IT Pros: AI and Robotics Adoption Accelerating

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IT professionals, it just may be time to blow the dust off those Computer Science textbooks you have laying around. The rate of adoption of AI and machine learning is really starting to accelerate. Tools needed for IT pros to remain relavant? Try Lisp, Prolog, Scheme, and Clojure, and yes of course C++.

 

If AI does not interest you then how about some mobile app development with C#. Here is a great free online course from Harvard named Mobile Software Engineering. Mobile app development is a growth industry. Master this course and you can master your destiny.

 

Gartner predicts one in three jobs will be converted to software, robots and smart machines by 2025, according Peter Sondergaard, a Gartner research director. "New digital businesses require less labor; machines will make sense of data faster than humans can," he said.

 

Smart machines are an emerging "super class" of technologies that perform a wide variety of work, both the physical and the intellectual kind. Machines, for instance, have been grading multiple choice test for years, but now they are grading essays and unstructured text. This cognitive capability in software will extend to other areas, including financial analysis, medical diagnostics and data analytic jobs of all sorts, says Gartner. "Knowledge work will be automated."

 

To further support this supposition, condider the reponses from Jeremy Howard and Erik Brynjolsson to a question posed to them regarding layered machine learning in a recent Mckinsey report.  (Note: All quotes below from the report.)

Jeremy Howard: The difference here is each thing builds on each other thing. The data and the computational capability are increasing exponentially, and the more data you give these deep-learning networks and the more computational capability you give them, the better the result becomes because the results of previous machine-learning exercises can be fed back into the algorithms. That means each layer becomes a foundation for the next layer of machine learning, and the whole thing scales in a multiplicative way every year. There’s no reason to believe that has a limit.

 

Erik Brynjolfsson: With the foundational layers we now have in place, you can take a prior innovation and augment it to create something new. This is very different from the common idea that innovations get used up like low-hanging fruit. Now each innovation actually adds to our stock of building blocks and allows us to do new things.

 

One of my students, for example, built an app on Facebook. It took him about three weeks to build, and within a few months the app had reached 1.3 million users. He was able to do that with no particularly special skills and no company infrastructure, because he was building it on top of an existing platform, Facebook, which of course is built on the web, which is built on the Internet. Each of the prior innovations provided building blocks for new innovations. I think it’s no accident that so many of today’s innovators are younger than innovators were a generation ago; it’s so much easier to build on things that are preexisting.

 

 

 

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Bill has been a member of the technology and publishing industries for more than 25 years and brings extensive expertise to the roles of CEO, CIO, and Executive Editor. Most recently, Bill was COO and Co-Founder of CIOZone.com and the parent company PSN Inc. Previously, Bill held the position of CTO of both Wiseads New Media and About.com.

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