What it was:
Talks by subject matter experts on AI hosted by the Civil Service Alumni, at the Royal College of Defence Studies, London on 18 December 2017
What I learned:
AI is permeating business:
- No single agreed definition of “AI”
- ‘Narrow’ AI does specific tasks, as opposed to ‘General’ AI which can solve new problems
- AI is already in use in hundreds of Google products
- AI is increasingly in the plans of the forward-looking global industrials
- Huge landscape of firms involved in different sectors
Deep Neural Networks:
- The workhorse of modern AI is the deep neural network, where each layer of neurons is connected to the prior layer
- Supervised versus unsupervised learning
- AI performance is better than humans in many narrow case, using supervised data and deep neural networks
Conditions for success:
- Many AI applications can be characterised and understood as input X leading to output Y to solve problem Z. For example, medical scan data leading to disease symptom recognition to solve diagnosis.
- A precondition for the successful deployment of AI is a clear definition of the system or process, and an understanding of what AI can do
- A social technical approach, including human impact, is typically desirable
- AI typically requires widespread digitisation of the system
- Data requirements: Common definitions, high quality, accessible, sufficient size
- Domain expertise is key
Impact on Government?
- A viable, shared business model for both public and private sector is needed
- Key challenges for government in terms of capability, ethics, regulation, getting on front foot in terms of impact, and in nurturing the wider AI economy
- There is a significant discussion happening now around ethics, bias, regulation
What I will aim to do differently as a result:
- Think about ethics and regulation of AI
- Think about impact of AI on Government, including potential oportunities
- Continue my learning theme on AI!