transforming industry: the role of artificial intelligence
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TRANSFORMING INDUSTRY: THE
ROLE OF ARTIFICIAL INTELLIGENCE
Dirk Pilat
Deputy Director
Directorate for Science, Technology and Innovation
dirk.pilat@oecd.org
7th Concordi Conference: Innovation for Industrial Transformation Seville, 25 September 2019
Outline
1. AI – context, applications and some trends
2. Transforming industry – some key issues for policy and analysis
3. OECD’s work on AI
4. Launch of JRC-OECD report: World Corporate Top R&D Investors
What is Artificial Intelligence?
4
• No agreed definition: AI consists of simulating certain learning processes of human intelligence, to learn from it and replicate it. AI rests on two key elements: data and algorithms (and computing power).
• OECD definition of an “AI System”: An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.
• Today’s Artificial Intelligence is “narrow” or “applied” and designed to accomplish a specific problem-solving or reasoning task. Even the most advanced AI systems available today, such as IBM’s Watson or Google’s AlphaGo, are still “narrow”.
• Applied AI can be contrasted to a (hypothetical) Artificial General Intelligence (AGI), in which autonomous machines would become capable of general intelligent action, like a human being, including generalising and abstracting learning across different cognitive functions.
Today, the science underlying AI continues to advance …
5
Source: OECD calculations based on Scopus Custom Data, Elsev ier, Version 1.2018 and 2018 Scimago
Journal Rank from the Scopus journal title list (accessed March 2018), January 2019. Statlink:
https://doi.org/10.1787/888933928445
Trends in scientific publishing related to artificial intelligence, 2006-16 Index of publication counts, 2006=100
100
120
140
160
180
200
220
240
260
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Index 2006 = 100
Total scientific publications Scient ific publications related to AI
0
20
40
60
80
100
2006 2016
%CHN EU28 USA Other
Share of world's AI publications
.. with EU countries making an important contribution
6
Source: OECD calculations based on Scopus Custom Data, Elsev ier, Version 1.2018 and 2018 Scimago
Journal Rank from the Scopus journal title list (accessed March 2018), January 2019.
0
5
10
15
20
25
30
35
% Top 10 economies
2016 2006
0
0.5
1
1.5
2
2.5
%Other economies
Top-cited scientific publications related to AI, 2006 and 2016 Economies with the largest number of AI-related documents among the 10% most
cited publications, fractional counts
Patenting is increasing rapidly
7
Source: OECD, STI Micro-data Lab: Intellectual Property Database, http://oe.cd/ipstats, January 2019,
Statlink: https://doi.org/10.1787/888933928331
Technology developments in artificial intelligence, 1990-2016 Index 1990=1 based on the number of IP5 patent families
0
2
4
6
8
10
12
Index 1990 = 1
AI-related paten ts All patents
0
20
40
60
80
100
Identification of AI-relatedpatents, 2012-16
%
IPC codes onlyIPC codes and keywordsKey words only
Transport: autonomous vehicles with potential cost, safety and
environmental benefits
Science: accelerate discovery, facilitate reproducibility and lower
experimentation costs.
Health: detect health conditions, deliver preventative services, discover
treatments, facilitate clinical research, and optimise health systems.
Criminal justice: predictive policing, assessing recidivism risk, and
predicting court procedure outcomes.
Security: detect anomalies and threats in digital security and surveillance.
Agriculture: agricultural robots, crop and soil monitoring and impact
prediction.
Financial services: detect fraud, assess credit worthiness, automate
trading, etc.
Marketing and advertising: target and personalise content, advertising
and products, demand forecasting, translation, etc
And, of course, economic analysis.
AI has a wide range of applications, …
… as emerges from patenting data, …
9
Top fields of application of AI-related technologies, 2012-16
Share of application fields in AI-related patents, IP5 patent families
Source: OECD, STI Micro-data Lab: Intellectual Property Database, http://oe.cd/ipstats, January 2019;
Statlink: https://doi.org/10.1787/888933928350
0
1
2
3
4
%Other technology fields
0
10
20
30
40
% Top 5 fields65
… with AI combining with many other technologies
10
Top technologies combined with AI, by field of application, 2012-16
Share of technology (IPC) classes in AI-related patents, IP5 patent families
Source: OECD, STI Micro-data Lab: Intellectual Property Database, http://oe.cd/ipstats, January 2019;
Statlink: https://doi.org/10.1787/888933928369
0
1
2
3
4% Other technologies
0
2
4
6
8
10
12
14% Top 5 technologies
Computer technologies Audiovisual technologies IT methods Telecommunications Medical technology Digital communication Control
Private investment in AI start-ups is growing
11
Total estimated investments in AI start-ups (USD billion), 2011-2018 (Jan-Jun)
By start-up location
Source: OECD estimates based on Crunchbase (July 2018), www.crunchbase.com, Note: Estimates for 2018 may be conservative, as they do not account for a likely lag in reporting.
… with the UK accounting for the bulk of start-up investments in the EU …
12
Investments in AI start-ups based in the EU (USD billion), 2011-mid-2018
Percentage of total amount invested in EU-based start-ups over period
Source: OECD estimates based on Crunchbase (July 2018), www.crunchbase.com,
.., and investments spanning a range of industries
13
Top sectors of AI Start-ups from 2011 to 2017
Source: OECD estimates based on Crunchbase (April 2018), www.crunchbase.com,
1. Who has access? – Mainly to data (but also algorithms and computing)
• AI relies on, and leverages data in fundamentally new ways
• Network and scale effects
How to enhance access to data
• Public research data
• Curated and accurate data
• SME access
• Public interest and global challenges (e.g. a Global Data Commons)?
• Markets for data
For example, machine learning requires large amounts of training data
17
https://medium.freecodecamp.org/chihuahua-or-muffin-my-search-for-the-best-computer-vision-api-cbda4d6b425d
2/3. Use and Innovation: can AI help strengthen productivity growth?
Probably, but:
– The diffusion of AI has only just started.
– AI may contribute to the productivity divergence between leaders & laggards
– Its impacts will depend not just on AI technologies, but also on organisational change, skills and new processes.
– It will require structural change to enable the growth of AI business models, including flexible regulations.
– All of this may take time to have an impact – experience suggests that productivity may initially slow down.
0.00
0.05
0.10
0.15
0.20
0.25
Digital intensity Sh. High-skilled KIS
Low High
The rate of catch-up in productivity is lower in digital and skill intensive industries
Source: Berlingieri et al., 2019, Last but not least: laggard firms, technology diffusion and its structural and policy determinants
Source: OECD (2019), Measuring the Digital Transformation, based on OECD, ICT Access and Usage by Businesses Database, http://oe.cd/bus, December 2018.
Enterprises performing big data analysis, by size, 2018 As a percentage of enterprises in each employment size class
Large gaps across and within countries in technology use, e.g. big data …
4. How will AI affect jobs and skills?
AI and automation typically only affect certain tasks; most jobs involve many tasks. Highest risk in routine jobs with low skill and education requirement BUT many jobs will be affected
Source: OECD, Science, Technology and Innovation Scoreboard 2017, based on OECD, Education Database, September 2017.
0
2
4
6
8
10
% Women Men
Tertiary graduates in information and communication technologies, by gender, 2015
As a percentage of all tertiary graduates
And how can countries foster AI talent?
• Privacy: Profiling, monitoring,
automated decision-making,
algorithmic bias
• Safety and responsibility: What do
concepts such as “product”, “safety”,
“defect”, “damage” mean for self-
learning and autonomous systems?
What about cybersecurity risks that
could impact safety (e.g. if hackers
take control of car)?
• Transparency: Understanding and
explaining how systems operate,
which factors influence result, level of
certainty. Detecting bias. Being able to
challenge results.
5/6. Society and Trust: How can we make
AI trustworthy?
7. Market openness and competition:
how does AI affect competition? Value of M&A deals per Year by Target Firm Industry
Source: Zephyr M&A database
Value of M&As in Digital Sectors – Normalised (2005 = 100)
• Agreement on Council Recommendation on Artificial Intelligence [C/MIN(2019)3]] adopted by 42 countries (36 members + 6 others) at the OECD Ministerial Council Meeting of 22-23 May 2019, and also the basis for G20 AI Principles, as agreed by Digital Economy Ministers on 8 June. Work now underway on implementation of the principles.
• Measurement and analytical work – report on AI in Society – 11 June 2019
• Establishment of an AI “Observatory”, with several pillars of work, to be launched in Q4 of 2019
OECD work on Artificial Intelligence
AI Policies & Initiatives
AI PolicyObservatory
Jobs, skills, data, etc.Health, transport, etc.
National AI policiesInitiatives
OECD metricsPartner metrics
AI Principles
OECD AI PrinciplesPractical guidance
AI Public Policy Topics
AI Metrics & Measurement
Governments that have adhered to the OECD or OECD-based G20 AI principles
OECD members Adherents G20 principles, based on OECD
• Relies on EU-JRC Scoreboard 2017 data
on world’s top 2 000 R&D investors (2016),
linked to IP assets and scientific publications (NEW!)
• 2019 edition:
Broad overview of the innovative activities
of top R&D investors
Changes between 2012 and 2016 samples
Special focus on developments in Artificial Intelligence
2019 report: Shaping the future of technologies
and of AI
Share of economies in the top 2000 R&D companies
Location of world’s top R&D investors,
Headquarters, 2016
Source: JRC-OECD, ca lculations based on EU Industrial R&D Investment Scoreboard (2017), May 2019.
Share of economies in total number of subsidiaries
Location of world’s top R&D investors,
Subsidiaries, 2016
Source: JRC-OECD, ca lculations based on EU Industrial R&D Investment Scoreboard (2017), May 2019.
Share of economies in the top 2000 R&D companies
Cumulative percentage shares within the top 2000 R&D companies, 2014-16
R&D investment, publications, IP bundle
Distribution
0
10
20
30
40
50
60
70
80
90
100
0 250 500 750 1 000 1 250 1 500 1 750 2 000
%
R&D ranking of companies
R&D expenditures Patents Scientific publications Trademarks
Note: Data relate to companies in the top 2 000 corporate R&D sample, ranked by R&D investment in 2016. The IP bundle refers to the number of patents and trademarks
fi led in 2014-16, and owned by the top R&D companies, and the number of scientific articles are those published by authors affiliated in the top R&D companies during the same time-period, using fractional counts. Source: JRC-OECD, COR&DIP© database v.2., 2019.
Share in total IP5 patents, trademarks & publications, resp.
Patents, trademarks and publications
of world’s top 2000 R&D investors, 2014-16
Source: JRC-OECD, COR&DIP© database v.2., 2019.
Top R&D investors Other
Patents Trademarks Publications
Leading companies:
publications, patents, and trademarks, 2014-16
Top 50 patenting Top 50 publishing Top 50 trademarking
Source: JRC-OECD, COR&DIP© database v.2., 2019.
Changes among the top R&D investors
2012 versus 2016
Top 20 companies
Note: Bold orange lines indicate an increase in ranking of more than 10 positions.
Source: JRC-OECD, ca lculations based on EU Industrial R&D Investment Scoreboard (2013 and 2017), May 2019.
New top R&D investors, by sector & HQ, 2016
AI developments by top R&D investors, 2014-16
Contribution of world top R&D investors
to Artificial Intelligence
Source: JRC-OECD, COR&DIP© database v.2., 2019.
Top 50 patenting
0
25
50
75
100% Top R&D investors
Top 50 AI patenting companies own 54% of AI patents
Share of AI in patents, trademarks and publications,
top R&D investors and other actors, 2014-16
Contribution of world top R&D investors
to Artificial Intelligence
Source: JRC-OECD, COR&DIP© database v.2., 2019.
0.0
0.5
1.0
1.5
2.0
2.5
Publications Patents Trademarks
% Top R&D investors Other
Leading sectors in AI:
patents, TMs and publications
Top 5 sectors in AI, word top R&D investors, 2014-16
Source: JRC-OECD, COR&DIP© database v.2., 2019.
0
10
20
30
40
50
%Patents
0
10
20
30
40
50
%Publications
0
10
20
30
40
50
%Trademarks
• Raw data
• Free download
• 3rd version of COR&DIP©
JRC-OECD COR&DIP© database v.2, 2019
World Top 2000 Corporate
R&D Investors
Financial2013-16
R&D, Sales, Employees, etc.
Trademark Portfolio2014-16
EUIPO, USPTO
Trademark Classes
NICE Class
Scientific Publications
Counts by ASJC fields
Patent Classes
IPC & WIPO technologies
Patent Portfolio2014-16
IP5 families
Further information and
data access: http://oe.cd/ipstats http://iri.jrc.ec.europa.eu/other-reports.html
THANK YOU!
OECD team
Hélène DERNIS Shohei NAKAZATO
Brigitte VAN BEUZEKOM Mariagrazia SQUICCIARINI
JRC team
Petros GKOTSIS Nicola GRASSANO Antonio VEZZANI
Thank you
41 41
Contact: dirk.pilat@oecd.org
OECD Going Digital website: http://oe.cd/goingdigital
OECD work on AI: http://www.oecd.ai
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