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Perspectives on AI

Neil D. Lawrence

Sheffield ML Meetup, Kollider, Castle House Castle Street, Sheffield

Are we close to creating intelligence?

What is Intelligence?

  • Poorly defined.
    • My definition: use of information to achieve goals more efficiently.
    • Efficiency is defined through use of less resource.
  • Automated decision making: we are continuing to progress well.

The Silicon Factor

  • BBC 1, London, 14th September 1980
  • Series of three programmes investigating the so-called microelectronics revolution.
  • The promise (and perils) of silicon was broadly similar to that for AI today.

Public Definition

  • Doing things that humans do.
    • There is a narcissistic element to our understanding of artificial intelligence
  • It’s a shifting definition.
    • Intelligence is the stuff I can do computers can’t.

Cybernetics and the Ratio Club

  • Ideas came out of the Second World War.
    • Researchers explored the use of radar (automated sensing) e.g. Donald MacKay
    • Automatic computation for decryption of military codes (automated decision making) e.g. Jack Good and Alan Turing
    • Post-war potential for electronic emulation of what had up until then been the preserve of an animallian nervous system.

The Centrifugal Governor

Figure: Science on Holborn Viaduct, cradling the Centrifugal Governor.

On Governors, James Clerk Maxwell 1868

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

From Model to Decision

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

Artificial vs Natural Systems

  • Consider natural intelligence, or natural systems
  • Contrast between an artificial system and an natural system.
  • The key difference between the two is that artificial systems are designed whereas natural systems are evolved.

Natural Systems are Evolved

Survival of the fittest

?

Natural Systems are Evolved

Survival of the fittest

Herbet Spencer, 1864

Natural Systems are Evolved

Non-survival of the non-fit

Mistake we Make

  • Equate fitness for objective function.
  • Assume static environment and known objective.

.

Engineering Systems Design

  • Major component of all Engineering disciplines.

  • Details differ: there is a common theme: achieve your objective with the minimal use of resources to do the job.

  • This provides efficiency.

  • Engineering designer imagines a solution that requires the minimal set of components to achieve the result.

  • A water pump has one route through the pump.

Don’t Fail

  • First criterion of a natural intelligence is don’t fail.

  • In contrast, mantra for artificial systems is to be more efficient.

  • Artificial systems are given a single objective (in machine learning it is encoded in a mathematical function)

  • Aim to achieve that objective efficiently.

Designing out Failure

  • Even if we wanted to incorporate don’t fail in some form, it is difficult to design for.

  • To design for “don’t fail”, you have to consider every which way in which things can go wrong, if you miss one you fail. These cases are sometimes called corner cases.

Corners Everywhere

  • In an uncontrolled environment, almost everything is a corner.

    • It is difficult to imagine everything that can happen.

    • Most of our automated systems operate in controlled environments (e.g. a factory, a set of rails.)

Deployment in Uncontrolled Environments

  • Requires a different approach to systems design.
  • One that accounts for uncertainty in the environment
  • One that is robust to unforeseen circumstances.

An Intelligent System

Joint work with M. Milo

An Intelligent System

Joint work with M. Milo

  • Need to deal with uncertainty and increase robustness.
  • Today, it is easy to make a fool of an artificial intelligent agent.
  • Technology needs to address the challenge of the uncertain environment to achieve robust intelligences.

  • Successful deployments of intelligent systems are common.
  • But they are redefined to be non-intelligent.
  • My favourite example is the Centrifugal governor.

Story

  • A man and his dog

Jeff and His Dog

Computers

  • A hundred years ago computers were human beings.
  • Digital computers originally called automatic computers
  • Do we think of such a computer as intelligent?

Are we close to creating intelligence?

Two Answers

  1. Current technology is a long way from emulating all aspects of human intelligence: there are a number of technological breakthroughs that remain before we crack the fundamental nature of human intelligence.
  1. More controversially, I believe that there are aspects of human intelligence that we will never be able to emulate, a preserve that remains uniquely ours.

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.

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

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!

Thanks!

References