This blog discusses key AI and machine learning (ML) terms that every board director and CEO must know to stay relevant and advance their duty of care.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningThis blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.

My last two blogs focused on the importance of AI professionals having some foundation in science discipline as a cornerstone for designing and developing AI models and production processes, and explored value of computing science, the richness of complexity sciences and the value of physics to appreciate the importance of integrating diverse disciplines into complex AI programs – key for successful returns on investments (ROI).

c If you want a good starter on the responsibility and duty of care, I recommend you read my earlier blog here.

In the Brain Trust Series, I have identified over 50 skills required to help evolve talent in organizations committed to advancing AI literacy. The last few blogs have been discussing the technical skills relevancy. To see the full AI Brain Trust Framework introduced in the first blog, reference here. We are currently focused on the technical skills in the AI Brain Trust Framework advancing the key AI and machine learning terms.

  1. Research Methods Literacy
  2. Agile Methods Literacy
  3. User Centered Design Literacy
  4. Data Analytics Literacy
  5. Digital Literacy (Cloud, SaaS, Computers, etc.)
  6. Mathematics Literacy
  7. Statistics Literacy
  8. Sciences (Computing Science, Complexity Science, Physics) Literacy
  9. Artificial Intelligence (AI) and Machine Learning (ML) Literacy
  10. Sustainability Literacy

Understanding Key AI Terms

The AI field is a deep and rich field that includes many fields, including statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.

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