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The Great AI Fallacy

Neil D. Lawrence

Webinar for The Cambridge Network

The Great AI Fallacy

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.
bits/min
billions
2000
6
billion
calculations/s
~100
a billion
a billion
embodiment
20 minutes
5 billion years
15 trillion years

For sale: baby shoes, never worn

Computer Science Paradigm Shift

  • Von Neuman Architecture:
    • Code and data integrated in memory
  • Today (Harvard Architecture):
    • 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.

Intellectual Debt

Technical Debt

Lean Startup Methodology

The Mythical Man-month

Separation of Concerns

Intellectual Debt

  • Technical debt is the inability to maintain your complex software system.

  • Intellectual debt is the inability to explain your software system.

The Three Ds of Machine Learning Systems Design

The Three Ds of Machine Learning Systems Design

  • Three primary challenges of Machine Learning Systems Design.
  1. Decomposition
  2. Data
  3. Deployment

The Three Ds of Machine Learning Systems Design

  • Three primary challenges of Machine Learning Systems Design.
  1. Decomposition
  2. Data
  3. Deployment

Decomposition

  • ML is not Magical Pixie Dust.
  • It cannot be sprinkled thoughtlessly.
  • We cannot simply automate all decisions through data

Decomposition

We are constrained by:

  1. Our data.
  2. The models.

Decomposition of Task

  • Separation of Concerns
  • Careful thought needs to be put into sub-processes of task.
  • Any repetitive task is a candidate for automation.

Pigeonholing

Pigeonholing

  1. Can we decompose decision we need to repetitive sub-tasks where inputs and outputs are well defined?
  2. Are those repetitive sub-tasks well represent by a mathematical mapping?

A Trap

  • Over emphasis on the type of model we’re deploying.
  • Under emphasis on the appropriateness of the task decomposition.

Chicken and Egg

Co-evolution

  • Absolute decomposition is impossible.
  • If we deploy a weak component in one place, downstream system will compensate.
  • Systems co-evolve … there is no simple solution
  • Trade off between performance and decomposability.
    • Need to monitor deployment

Data Science as Debugging

80/20 in Data Science

  • Anecdotally for a given challenge
    • 80% of time is spent on data wrangling.
    • 20% of time spent on modelling.
  • Many companies employ ML Engineers focussing on models not data.

Deployment

Premise

Our machine learning is based on a software systems view that is 20 years out of date.

Continuous Deployment

  • Deployment of modeling code.
  • Data dependent models in production need continuous monitoring.
  • Continous monitoring implies statistical tests rather than classic software tests.

Continuous Monitoring

  • Continuous deployment:
    • We’ve changed the code, we should test the effect.
  • Continuous Monitoring:
    • The world around us is changing, we should monitor the effect.
  • Update our notions of testing: progression testing

Data-oriented Architectures

Milan

  1. A general-purpose stream algebra that encodes relationships between data streams (the Milan Intermediate Language or Milan IL)

  2. A Scala library for building programs in that algebra.

  3. A compiler that takes programs expressed in Milan IL and produces a Flink application that executes the program.

Context

Stateless Services

Meta Modelling

Conclusion

  • AI will not (yet) adapt to the human.
  • Humans must adapt to the machine.
  • Good design more important than ever.

Thanks!

References