For all its promised benefits and applications across industries, AI remains neither fully transparent nor explainable. For this and other reasons, AI is challenging us in myriad ways, both in what we must learn and how we must learn it—and it is doing so at warp speed.
The Computer article, “Grow Your Artificial Intelligence Competence,” by Christof Ebert and Ulrich Hemel outlines these challenges and spells out required AI competences and how individuals and organizations can build them.
Critical AI Competences
AI competences span a range of hard and soft skills, as well as explicit and implicit knowledge:
- Explicit competences are built by intentionally learning information, such as when we study and learn words in a new language.
- Implicit competences grow “in the absence of conscious awareness” in discrete episodes over time, as when we learn how to speak a new language.
To highlight the challenges AI entails, the authors compare the competences that exist in the workforce today with those that are urgently needed; both are growing in importance and will continue to do so.
Existing AI competences include:
- hard competences such as data science, algorithms, security, and AI frameworks; and
- soft competences such as problem solving, abstraction, and communication.
AI competences that are less common in the workforce today include:
- hard competences such as systems engineering, model selection and evaluation, and copiloting; and
- soft competences such as empathy, creativity, and ethics.
Using AI to Build AI Competence
The authors offer many suggestions for increasing AI competence and offer an extensive “Guide to Grow,” which includes specific tips for increasing competences at the individual and organization levels. They also offer a step-by-step approach for using AI itself to improve AI training.
As Ebert and Hemel describe, using GenAI-based e-learning can create personalized, interactive training to address specific skill gaps and prepare employees for upcoming challenges.
Based on their experiences using GenAI to build competence, the authors recommend the following four steps:
-
- Analyze knowledge gaps. Develop a tailored curriculum that identifies specific learning needs and skill gaps using standardized questions and small case studies.
- Create the training. Use GenAI to synthesize and aggregate existing training materials—as well as materials on corporate rules, standards, and so on—into individualized training formats that coherently address Step 1 gaps.
- Deliver the training. Use GenAI to enhance existing e-learning platforms and deliver digestible Step 2 content that is automatically adjusted to the trainee’s available time, knowledge gaps, learning styles, learning goals, project role, and the project’s needs.
- Get feedback. Use GenAI to offer immediate data-driven feedback on learning progress, then use that feedback to automatically update training materials. Examples of updates might include restating learning questions and selecting sources to more thoroughly explore specific types of content.
What’s at Stake?
In various areas, increasing AI competence is not only a good idea but an urgent need on the part of companies, institutions, and societies.
For example, Ebert and Hemel provide a compelling discussion of AI ethics, including how AI mirrors the values and thinking of its human creators, who form the foundation of “the morality of AI” through the data they choose and the algorithms they create.
The authors also discuss the stakes of the current regulatory chaos surrounding AI, offering a cautionary analogy regarding the introduction of automobiles: At first, only experts drove, and there was little need for regulation. Once driving gradually became a more common option, rules were implemented and licenses required to prove driving competency.
In contrast, AI has been quickly released into the wild, fueled by corporate pursuits of growth, competition, and engagement at all costs as governments and standardization bodies struggle to catch up.
Dig Deeper
To learn more about these stakes and approaches to developing AI competence, read “Grow Your Artificial Intelligence Competence.”
To dig even deeper, attend the international IEEE Conference on Artificial Intelligence (IEEE CAI) May 5–7 in Santa Clara, California. In addition to showcasing the latest AI research and breakthroughs, IEEE CAI emphasizes applications and key subject areas, from human-centered AI and sustainability to issues and industry-specific applications in healthcare, transportation, and engineering and manufacturing.
