Artificial Intelligence at Imperial

Dr. Simon Colton

Computational Bioinformatics Laboratory

Department of Computing

Dr. Simon Colton

• Lecturer:– Artificial Intelligence & Bioinformatics

• Researcher:– Computational Creativity

• In maths, science (bioinformatics) and arts

• Administrator:– Next year’s admission’s tutor

What AI Isn’t

• It is not what you read in the press– Robots will take over the earth [Prof. Warwick]– Computers will never be clever [Prof. Penrose]

• These are two extremes– Real AI researchers and educators believe in the

middle ground: • Computers will increase in intelligence, but not be a threat

AI in General

• AI usually seen as problem solving– Problems would require intelligence in humans– This is the way AI is taught

• Some of us see AI more as artefact generation– Producing pieces of music/theorems/poems, etc.

A Characterisation of AI

• As answers to:– “How can I get my machine to be clever”

• Seven answers over the years:– Use logic– Use introspection– Use brains– Use evolution– Use the physical world– Use society– Use ridiculously fast computers

Elementary, my dear Watson

• Logical approach– Idea: represent and reason

• “It’s how we wish we solved problems…– Just like Sherlock”

• Very well respected– Established

• 3000 years of development

– Techniques for reasoning • Deduction & induction

– Programming languages

Introspection

• Logic has limits– Combinatorial explosion

• “Maybe we’re not logical– But we are intelligent”

• Use introspection– Can be highly effective

– Can be problematic

• Heuristic search– Using rules of thumb to guide

the solving process

BrainWare

• “Maybe we don’t know our psychology– But it’s our brains which do the

intelligent stuff”

• And we do know– Some neuroscience

• Idea is to build:– Artificial Neural Networks– Simulate neurons firing

• Networks configuring themselves

• Mostly used for prediction– E.g., stock markets (badly)

Evolve or Perish

• “Our brains give us our smarts, – But what gave us our brains?”

• Idea: evolve programs– Simulate reproduction and survival

of fittest

• Problem Solving:– Genetic algorithms (parameters)

– Genetic programming (program)

• Artificial Life– Can we evolve “living” things

The More the Merrier

• “We live and work in societies– Each of us has a job to do”

• Idea to simulate society– Autonomous agents

• Each has a subtask– Together solve the problem

• Agencies have structure• Agents can

– compete, co-operate, haggle, argue, …

The Harsh Realities of Life

• “But we evolved intelligence for a reason”

• Idea: get robots to do simple things in the physical world– Dynamic & dangerous

• From survival abilities– Intelligence will evolve

• Standing up is much more intelligent than– Translating French to German– In Evolutionary terms

Brute Force

• “Let’s stop being so clever and use computers to their full”– Processor/memory gains have

been enormous

• Can solve problems in “stupid” ways– Relying on brute force

• The Deep Blue way– Little harsh on IBM

A Good Example• Robotic museum tour guide

– Robot + computers– And worried researchers

• Who didn’t intervene

• Highly successful– 18.6 kilometres, 47 hours– 50% attendance rise– 1 tiny mistake

• No breakage/injury

• Great science– Using many approaches– Won best paper award

AI at Imperial

• Mainly in Computing and Electrical Engineering– Also in biochemistry, maths, …

• AI in the Department of Computing– Introduction courses– Logic courses– Advanced courses– Programming courses– Application courses

Logic• Logic is taught for two reasons

– To enable students to think analytically and at an abstract level• The mark of good computer scientists

– To give them tools for AI techniques & other areas

• Logic courses– First year introduction

– Computational Logic

– Automated reasoning

– Modal and temporal logic

– Practical logic programming

Advanced Courses

• Advances in Artificial Intelligence• Decision analysis• Knowledge management techniques• Knowledge representation• Multi-agent systems• Natural language processing• Probabilistic inference and data-mining• Robotics• Vision

My Research

• Computational Creativity– Getting computers to create artefacts

• Which we say require creativity in humans

• Past/ongoing– Automatic generation of mathematical concepts,

conjectures and theorems (theories)

• Current– Machine learning in bioinformatics

• Future– Automating the creative aspects of graphic design

Bioinformatics Research

• Computational Bioinformatics Laboratory– Head: Prof. Stephen Muggleton

• Robot scientist project– Robot attached to an AI system

• Performs experiments, analyses the results, designs better experiments, starts again

– Published in Nature (& reported everywhere)

• Metalog project– Looking at biochemical networks– Filling gaps, making predictions– Funded by the DTI

Student Projects

• Students gain a great deal from undertaking projects– Abilities to research– To be self sufficient– Understanding of a particular subject area

• Projects can also be fun…

Student Projects - Mathematics

• Automatically generating number theory exercises– Try to beat his classmates

• Inventing integer sequences– For entry into an encyclopedia

• Making graph theory conjectures– Try to beat a program called Graffiti

Student Projects - Bioinformatics

• Bioinformatics for the web– Set of tutorial web pages with little programs in

• Evolving protein structure prediction algorithms– Using nature-inspired techniques to mimic nature

• Substructure server– Predicting the toxicology of drugs

Student Projects - Creativity

• Anomaly detection in musical analysis– Learning reasons why melodies are different

• Automated puzzle generation– Next in sequence, odd one out, A is to B…

• Pun generation via conceptual blending– What do you call a vegetable that you wear?

• Evolving image filters– Growing graphic design algorithms

Evolving Images © Machedo