Administrative information
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Title
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Introduction to resurgence of AI and ML |
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| Duration |
45-60 |
| Module |
C |
| Lesson Type |
Lecture |
| Focus |
Technical - Future AI |
| Topic |
Introduction |
Keywords
Turing test, Birth of AI, Resurgence of AI, AI definition,
Learning Goals
- Understanding emergence of AI
- Knowledge of events leading to the AI winters
- Raionale key factors responsible for the Resurgence of AI
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Machine learning concepts
- Deep learning concepts
Optional for Students
None.
References and background for students
None.
Recommended for Teachers
None.
Lesson materials
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 a brief history of AI and the events/developments that have lead to an explosion of AI applications and the current wave of AI research, investment and the call for AI regulation. It should set the stage for more in-depth studies of advanced AI concepts, technological and regulatory developments that will shape future AI. The lecture should:
- Present a chronological timeline of AI developments from past to present
- Use real-world examples to illustrate AI landmarks through time
- Focus on the impact and direction of AI research leading to the current wave of AI applications
- Pay particular attention to the recent explosion of AI, its ubiquitous nature and the need to focus on ethical considerations
Outline
| Duration |
Description |
Concepts |
Activity |
Material |
| 10 min |
Birth of AI: tracing the first notions of AI |
AI in Greek mythology, automotons, early science fiction, 3 laws of robotics (Asimov), questions driving AI, categorising AI |
Taught session and examples |
Lecture materials |
| 5 min |
Events & developments leading to the first AI winter |
Formal logic and AI, thinking machines, turing test, early success stories (Arthur Samuel checkers program 1955), early machine translation, Dartmouth summer project (1956), Rosenblatt's perceptron (1957), fall of connectionism (Minsky & Papert 1969), Lighthill report (1973) |
Taught session and examples |
Lecture materials |
| 5 min |
Events & developments leading to the second AI winter |
Expert systems (DENDRAL, MYCIN 1972), Japanese fifth generation project (1982), Backpropagation (1986), early character recognition (LeNeT-1 1989), commercialisation of AI, limitations of expert systems, slow progress in nerual network development (Support Vector, Bayesian style methods) |
Taught session and examples |
Lecture materials |
| 10 min |
Big data: how the collection of big data has impacted AI and machine learning |
Web 2.0 and explosion of data, knowledge bottleneck (Halevy et al. 2009), growth of social media (semi-structured and unstructured data), mobile device and health data, sensor-based internet enabled devices (IOT), race to extract meaningful data |
Taught session and examples |
Lecture materials |
| 10 min |
Resurgence of AI: how data and computational power has given rise to a new wave of ubiquitous AI and the call for regulation |
GPU-based computation (CUDA 2012), rise of personal assistants (Google, Apple, Amazon, Microsoft), AlexNet (ImageNet 2012), Google Brain (2012), Tensor Processing Units (2016), AlphaGo & AlphaFold (2016, 2020), self-driving cars Waymo (2020), EU AI Act (2021) |
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
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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
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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