The Computational WorldThe Computational World

Stephen Pulman*Stephen Pulman*

University of Oxford Computing LaboratoryUniversity of Oxford Computing Laboratory

www.comlab.ox.ac.ukwww.comlab.ox.ac.uk

September 2009September 2009*with contributions from Bob Coecke, Karo Moilanen, and Nic Smith*with contributions from Bob Coecke, Karo Moilanen, and Nic Smith

Brief Intro to Comlab

• Established 1957. In 2009:• 55 permanent faculty (35 in 2005)• c.110 DPhil (PhD) students• c.40 postdoc researchers• c.60 MSc students• c.200 undergraduates• c.£30m research funding (up from £6m in

2005)

ComputationalBiology

Information systems

NumericalAnalysis

Software EngineeringProgramming

Languages

Foundations, Logicand structures

Verification

Medicine

Biology Biochemical

Machine Learning

BuildingSpatial

reasoning

ComputationalLinguistics

Web Technologies

KnowledgeRepresentation

ProgrammingTools

InformationModelling

Medical Informatics

Robotics

Sensor NetworksTransportation

Security Infrastructures

Animal behaviour

Requirements

GameSemantics

Aircraft Engineering

Power Management

Nanotechnology

Climate prediction

Music

Finance

Fluid Dynamics

VirtualPhysiological

Human

CardiacModelling

Systems Biology

Interdisciplinarity...

Humanities

CommerceRetail

Quantum InformationProcessing

Physics

Chip design

Just three examples:

• Heart Modelling (Computer Science + Physiology) = better diagnosis and treatment.

• Quantum Information Processing (Computer Science + Physics) = potentially a whole new way of doing computing.

• Computational Linguistics (Computer Science + Linguistics) = new ways of processing and acquiring information.

Modelling the Human HeartIn Comlab, David Gavaghan, Nic Smith, and Banca Rodriguez are involved in several projects aimed at heart modelling, to:

- better understand how the heart functions/goes wrong

- better predict the outcome of particular types of treatment

- give clinicians better diagnostic tools and more information

about individual patients than they currently have.

Modelling the Human Heart

Heart disease is an enormous health and financial problem:

Hospital outpatient care

8%

Outpatient 6%

Drugs 9%

Hospital inpatient care

60%Primary care 17%

gene sequence

SNPs gene

expressionphysiology

clinicalmedicine

35,000genes

100,000 (?)proteins

10organs

1body

100 celltypes

4 tissuetypes

Scales in the Heart

The spatial and temporal scales• 1 m person• 1 mm electrical length scale of cardiac tissue• 1 µm cardiac sarcomere spacing• 1 nm pore diameter in a membrane proteinRange = 109

• 109 s (70 yrs) human lifetime• 106 s (10 days) protein turnover• 103 s (1 hour) digest food• 1 s heart beat• 1 ms ion channel HH gating• 1 µs Brownian motionRange = 1015

Ingredients of a Whole Organ Heart Model

Drug DiscoveryClinical Applications

Tissue Structure

Tissue Properties

Cellular Properties

Anatomy

Model Validation

Blood flow

Stress

Coronary vessels

Computational Modelling Applications

Stresses during contraction

Regional work through the cardiac cycle

Cardiac activation and contraction

Coronary Blood flowVentricular Blood flow

D:\Old Data\presentation\movies\virtual_human_H_coronary_flow.mov

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Quantum mechanics: a bluffer's guide

• The state of a system (e.g. electron + nucleus) is described by elements of a complex vector space. This allows calculation of probabilities of measurements (e.g. position or direction of spin) of a particle. You can't simultaneously measure both accurately (Heisenberg)

• Measurement of a physical observable is represented by an operator on the vector space. When you measure something the state `collapses' to a definite value, but this blurs the values of other observables.

• `Superposition' of states: i.e. a system can be partly in one state (e.g. spin up), partly in another (spin down), simultaneously. A pair of particles can be in any superposition of pairs of positions.

Quantum mechanics: a bluffer's guide

• It is possible to create pairs of particles that are `entangled': i.e. some of their their properties are correlated: e.g. direction of spin or charge.

• For example, both might have the same, or opposite directions of spin or charge. This means that (if you know which way the entanglement works, from the way the particles were constructed) measuring one particle tells you about the other.

• A `qubit' is the quantum analogue of a classical `bit': think of it as a superposition of proportions of states 0 and 1.

• Particles representing qubits can be entangled in the same way.

Quantum mechanics: a bluffer's guide

• The very weird thing about entanglement is that it can happen over long distances: many kilometres in fact - the current record is from Geneva to the Canary Islands!

• In quantum cryptographic key distribution we can use this fact to transmit information in a completely secure way.

• We can also carry out an operation which allows us to transmit the state of a qubit using only two classical bits.

• This is known as `quantum teleportation' ... but not teleportation as we know it, Jim.

Quantum teleportation

• Create an entangled qubit BC, where Alice (in Oxford) has B and Bob (in the other place) has C. Alice wants to transmit qubit A (in an unknown state) to Bob.

• She then jointly measures A and B and gets two classical bits as the result. A and B are now entangled by the measurement.

• B is no longer entangled with C - but C now potentially contains information about A.

• Alice sends the two classical bits to Bob, which encode one of 4 states (00,01,10,11).

• This tells Bob which operation to perform on C to reconstruct A.

Quantum entanglement

• The possibility of teleportation shows that by measuring (i.e. observing) one can process continuous quantum data..

• This leads to a model of quantum computing where a classical computer controls a huge quantum state.

• Such a computer is expected to be exponentially faster than a classical computer on its own.

• Entanglement based communication protocols are secure because although an eavesdropper could intercept the classical communication, the quantum channel cannot be intercepted without destroying the quantum state.

• But how can be sure that such quantum computational processes have the right properties?

Quantum entanglement

• In Comlab, Samson Abramsky and Bob Coecke have been developing a graphical calculus to reason about such quantum processes, and prove they do have the right properties:

Quantum teleportation

• Here is their proof of correctness of the teleportation protocol:

Quantum teleportation• And here is the textbook proof:

Quantum teleportation

• How do we know the graphical calculus is telling us the truth?

• Because it has a mathematical interpretation in Category Theory, one of the basic tools of Computer Science.

The Future:• The Comlab Quantum Information Processing group

collaborates with Materials Science and Physics to advance this research.

• Quantum Computing will be the next real computer revolution...but not for a while.

WHAT IS COMPUTATIONAL LINGUISTICS?

We aim to create computer programs that behave as if they had some understanding of English (or French, Urdu, Hixkaryana, etc...)

To enable:Better searching for information: just ask a

question like `How many colleges are there in Oxford University?'

Automatic translation, summarisation of documents.

You can interact naturally with the computer to solve problems or perform tasks that would otherwise be too difficult, take too long, or require specialist computational expertise.

Why is this difficult?Computer languages:● simple● not ambiguous (a sentence only has one meaning)● not dependent on context

- a sentence always means the same thing.

Human languages:● not simple, and very ambiguous● the same sentence can mean different things in different contexts: e.g. `he's here now'● require non-linguistic knowledge (`common sense') to resolve ambiguities.

How can we get the computer to understand?

• we find the syntactic or grammatical structure of sentences

• we use that to translate the sentence into a simpler language that the computer can understand: a `meaning representation'

• but we also have to solve the problems of ambiguity and `common sense'

• and the problem of context changing meaning

Syntactic analysis Sentences have a hierarchical structure: words

combine into phrases, and phrases into sentences:

Name Name Verb Det Adjective Noun

Barack Obama won the presidential election

Noun Phrase

Noun Phrase

Verb Phrase

Sentence

We can describe the structure of sentences with rules like:

Sentence → Noun Phrase + Verb Phrase

Noun Phrase → Name + Name

Noun Phrase → Determiner + Adj + Noun

Verb Phrase → Verb + Noun Phrase

etc.

We call a collection of rules like this a `grammar' and we can use it to find the structure of new sentences automatically. We call this process `parsing'.

Syntactic Analysis

Ambiguity

Many sentences have more than one possible syntactic analysis, corresponding to different interpretations:

He photographed the man with the camera

Either: He used a camera to photograph a manOr: He photographed a man who had a camera.

Ambiguity

But often some of the syntactic possibilities do not make sense:

He photographed the man with a bicycle

- can't (easily) mean: He used a bicycle to photograph a man - because we know that you can't take

photographs with a bicycle. But it is very hard to get a computer to `know' this.

Ambiguous words

• Many words are ambiguous: for example, the English word `bank' has several meanings: financial, and river-related

• Humans do not find this a problem:

He went to the bank to get some money The fish jumped out of the water on to the bank

but computers do not know that fish do not jump on to a financial institution (yet), or that you do not (usually) get money from a riverbank

The state of the art

How well do we do?

• Parsing - 70-80% on newspaper text, including disambiguation.

• Word sense ambiguity: around 60%

• Context - `he's here now' etc. Really hard. Lucky to get it right 50% of the time!

A practical application: detecting positive (POS), neutral (NTR), or negative (NEG)

attitudes in text.Used for: • brand management (what do people

think of your company or reputation?)• consumer research (what do people like

and dislike about your product?)• monitoring government policy (will you

get re-elected!)• we call this Sentiment Analysis

Automated Sentiment Analysis

Typically done by spotting key words:• This is a great film!• This is the worst film I've seen this year.

Not always so simple: • publicity for the Blackberry Storm • adverse publicity for the Blackberry Storm • counteract adverse publicity for the Blackberry

Storm• fail to counteract adverse publicity for the

Blackberry Storm

Using syntactic analysis we can deal with these more sophisticated sentiment phenomena, using a set of `sentiment logic' rules:

• adverse + NTR = NEG (e.g. adverse circumstances)• counteract + POS = NEG (e.g. counteract progress)• counteract + NEG = POS (e.g. counteract disease)• fail + NEG = POS (e.g. fail to counteract progress)• fail + POS = NEG (e.g. fail to counteract disease)

Syntax + sentiment logic:

fail to counteract adverse publicity for the Blackberry

MOD:NTR

NOM:NTR

NP:NEG

VP:POS

VP:NEG

We can classify individual entities rather than whole documents:

Sentiment analysis from Twitter:

David Cameron:

David Beckham:Gordon Brown:

Nick Clegg:

We can track sentiment over time, or across a series of documents:

It's a computational world, but not this one:

Our computational world

• Computers are not going to go away - if anything, they will become ever more present. They are in your watch, phone, car, credit card, magazine page...

• They are deeply involved in every aspect of manufacturing, commerce, physical science and technology, and increasingly in the medical and life sciences.

• And are beginning to be indispensable in the humanities and the arts.

• I can't imagine how we managed for so long without them...