The Computational World

1. The Computational World Stephen Pulman* University of Oxford Computing Laboratory www.comlab.ox.ac.uk September 2009 *with contributions from Bob Coecke, Karo Moilanen, and Nic Smith

2. 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)

3. Interdisciplinarity... Systems Biology Computational Biology Verification Numerical Analysis Cardiac Modelling Virtual Physiological Human Climate prediction Foundations, Logic and structures Security Infrastructures Chip design Robotics Medicine Music Humanities Animal behaviour Biochemical Biology Game Semantics Medical Informatics Web Technologies Physics Quantum Information Processing SensorNetworks Fluid Dynamics Transportation Aircraft Engineering Spatial reasoning Building Commerce Power Management Retail Finance Nanotechnology Machine Learning Computational Linguistics Programming Tools Information Modelling Requirements Information systems Software Engineering Programming Languages Knowledge Representation

4. 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.

5. Modelling the Human Heart In 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.

6. Modelling the Human Heart Heart disease is an enormous health and financial problem:

7. 4 tissue types 35,000 genes 100,000 (?) proteins 100 cell types 10 organs 1 body Scales in the Heart clinical medicine gene expression gene sequence physiology SNPs

8. 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 protein Range = 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 motion Range = 1015

9. Tissue Structure Anatomy Tissue Properties Cellular Properties Model Validation Ingredients of a Whole Organ Heart Model Drug Discovery Clinical Applications

10. Blood flow Coronary vessels Stress

11. Computational Modelling Applications Stresses during contraction Cardiac activation and contraction Ventricular Blood flow Coronary Blood flow Regional work through the cardiac cycle

14. 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.

15. 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.

16. 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.

17. 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.

18. 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?

19. 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:

20. Quantum teleportation • Here is their proof of correctness of the teleportation protocol:

21. Quantum teleportation • And here is the textbook proof:

22. 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.

23. 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.

24. 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.

25. 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

26. Syntactic analysis Sentences have a hierarchical structure: words combine into phrases, and phrases into sentences: Sentence Verb Phrase Noun Phrase Noun Phrase Name Name Verb Det Adjective Noun Barack Obama won the presidential election

27. Syntactic Analysis 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'.

28. 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 man Or: He photographed a man who had a camera.

29. 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.

30. 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

31. 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!

32. 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

33. 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

34. 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)

35. Syntax + sentiment logic: VP:NEG VP:POS NP:NEG NOM:NTR MOD:NTR fail to counteract adverse publicity for the Blackberry

36. We can classify individual entities rather than whole documents: Sentiment analysis from Twitter: David Cameron: Nick Clegg: Gordon Brown: David Beckham:

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

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

39. 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...