Definition
Deep learning is a subset of machine learning in artificial intelligence that uses neural networks with multiple layers (hence “deep”) to analyse various factors of data. It imitates the way humans gain certain types of knowledge, with artificial neural networks that simulate the structure and function of the human brain.
Example
AlphaGo
A real-world example of deep learning in action is Google’s AlphaGo, which defeated world champion Lee Sedol in the complex game of Go in 2016. AlphaGo used deep learning to analyse millions of moves from expert Go players and then played against itself to develop strategies beyond human capability.
Ask yourself
- How might deep learning affect decision-making processes in my industry?
- How can we adapt our thinking to create novel insights using deep learning?
- What ethical considerations should we keep in mind as deep learning becomes more prevalent?
- How can we ensure that deep learning algorithms are transparent and explainable?
- How might deep learning change the nature of work and employment in the future?
Tools
- Scenario Planning: To explore potential futures where deep learning is more advanced and ubiquitous.
- Trend Analysis: To track the development and adoption of deep learning across various sectors.
- Cross-Impact Analysis: To understand how advancements in deep learning might affect other technological and societal trends.
- Delphi Method: To gather expert opinions on the future trajectory of deep learning.
- Futures Wheel: To map out the primary, secondary, and tertiary effects of widespread deep learning adoption.
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