artificial intelligence at imperial dr. simon colton computational bioinformatics laboratory...
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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