staffers. Work conditions in its warehouse-like fulfillment centers have been described as demanding and even dangerous . Those divergent approaches to labor could well clash when the acquisition closes.

Read more:

Amazon: Whole Foods Plan Hints at Price Cuts, Automation - Fortune

"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Todays column is written by Dennis Buchheim, senior vice president of data and ad effectiveness at the Interactive Advertising Bureau (IAB) and general manager of theIAB Data Center of Excellence.

Soon, truckers may be out of a job. Why? Automation. Factory work continues to dwindle, again, because of automation. Artificial intelligence, the driver du jour of high valuations in tech, is all about you guessed it enhancing automation.

Already this year, the Harvard Business Review has published more than a dozen articles related to the topic. Automation is a global force across all industries, and its being wrestled with at the highest and lowest echelons of organizations and society in general.

But we in advertising dont talk about automation. Instead, we use the word programmatic, an increasingly ambiguous industry term that cant hold the weight of the conversations that need to be had. Now dont get me wrong: Jargon can be OK. Every industry has it. But any language we choose to use must enable rather than disable communication. And in this case, its beginning to hurt.

A semantic and contextual shift is necessary. To achieve new levels of efficiency and effectiveness, digital advertising leaders need a new lens through which to perceive and discuss the massive changes going on in their companies because of, yes, automation.

The term programmatic originated years ago to describe elements of digital advertising campaigns performed by software. The word was generally used to distinguish transactions executed by machines, especially via real-time bidding, from those negotiated and executed by flesh-and-blood salespeople and media buyers. At first, the word was apt. But times have changed.

The rapid evolution and extensive adoption of data-fueled technology has pushed us into a post-programmatic world. Today, partially or wholly machine-driven processes are woven throughout the entire digital advertising supply chain. We need to talk in depth about automation across not only transactions but also planning, creative development, data management, decisioning, personalization, measurement, verification, optimization and more. This is all more far-reaching and complicated than the original notion of programmatic advertising. And yet were still using the term and concept of programmatic. Its a vast oversimplification and cause of confusion.

Automation is a universally understood term that refers to all of the ways that technology is replacing or augmenting humans. Use of this term within advertising will force all of us to unpack whats been obscured by the idea of programmatic, and it will empower discussions about the role automation plays or could play in specific supply chain processes, as well as the utility that can be provided by technology and data relative to their costs and, most importantly, the value that can be created or destroyed by automation.

These conversations need to be had and can lead to a spectrum of benefits. The benefits notably include increased transparency and control related to tools, data, ad inventory, vendors and associated costs; more efficient and more effective advertising; and better consumer experiences. The industry has long tended to rush toward adopting automated technologies, but relying on zeros and ones instead of a person isnt always cheaper or faster and the black boxes that have developed between buyers and sellers often cause more cost than benefit.

Questions such as When does automation save time? and When doesnt it? and What is the optimal mix between humans and machines? need to be answered for each application of automation in a business.

Now, a change in language alone clearly cant resolve all of these open issues. But a shift in perspective toward the challenges and opportunities created by automation across the supply chain is the foundation of these explorations.

The digital advertising ecosystem has achieved a scale so gargantuan and personalized that humans cant do all of the driving. Its up to business leaders to determine when their teams should step on the gas, tap the brakes or even change lanes and when a machine should do this for them.

Follow the IAB (@iab) and AdExchanger (@adexchanger) on Twitter.

Visit link:

Unpacking Programmatic: How Automation Impacts The Digital ... - AdExchanger

(Image: PRNewsfoto/ American Staffing Association)

Workplace automation is a polarizing issue for Americans, according to the results of a new American Staffing Association Workforce Monitor survey. About equal percentages of respondents say that automation in the workplacerobots or artificial intelligencewill be a good or a bad thing for the future world of work.

About one-third (34%) of Americans say automation will be a positive development for the workforce in the next 10 years or morecompared with 31% who say it will be negative: 35% are neutral on the matter or just dont know.

However, more than four in five Americans think that increased automation will revolutionize work (83%)and that this transformation is inevitable (82%). A substantial majority think that automation will fundamentally change the quantity (79%) and types (68%) of jobs available in the U.S. Seven in 10 (72%) say its increased use will lead to higher unemployment.

But most Americans are in denial that automation will ever affect their work life. Nearly three quarters (73%) do not believe that their work can be easily replaced by robots or artificial intelligence, and 85% agree that the human factor outweighs any benefits from mechanizing their job. Nine in 10 (90%) say that there are some tasks that automation will never be able to take over from humans.

Automation is revolutionizing the who, what, where, and how people will work in the future, said Richard Wahlquist, ASA president and chief executive officer. The ASA Workforce Monitor found that nearly nine out of 10 (87%) Americans believe that to succeed in this new world of work, additional training will be needed.

Harris Poll conducted the survey online within the U.S. on behalf of ASA March 79, 2017, among a total of 2,133 U.S. adults age 18 and older. Results were weighted on age, education, race/ethnicity, household income, and geographic region where necessary to bring them into line with their actual proportions in the U.S. population.

Read more here:

Americans Split On Impact of Workplace Automation - Facility Executive Magazine

The Drive

GM's Cruise Automation Wades Into HD Mapping to Aid ... Wall Street Journal (subscription) A small autonomous-cars company owned by General Motors Co. is getting into the high-definition mapping business, a move that could help the Detroit auto ... GM's Cruise Automation Wants to Make HD Maps for Self-Driving ...The Drive GM lets Cruise Automation run free - General Motors Company ...Seeking Alpha GM lets its Cruise Automation self-driving unit work autonomouslyAutomotive News (subscription) (blog) all 6 news articles »

Originally posted here:

GM's Cruise Automation Wades Into HD Mapping to Aid ... - Wall Street Journal (subscription)

For some time, we said that data is the new oil to indicate how much value and power there is in having data available. Having data means having knowledge of whats happening and being able to objectively evaluate phenomena that otherwise could only be guessed. Then we started to say that having the data wasnt enoughits more important to apply context to data so that it can be transformed into information. Data is important, but providing context makes it much more meaningful, and the information can then be used to make better informed decisions.

This is even more significant when we are talking about the high volumes of data collected from manufacturing operations. These huge amounts of dataprocess and production datacan be difficult to interpret if you look at it without context. You can, for example, collect the temperature of an oven every second in several areas of the equipment, and have a very detailed dynamic view of whats happening in it. But it doesnt mean a whole lot if you dont know which SKU was being produced, what the setpoint was, and maybe what the humidity was of the semi-finished good that you were cooking or drying. So context transforms data in information; context is the refinery of data.

But today, even information is not enough. Transforming Big Data into Big Information is powerful, but it can still be difficult to interpret and understand. Moreover, when you apply context to data, you are basically applying a model that combines variables you know are correlated in some way. But is that the only existing correlation? Or are some variables correlated to others in way you do not know and maybe are not so evident? The exponential growth of available data and information makes it difficult, if not impossible, to evaluate all the possible relationships, especially when you start to consider data coming from different domains (e.g. process and business data) or data coming from different stages of the value chain.

This is when analytics becomes important and can transform information in the same way context transforms data. Analytics is a generic word to identify a set of different activities or applications of statistical analysis or business intelligence, sometimes related to a specific domain, others to a specific type of content. Frequently, it indicates the capability to apply statistical models or mathematical algorithms to a data set, distilling information that otherwise couldnt be retrieved and that can be used to predict possible situations or to support manual decisions or even to implement automatic decision processes.

In manufacturing, analytics often refers to a system that can analyze a set of data and automatically identify relationships between variables. In this way, the system builds a mathematical model that can be used to predict the state or value of a single variable based on the behavior of the others. One of the most used examples is predictive maintenance where, based on the data collected from several sensors installed on an asset, the system can predict if the asset will fail in the near futureoptimizing the maintenance process, and minimizing the maintenance costs and possible impact of a failure on production at the same time.

But this is just a very simple case to understand. Even within manufacturing operations that are considered best in class, the use of advanced analytics could reveal further opportunities to increase yield. This was the case at one established European maker of functional and specialty chemicals. It boasted a strong history of process improvements since the 1960s, and its average yield was consistently higher than industry benchmarks, so they were skeptical that there was much room for improvement. However, several unexpected insights emerged when the company used neural-network techniques (a form of advanced analytics available in many products) to measure and compare the relative impact of different production inputs on yield. By adjusting the process parameters based on the evidence, the chemical company was able to reduce its waste of raw materials by 20 percent and its energy costs by about 15 percent, thereby improving overall yield.

More and more opportunities become available by mixing data coming from different contexts. In this case, not only can analytics apply a mathematical model to very large volumes of data, but it can identify patterns and correlations that otherwise would be extremely difficult to identify, since nobody has full knowledge of the data set.

Analytics can really change the way a company is run, providing insights with a much larger value than information and data. Analytics is the third level of knowledge that promises to transform dramatically how people will manage factories, both at the operations level and at the business level.

Luigi De Bernardini is president of Autoware Digital and CEO of Autoware, a certified Control System Integrators Association (CSIA) member based in Vicenza, Italy. For more information about Autoware, visit the Autoware profile on the Industrial Automation Exchange.

See the rest here:

AnalyticsBeyond the Value of Information - Automation World

There's no shortage of studies and analysis suggesting that robots can potentially take our jobs. But exactly how far away are we from losing our livelihoods to automation?

Artificial intelligence experts with the BBC surveyed 352 scientists about automation, including some of the world's leading experts on machine learning. According to the BBC analysis, there is a 50% chance that machines can take over all human jobs in 120 years.

But some fields are at greater risk than others. Let's focus on one big one: trucking.

Truck drivers may be replaced by automated technology as early as 2027. According to the researchers, artificial intelligence could be maneuvering trucks on the road within the next decade.

"All jobs are being impacted by technological change some more than others," said Nicholas Wyman, CEO of the Institute for Workplace Skills and Development and author of Job U: How to Find Wealth and Success by Developing the Skills Companies Actually Need. "Driverless trucks are now used extensively in the mining industry and it's certain this technology will impact other parts of transport and distribution."

Estimates from the American Trucking Association suggest there are 3.5 million professional truck drivers in the United States and the industry, as a whole, employs more than 8.7 million people. According to the Los Angeles Times, 1.7 million American truckers could be replaced by self-driving trucks over the next decade.

Trucking jobs are the most common jobs in 29 out of 50 states in the U.S., and there are millions of people working for the trucking industry in non-driving positions.

Uber-owned Otto is perfecting the technology that will let trucks drive themselves.

It's not such a far-fetched idea, as progress is being made in automating truck driving. Take self-driving truck company Otto, which was created by former Google employees and acquired by Uber last August. The company's system lets trucks drive for long stretches of time without needing a human driver. The technology was successfully tested in Colorado in October when a self-driving truck delivered 50,000 cans of Budweiser.

If perfected, autonomous trucks could offer heightened efficiency and safer roads; one in seven fatal truck accidents is caused by driver fatigue. For those currently in the trucking industry, it's not all bad news. How much of a threat self-driving trucks pose to drivers depend on the level of automation: if drivers are still required to be in trucks, then jobs are secure, MIT Technology Review reports.

Wyman says truck drivers need to be open to adapting to the changing landscape of their field. "They need to embrace change it's happening so hoping it will go away is not an option," Wyman said. "Truck drivers should look for opportunities to refresh and reboot their current skill sets."

Original post:

When will automation take over the trucking industry? Scientists now have an estimate - Mic