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What it Takes to Build Artificial Intelligence Skills

Artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time., , is all the rage these days – analysts are proclaiming it will change the world as we know it, vendors are -washing their offerings, and business and IT leaders are taking a close look at what it can potentially deliver in terms of growth and efficiency.

For people at the front lines of the revolution, that means developing and honing skills in this new dark art. In this case, requires a blend of programming and data analyticsData analysis is simply put the study of data in order to take better informed decisions. All business decisions should be made with the best information available, data analysis tries to provide this information through different algorithms and statistical techniques. skills, with the necessary business overlay. In a recent report at the Dice site, William Terdoslavich explores some of the skills people will need to develop a repertoire in the space, noting that these skills are in high demand, especially with firms such as Google, IBM, Apple, Facebook, and Infosys absorbing all available talent.

Machine and Math

Machine is the foundational skill for , and online courses such as those offered through Coursera offer some of the fundamental skills. Abdul Razack, senior VP and head of platforms at Infosys, notes that another way to develop expertise is to “take a statistical programmer and training them in data strategy, or teach more statistics to someone skilled in data processing.” Mathematical knowledge is also foundational, Terdoslavich adds, requiring a “solid grasp of probability, statistics, linear algebra, mathematical optimization–is crucial for those who wish to develop their own algorithmsAn algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. or modify existing ones to fit specific purposes and constraints.”

Programs popular with developers include R, Python, Lisp, Prolog and Scala, Terdoslavich’s article states. Older standbys — such as C and C++ and Java — are also being employed, depend upon applications and performance requirements. Platforms and toolsets such as TensorFlow also provide capabilities. Ultimately, becoming adept in also requires a degree of a change in conceptual thinking as well, requiring deductive reasoning and decision-making. […]

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