Kick-off

A cohort of participants is formed and they get ready for the journey ahead.

1 hour meeting

online or live

What happens?

• The facilitator introduces the course
• The participants login to study materials, fill starting surveys and book study time for the first self study period
• This time can be used to confirm and refine the program schedule, check accessibility to tools and materials,  and answer questions any questions that might come up

Outcomes:

After the kick-off participants are ready and equipped to start the first self-study period.

What is AI?

Participants dive into the fundamentals of AI and begin uncovering a shared understanding.

1 study time

Independent study

What happens?

• Participants study Chapter 1 – What is AI?
  
• How should we define AI?
• Related fields
• Philosophy of AI

Self-paced learning on our award-winning Elements of AI course – co-created with the University of Helsinki. The study includes reading, quizzes, and written exercises

Outcomes:

After completing the chapter participants can

• Explain autonomy and adaptivity as key concepts for explaining AI
• Distinguish between realistic and unrealistic AI (science fiction vs. real life)
• Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment

A mysterious discovery

The participants encounter an unknown intelligence. In order to find their place in this new reality they are forced to challenge their way of thinking.

2.5 hour workshop

online or live

What happens?

• The cohort agrees on ways of working
• Defining AI: Define AI to a colleague or customer
• AI Lab: Explore the use of a large language model and its limits in practice.
• The Landscape: Define your business landscape concerning AI

Learning outcomes:

After the workshop participants can…

• Explain AI and its relevance to their work
• Understand how to use large language models to produce valuable outcomes
• Understand restrictions of large language models
• Map their business to industry AI development

Machine Learning

The participants study the fundamentals of machine learning & neural networks – the key ingredients in generative AI.

2.5 study time

Independent study

What happens?

• Participants study Chapter 4 – Machine Learning
  
• The types of machine learning
• The nearest neighbor classifier
• Regression

Self-paced learning on our award-winning Elements of AI course – co-created with the University of Helsinki. The study includes reading, quizzes, and written exercises

Outcomes:

After completing the chapter participants can

• Explain why machine learning techniques are used
• Distinguish between unsupervised and supervised machine learning scenarios
• Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression

Neural Networks

The participants study the fundamentals of machine learning & neural networks – the key ingredients in generative AI.

2.5 study time

Independent study

What happens?

• Participants study Chapter 5 – Neural Networks
  
• Neural network basics
• How neural networks are built
• Advanced neural network techniques

Self-paced learning on our award-winning Elements of AI course – co-created with the University of Helsinki. The study includes reading, quizzes, and written exercises

Outcomes:

After completing the chapter participants can

• Explain what a neural network is and where they are being successfully used
• Understand the technical methods that underpin neural networks

Trial by fire

Is this strange intelligence a friend or foe? A challenge is revealed and the participants have to split up to find answers.

2.5 hour workshop

online or live

What happens?

• Generative AI: Identify what makes generative AI tick. Create a presentation on what you’ve learned.
• AI experiment: Hypothesis – Identify problems to solve with AI solutions. Make a plan for how to test your solution.

Learning outcomes:

After the workshop participants can…

• Explain how machine learning and neural networks relate to generative AI
• Plan agile experiments to enhance existing business processes with the latest AI tools

Implications

The participants study the fundamentals of machine learning & neural networks – the key ingredients in generative AI.

2.5 study time

Independent study

What happens?

• Participants study Chapter 6 – Implications
  
• About predicting the future
• The societal implications of AI
• Summary

Self-paced learning on our award-winning Elements of AI course – co-created with the University of Helsinki. The study includes reading, quizzes, and written exercises

Outcomes:

After completing the chapter participants can

• Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
• Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work

Triumph of the human mind

Weary from their long journey and forever changed – the participants have found their answers. Now they have what it takes to encounter what lies ahead.

2.5 hour workshop

online or live

What happens?

• Implications: Design a magazine cover that depicts the future of your business or industry.
• AI experiment – Results: Sharing what was done, why, and what were the learnings?
• Participants vote on the best experiment based on the value it brings and how well the experiment tests the hypothesis made.
• Participants fill out their final evaluation survey
• The cohort graduates and receive their certificates

Learning outcomes:

After the workshop participants can…

• Identify and communicate future AI opportunities and challenges for their business
• Make more informed decisions concerning AI