Artificial Intelligence (AI) for Business & Government + Examen

The AI for Business training is aimed at executives, managers and professionals who wish to progress beyond basic awareness to acquire an understanding of what AI is, how it works and where it can be applied – including both benefits and risks.

It is therefore ideal for people for whom the concept of AI is relatively new and who wish to become competent in AI, are considering becoming involved in the application of AI or who must make informed decisions about the application of AI in their business domain.

• Appreciates what AI is, why it is important now and what benefits it brings
• Is aware of fundamental concepts such as robotics, algorithms and machine learning approaches
• Is aware of how to organize a team for AI, approaches and common tools required
• Understands the dependence AI has upon data and how to manage data for AI
• Is aware of how to assess risks and ethical dilemmas for Trustworthy AI
• Can describe how humans and machines will coexist in an organization
• Is aware of the future directions of AI

Section 1: AI Fundamentals

1.       Applications of AI and their benefits

Describes a range of applications of AI, how they impact organisations & society, what value they create and their underlying use of data, algorithms and learning approaches. Describes the role of the NL AIC in promoting the beneficial and ethical use of AI.  Includes examples of key domains such as classification systems, forecasting, cluster analysis, voice, image and natural language processing.

2.       Data, Robots and Artificial Intelligence including definitions

This sets out a common vocabulary around data, data science, algorithms, human logical thinking versus intelligent agents and provides definitions for key items.  Describes Intelligent Agent types, robotics and agent models.

3.       Predictions, Algorithms, Machine and Deep Learning

Introduces the different levels of predictions, an overview of the key algorithms and the learning approaches.  Highlights which types of algorithms address which types of problems. 

Section 2: Applying AI in practice

4.       Building and assessing an AI application

Describes a basic approach towards building a simple AI application.  The CRISP-DM method is described highlighting the steps involved and raising awareness of the business context and trustworthiness assessment at each step. Highlight pitfalls including overfitting, underfitting and bias.  Addresses need for innovation and creativity including team organisation.

5.       Managing Data for AI

Raises awareness of the dependence upon data and how to acquire, prepare, manage and provide and scale data for AI applications.  Addresses the role of the cloud for managing data and processing capability.  Emphasise the risks that arise in data and impact on trustworthiness.

Section 3: Ethics, Trustworthiness and Human Machine Coexistence

6.       Ethics, Risks and Trustworthiness

Addresses the risks and ethical dilemmas associated with AI including the need for explainable AI.  Introduce EU Ethical Guidelines and the need to maintain the trust of society in the use of AI.

7.       Human and Machine Coexistence

Covers the combination of human and machine capability in an organisation addressing question of whether AI will replace humans (singularity). Includes key roles of business management, domain expertise, analytics and data managers, and how these roles work together.

Section 4: Future developments of AI

8.       The future developments of AI

Highlight future directions and applications of AI.