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With this logic, many sectors of the economy have moved ahead with remarkable speed. Technology, Healthcare, and Finance are all areas in which the intensive investment in Human Capital has yielded enormous fruit in terms of growth, profit, and innovative advancement. Unwilling to be left behind, “traditional” sectors like Manufacturing, Energy, and Transportation, too, have adopted similar attitudes, programs, and processes to ensure that they optimize their benefit from the maximal use of Human Capital, both internal and external to the organization.
Productivity and Creativity are the keys to this connection between “people-power” and business success. Both of these elements are essential to the development of a progressive, “learning” organization that is able to create and capitalize on new ideas, new trends, and new offerings. It is here, in the combination of two seemingly disconnected or even antagonistic factors, that the conundrum lies: how does an organization manage to both maximize “output” and to pivot creatively? In the common conception, the former is about operational efficiency, and the latter is about artistic license; these two ideas can be in conflict, but great organizations create a space in which both are possible and—you guessed it—technology plays a huge role in this harmonization.
Connection between “people-power” and business success
With regard to Productivity which, put in a different lexicon, implies the ability to produce outcomes at scale, the recent adoption of Machine Learning offers enormous potential. Beneath the technical surface, Machine Learning implies both intelligence and automation powered by data and prediction. In this way, ML allows for intelligent systems in which agility and automation create speed and scale at a heretofore unimaginable magnitude. Advances in both software and hardware allow organizations to avail of the benefits of ML at a manageable cost; as such ML is far more widespread than it was even a year ago and is growing exponentially.
While ML is undergirded by Human Capital insofar as brilliant engineers and scientists are required to create “agility by algorithm,” many believe that it will undercut Human Capital because, frankly, it is too powerful a tool for intelligence and automation. Why do we need humans, they ask, if machines are adaptive and smart? The debate about the extent that ML will either produce or reduce human capabilities and jobs is far beyond the scope of this piece, but suffice it to say that whatever one’s views on the matter, it is clear that the combination of ML and human creativity is the most potent.
A combination of ML and human creativity is the most potent
This is true for a variety of reasons that underpin, the idea espoused at the beginning of this piece, namely that Human Capital and creativity are core sources of advantage. The argument for this is simple-yet-powerful.
First, ML itself is based on human ingenuity. While there are arguments that machines will soon pass the famous “Turing Test,” we are still not at the stage in which human creativity, with its infinite permutations, is replaceable by machine.
Second, given the infinitude not only of ideation and creativity, but of consumer behavior, the acts of “human understanding and empathy” is not matchable by machine. Given that most businesses require products and services that humans, with their seemingly infinite frailties, variations in taste, and temporal behavioral changes, have to buy, the human-to-human connection prevails as the most important source of inspiration. Creativity and empathy provide the motive force, while ML creates the scale and speed required in the modern enterprise. Both are necessary while neither is sufficient.
Third, much of business is built on relationships and not optimal transactions. This point requires no explanation.
Fourth, since presumably ML is an “equalizer” insofar as any relatively solvent organization can pay for platforms that are ML-enabled, people are the differentiator, not systems.
Great organizations emphasize and laud Human Capital but also provide humans with the tools required for massive operational success. ML is just such a tool and when put in the hands of great teams, is the source of sustainable advantage.
Bellevue, Wa – OpenCV 3.0 is finally here, and we couldn’t be more proud of our team that helped make this new update the best it has ever been. OpenCV 3.0 is claimed to be the most functional and fastest OpenCV ever.
Open Source Computer Vision (OpenCV) is a permissive free software designed to build efficiency with real-time applications. With more than 9 million downloads, OpenCV is typically used for interactive art, stitching maps on the web, mines inspections and advanced robotics.
Here at Akvelon, we help our clients throughout the world through innovative applications of software. We ideate, define and implement better ways of doing things, which create better processes to achieve better results.
Our very own team – Max Kostin and Evgeny Agafonchikov – worked to bring OpenCV into Windows Runtime realm. Most of OpenCV modules are now available to be used from WinRT CX/C++ code and components. Over 75,000++ lines of code through 15++ pull requests have been contributed and merged in since 2.4 – Remember, this is the most functional and fastest OpenCV to date! Through thousands of tests, OpenCV 3.0 is stable, and behaves successfully on every operating system.
What’s more are the multiple changes in 3.0, one in particular that called out our very own Max Kostin:
There are multiple improvements and bug-fixes for WinRT port (as well as Windows 8.x port) of OpenCV by Microsoft guys (big thanks to Max Kostin)!In particular, parallel_for is enabled on WinRT, so the code should run much faster on multi-core devices.Also, the WMF video capturing backend has been greatly improved.
In particular, parallel_for is enabled on WinRT, so the code should run much faster on multi-core devices.
Also, the WMF video capturing backend has been greatly improved.
What is WinRT?
What are the benefits of the WinRT improvements to OpenCV?
Developers can now mix Win8 code – based on WinRT and Windows Runtime for Modern apps – with OpenCV code. If they choose to use their existing codes, they can do that as well. Developers can also use XAML to define the screen layout (using the familiar XAML controls, binding, etc) and combine it with OpenCV code, or directly use the main screen as a drawing surface for OpenCV code. OpenCV can now also run within XAML Phone applications, so developers can use OpenCV in their apps and publish it to the Windows Phone store. As with WinRT, developers can use XAML, OpenCV or a combination of the two.
Thus, our work not only targets Windows 8/8.1, but also provides early access for developers to use OpenCV on Windows 10. This is very important when considering the transformation to Windows 10 and Windows 10 support for many of their internal and external products and technologies.
Furthermore, our latest contribution of open source code to the OpenCV project, completes the relevant OpenCV libraries to enable video modules to run on any modern Windows OS -Windows 8.1, Windows Phone 8.1, as well as Windows 10 preview.
Read more on Running Video with OpenCV and WinRT
It means that all the parallel processing should be available out-of-the-box on any POSIX-compatible OS, including QNX and such. Try it out!
smart pointers (Ptr<>) can now be created in both 2.4 and 3.0 style (new ClassName(params) vs makePtr<ClassName>(params))
trained and stored stat models from opencv_ml 2.4 can now be loaded and used by opencv_ml 3.0 as-is.
OPENCV WIKI CHANGE LOG
We have been really delighted to work with [the consultant]. We have assigned additional items for him to develop as a result. I hope we can work with him again on other projects as well.
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