News broke Tuesday after more than a year of waiting that Amazon would build two headquarters in Queens, N.Y., and Crystal City, Va..
Elected officials in cities like Pittsburgh and Philadelphia were disappointed they would miss the boost an Amazon HQ would deliver to their tax bases. But some technology entrepreneurs had a different reaction, saying Pittsburgh and other cities might be better off.
“We have a lovely standard of living,” Kevin Kelly, co-founder and CEO at Rhabit Analytics, told the Pittsburgh Post-Gazette.
“… If you have sudden, explosive growth, there are great benefits that come with that, but [also] some of the disproportionate negative things that you’ll find in markets like San Francisco, where hyper growth has really started to create a stratum of social challenges.”
Pittsburgh entrepreneurs weren’t alone.
The Raleigh News & Observer reported that support in Raleigh “seemed to lag, as many in the area seemed concerned about its potential impact to home prices and traffic in an already booming market.” The newspaper cited an Elon University poll that found 43 percent of Raleigh locals “strongly supported” bringing in HQ2.
In Denver, Westworld, an alt-weekly, gathered a flurry of social media posts from Denverites pleased that Amazon was going elsewhere.
The prevailing feeling among cities that lost was that they were better off for having gone through the process. Officials in Columbus, Ohio, said they’re better prepared.
“Even though we didn’t win, it put us in a much better position for whatever is next,” Columbus Mayor Andrew J. Ginther told the Columbus Dispatch. “Our future is very bright. America is starting to recognize that Columbus is a great place to do business.”
One of Gartner’s Top 10 strategic trends for the Internet of Things could impact the rest of the list: the social, legal and ethical issues surrounding IoT growth.
“Successful deployment of an IoT solution demands that it’s not just technically effective but also socially acceptable,” said Nick Jones, research vice president at Gartner. “CIOs must, therefore, educate themselves and their staff in this area, and consider forming groups, such as ethics councils, to review corporate strategy.
Intentional and unconscious bias woven into the algorithms that gather insights from big data sets is getting scrutiny from news organizations and academic researchers. They are raising concerns that the invisible inner-workings of machine-learning algorithms could ingest biased information and make conclusions that are unethical, unwise or just wrong.
A recent piece in Scientific American said, “According to a U.S. government study on big data and privacy, biased algorithms could make it easier to mask discriminatory lending, hiring or other unsavory business practices.”
One author suggested the need for a regulatory agency to examine algorithms.
“So I think we need something like the FDA for algorithms.
“A regulatory body that can protect the intellectual property of algorithms, but at the same time ensure that the benefits to society outweigh the harms,” said UK mathematician Hannah Fry, whose new book “Hello World” examines the hopes for and dangers of AI.
The stakes are high. That’s particularly true when applied to major government institutions like the criminal justice system. In 2016, ProPublica uncovered racial bias in software that was supposed to help courts assess the probability that someone would reoffend.
For some more recent research about bias in automated decision-making, check out this recent University of California Berkley study.
Here’s Gartner’s entire list of IoT trends:
Asset Performance Management is all about increasing reliability and managing your maintenance program.
Unless you’re really ahead of the game, you could probably be doing more to get the most out of your APM strategy.
That’s why our intelligent assets team, led by GrayMatter’s Paul Casto, put together a list of ways to make sure you’re leveraging the most out of your assets. This is a quick guide to get you started on a deeper conversation. Here’s a look at our 5 approaches.
We can analyze the impact of bad actors in several ways, for example: maintenance cost, total cost, work orders, stop duration, number of stops, type of stop. It’s also helpful to sort findings by shift, day, operator, etc.
Using information from the MES, we can automatically trigger a Root Cause Analysis event (based on production loss, number of shutdowns, etc.)
Leads to lower maintenance costs and higher equipment uptime.
We have all had operators tell us: “I could have told you a week ago this was going to break.” Use operators to trigger plant maintenance.
The risk of wear-out failures increases over time, but the wear rate is not linear. We can use data to get a better view of the probability of equipment failure over time.
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