
# AI and Data Science

Systems, Data Science, Biology, Medicine

Autumn Data Science School, Cambridge

## 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 $\mappingFunction (\cdot)$ includes our beliefs about the regularities of the universe
• an objective function $\errorFunction (\cdot)$ 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

## Machine Learning in Supply Chain

• Supply chain: Large Automated Decision Making Network
• Major Challenge:
• We have a mechanistic understanding of supply chain.
• Machine learning is a data driven technology.

## Deploying Artificial Intelligence

• Challenges in deploying AI.
• Currently this is in the form of “machine learning systems”

## Internet of People

• Fog computing: barrier between cloud and device blurring.
• Computing on the Edge
• Complex feedback between algorithm and implementation

## Deploying ML in Real World: Machine Learning Systems Design

• Major new challenge for systems designers.
• Internet of Intelligence but currently:
• AI systems are fragile

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 Mubaganzi (Makerere University, Uganda)
• See AI-DEV Group.

## Malaria Prediction in Uganda

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

## Conclusion

• Separation between machine learning and AI

• Real world challenges such as matching supply to demand

• Example of predicting disease via Gaussian processes

## 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

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.