Total factor productivity Logarithmed levels -3.3  -3.3 Actual TFP  -3.4  -3.4  TFP, forecast Potential TFP  -3.5  -3.5  -3.6  -3.6  -3.7  -3.7  -3.8  -3.8  -3.9  -3.9 80  85  90  95  00  05  10  15  20  Source: The Riksbank  10  BIS central bankers’ speeches  5. Capital stock Logarithmed level 29.2  29.2  29.0  29.0  28.8  28.8  28.6  28.6  28.4  28.4  28.2  28.2  28.0  28.0  27.8  27.8 80  85  90  95  00  05  10  15  20  Note. Capital services Sources: The OECD and the Riksbank  6. Number of hours worked Logarithmed levels 12.2  12.2  Hours Hours, forecast Hours, potential  12.2  12.2  Hours, HP trend  12.1  12.1  12.1  12.1  12.0  12.0  12.0  12.0  11.9  11.9 80  85  90  95  00  05  10  15  20  Sources: Statistics Sweden and the Riksbank  BIS central bankers’ speeches  11  7.
Cyborg supervision – the application of advanced analytics in prudential supervision Speech given by James Proudman Executive Director, UK Deposit Takers Supervision  Workshop on research on bank supervision Bank of England 19 November 2018  I am grateful to Sian Besley, David Bholat, Charlotte Bull, Stephen Denby, Ryan Lovelock, Clair Mills, Philip Sellar, Pete Thomas and Sam Woods for their assistance in preparing these remarks and conducting background research and analysis. 1  All speeches are available online at www.bankofengland.co.uk/speeches  I. INTRODUCTION  Recognising faces comes instinctively to humans. Until fairly recently, however, it proved beyond the ability of computers. Advances in artificial intelligence (AI) - the use of a machine to simulate human behaviour – and its subset, machine learning (ML) – in which a machine teaches itself to perform tasks – are now making facial recognition software much more widely available. You might even use it to access your bank account. Because it is so easy for us but so hard for computers, facial recognition is a good illustration of the challenges faced in developing AI. Enabling a machine to teach itself to recognise a face requires sophisticated algorithms that can learn from data. Advances in computational power and algorithmic techniques are helping machines become more human and super-human like. ML also requires lots of data from which to learn: data are the fuel that powers it – the more data used to train the algorithms, the more accurate their predictions typically become.