Week #6 - MBA 6101 - Surfing the Tsunami, Chapter 6: Level 3: Become Adept in AI

Through the course of his book, Surfing the Tsunami, Dr Todd Kelsey encourages that some familiarity with artificial intelligence and machine learning is crucial in the job market today and for the foreseeable future. At an organizational level, AI and ML are and will continue to be critical to creating and keeping a competitive edge.

Kelsey provides three options or levels of proficiency to aspire for. The beginner level, to adapt, is predominantly a passive option.  According to Kelsey, the AI/ ML beginner is learning and paying attention to how artificial intelligence and machine learning are being integrated and applied in the workspace. At this stage, while acknowledging the potential impact of AI/ ML, the adapt stage still allows for some cynical-tinted skepticism despite the “writing on the wall.”

At the intermediate/ proficiency level, the user has moved beyond adapt and embraced adoption. According to Kelsey, the adopt option is for the user that looks for a “hands on” approach to artificial intelligence tools and platforms.  Integration may include basic tools like virtual assistants (Google Assistant, Amazon Alexa, or Cortana), using software that is AI-based like Salesforce’s Sales Cloud Einstein, or using a platform built on programs using Microsoft’s Azure Machine Learning or Google Cloud Machine Learning.

The third possibility is to become adept. According to Kelsey, the adept user has achieved a fluency with artificial intelligence and machine learning. The user has developed the knowledge and skills to engage directly in creating, developing, or implementing AI applications. While bridging the gap between intermediate/ proficient (adopt) and fluent (adept) may feel intimidating, Kelsey favors short steps over long leaps.

Kelsey suggests “finding ways to simplify the process of learning about AI” and above all, to find ways of learning that are “fun and interesting.” For example, novice learners can find inspiration on sites like YouTube. Videos, especially those that focus on application rather than abstraction, are accessible, convenient first steps.

Another method for users looking to move from adopt to adept (and beyond) are games. Educational games are effective tools that can be used to teach a diverse audience of learners a range of concepts across many subjects including data science, artificial intelligence, and coding. Games/ sites like CodinGame, SQL Murder Mystery, and Grid Garden and Flexbox Defense are aimed at introducing beginners to coding through gamification. 

Videos and games provide a reasonably thorough introduction to artificial intelligence, data science, and coding, there are sites and apps that offer a more “traditional” online experience to learning.  Kelsey notes the advantages of a structured, community-based, mentor/ instructor led approach to learning as the most helpful, but suggests offerings from sites like Coursera, DataCamp, and Khan Academy as simple ways to get started and, importantly, to “maintain momentum.”    

There are many different paths that lead towards fluency in artificial intelligence. Ultimately, according to Kelsey, “it comes down to trying a path” and making learning a habit.