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The commercial applications of drones: Big consulting firms don’t invest in little markets. So when a major player, like PwC, establishes a new global center of excellence around an emerging technology, like drones, it’s probably worth swooping in for a look.
If you do, you’ll find a new PwC report that pegs the commercial applications of “drone powered solutions” at more than $127 billion. That’s the current value of the business and labor — in sectors including infrastructure, transport, insurance, media and entertainment, telecommunication, agriculture, security, and mining — that could be supplanted by drone technology in the coming years.
“Drone powered solutions are best suited to sectors that require both mobility and a high quality of data,” write PwC consultants Michał Mazur and Adam Wiśniewski. “Specifically, businesses that manage assets dispersed over large areas have a long history of issues that new drone powered solutions can address.” Some of the applications are obvious, such as Amazon’s ongoing bet that drones will enable it to cut delivery time and costs. Then there’s the host of less-publicized applications in various stages of development, such as combining 3D printers and drones to produce on-site replacement parts; sending drones to assess damage in insurance claims; providing temporary broadband and mobile service when a network is down; spraying crops; conducting security patrols; surveying mines; etc. And, of course, there’s the applications that you’ll think up for your company.
A digital renovation of the corporate university: A lot of technology has passed under the bridge since the 1980s, when Jack Welch revamped GE’s sleepy Crotonville leadership center in order to produce the scores of hard-driving leaders needed to mount a do-or-die effort to push the company’s business units to the top of their markets. “Now a new phase is unfolding at [corporate learning] organizations, which must grapple with tools and platforms that facilitate knowledge sharing and employee interactions on an almost limitless scale, challenging — and sometimes appearing to sweep away — the old brick-and-mortar model,” write McKinsey consultants Richard Benson-Armer, Arne Gast, and Nick van Dam.
In a new article in McKinsey Quarterly, which draws on survey responses from 120 chief learning officers at major companies, the authors find that, on the one hand, corporate learning is still in high demand. Over the next three years, they report, “more than 60 percent of the respondents’ companies plan to increase their learning-and-development spending and 66 percent to increase the number of formal-learning hours per employee.”
On the other hand, the article notes, a large number of the CLOs also believe that their organizations are not properly equipped to meet the learning challenge. What enterprise learning needs is a digital update. The authors recommend that companies adopt cloud-based learning platforms capable of hosting and running “such personalized applications as MOOCs (massive open online courses), SPOCs (small private online courses), instructional videos, learning games, e-coaching, virtual classrooms, online performance support, and online simulations.”
There will still be a place for corporate universities, but they, too, need a digital renovation. “Ultimately, we believe, the future of corporate academies lies in blended learning, which combines classroom forums, in-field applications, personal and results-oriented feedback, and online engagement,” conclude the consultants. “There is no magic number for allocating time between digital and in-person learning; different industries, and different companies within them, must determine the mix that makes the most sense for their circumstances and capability-development priorities.”
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A data scientist at every desk: Everybody knows that machine learning will revolutionize the way in which companies are run, but unless you’re going to give everyone a data scientist as an assistant, the challenge of using it across the organization remains a significant obstacle. For hints on how that might be solved, you might want to check in with Facebook.
“Many of the experiences and interactions people have on Facebook today are made possible with AI,” wrote Facebook software engineer Jeffrey Dunn in a post on the company’s blog last week. In it, he called AI out as the mechanism behind ranking and personalizing news feed stories, blocking offensive content, identifying trending topics, and ranking search results. “There are numerous other experiences on Facebook that could benefit from machine learning models, but until recently it’s been challenging for engineers without a strong machine learning background to take advantage of our ML infrastructure,” Dunn explained. “In late 2014, we set out to redefine machine learning platforms at Facebook from the ground up, and to put state-of-the-art algorithms in AI and ML at the fingertips of every Facebook engineer.”
The result is FBLearner Flow, which Dunn calls “Facebook’s AI backbone.” He reports that more than 25% of the company’s engineering team is using the software. “Since its inception, more than a million models have been trained,” he writes, “and our prediction service has grown to make more than 6 million predictions per second. Eliminating manual work required for experimentation allows machine learning engineers to spend more time on feature engineering, which in turn can produce greater accuracy improvements … FBLearner Flow provides the platform and tools to enable engineers to run thousands of experiments every day.”
Dunn does such a clear job of explaining how the software works that even I kinda understand it. At least, I understand it well enough to know that someone who isn’t a software engineer probably isn’t going to use without tech assistance … yet. But it does seem like a step in that direction.