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AutoAI

Putting Systems at the Heart of Machine Learning

Neil D. Lawrence

10 Minute Talk, Wednesday Meeting, Department of Computer Science and Technology, University of Cambridge

Introduction

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!

Ride Allocation Prediction

The Promise of AI

  • Automation forces humans to adapt, we serve.

  • We can only automate by systemizing and controlling environment.

  • AI promises to be first wave of automation that adapts to us rather than us to it.

That Promise …

… will remain unfulfilled with current systems design.

Computer Science Paradigm Shift

  • Von Neuman Architecture:
    • Code and data integrated in memory
  • Today:
    • Code and data separated for security

Computer Science Paradigm Shift

  • Machine learning:
    • Software is data
  • Machine learning is a high level breach of the code/data separation.

Technical Consequence

  • Classical systems design assumes decomposability.
  • Data-driven systems interfere with decomponsability.

Bits and Atoms

  • The gap between the game and reality.
  • The need for extrapolation over interpolation.

Data Oriented Architectures

  • Convert data to a first-class citizen.
  • View system as operations on data streams.
  • Expose data operations in a programmatic way.

Streaming System

  • Move from pull updates to push updates.
  • Operate on rows rather than columns.
  • Lead to stateless logic: persistence handled by system.
  • Example Apache Kafka + Apache Flink

Hypothetical Streams

  • Real stream — share prices
    • derived hypothetical stream — share prices in future.
  • Hypothetical constrained by
    • input constraints.
    • decision functional
    • computational requirements (latency)

Hypothetical Advantage

  • Modelling is now required.
  • But modelling is declared in the ecosystem.
  • If it’s manual, warnings can be used
    • calibration, fairness, dataset shift
  • Opens door to auto-adaptable ML.

Ride Sharing: Service Oriented

Ride Sharing: Data Oriented

Ride Sharing: Hypothetical

Information Dynamics

  • Potential for information feedback loops.
  • Hypothetical streams are instantiated.
  • Nature hypothesis (e.g. price prediction) can effect reality.
  • Leads to information dynamics, similar to dynamics of governors.
  • See e.g. Closed Loop Data Science at Glasgow.

Auto AI

  • Auto ML is great but not sufficient
  • Interacting components in an ML system
  • Identify problems, and automatically deploy solutions

Conclusion

  • Paradigm shift for computer science.
  • Want to study deployed interacting ML systems.
  • Need to put the data flows at the heart, not models or services.
  • Need expertise in
    • Security, Programming languages, Systems
  • Implications for Hardware and network design

Thanks!

References