Future of the Technician Workforce Study


TOOLS Software:  Analytics/dashboard


 Basic, advanced math skills  Statistics & modeling; Evaluating model robustness  Data collection & analysis; data processing  Integrating data/info from diverse sources to create “big data” set  Understanding of how biases are introduced into process models  Knowledge of cognitive processes and operations  Deep understanding of optimization goals for current processes  Defining problem/question to be analyzed by AI  Ability to communicate intent and algorithm use cases  Human explanation techniques for ML systems  Formal methods of logical thinking (i.e., philosophical reasoning Programming & Coding:  Training neural networks  Use of AI/ML software suites (i.e., Oversight)  Programming and software development skills  Simulation modeling  Design of user-friendly interfaces (i.e., UX/UI)  System developer and data security understanding  Connectivity of different generations of equipment SOFT SKILLS  Critical thinking  Team dynamics/communication  Ability to provide effective feedback and evaluation  Ethics of using AI technologies  Ability to understand business problem statements and value proposition  Change management (i.e., in support of adopting AI methods)

software: Qualtrics, Tableaux, Power BI  Language processing and voice recognition tools  Deep Learning Frameworks: TensorFlow, PyTorch  Neural network mapping software Data Libraries:  Neural network libraries (i.e., Keras)  Machine learning libraries: (i.e., Scikit-learn) Infrastructure & Computing Platforms:  Edge computing/data score (for real-time analytics/ control)  Mobile platforms  Cybersecurity systems Hardware:  Hardware for neural network mapping (i.e., Siemens 57-1500 TM neural processing unit [NCU])  Use of AI/NN in Industrial Controller (Siemens 57-1500 TET 200 MP I/O)

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