Harnessing the power of data science through research

Harnessing the Power of Data Science Through Research

Chair:

John PullingerNational Statistician

Professor Andrew BlakeDirector, The Alan Turing Institute

The Alan Turing Institute 2

Professor Andrew BlakeInstitute Director

The Alan Turing Institute

3

Harnessing the power of Data Science through research


The UK’s National Institute for Data Science

‘We will found the Alan Turing Institute to ensure Britain leads the way again in the use of big data and algorithm research’

George OsborneBudget Speech, March 2014


The data economy – principal players

01/05/2023

5

a decreasing number of people will gather to themselves the knowledge that comes from owning giant databases

– The Telegraph on Jaron Lanier’s “Who owns the future?”


... and powering growth for mid-size companies

01/05/2023

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... and creating value via smaller companies

01/05/2023

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$5bn$3bn

~ $500m

~ $100m

~ $100m


An Institute without disciplinary boundaries


Interdisciplinary science


Priorities for research

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Working with government – relationship with ONSCollaboration between Turing Fellows Suzy Moat, Tobias Preis and the ONS Data Science Campus

Barchiesi, Moat, Alis, Bishop and Preis (2015)

“Quantifying International Travel Flows Using Flickr”, PLOS ONE 10.

A map of the world built only from GPS locations of Flickr photos


HSBC programme in data-driven economics

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The HSBC – Turing Strategic Partnership ...

The UK’s National Institute for Data Science

“… we are excited about the prospect of working with the Institute’s world leading scientists using big data analytics to better understand economic trends.”- Douglas Flint, HSBC Chairman

... aims to help economists, researchers, policymakers and businesses to better understand the UK economy and its interconnection with global markets.

www.turing.ac.uk


Resilient networks

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Evaluation of Velocity Fields via Sparse Bus Probe Data in Urban Areas

Richard Gibbens and Andrei Bejan, Computer Laboratory, University of Cambridge, 2015

The Alan Turing InstituteRichard Gibbens and Andrei Bejan, Computer Laboratory, University of Cambridge, 2015

10am, Tuesday 15th June 2010 10am, Tuesday 22nd June 2010

Urban traffic velocity fields via “bus probe”


Machine Learning meets mathematics

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dd


Rough paths to Chinese characters


Rough Paths to Chinese Chars


AI for data analytics

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... AI for data analytics

Zoubin Ghahramani

James R. Lloyd


Empirical classifier– black box, optimize predictive power, efficient

Generative model– explain observations, Bayesian inference

? Which wins

Machine learning:– big data or explanatory models?


Example: online recommender system

MovieRecommender

• Generative model – hidden variables

• Performance engineered – predictive black box

eg Netflix challenge


Black box (RBM)

(eg Salakhutdinov et al. 2007)

Y

X

W

User

Movie

𝑝 (𝑌∨𝑋 ;𝑊 )


Generative model

(eg Stern et al. 2009)


Trait space


Deep neural networks

(Krizhevsky, Sutskever, Hinton, 2012)

(LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P., 1998)


Deep neural network for speech classification

© Microsoft Research


Captioning an image

Microsoft team

Also teams from Google, Stanford, ..


Transparency Fairness

Full value from data

Generative models: set to make a comeback?


Fast mapping (Bloom, 2000)


Tufas and other objects learned from very few labels


“Fast mapping” with logic


Learning: the data efficiency trade-off

??

1 10 100 1000

Tolerance to noise

No of examples

?


Black box recognisers

Generative models Transparency and fairness

Efficiency of learning – logic?


The Turing’s strategic priorities

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Harnessing the power of data science through research

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