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# Christmas Presentation at Aarhus: What I do

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### Christmas Presentation at Aarhus: What I do

1. 1. GOOD YULE ● It's December ● Apparently there is an extra deadline this month – 24 December: submit presents – Reviews should be polite and positive
2. 2. WHAT LEON DOES Natural Language Processing: part of Artificial Intelligence ..which is Really Cool
3. 3. HARD MATHS AND BORING PROOFS Pruned Hidden Markov Model Semi-supervised Conditional Random Field Entropy Gradient
4. 4. HARD MATHS AND BORING PROOFS from scipy import HMM Pruned Hidden Markov Model from scipy import CRF Semi-supervised Conditional Random Field Entropy Gradient → optimised drinking time
5. 5. EVALUATING ARTIFICAL INTELLIGENCE ● ● “Machine Learning”: getting the computer to write the program for itself, so you can go drinking Assumes that the human is intelligent Is this an appropriate assumption? Can you think of any counter-examples?
6. 6. EVALUATING ARTIFICAL INTELLIGENCE ● ● “Machine Learning”: getting the computer to write the program for itself, so you can go drinking Assumes that the human is intelligent Is this an appropriate assumption? Can you think of any counter-examples?
7. 7. BIG DATA & SOCIAL MEDIA ● “3V” definition of Big Data: – 1. Velocity: 6000000000000000 tweets / second – 2. Volume: 9999999999999 active teenagers / day
8. 8. BIG DATA & SOCIAL MEDIA ● “3V” definition of Big Data: – 1. Velocity: 6000000000000000 tweets / second – 2. Volume: 9999999999999 active teenagers / day – 3. Variety: Virus prediction, earthquake detection,
9. 9. BIG DATA & SOCIAL MEDIA ● “3V” definition of Big Data: – 1. Velocity: 6000000000000000 tweets / second – 2. Volume: 9999999999999 active teenagers / day – 3. Variety: Virus prediction, earthquake detection, 90% BIEBER
10. 10. BIG DATA & SOCIAL MEDIA ● “3V” definition of Big Data: – 1. Velocity: 6000000000000000 tweets / second – 2. Volume: 9999999999999 active teenagers / day – 3. Variety: Virus prediction, earthquake detection, 90% BIEBER