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Implementation Strategies: AI and Cognitive Computing

Gain an in-depth understanding of AI technology standards and terminology, and acquire skills to successfully evaluate and implement AI and cloud computing technology within an enterprise architecture context.

Implementation Strategies: AI and Cognitive Computing

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Starts Oct 14
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Estimated 6 weeks
3–4 hours per week
Self-paced
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Free
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Implementation Strategies: AI and Cognitive Computing

About this course

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Artificial Intelligence (AI) is set to radically disrupt how we do business. Yet for all the hype, many businesses lack the concrete knowledge to successfully implement AI and cognitive computing initiatives.

Designed for leaders operating at the intersection of IT and business, this course builds on the teachings in Course 1: Emerging Technologies in an Enterprise Environment but this time delving specifically into AI technology standards and terminology, focusing on the framework introduced by National Institute of Standards and Technology.

Drawing on real-life case studies and applications, analyze ethical considerations and learn about AI breakthroughs and success stories. Learn the mechanisms and implementation steps needed to successfully enable and implement your organization’s business goals.

Learners will review AI and cognitive computing within an overarching enterprise architecture context, considering factors such as risk management, open source vs. proprietary, redundancy, security and growth. You will apply program teachings to your own specific use cases, and will get to complete the end-to-end process from technology selection to workplace adoption.

It is recommended you complete Course 1: Emerging Technologies in an Enterprise Environment before enrolling in this course. Verified learners who complete all three courses in this series will receive an edX Professional Certificate in AI and Cloud Computing: Implementation Strategies for Business.

At a glance

What you'll learn

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  • Learn the origins, standards and terminology of AI and cognitive computing technology
  • Become familiar with, and critically appraise the leading AI technologies and vendors;
  • Learn how to effectively draft, provide input to, and/or summarise your organization’s AI strategy;
  • Identify AI use cases and strategically implement AI technology in the context of organization business strategy.

Syllabus

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Week 1, Module 1 Framework and Standards:

An overview of AI history, technology standards and terminology based on the framework introduced by the National Institute of Standards and Technology.

Week 2, Module 2 Technology Dive In:

Delve deeper into AI technology, including Machine Learning, Neural Networks, Robotics, Expert Systems, Natural Language Processing.

Week 3, Module 3 Challenges and Success Stories:

Discuss inspiring breakthrough AI technologies, including IBM’s Deep Blue and Watson, AlphaOne, Google Map, Google Translate, and more, and discuss common concerns in the AI field, including data and privacy, ethics, and AI autonomy.

Week 4, Module 4 AI Implementation Business Strategy:

Leverage AI to increase efficiencies and meet your organization’s business objectives, and learn how you can leverage AI and cognitive computing to enable and implement your organization’s business strategy.

Week 5, Module 5 AI Implementation - Follow the Data:

Two areas are essential to A: one is business domain knowledge, and the other is data. Without either, Learn the mechanisms and implementation steps to leverage AI technology in the context of enterprise architecture, so as to produce business intelligence for your organization.

Week 6: AI is Innovation:

While realizing the potential business benefits, AI is disruptive. Organizational culture is a key enabler for the success adoption of AI technology. This module discusses the steps that can facilitate the adoption of AI through enterprise-wide change management.

About the instructors

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