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Towards Machine Learning Systems Design

Neil D. Lawrence

Department of Computing Science, University of Glasgow

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

Embodiment Factors
compute \[\approx 100 \text{ gigaflops}\] \[\approx 16 \text{ petaflops}\]
communicate \[1 \text{ gigbit/s}\] \[100 \text{ bit/s}\]
(compute/communicate) \[10^{4}\] \[10^{14}\]

See “Living Together: Mind and Machine Intelligence” Lawrence (2017)

.

Evolved Relationship

Evolved Relationship

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

Olympic Marathon Data

  • Gold medal times for Olympic Marathon since 1896.
  • Marathons before 1924 didn’t have a standardised distance.
  • Present results using pace per km.
  • In 1904 Marathon was badly organised leading to very slow times.
Image from Wikimedia Commons http://bit.ly/16kMKHQ

Olympic Marathon Data

Alan Turing

Probability Winning Olympics?

  • He was a formidable Marathon runner.
  • In 1946 he ran a time 2 hours 46 minutes.
    • That’s a pace of 3.95 min/km.
  • What is the probability he would have won an Olympics if one had been held in 1946?

Olympic Marathon Data GP

Deep GP Fit

  • Can a Deep Gaussian process help?

  • Deep GP is one GP feeding into another.

Olympic Marathon Data Deep GP

Olympic Marathon Data Deep GP

Olympic Marathon Data Latent 1

Olympic Marathon Data Latent 2

Olympic Marathon Pinball Plot

Supply Chain

Cromford

Deep Freeze

Deep Freeze

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

Machine Learning Systems Design

Fragility of AI Systems

  • They are componentwise built from ML Capabilities.
  • Each capability is independently constructed and verified.
    • Pedestrian detection
    • Road line detection
  • Important for verification purposes.

Pigeonholing

Robust

  • Need to move beyond pigeonholing tasks.
  • Need new approaches to both the design of the individual components, and the combination of components within our AI systems.

Rapid Reimplementation

  • Whole systems are being deployed.
  • But they change their environment.
  • The experience evolved adversarial behaviour.

Machine Learning Systems Design

Adversaries

  • Stuxnet
  • Mischevious-Adversarial

An Intelligent System

Joint work with M. Milo

An Intelligent System

Joint work with M. Milo

Peppercorns

  • A new name for system failures which aren’t bugs.
  • Difference between finding a fly in your soup vs a peppercorn in your soup.

Peppercorns

Turnaround And Update

  • There is a massive need for turn around and update
  • A redeploy of the entire system.
    • This involves changing the way we design and deploy.
  • Interface between security engineering and machine learning.

Emukit

Emukit

Emukit

  • Work by Javier Gonzalez, Andrei Paleyes, Mark Pullin, Maren Mahsereci, Alex Gessner, Aaron Klein.
  • Available on Github
  • Example sensitivity notebook.

Emukit Software

  • Multi-fidelity emulation: build surrogate models for multiple sources of information;
  • Bayesian optimisation: optimise physical experiments and tune parameters ML algorithms;
  • Experimental design/Active learning: design experiments and perform active learning with ML models;
  • Sensitivity analysis: analyse the influence of inputs on the outputs
  • Bayesian quadrature: compute integrals of functions that are expensive to evaluate.

Conclusion

  • Artificial Intelligence and Data Science are fundamentally different.

  • In one you are dealing with data collected by happenstance.

  • In the other you are trying to build systems in the real world, often by actively collecting data.

  • Our approaches to systems design are building powerful machines that will be deployed in evolving environments.

Thanks!

References

Lawrence, N.D., 2017. Living together: Mind and machine intelligence. arXiv.

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