Administrative information

Title	Generalizability and Artificial General Intelligence (AGI)
Duration	45 - 60
Module	C
Lesson Type	Lecture
Focus	Technical - Future AI
Topic	Open Problems and Challenges

Keywords

AGI,Generalizability,LLMs,Transformers,

Learning Goals

Limitations of currrent AI approaches
Definition of Artificial General Intelligence (AGI)
Capabilities & core requirements of AGI
How can we test for AGI
How far away is AGI and what are the benefits and risks

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

Introduction to machine learning and deep learning concepts given in previous lectures

Optional for Students

None.

References and background for students

None.

Recommended for Teachers

Lesson materials

Lecture slides

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

The goal of this lecture is to provide students with an introduction to the idea of Artificial General Intelligence (AGI). It should set the stage for more in-depth discussions and debates about AGI. The lecture should:

Clarify the differences between the levels of AI
Discuss the expected characteristics of AGI
Outline the limitations of the current state-of-the-art AI in terms of the characterists of AGI
Present possible ways in which we might test for AGI
Look at various expert viewpoints on how far away AGI is
Discuss the possible benefits and risks of AGI in terms of Human Centered AI

Outline

Duration	Description	Concepts	Activity	Material
10 min	Limitation of current AI approaches	Reliance on data and teaching (learning from limited data), Human scale nerual networks, offline learning versus continuous learning and adaptation of beliefs, integration into a complete AI stack	Taught session and examples	Lecture materials
5 min	Definition of Artificial General Intelligence (AGI)	How can we define AGI, levels of AI (weak, strong, super)	Taught session and examples	Lecture materials
10 min	Capabilities and core requirements of AGI	Sensory perception, motor skills, natural language understanding, knowledge retention, problem solving, common sense, creativity, consciousness, pattern recognition versus modeling the world	Taught session and examples	Lecture materials
5 min	How can we test for AGI?	AGI Turing test, Coffee test, Robot college student, Employment test	Taught session and examples	Lecture materials
5 min	How far away is AGI and what are the benefits and risks?	Metrics (time, speed of technological advancement, singularity breakthrough), Expert views and predictions, Possible outcomes and ethical concerns (Utopia, Status Quo, Distopia)	Taught session and examples	Lecture materials
5 min	Conclusion, questions and answers	Summary	Conclusions	Lecture materials

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities