robert dale – how can robot journalists help the media?

Robot Journalism 2.0

news:wired, London, 1st December 2015

© 2015 ARRIA NLG plc. Proprietary and Confidential

Key Points

• Current robot journalism uses a simple form of NLG called smart templating

• More sophisticated forms of NLG are already being used in other domains

• The challenge: How can we utilise more sophisticated NLG in the media?

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3© 2015 ARRIA NLG plc3

How Robot Journalism Works

RAW DATA REFINED DATA NARRATIVEDATA PROCESSING SMART TEMPLATES



How Natural Language Generation Works

RAW DATA INFORMATION NARRATIVEDATA ANALYTICS

AND INTERPRETATION

INFORMATION DELIVERY



Loan Id Loan Amount Interest Rate Term Repayment per Annum Start Date

DEB001 250000 5.0% 30 12 20/03/2006

DEB002 20000 4.0% 10 12 01/07/2008

DEB003 125000 5.0% 10 4 01/08/2010

DEB004 6500000 5.0% 25 4 01/01/2011

DEB005 3000000 4.5% 30 4 01/01/2011

DEB006 2000000 2.0% 10 4 06/06/2006

DEB007 5000000 5.0% 10 4 06/06/2005

DEB008 10000000 4.5% 15 12 10/10/2005

DEB009 45000000 4.5% 15 12 10/10/1990

DEB010 20000 10.0% 10 4 10/10/2008

DEB011 30000 8.0% 10 4 10/11/2008

DEB012 40000 4.0% 10 4 01/03/2009

DEB013 40000 4.0% 10 4 01/06/2009

DEB014 40000 4.0% 10 4 01/09/2009

DEB015 40000 4.0% 10 4 01/12/2009

Id Loan Id Closing Balance Interest Paid

0 DEB001 354827.1 10397.11

1 DEB002 125954.35 284.93

2 DEB003 207112.01 3739.87

3 DEB004 8157962.68 294971.32

4 DEB005 3978977.32 125319.69

5 DEB006 409962.2 4905.14

6 DEB007 192818 5887.05

7 DEB008 4084924.1 196840.28

8 DEB010 108572.61 958.27

9 DEB011 211597.69 1094.08

10 DEB012 345781.61 696.2

11 DEB013 257841.42 737.55

12 DEB014 119890.74 778.5

13 DEB015 118987.67 819.04

OBJECTIVE

To document audit tests on all loans outstanding at 31 December 2015 and interest charged duringthe period 1 January 2015 to 31 December 2015.

EXECUTIVE SUMMARY

At the balance sheet date of 31 December 2015, the client had stated aggregate loans outstanding of£18,675,209 and a total interest charged as recorded in the client’s profit and loss account of£647,429. In total there were 14 separate loan agreements.

All loans have been tested and 14 out of 14 loans have a misstated closing balance. The misstatedloans have variances on closing balance which are above the materiality threshold of 5%. Please checkthe misstated loans. A detailed overview of the loans can be found in Section 4.2. The total closingbalance variance is £5,675,047, which needs to be added to the Schedule of Misstatements.

The interest charged has been recalculated for all loans and the variances are within the materialitythreshold of 5%.

TESTING PERIOD

We have recalculated the closing balances of all the individual loans on the company’s book at 31December 2015 and recalculated the interest charged in the period between 1 January 2015 and 31December 2015. We have compared the calculated balances against the closing balances recorded inthe company’s accounts as at 31 December 2015 and the calculated interest charged against theinterest charged recorded in the company’s profit and loss account for the period. A materialitythreshold of £100,000 (5% of materiality) was applied to determine misstatement.

In this document, the calculations use the amortization formula.


Situational analysis:There was a Radial Bearing Temperature alert on Asset #1FGC1 GP at Nov 12 2012 13:08. The alert had been intermittently active since Nov 9 2012 22:44. An analyst had previously examined this alert during the intermittent period and did not turn it into a service. GP No. 2 & 3 Bearing Drain Temperature was stable at around 226 °F from Nov 12 2012 07:09 to 13:06, but the typical operating range is 140 °F to 220 °F. Bridge high setpoint was 220 °F when the alert triggered. FGC1 was on during this period.

Lube Oil Header Temperature and Tank Temperature were stable within the typical operating range.

Summary since the alert was run:

GP No. 2 & 3 Bearing Drain Temperature was stable at around 224 °F from 13:13 to 19:04, but the typical operating range is 140 °F to 220 °F. Bridge high setpoint was 220 °F when the alert triggered. FGC1 was on during this period.

Lube Oil Header Temperature fell from 142 °F to 134 °F. Tank Temperature fell from 166 °F to 159 °F.

There was one closed service that had examined this alert: Service 8077 was closed on Aug 16 2012 12:37. An action was taken: ‘Changed alarm high to 222 *F'.

Recommendation:There is likely to be a problem, which may be fixed by raising the set point.

Problem reasons: A long term rising trend was detected. However, the main tag went back to normal after the alert.

Action reasons: There are many active alerts.

The Radial Bearing Temperature alert was last modified on Apr 11 2011. Since then, the alert has been marked as ‘No Action’ 25 times. The alert was turned into a service 3 times.

Summary over previous 90 days:

GP No. 2 & 3 Bearing Drain Temperature rose from 215 °F to 227 °F between Aug 14 2012 14:09 and Nov 12 2012 01:09. Bridge high setpoint is 220 °F. FGC1 was started 30 times during this period.

Lube Oil Header Temperature and Tank Temperature were stable within the typical operating range.

Information: The test tag was GP No. 2 & 3 Bearing Drain Temperature. The related tags were Lube Oil Header Temperature and Tank Temperature. The run tag was GP Speed.


Asset Integrity Management

The summary integrates alert data, historical data, service data, and maintenance history.

The application selects the most important additional information for greater context.

The recommendation is in line with the standard process, and reinforces agreed practice.

The application also uses importance and relevance to select related information and historical summary.

BABYTALK NLG:

Personalised Messaging

© 2015 ARRIA NLG plc7

Input Data Output Reports

Fine grained sensor data

from multiple sources,

combined with medical and health data.

NLG creates different reports from the same inputs using language appropriate for doctors, nurses and parents.

From 1.0 to 2.0


Current Robot Journalism The Next Generation

Short data distance Significant data distance

Simple analytics Complex AI-based analytics

Self-similar templated texts Contextually-driven variation

One size fits all Fine-grained personalisation


Where To Next?


• Machines won’t match human authoring capabilities any time soon

• But they will increasingly handle more sophisticated writing based on data

• How do we best combine human and machine authoring?

robert dale – how can robot journalists help the media?

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