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

The Engineer in Society

Neil Lawrence
Professor of Machine Learning

## 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

## 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.

## 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

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

## Malaria Prediction in Uganda

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

## 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.