What it takes to build artificial intelligence skills

An AI repertoire is a unique blending of programming, data science and business knowledge.

Artificial intelligence, AI, is all the rage these days -- analysts are proclaiming it will change the world as we know it, vendors are AI-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.

Photo: IBM Media Relations

For people at the front lines of the revolution, that means developing and honing skills in this new dark art. In this case, AI requires a blend of programming and data analytics 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 AI 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 learning is the foundational skill for AI, 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 AI 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 algorithms or modify existing ones to fit specific purposes and constraints."

Programs popular with AI 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 AI capabilities.

Ultimately, becoming adept in AI also requires a degree of a change in conceptual thinking as well, requiring deductive reasoning and decision-making.

AI skills -- again, which blend expertise n programming, data, and business development -- may continue to be in short supply, and David Kosbie, Andrew W. Moore, and Mark Stehlik sounded the alarm in a recent Harvard Business Review article, calling for an overhaul of computer science programs at all levels of education. AI is "not something a solitary genius cooks up in a garage," they state. "People who create this type of technology must be able to build teams, work in teams, and integrate solutions created by other teams."

This requires a change in the way programming is taught, they add. "We're too often teaching programming as if it were still the 90s, when the details of coding (think Visual Basic) were considered the heart of computer science. If you can slog through programming language details, you might learn something, but it's still a slog -- and it shouldn't be. Coding is a creative activity, so developing a programming course that is fun and exciting is eminently doable."

What's in demand right now in terms of AI skills? A perusal through current job listings yields the following examples of AI jobs:

Senior software developer - artificial intelligence and cognitive computing (insurance company): "Lead the application prototyping and development for on premise cognitive search and analytics technologies. Candidate should have experience with AI, machine learning, cognitive computing, text analytics, natural language processing, analytics and search technologies, vendors, platforms, APIs, microservices, enterprise architecture and security architecture."

Artificial intelligence engineer: (aerospace manufacturer): "Will join a fast-paced, rapid prototyping team focused on applied artificial intelligence. Basic qualifications: 5 years experience in C/C++ or Python. Algorithm experience. Experience with machine learning and digital signal processing (computer vision, software defined radio) libraries."

Artificial intelligence innovation leader (financial services firm): "Oversee strategic product development, product innovation and strategy efforts. Evaluate market and technology trends, key providers, legal/regulatory climate, product positioning, and pricing philosophy.... Work closely with IT to evaluate technology viability and application. Qualifications: 7+ years of senior level management experience, PhD/masters in computer science, AI, cognitive computing or related field."

Artificial intelligence/machine learning engineer (Silicon Valley startup): "Deal with large-scale data set with intensive hands-on code development. Collect, process and cleanse raw data from a wide variety of sources. Transform and convert unstructured data set into structured data products. Identify, generate, and select modeling features from various data set. Train and build machine learning models to meet product goals. Innovate new machine learning techniques to address product and business needs. Analyze and evaluate performance results from model execution." Qualifications: "Strong background and experience in machine learning and information retrieval. Must have experience managing end-to-end machine learning pipeline from data exploration, feature engineering, model building, performance evaluation, and online testing with TB to Petabyte-size datasets."


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