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