Control Engineering | Bridging the artificial intelligence skills gap for machine manufacturers

The cyclic nature of artificial intelligence and machine learning (AI/ML) projects, as presented in Dimecc’s Machine Learning Academy. Courtesy: Dimecc Ltd.

 

Learning Objectives

  • An AI skills gap exists for ML applications.
  • For Industry 4.0 environments more AI and ML knowledge is needed.
  • Education needs to change to help AI and ML.

Artificial intelligence (AI) talent is difficult to find, and few industrial companies have enough in-house AI talent. AI will transform many jobs, and companies should give every employee the knowledge they will need to adapt to new AI-enhanced roles. AI resources help implement new business models and better services, but user acceptance is required.

During the last decade, AI design, development and implementation has expanded in many sectors. Organizations are struggling with AI business potential understanding and with finding AI talent.

A growing number of countries have recognized the opportunities provided by artificial intelligence and have prepared a national

Read More