Why is Artificial Intelligence important?

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The AI learning adventure explores intelligence and its connection to engineering and technology.  Using ideas about human intelligence and intelligence more broadly, engineers can create “artificial intelligence,”; that is, impart “human” intelligence into machines or technology (Classical AI) or design technology that can itself “create” intelligence (future AI).  In fact, understanding how the brain works—”reverse-engineering the brain”—and understanding how engineers design intelligent machines—machines that replicate human intelligence—is one of the “Grand Challenges of Engineering” as set forth by The National Academy of Engineering (NAE). The implications and benefits of understanding the brain are many.  In addition to advances in the treatment of brain injuries and diseases and advancements in communications technology and computer simulations, understanding the brain will allow the design of intelligent machines with even more signicant societal impacts.  Already, mac
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Challenges or provocations solved by DevOps | NIIT digiNxt



Prior to DevOps application development, teams were in charge of gathering business requirements for a software program and writing code. Then a separate QA team tests the program in an isolated development environment, if requirements were met, and releases the code for operations to deploy. The deployment teams are further fragmented into siloed groups like networking and database. Each time ⌚ a software program is “thrown over the wall” to an independent team it adds bottlenecks.

The problem with this paradigm is that when the teams work separately:

·         Dev ✌ is often unaware of QA and Ops roadblocks that prevent the program from working as anticipated.
·         QA and Ops are typically working across many features and have little context of the business purpose and value of the software.
·         Each group has opposing goals that can lead to inefficiency and finger pointing when something goes wrong.
DevOps addresses these provocations by establishing collaborative cross-functional teams that share responsibility for maintaining the system that runs the software and preparing the software to run on that system with increased quality feedback and automation issues.


A Recurrent Pre-DevOps Storyline
The Dev team that has a goal to ship as many features as possible, kicks a new release “over the wall” to QA. Then the tester’s goal is to find as many bugs as possible. When the testers identifies and bring their findings to Dev, the developers become defensive and blame the testers that are testing the environment for the bugs. The testers respond that it isn’t their testing environment, but the developer’s code that is the problem.
Eventually the issues get worked out and QA kicks the debugged new release “over the wall” to Ops. The Ops team’s goal is to limit changes to their system, but they apprehensively release the code and the system crashes. The finger pointing resumes.

Ops says that Dev provided them faulty artifacts. Dev says everything worked fine in the test environment. The fire drills begin to debug the system and get production stable. The production environment isn’t Dev’s and QA’s responsibility, so they keep hands off while Ops spends all night fixing the production issues.


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