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What is Artificial Intelligence?

Neil D. Lawrence

Council on Ethics, The Norwegian Government Pension Fund

What is Machine Learning?

\[ \text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}\]

  • 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?

\[\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}\]

  • 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

What does Machine Learning do?

  • Automation scales by codifying processes and automating them.
  • Need:
    • Interconnected components
    • Compatible components
  • Early examples:
    • cf Colt 45, Ford Model T

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ \text{odds} = \frac{p(\text{bought})}{p(\text{not bought})} \]

\[ \log \text{odds} = \beta_0 + \beta_1 \text{age} + \beta_2 \text{latitude}.\]

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ p(\text{bought}) = \sigmoid{\beta_0 + \beta_1 \text{age} + \beta_2 \text{latitude}}.\]

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ p(\text{bought}) = \sigmoid{\boldsymbol{\beta}^\top \inputVector}.\]

Codify Through Mathematical Functions

  • How does machine learning work?
  • Jumper (jersey/sweater) purchase with logistic regression

\[ \dataScalar = \mappingFunction\left(\inputVector, \boldsymbol{\beta}\right).\]

We call \(\mappingFunction(\cdot)\) the prediction function.

Fit to Data

  • Use an objective function

\[\errorFunction(\boldsymbol{\beta}, \dataMatrix, \inputMatrix)\]

  • E.g. least squares \[\errorFunction(\boldsymbol{\beta}, \dataMatrix, \inputMatrix) = \sum_{i=1}^\numData \left(\dataScalar_i - \mappingFunction(\inputVector_i, \boldsymbol{\beta})\right)^2.\]

Two Components

  • Prediction function, \(\mappingFunction(\cdot)\)
  • Objective function, \(\errorFunction(\cdot)\)

Deep Learning

  • These are interpretable models: vital for disease modeling etc.

  • Modern machine learning methods are less interpretable

  • Example: face recognition

DeepFace

Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three locally-connected layers and two fully-connected layers. Color illustrates feature maps produced at each layer. The net includes more than 120 million parameters, where more than 95% come from the local and fully connected.

Source: DeepFace (Taigman et al., 2014)

Deep Learning as Pinball
bits/min
billions
2000
6
billion
calculations/s
~100
a billion
a billion
embodiment
20 minutes
5 billion years
15 trillion years

Turing AI Fellowship

Project Description

It used to be true that computers only did what we programmed them to do, but today AI systems are learning from our data. This introduces new problems in how these systems respond to their environment.

We need to better monitor how data is influencing decision making and take corrective action as required.

Aim

  • Scale safe and reliable AI solutions.
  • Move from Auto ML to Auto AI
  • Bayesian Optimisation to Bayesian System Optimisation

Motivating Examples

SafeBoda

SafeBoda

With road accidents set to match HIV/AIDS as the highest cause of death in low/middle income countries by 2030, SafeBoda’s aim is to modernise informal transportation and ensure safe access to mobility.

Turing AI Fellowship

and
Your Name Here

Inclusive Project

There is no way that the team we’re building will be able to deliver on this agenda alone, so please join us in addressing these challenges!

Motivation

  1. Indsidious decision-making that has downstream instrumental effects we don’t control.
  2. A power-asymmetry between data-controllers and data-subjects
  3. A loss of personhood in the re-representation of ourselves in the digital world.
  4. The GDPR’s endeavour to curb contractual freedom cannot by itself reverse the power-asymmetry between data-controllers and data-subjects.

Analogy

  • Digital Democracy vs Digital Oligarchy Lawrence (2015a) or Information Feudalism Lawrence (2015b)
  • Data subjects, data controllers and data processors.

Five AI Myths

  1. AI will be the first wave of automation that adapts to us.
  2. Hearsay data has significant value.
  3. The big tech companies have the landscape all ‘sewn up’
  4. ‘data scientists’ will come and solve all problems.
  5. The normal rules of business don’t apply to AI.

Mythbusting

  • Area of good data:
    • Finance

Criteria for Success

  • Executive sponsorship (Office of CEO).
  • Technical Expertise (Open minded expert).
  • Financial buy in (CFO).
  • Assimilated knownledge (CTO).

Normal Organisational Rules Apply

  • AI is not magical pixie dust
  • Standard organisational instincts apply
  • Disruption requires agile thinking.
    • Don’t be the Grand Old Duke of York
    • Be Special Forces

Thanks!

References

Delacroix, S., Lawrence, N.D., 2019. Bottom-up data trusts: Disturbing the “one size fits all” approach to data governance. International Data Privacy Law. https://doi.org/10.1093/idpl/ipz014

Edwards, L., 2004. The problem with privacy. International Review of Law Computers & Technology 18, 263–294.

Lawrence, N.D., 2016. Data trusts could allay our privacy fears.

Lawrence, N.D., 2015a. Beware the rise of the digital oligarchy.

Lawrence, N.D., 2015b. The information barons threaten our autonomy and our privacy.

Taigman, Y., Yang, M., Ranzato, M., Wolf, L., 2014. DeepFace: Closing the gap to human-level performance in face verification, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2014.220