How Engineers Solve Big and Difficult Problems Part 1: The Challenges/Opportunities Presented to Engineers by AI/ML

Neil Lawrence Professor of Machine Learning

Complexity in Action

Data Selective Attention Bias

BMI Steps Data

BMI Steps Data Analysis

A Hypothesis as a Liability

“ ‘When someone seeks,’ said Siddhartha, ‘then it easily happens that his eyes see only the thing that he seeks, and he is able to find nothing, to take in nothing. […] Seeking means: having a goal. But finding means: being free, being open, having no goal.’ ”

Hermann Hesse

The Scientific Process

What is Machine Learning?

\[ \text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

data : observations, could be actively or passively acquired (meta-data).

model : assumptions, based on previous experience (other data! transfer learning etc), or beliefs about the regularities of the universe. Inductive bias.

prediction : an action to be taken or a categorization or a quality score.

Royal Society Report: Machine Learning: Power and Promise of Computers that Learn by Example

What is Machine Learning?

\[\text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

To combine data with a model need:
a prediction function \(f(\cdot)\) includes our beliefs about the regularities of the universe
an objective function \(E(\cdot)\) defines the cost of misprediction.

Machine Learning

Driver of two different domains:
1. Data Science: arises from the fact that we now capture data by happenstance.
2. Artificial Intelligence: emulation of human behaviour.
Connection: Internet of Things

Machine Learning

Driver of two different domains:
1. Data Science: arises from the fact that we now capture data by happenstance.
2. Artificial Intelligence: emulation of human behaviour.
Connection: Internet of ~~Things~~

Machine Learning

Driver of two different domains:
1. Data Science: arises from the fact that we now capture data by happenstance.
2. Artificial Intelligence: emulation of human behaviour.
Connection: Internet of People

Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (1981/1/28)

What does Machine Learning do?

ML Automates through Data
- Strongly related to statistics.
- Field underpins revolution in data science and AI
With AI:
- logic, robotics, computer vision, speech
With Data Science:
- databases, data mining, statistics, visualization

Evolved Relationship with Information

New Flow of Information

Evolved Relationship

Data Science Africa is a bottom up initiative for capacity building in data science, machine learning and AI on the African continent

Example: Prediction of Malaria Incidence in Uganda

Work with Ricardo Andrade Pacheco, John Quinn and Martin Mubangizi (Makerere University, Uganda)
See AI-DEV Group.
See UN Global Pulse Disease Outbreaks Site

Malaria Prediction in Uganda

(Andrade-Pacheco et al., 2014; Mubangizi et al., 2014)

Tororo District

Malaria Prediction in Nagongera (Sentinel Site)

Mubende District

Malaria Prediction in Uganda

GP School at Makerere

Kabarole District

Early Warning System

Early Warning Systems

Thanks!

twitter: @lawrennd
podcast: The Talking Machines
Guardian article on How African can benefit from the data revolution
Guardian article on Data Science Africa
blog posts:

Open Data Science

What is Machine Learning?

System Zero

References

Andrade-Pacheco, R., Mubangizi, M., Quinn, J., Lawrence, N.D., 2014. Consistent mapping of government malaria records across a changing territory delimitation. Malaria Journal 13. https://doi.org/10.1186/1475-2875-13-S1-P5

Lawrence, N.D., 2010. Introduction to learning and inference in computational systems biology.

Mubangizi, M., Andrade-Pacheco, R., Smith, M.T., Quinn, J., Lawrence, N.D., 2014. Malaria surveillance with multiple data sources using Gaussian process models, in: 1st International Conference on the Use of Mobile ICT in Africa.