The State of AI 2019: Divergence

H
ighlights

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“The future is already here.
It’s just unevenly distributed.” 
(William Gibson)
The State of AI 2019: Divergence

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For entrepreneurs, executives, investors and policy-makers, The State of AI 2019 is an accessible, comprehensive guide to the
reality of AI today, what’s to come and how to take advantage. Download at www.stateofai2019.com.
Highlights:
1. The race for adoption 
Increasing adoption of AI masks a growing divergence among nations, across industries and between companies.
2. The war for talent 
While demand for AI professionals exceeds supply, winners and losers are emerging in the war for talent.
3. The advance of technology 
Emerging techniques (transfer learning, reinforcement learning and generative AI) are enabling new possibilities.
4. The Disruptors: Europe’s AI startups 
Our unique analysis of Europe’s 1,600 AI startups reveals a maturing ecosystem bringing creative destruction to new industries.
5. The implications of AI
AI will reshape sector value chains and enable new business models, while risking greater inequality and the erosion of trust.
3

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MMC Ventures is a research-led venture capital ﬁrm.
We’ve backed over 60 early-stage, high growth
technology companies since 2000.
AI is a core area of research and investment. 
We’ve made 20 investments into many of the UK’s 
most promising AI companies.
If you’re an early stage AI company, get in touch 
to see how we can accelerate your journey.
www.stateofai2019.com
We’re MMC Ventures
4
MMC Ventures Research
David Kelnar: Partner & Head of Research 
Asen Kostadinov, CFA: Research Manager
Explore MMC’s cutting-edge research at
mmcventures.com/research.

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AI: Coined in 1956, a general term referring to hardware or
software that exhibit behaviour which appears intelligent.
Machine learning: Modern AI, which enables software to
perform diﬃcult tasks more eﬀectively by learning through
training instead of following sets of rules.
Deep learning: A form of machine learning that approximates
the function of neurons in a brain to solve problems.
New to AI? Read our primer in www.stateofai2019.com
5
Before we begin…

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1. The race for adoption

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AI may be the fastest paradigm shift in technology history.
But increasing adoption masks a growing divergence
between leaders and laggards – among nations, between
industries and across companies.
The race for adoption
The State of AI 2019: Divergence 7

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AI adoption is proliferating
8
One in seven large companies has deployed AI
Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, ﬁgure 1, 3 January 2019. Base: All answering, n = 2,882. What are your organisation’s plans in terms of artiﬁcial intelligence?
One in seven large companies have deployed AI
Enterprise plans to deploy AI
No interest Plan to deploy in 2 to 3 years Will deploy in 12 to 24 months Will deploy in next 12 months Have already deployed
9% 29% 25% 23% 14%
• Today, one in seven large companies has adopted AI.
• In two years, two thirds of large companies will have live AI initiatives.

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AI is ‘crossing the chasm’
9
In 2019, AI ‘crosses the chasm’ to the early majority
Source: Everett Rogers, Geoﬀrey Moore
• By the end of 2019, over a third of enterprises will have deployed AI.
• In 2019, AI ‘crosses the chasm’ to the early majority.
• Adoption of AI has progressed rapidly from innovators and early adopters to the early majority.
AI adoption is ‘crossing the chasm’ to the early majority
Innovators Early adopters
CHASM
Early majority Late majority Laggards
Population
2.5% 34%
34% 16%
13.5%

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Adoption has been enabled by:
• the prior paradigm shift to cloud computing
• the availability of plug-and-play AI services from global technology vendors
• a thriving ecosystem of AI-led software suppliers.
10
AI: the fastest paradigm shift in enterprise tech history?
Over three years, the proportion of companies with AI initiatives will
have grown from one in 25 to one in three (Gartner).

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• Adoption is being catalysed by companies’ growing conviction in AI’s potential.
• A greater proportion of executives believe AI will be a ‘game changer’ than any other
emerging technology – including cloud, mobile, IoT, blockchain or APIs.
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Great expectations are fuelling adoption
2019 CIO Agenda 
Which technology area do you expect will be a game-changer for your organisation.
Top performers 
(n=230)
Typical performers 
(n=2,329)
Trailing performers 
(n=276)
AI / Machine Learning 40% 25% 24%
Cloud (including XaaS) 12% 10% 14%
Mobile (incl. 5G) 7% 6% 5%
Internet of Things 6% 10% 11%
Blockchain 5% 4% 5%
ERP 1% 3% 3%
AI tops the list of technologies companies perceive as ‘game-changing’
Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, ﬁgure 1, 3 January 2019.

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Use of AI applications is advancing across a broad front
• Today’s enterprises are using multiple types of experiential and analytical AI applications.
• One in ten enterprises now uses ten or more AI applications (Gartner).
Chatbots have displaced fraud detection as the top use of AI in 2019
Does your organisation use any of these artiﬁcial intelligence (AI) based applications? 2019: n = 2,791; 2018: n = 2,672. Multiple responses allowed.
Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, ﬁgure 1, 3 January 2019.

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Popular use cases: asset management
• Investment strategy 
Improve a ﬁrm’s investment strategy by synthesising research and data, and incorporating broader
data sets including unstructured information.
• Portfolio construction 
Augment or automate an asset manager’s process of portfolio construction.
• Risk management 
Improve risk management by incorporating broader data sets and enhanced analytical processing.
• Client service 
Natural language AI enables client enrolment, support and self-service. Chatbots enable account
managers to query client details and understand developments with client portfolios in seconds.

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Popular use cases: healthcare
• Diagnosis 
Replace complex, human-coded sets of probabilistic rules and identify subtle correlations between
vast, multi-variate data sets to deliver scalable, automated diagnosis.
• Drug discovery 
Synthesise information and oﬀer hypotheses from the 10,000 research papers published daily;
predict how compounds will behave from an earlier stage of the testing process; identify patients
for clinical trials.
• Monitoring 
Synthesise signals from inexpensive wearable devices worn by patients to deliver clinical-grade
monitoring; enable large patient groups to be monitored in real-time by a single nurse.

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• Risk assessment 
Assess the risk of individual policies more accurately than rules-based systems by detecting 
non-linear patterns in multi-variate data sets and making more accurate projections.
• Claims processing 
Automatically extract and classify structured and unstructured information from insurance policies
and claim forms.
• Fraud detection 
Identify fraudulent transactions, while reducing false positives, more eﬀectively.
• Customer service: 
Chatbots using natural language processing can oﬀer 24/7 product information and answers to
policyholders’ enquiries in a scalable, inexpensive and personalised channel.
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Popular use cases: insurance

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Popular use cases: law and compliance
• Case law, discovery and due diligence 
Natural language processing AI can identify, classify and utilise content from databases and
unstructured documents at scale and speed, saving legal firms time and cost for document review.
• Litigation strategy 
Analyse past judgements at greater speed, granularity and subtlety. By anticipating the probability
of different outcomes, inform and enhance strategic decision-making.
• Compliance 
Flag potential compliance breaches in real-time, before they occur, with sufficient accuracy to
enable broad deployment.

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Popular use cases: manufacturing
• Predictive maintenance 
Identify subtle patterns in data from vibration, temperature, pressure and other sensors to identify
leading indicators of equipment failure and save expensive, unplanned downtime.
• Asset performance 
Improve the operation of high value assets, including gas and wind turbines, to optimise yield.
•Utility optimisation 
Anticipate, and align, utility consumption with process requirements in real-time, lowering utility
consumption by 5% or more.

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Popular use cases: retail
• Customer segmentation 
Access additional data sets (e.g. social media data) and undertake more granular analysis than
rules-based systems allow, to optimise segmentation, channel selection and messaging.
• Content personalisation 
Access additional unstructured data sets for analysis, and improved multivariate analysis to
identify more subtle correlations than rules-based systems can detect.
• Price optimisation 
Identify correlations within and between data sets, to better optimise for factors including price
elasticity, revenue, proﬁt, availability and phases in product’s lifecycle (introduction or end-of-life).

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Popular use cases: transport
• Autonomous vehicles 
Enable vehicles to sense and identify physical features and dynamics in their environment, from
road lanes to pedestrians and traffic lights, with a high degree of accuracy.
• Infrastructure and system optimisation 
Detect patterns and optimise complex data to address traffic, congestion and infrastructure
challenges in transport systems.
• Fleet management 
Optimise pick-ups, route planning and delivery scheduling to maximise asset utilisation, while
considering economic, social and environmental impacts.
•Control applications 
Address prediction and optimisation challenges presented by air traffic control, vehicle traffic
signalling, and train control.

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Popular use cases: utilities
• Supply management 
Predict changes in supply, including those caused by the intermittency of renewable resources,
enabling smaller reserves and cost savings.
• Demand optimisation 
Identify detailed patterns in consumer behaviour to move consumption of energy from periods of
peak use and high prices to times of lower demand and cost.
• Security 
Identify abnormal patterns in network behaviour to detect breaches in network security that elude
traditional programs.
•Customer experience 
Chatbots, powered by deep learning algorithms, oﬀer consumers self-service account
administration, product information and customer service.

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‘Deployed AI’ (% of companies)
Source: Gartner
‘Deploying AI in the next 12 months’ (% of companies)
Source: Gartner
21
Globally, China leads the race in AI adoption
• Twice as many enterprises in Asia have adopted AI, compared with companies in North
America, due to: government engagement; a data advantage; and fewer legacy assets.
• Chinese companies have a dual advantage: more permissive policies than Europe
regarding use of personal data; and less siloed data within companies.
Deployed AI
25%
1%
Deploying AI in the next 12 months
30%
1%
Rapid adoption of AI overall masks a growing divergence – among countries, across industries and
between companies.

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Adoption of AI is uneven across, and within, sectors
Source: Gartner
22
Sector adoption is in a state of flux
Divergence is also evident across industries:
• ‘Early adopters’ (financial service and high-tech companies)
invested early in AI, sustained their investment, and today
maintain a lead.
• ‘Movers’ (retail, healthcare and media) have awoken to AI’s
potential and are rapidly catching up.
• Government agencies, education companies and charities are
laggards in AI adoption. Utilities
Wholesale trade
Government
Education
Charity
Natural Resources
Financial Services
Health (Providers)
Transport
Manufacturing
Media
Health (Payers)
Retail
Telecoms
Software & IT Services
Insurance
Adoption of AI is uneven across, and within, sectors
Percentage of respondents that have
deployed AI or plan to within 12 months
48%
46%
44%
44%
44%
42%
40%
39%
38%
37%
35%
33%
32%
26%
26%
25%
Vulnerable members of society may be among the last
to benefit from AI.

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Leaders and laggards face different adoption challenges
23
Leaders and laggards face different challenges to adoption
Source: S. Ransbotham, P. Gerbert, M. Reeves, D. Kiron, and M. Spira, “Artificial Intelligence in Business Gets Real,” MIT Sloan Management Review and The Boston Consulting Group, September 2018.
• Among companies, laggards are struggling to gain leadership support for AI and to 
define use cases.
• Leaders’ challenges have shifted from ‘if’ to ‘how’. Leaders are seeking to overcome the
difficulty of hiring talent and addressing cultural resistance to AI.
20%
40%
60%
80%
100%
0%
Attracting,
acquiring and
developing the
right AI talent
Competing
investment
priorities
Security concerns
resulting from
AI adoption
Cultural
resistance to
AI approaches
Limited or
no general
technology
capabilities
(e.g. analytics,
data, IT)
Lack of
leadership
support for
AI initiatives
Unclear or no
business case for
AI applications
Passives Experimenters Investigators 2017 data
What gets in the way of AI adoption?
Pioneers
Leaders and laggards face different challenges to adoption

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There is a growing gulf in corporate understanding of AI
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• Among AI laggards, fewer than two in ten
understand the implications of AI.
• Among leaders the reverse is true; eight in
ten understand its dynamics.
There is a gulf between leaders’ and laggards’ understanding of the implications of AI
Source: “Reshaping Business With Artiﬁcial Intelligence”, MITSloan Management Review in collaboration with The Boston Consulting Group
Leaders are extending their advantage in AI
through better understanding, faster learning
and greater investment.

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The smart are getting smarter
Source: “Reshaping Business With Artificial Intelligence”, MITSloan Management Review in collaboration with The BCG.
AI leaders are extending their advantage through greater investment
Source:“Artificial Intelligence in Business Gets Real,” MIT Sloan Management Review and The BCG, September 2018.
25
Leaders are learning faster and investing more
• The smart are getting smarter. In the last year, between half and two thirds of AI leaders
improved their understanding of AI to a great extent. Just a tenth to a fifth of laggards did so.
• Leaders are extending their advantage through greater investment. Nine in ten AI pioneers
have increased their investment in AI in the past year compared with one in five passives.
10%
20%
30%
40%
50%
60%
70%
0%
Passives Experimenters Investigators Pioneers
Fig. 4b: How much are organisations learning about AI?
11%
20%
50%
69%
Percentage of respondents whose understanding of AI
has changed a lot or to a great extent in the past year
10%
20%
30%
40%
50%
60%
70%
80%
90%
0%
Passives Experimenters Investigators Pioneers
Percentage of respondents with an increased investment in AI in the past year
19%
62% 62%
88%
Percentage of respondents with an increased
investment in AI in the past year

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Laggards are falling further behind in AI strategy
26
While more companies have an AI strategy, the proportion of laggards with an AI strategy is unchanged
Source: S. Ransbotham, P. Gerbert, M. Reeves, D. Kiron, and M. Spira, “Artiﬁcial Intelligence in Business Gets Real,” MIT Sloan Management Review and The Boston Consulting Group, September 2018.
• Laggards’ sense of urgency regarding AI is weakening.
• The proportion of companies that have implemented an AI strategy has increased – but the
proportion of laggards that have done so is unchanged.
How are organisations planning for AI?
Developing a strategy for
AI is urgent for our organisation
Percentage of all respondents
We have a strategy for what
we are going to do with
AI on our organisation
Percentage of respondents
who expressed urgency
for an AI strategy
Somewhat or strongly agree Do not somewhat or strongly agree 2017 data
60% 39% 61% 67% 33% 69% 31% 85% 15%
40%
57% 14% 86% 50% 50% 63% 37% 90% 10%
43%
Investigators Pioneers
Experimenters
Passives
OVERALL

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AI initiation is shifting from the C–suite to IT department
27
Initiation of AI projects has shifted from the C-Suite to the IT department
Source: Gartner (June 2018)
• Two years ago CEOs, CIOs or CTOs initiated two thirds of AI projects.
• Today, just one in eight survey respondents highlight corporate leadership as the primary initiator
of AI projects. Interest in AI has shifted from the C-suite to the IT department.

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Companies prefer to buy, not build, AI
28
Nearly half of companies favour buying AI solutions from third parties
Source: Gartner (June 2018).
• Nearly half of companies favour buying AI solutions from third parties.
• A third of companies intend to build custom solutions.
Buy an AI solution and then
customise it to our industry
Buy an AI solution already
customised to our industry
Build custom solution internally
using open source where possible
Outsource custom solution
development to third party
Wait for AI to be embedded in
our favoured software products
Build custom solution internally
Other
0 5 10 20
15 25 30
Sourcing strategies for AI initiaves
18
27
BUY = 44%
BUILD = 33%
1
10
11
18
15
Communications = 32%
Education = 25%
Government = 25%

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Workers’ views vary widely regarding the impact of AI on their activities
Source: Survey Analysis: How AI Will Impact Industries From the Workers’ Perspective, Gartner 2018
On balance, workers expect AI to decrease job security
Source:Survey Analysis: How AI Will Impact Industries From the Workers’ Perspective, Gartner 2018
29
Workers are concerned about job security
• Workers expect AI to increase the safety, quality and speed of their work.
• As companies’ AI agendas shift from revenue growth to cost reduction initiatives, workers are
concerned about job security.
0
Fig. 19: Workers views regarding the impact of AI on their activities vary widely
AI impact on jobs
Variety
% DECREASE % INCREASE
Job security
Customer contact
Interesting
Safety
Co-worker collab
Quality
Pace of work
21
-20
14
-33
16
-22
15
-22
23
-18
38
-8
34
-10
34
-17
0
-20
20
40
Safety
AI impact on jobs
(net % of respondents)
Quality
Pace
Interest
Co-worker
collaboration
Customer contact
Variety
Job security
How AI will impact industries from the workers' perspective

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2. The war for talent

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While supply of AI talent is increasing, demand for AI
professionals continues to exceed supply.
Winners and losers are emerging in the competition 
to capture talent.
High job satisfaction is intensifying the war for talent.
The war for talent

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Demand for AI talent has doubled in 24 months
32
• In the UK, job listings for AI roles have increased 485% since 2014 (Indeed).
•Demand for AI talent has doubled in 24 months.
•There is a gulf between demand and supply, with 2.3 roles available for every AI professional.
AI-related job postings as a proportion of total job postings have doubled in 18 months
Source: Indeed
Fig. 1: AI-related job postings as a proportion of the total have doubled in 18 months
(AI-related postings per million postings)
0
200
400
600
800
1000
1200
2015 2016 2017 2018
AI-related postings per million postings

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• Machine learning has become the top emerging field of employment in the United States.
• Greater supply is being driven by: high pay; the inclusion of AI modules in university computer
science courses; companies’ investment in staff training; and AI technology companies ‘pump
priming’ the market with free resources.
• Over time, AI tools offering greater abstraction will make AI accessible to less specialised developers.
In the US, machine learning is the top emerging job
Source: LinkedIn
Technology companies are offering free educational AI resources
Source: Google, Udacity
Brand Partner
Full Stack Developer
Director of Data Science
Unity Developer
Big Data Developer
Full Stack Engineer
Customer Success Manager
Sales Development Representative
Data Scientist
Machine Learning Engineer
0x 1x 2x 3x 4x 5x 6x 7x 8x 9x 10x
Fig. 2: In the US, machine learning is the top emerging job
Increase in individuals’ listed professions
compared with five years ago
Supply is increasing…

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• AI demands advanced competencies in mathematics, statistics and programming.
• AI developers are seven times more likely to have a Doctoral degree than other developers.
• Estimates vary; there may be as few as 22,000 highly-trained AI specialists (Element) and up to
300,000 AI researchers and practitioners within broader technical teams (Tencent).
60% of AI developers have a Master’s or Doctoral degree
Source: Kaggle
AI developers are seven times more likely to have a Doctoral degree than others
Source: Kaggle, StackOverﬂow
Fig. 5: 60% of AI developers have a Master’s or Doctoral degree
(AI developers’ level of education)
Master's degree
Bachelor's degree
Doctoral degree
Other
42%
32%
16%
11%
0%
10%
20%
30%
40%
50%
60%
Secondary
school
Some
university
Bachelor’s
degree
Master’s
degree
Doctoral
degree
AI Developers
General Developers
Developers (%)
Fig. 6: AI professionals have a greater proportion of advanced degrees
Developers’ levels of education
34
…but the pool of AI talent remains small

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35
Over time, AI democratisation will mitigate talent shortages
• Over time, a larger talent pool and more accessible AI tools will alleviate much of the shortfall
in AI talent.
• Governments’ investment in education – in science, technology, engineering and mathematics
(STEM) subjects – will be vital for countries to broaden their pools of AI talent.
• AI courses and resources from universities and technology companies, and market demand, will
also boost supply.
• Development environments oﬀering higher levels of abstraction will also reduce the burden
on developers’ knowledge. In 2005, NumPy abstracted portions of required mathematics. In
2010 and 2015, Python libraries and TensorFlow progressively abstracted network development.

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• AI professionals are among the best paid developers and their salaries continue to increase.
• Half enjoyed salary growth of 20% or more in the last three years.
AI professionals are among the highest paid developers
Source: StackOverﬂow
Most AI professionals’ salaries have increased in the last three years
Source: Kaggle
Mobile developer
Game or graphics developer
Educator or academic reseracher
Designer
Front-end developer
Database administrator
QA/test developer
System administrator
Back-end developer
Desktop or enterprise applications developer
Full stack developer
Embedded applications/devices developer
Data or business analyst
Data scientist or machine learning specialist
DevOps specialist
Engineering manager
$0 $10 $20 $30 $40
Median annual salary ($000s)
$50 $60 $70 $80 $90
Fig. 8: AI professionals are among the highest paid developers
(Global average developer salaries by category)
Global average developer salaries by category
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0
Declined Unchanged
(+/- 5%)
Higher
(+6% to +19%)
Higher
(+20% or more)
Fig. 11: Most AI professionals’ salaries have increased in the last three years
3%
18%
19%
45%
Compensation changes in the past three years
for data scientists/machine learning engineers
36
Today, talent shortages are sustaining high salaries

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• Data scientists enjoy among the greatest salary premium relative to their level of experience.
AI professionals are paid highly relative to their level of experience
Source: StackOverﬂow
$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
6 7 8 9 10 11
Average years of professional programming experience
Median salary ($USD)
Fig. 10: AI professionals are paid highly relative to their level of experience
(Developer salary vs. years of professional programming experience)
Developer salary vs. years of professional programming experience
Number of respondents
10,000 20,000
DevOps specialist
Engineering manager
CTO/CEO/etc
Product manager
Data scientist
Full-stack developer
Embedded/devices developer
Back-end developer
QA or test developer
Data or business analyst
Mobile developer
Game or graphics developer
Educator or academic researcher
Desktop of enterprise application developer
System administrator
Database administrator
Designer
Front-end developer
Today, talent shortages are sustaining high salaries
37

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Technology and financial services firms are absorbing nearly 60% of AI talent
Source: The Burtch Works Study – May 2018. N=2,212 . Shows distribution of data scientists by sector.
44%
Technology
10%
Other
14%
Financial Services
6%
Healthcare/
Pharma
2%
Academia
1%
Gaming
1%
Government
5%
Retail & CPG
9%
Advertising/Marketing
8%
Consulting
Fig. 13: Technology and Financial Services absorb nearly 60% of AI talent
(distribution of data scientists by industry)
• The technology and financial services sectors are absorbing 60% of AI talent.
• The ‘brain drain’ from academia to industry is real and will have mixed implications, catalysing
AI’s impact while moving value from the public domain to select, private companies.
Winners and losers are emerging in the war for talent
38

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• Three quarters of AI professionals are satisfied in their current role.
• To optimise hiring and retention, companies should align roles to AI professionals’ primary
motivators – learning opportunities, environment, access to preferred technologies and impact.
Three quarters of AI developers are satisfied (6/10 or better) with their current role
Source: Kaggle
Learning, office environment and technologies are AI developers’ primary motivators
Source: Kaggle
Fig. 14: Three quarters of AI developers are satisfied (>6/10) with their current roles
(AI developers’ job satisfaction)
Respondents (%)
AI developers’ job satisfaction (scale of 1 to 10)
0%
5%
10%
15%
20%
25%
1 2 3 4 5 6 7 8 9 10
0%
10%
20%
30%
40%
50%
60%
70%
80%
Diversity
Work remotely
Leader reputation
Company funding/financials
Industry
Publishing opportunties
Commute
Department
Impact
Job title
Management
Experience level
Compensation
Technologies
Office environment
Learning
Fig. 15: Learning, office environment and the technologies they will use are
AI developers’ primary motivators
% respondents rating factor “very important”
High job satisfaction is intensifying the war for talent
39

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• Company websites and technology job boards are less effective than engaging with
recruiters, friends, family and colleagues, according to those already employed in the field.
The most effective route into AI is engagement with recruiters
Source: Kaggle
1. Recruiter
2. Friend, family, colleague
3. General job board
4. Other
5. Company website
6. Career fair or recruiting event
7. Tech job board
EMPLOYED IN FIELD
1. Company website
2. Tech job board
3. General job board
4. Friend, family, colleague
5. Recruiter
6. Career fair or recruiting event
7. Other
ENTERING FIELD
Fig. 16: The most effective route into AI work is engagement with recruiters
How do you look for, or find, work?
40
New talent follows sub-optimal paths to employment

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3. The advance of technology

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The advance of technology
Advances in AI technology are creating new possibilities.
Custom silicon is enabling a new generation of AI hardware.
Emerging software techniques – reinforcement learning,
transfer learning and generative AI – are delivering
breakthroughs in multiple domains and decoupling progress
from the constraints of human experience.

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• Vendors are optimising or customising hardware to support the use of popular deep learning
frameworks, enabling faster system training and performance with common AI frameworks.
• NVIDIA’ Tesla GPUs accelerate matrix computations to optimise deep learning on a range of frameworks.
Certain neural networks can be trained in one third of the previous time and operate 4x faster.
Tesla GPUs enable suitable neural networks to be trained in a third of the previous time
Source: NVIDIA
Tesla GPUs allow suitable neural networks to operate four times faster than previously
Source: NVIDIA
43
Tensor architectures are accelerating deep learning
8X Tesla P100
8X Tesla V100
0 4 8 12 16
Time to solution in hours
(Lower is better)
Server conﬁg: Dual Xeon E5-2699 v4 GHz | 8X NVIDIA®
Tesla® P100 or V100 | ResNet-50 Training on MXNet for
90 Epochs with 1.28M ImageNet Dataset
Title TBC
15.5 hours
5.1 hours
Tesla P4
1X CPU
Tesla V100
0 10X 20X
27X
7X
30X 40X 50X
Performance normalised to CPU
Workload: ResNet-50 | CPU: 1X Xeon
E5-2690v4 @ 2.6 GHz | GPU: Add 1X Tesla P4 or V100
Title TBC

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Tensor architectures are accelerating deep learning
44
• Google’s Tensor Processing Unit (TPU) is an application- specific integrated circuit (ASIC)
designed specifically to accelerate AI workloads on the popular TensorFlow framework.
• Pods of next-generation TPUs can reduce training time for select algorithms from 
five hours to seven minutes.
Google’s next-generation TPUs reduce further the time required to train an algorithm
Source: Google
Title TBC
ResNet-50 302.0
minutes
183.0
minutes
60.0
minutes
7.1
minutes
TPU v2 TPU v3 TPU v3 Pod TPU v3 Pod
NMT
SSD
40.4
minutes
26.3
minutes
9.7
minutes
96.9
minutes
74.9
minutes
17.8
minutes
Number of chips
4 4 16
4
256
4

View Slide

• Custom silicon, designed from inception for AI, offers transformational performance,
capability similar to existing systems for a fraction of the power or space, and greater versatility.
• Graphcore’s intelligence processing unit (IPU) combines a bespoke, parallel architecture with
custom software to offer greater performance than existing systems.
Graphcore’s IPU is designed, from inception, for AI
Source: Graphcore
Graphcore’s IPU could deliver 200-fold performance improvements in selected tasks
Source: Graphcore
45
The post-GPU era: custom silicon offers new possibilities
GPU (<2ms latency)
GPU (<7ms latency)
IPU (<7ms latency)
GPU (<5ms latency)
IPU (<5ms latency)
IPU (<2ms latency)
0 10000 20000
242X
182X
30000 40000 50000 60000 70000
Inferences per second
Single layer inference

View Slide

• A ‘barbell’ eﬀect is emerging as a new class of AI hardware is optimised for edge
computing instead of the data centre.
• Edge computing moves the processing of data from the cloud to the ‘edge’ of the internet –
to devices where data is created: autonomous vehicles; drones; sensors and IoT devices.
• Increasingly, edge computing is required as devices proliferate, and issues of connectivity
and latency demand on-device processing in multiple contexts.
46
Custom silicon is taking AI to the edge
In 2019, as well as enabling next generation AI in the cloud, custom
silicon will transform AI at the edge by coupling high performance
with low power consumption and small size.

View Slide

• As quantum computing matures it will create profound opportunities for progress in AI.
• Quantum-powered AI will enable humanity to address previously intractable problems, from
personalised medicine to climate change.
• While nascent, quantum computing is advancing rapidly. Google is developing quantum
neural networks. In 2018, an Italian team of researchers developed a functioning quantum
neural network on an IBM quantum computer.
47
Quantum computing is unlocking profound opportunities
Interested in learning more about quantum computing?
Listen to ‘Quantum Leap’ – Episode 6 – of our ‘Beyond the Hype: AI’ podcast: 
=> bit.ly/2LrNhn1 (Web)
=> apple.co/2GwMXEU (iTunes)

View Slide

Reinforcement learning is enabling powerful AI agents
48
Reinforcement learning enabled AlphaGo Zero, a system developed by DeepMind to play Go, to achieve unrivalled
capability after 40 days of play
Source: Google DeepMind
• Instead of learning from training data, reinforcement learning (RL) systems learn through
exploration. Progress towards a speciﬁed goal is rewarded and reinforced.
• Developments in RL are enabling the creation of powerful AI agents.
• AlphaGo Zero (Oct 2017), an RL system developed to play Go, achieved unparalleled capability within
40 days by playing against itself. AlphaZero (Dec 2017) can play a range of games at even higher levels.
Elo Rating
Days
-2000
-1000
0
1000
2000
3000
4000
5000
Title TBC
0 5 10 15 20 25 30 35 40
40 days
AlphaGo Zero supasses all other versions of AlphaGo and,
arguably, becomes the best Go player in the world. It does
this entirely from self-play, with no human intervention and
using no historical data.
AlphaGo Zero 40 blocks AlphaGo Lee AlphaGo Master

View Slide

Reinforcement learning is enabling eﬀective multi-agent collaboration
Source: OpenAI. AI agents playing Defence of the Ancients 2 (Dota2)
Reinforcement learning enabled the OpenAI 5 team to surpass rapidly most human teams
Source: OpemAI
49
Reinforcement learning is enabling multi-agent collaboration
Drafting
Picking
Composition
Mirror Death Prophet,
Witch Doctor, Gyrocopter, Earthshaker, Fidehunter
Mirror Necrophos, Lich,
Crystal Maiden, Viper, Sniper
2,000
3,000
4,000
5,000
6,000
22 April 6 May
OpenAI Five
OpenAI Dev Team
Blitz + Audience
Amateur Team
Semi-Pro Team
Test Team A
Test Team B
Caster Team
20 May 3 June 17 June 1 July 15 July 29 July 12 August
Reinforcement learning enabled the OpenAI 5 team to rapidly surpass the performance of most human teams
Approximate Team MMR
Date (2018)
• Developments in RL are enabling groups of agents to interact and collaborate with each other.
• OpenAI 5, an RL-powered team of agents developed to play the game Defence of the Ancients 2
(DOTA2), improved rapidly and can defeat all but the top professional human players.

View Slide

Reinforcement learning decouples progress from human knowledge
50
• RL is well suited to creating agents that can perform autonomously in environments for which we
lack training data.
• Progress in RL is signiﬁcant because it decouples system improvement from the constraints of
human knowledge.
• RL enables researchers to “achieve superhuman performance in the most challenging domains
with no human input” (DeepMind).

View Slide

Transfer learning can offer strong initial performance, faster improvement and better  
long-term results
Source: Torry, Shalvik
Interest in transfer learning has grown 7-fold in 24 months
Source: Google trends
51
Transfer learning enables more adaptable systems
• Transfer learning (TL) enables programmers to apply elements learned from previous challenges
to related problems.
• Interest in TL has grown seven-fold in 24 months and is enabling a new generation of systems with
greater adaptability.
• TL- powered models are improving the state of the art in language processing in areas of universal
utility – text classification, summation, text generation, question answering and sentiment analysis.
Interest in transfer learning has grown 7-fold in 24 months
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
APR-13
JUN-13
AUG-13
OCT-13
DEC-13
FEB-14
APR-17
JUN-17
AUG-17
OCT-17
DEC-17
FEB-18
APR-18
JUN-18
AUG-18
OCT-18
DEC-18
FEB-19
APR-14
JUN-14
AUG-14
OCT-14
DEC-14
FEB-15
APR-15
JUN-15
AUG-15
OCT-15
DEC-15
FEB-16
APR-16
JUN-16
AUG-16
OCT-16
DEC-16
FEB-17
Relative interest in transfer
learning (April 2013 = 1.0)
Performance
Transfer learning can offer strongerinitial performance,
faster improvement and better long-term results
Training
with transfer
without transfer
higher start
higher slope
higher asymptote

View Slide

52
Transfer learning is delivering leaps in language AI
• ELMo (‘Embeddings from Language Models’, March 2018): improved the state of the art for a broad
range of natural language tasks including Q&A and sentiment analysis.
• ULMFiT (‘Universal Language Model Fine-tuning for Text classification’, May 2018): underscored that TL can
be applied to language processing tasks and introduced techniques for fine-tuning language models. With
100 labelled examples, ULMFiT matched the performance of systems trained with 100-fold more data.
• OpenAI (mid-2018): Impressive results on a diverse range of language tasks from a single starting point;
task-agnostic model outperformed models with architectures crafted for specific tasks including
question answering.
• BERT (Bidirectional Encoder Representations from Transformers, Oct 2018): Open-sourced by Google, an
RL-based language processor achieved state-of-the-art results on 11 natural language benchmarks.

View Slide

53
Transfer learning will unlock further advances in AI
• Transfer learning is enabling complex systems to interact with the real world. 
Training AI systems, such as autonomous vehicles, can be laborious, expensive or dangerous.
Simulation enables emulation of environments instead of capturing real-life data. Applying transfer
learning enables learnings from a simulation to be applied to real-world assets.
• Transfer learning offers adaptability and progress towards AGI. 
By offering greater adaptability, TL supports progress towards artificial general intelligence (AGI) 
i.e. systems that can undertake any intellectual task a human can perform. 
While AGI is far from possible with current AI technology, developments in TL are enabling progress.

View Slide

“I think transfer learning is the key to general intelligence.
And I think the key to doing transfer learning will be the
acquisition of conceptual knowledge – knowledge that is
abstracted away from perceptual details of where you
learned it, so you can apply it to a new domain.”
Demis Hassabis, DeepMind
Transfer learning will unlock further advances in AI

View Slide

GANs’ ability to create lifelike media has rapidly improved
Source: Goodfellow et al, Radford et al, Liu and Tuzel, Karras et al, https://bit.ly/2GxTRot
GANs can generate artificial images that appear real (none of these individuals exist)
Source: NVIDIA
55
Generative AI will transform media and society
• Generative Adversarial Networks (GANs) enable the creation of artificial media, including
pictures and video, with exceptional fidelity.
• GANs will deliver transformational benefits in sectors including media and entertainment,
democratising the creation of high quality content at scale and low cost. GANs will also present
profound challenges to societies – beware ‘fake news 2.0’.

View Slide

Generative AI: two networks working in opposition
56
GANs operate with two networks working in opposition
Source: Naoki Shibuya
• GANs operate by two networks – a ‘generator’ and ‘discriminator’ – working in opposition to
create increasingly lifelike media.
• The generator receives a random input, such as a matrix of numbers, and follows a series of
mathematical transformations to convert the input into a picture.
• The discriminator assesses whether the image received from the generator is authentic or has
been artiﬁcially generated, with a feedback cycle between the networks promoting progress.
DISCRIMINATOR
GENERATOR
Training data
Latent sample
-0.19972104,
-0.27617624,
-0.7449326,
-0.02522890,
-0.86428853,
-0.84710504,
-0.41719925,
-0.88549566,
-0.42638235,
-0.0455994,
-0.305852,
-0.60752668,
-0.14700781,
-0.26471074,
-0.71651508,
-0.65559716,
-0.71335986,
-0.82961057,
-0.81311934,
-0.42092858,
-0.42457545,
-0.39863341,
-0.26192929,
-0.18518651,
Generated
image
1 (Real)
0 (Fake)
Title TBC

View Slide

57
Generative AI has use beyond pictures and video
• While GANs are frequently used to create images, their utility is broader. GANs enable:
- Alternative media: create music or text in the style of particular individuals.
- System Training: improve the training of AI classification systems.
- Data manipulation: Add/remove features in data by combining GANs with autoencoding techniques.
- Data normalisation: Normalise data from different sources.
- Network security: Detect anomalies in network access or activity.
- Data creation: Produce additional training data to improve the accuracy of AI classifiers.

View Slide

4. The Disruptors: 
Europe’s AI startups

View Slide

1 in 12 European startups is now an ‘AI startup’ 
putting AI at the heart of its value proposition.
Europe’s 1,600 AI startups are maturing, bringing creative
destruction to new industries and navigating unique
capital dynamics. While the UK is the powerhouse of
European AI, Germany and France may extend their inﬂuence.
Entrepreneurs’ greatest challenges are access to data,
recruiting talent and moving AI from ‘lab to live’.
The Disruptors: Europe’s AI startups

View Slide

Europe is home to 1,600 early stage AI software companies
60
• We individually reviewed the activities, focus and funding of 2,830 companies classiﬁed  
by popular tools as AI startups, in the 13 EU countries most active in AI.
• Together, these countries – Austria, Denmark, Finland, France, Germany, Ireland, Italy, the  
Netherlands, Norway, Portugal, Spain, Sweden and the UK – also comprise 90% of EU GDP.
• In approximately 60% of cases – 1,580 companies – there was evidence of AI material to 
a company’s value proposition.

View Slide

One in twelve new European startups is an AI company
Source: MMC Ventures (2018 data to October)
• In 2013, one in ﬁfty new startups embraced AI.
• Today, one in twelve put AI at the heart of their value proposition.
• We have entered the era of AI entrepreneurship.
AI entrepreneurship is becoming mainstream
61
0
1%
2%
3%
4%
5%
6%
7%
8%
9%
0.0pp
0.2pp
0.4pp
0.6pp
0.8pp
1.0pp
1.2pp
1.4pp
1.6pp
1.8pp
2011
2010 2012 2013 2014 2015 2016 2017 2018
AI startups as % of all startups founded each year
AI startups as % of all startups founded each year
AI startups as % of all startups founded each year (left axis)
YoY change in percentage points (right axis)
YoY change in percentage points

View Slide

• Two years ago, just one in 20 AI startups were ‘growth’-stage companies that had passed beyond
Angel and Seed funding rounds.
• Today, one in six European AI companies is a ‘growth’-stage company with over $8m of funding.
• Expect: acquisitions to recycle capital and talent; startups competing with ‘scale-ups’ as well as
incumbents; and increasing competition for talent.
Six in ten startups are at the Angel or Seed stages Larger ecosystems are typically more mature; Spain is an exception
Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
The European AI ecosystem is maturing
62
Six in ten AI startups
are at the Angel or Seed stages
Angel
27%
Early
Stage
28%
Seed
29%
Growth
16%
0% 20% 40% 60% 80% 100%
Fig. X: Larger ecosystems are typically more mature; Spain is an exception
31%
26%
28%
29%
28% 28%
34%
25%
19%
19%
27%
27%
27%
16%
16%
39%
21%
35%
32%
32%
11%
38%
24%
9%
6%
18%
26%
All
Seed
Early Stage
Growth
Angel
UK
France
Germany
Spain
Sweden
Finland
25%
Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn. 
Funding categories: Angel (<$0.5m), Seed ($0.5-2.0m), Early Stage ($2.0-8.0m), Growth (>$8.0m)

View Slide

With twice as many AI startups as any other country, the UK is the powerhouse of European AI entrepreneurship
Source: MMC Ventures,
Beauhurst, Crunchbase, Tracxn
• The UK is the heartland of European AI with a third of the continent’s AI startups.
• UK strengths include: talent from a quarter of the world’s top 25 universities; extensive venture capital;
many successful AI scale-ups and exits; and a global ﬁnancial services hub.
• Germany and France are thriving hubs where high quality talent, increasing volumes of capital and an
expanding roster of successful AI companies are creating feedback loops of growth and investment.
The UK is the powerhouse of European AI
63
479
UK
196
Germany
217
France
166
Spain
75
Ireland
66
Italy
45
Portugal
43
Austria
32
Norway
36
Denmark
49
Finland
73
Sweden
103
Netherlands
Fig. X: The UK is the heart of European AI entrepreneurship

View Slide

The ‘UK 500’: A map of early stage UK AI companies
64
Cambridge Quantum Computing
UK AI Landscape (Early stage companies)
ANGEL / SEED
EARLY STAGE / GROWTH
Skim Technologies
Instadeep Memgraph
Seldon
Invacio
Grakn Labs Hadean
Graphcore
Fetch.AI
Academy of Robotics Evolve Dynamics
ANGEL / SEED
FiveAI
Oxbotica
Latent Logic
Focal Point Positioning
Sky-Futures
Accelerated Dynamics Baro Vehicles
Headlight AI Humanising Autonomy Intelligent Robots
Machines With Vision Predina Wayve
React AI Third Space Auto
ANGEL / SEED
SLAMcore
MicroBlink
Spectral Edge WeSee
Blue Vision Labs
Emteq
Alchera Technologies FaceSoft
TouchByte
Vize.ai
Visio Ingenii
Machine Medicine Synthesia
Telectic
Audio analytic
Ditto AI
Mind Foundry
Prodo.ai
ANGEL / SEED
Bottr.co Chatbot Agency
Prowler.io
Neuron soundware
CloudNC
Senseye
QiO Technologies
ANGEL / SEED
Ai Build Flexciton
Materialize.X
Thingtrax
ANGEL / SEED
Mouldbox Houseprice.AI PerchPeek
Pupil
ANGEL / SEED
AskPorter
Skyscape
Ecosync
Brytlyt Hivemind
Instrumental Jukedeck Myrtle Software Mogees Popsa Spirit AI
ANGEL / SEED
Capito Systems Kami.ai
Poly AI
CrowdEmotion DAM Good Media
AI Music Antix
Let's Enhance
Lobster Logically
inﬂoAi Kompas
DeepAR
Deep Innovations
Gameway
Factmata
Pimloc Thingthing
Snaptivity
B2B
Angel/Seed: <$2m
Early stage/Growth: >$2m
Funding category
New company (since 2017)
B2C
KEY
iProov
Speechmatics
RETAIL
OTHER
LANGUAGE & SOUND
COMPUTER VISION
AI INFRASTRUCTURE
Core Technologies
AUTONOMOUS SYSTEMS
Sector
Cortexica Dressipi
Artlimes
Cocoon Emotech Mallzee Metail
Snap Tech Spoon Guru Thread
Aura Vision Boldmind
ANGEL / SEED
Hoxton Analytics JCC Bowers Measmerize
Cadouu CartMe
Orpiva
Pasabi Proximus See Fashion ThirdEye
Save your wardrobe
Olvin
EDITED
PROPERTY & REAL ESTATE
Gousto
Winnow
Nuritas
Henchman
ANGEL / SEED
Dinabite Limited
IntelligentX VideraBio
FOOD & BEVERAGE
MANUFACTURING MEDIA & ENTERTAINMENT
Function
Energi Mine
SenSat
Limejump nPlan
ClauseMatch
Eigen Technologies
Luminance
Cognitiv+
ThoughtRiver
DoNotPay
ANGEL / SEED
Crowd Connected
Verv by Green Running
Calipsa Disperse.io
ANGEL / SEED
Green Running Grid Edge NumberEight
TravelAI
OpenCapacity
Wluper
Rovco Ginie.ai
INFRASTRUCTURE & TRANSPORT
Cambridge Medical Robotics
Exscientia GTN
Drayson Technologies
BrainWaveBank
BenevolentAI
Babylon Health
Amiko BioBeats Cydar Medical Deontics
eCUORE
Antiverse Biorelate
Auro
ANGEL / SEED
LabGenius Lifebit
Cambridge Humanae
Cambridge Bio-Augmentation Systems
Aigenpulse Causaly Chronomics
HealthUnlocked Healx Kheiron Medopad Snap40 Synthace Thriva
Your.MD
Visulytix Viz
Sentireal
ThinkSono
Sime Diagnostics Transformative
HEALTH & WELLBEING LAW
FitWell InnersightLabs InsideDNA
Kraydel
Kaido Kiroku OME Health Resurgo Genetics
PetaGene
Optellum Peptone
ANGEL / SEED
Quantemplate Quantexa Vortexa
ArrayStream
AdviceGames Artiﬁcial Labs Banked Blue Lion Research Bud
Abaka
FINANCIAL SERVICES
Oxademy Synap
BridgeU
Gamar Kwiziq
ANGEL / SEED
Century Lingumi
Lingvist
Global Surface Intelligence
Hummingbird Technologies
Dogtooth Technologies
HeraSpace Observe
ANGEL / SEED
Cervest
KisanHub
Xihelm
Optimal Labs
AGRICULTURE EDUCATION
Tractable
Procensus
PriceHubble
Plum
Kirontech
Essentia Analytics
Arkera Floodlash
BMLL Technologies Digital Contact FriendlyScore
ForwardLane
Cleo AI Cytora
Mosaic Smart Data Multiply TradeTeq
Acuity Trading AlgoDynamix Brolly
Alpha-i
CUBE DealX Ducit.ai Knox EA
G-Research Metafused
Digital Clipboard Financial Network Analytics
Chip EnAlgo
Oseven Telematics
Proportunity
Yedup Zoral
Sns Analytics
ProvidensAI Torafugu Tech Win FX Financial Innovation
TradeRiser
Spixii
RightIndem
ANGEL / SEED
Skim Technologies
Instadeep Memgraph
Seldon
Invacio
Grakn Labs Hadean
Graphcore
Fetch.AI
ANGEL / SEED
FiveAI
Oxbotica
Latent Logic
Sky-Futures
ANGEL / SEED
SLAMcore
MicroBlink
Spectral Edge WeSee
Blue Vision Labs
Emteq
TouchByte
Vize.ai
Visio Ingenii
Telectic
Audio analytic
Prodo.ai
Prowler.io
Brytlyt Hivemind
ANGEL / SEED
Capito Systems
Poly AI
B2B
Angel/Seed: <$2m
Funding category
B2C
KEY
Spe
LANGUAGE
COMPUTER VISION
AI INFRASTRUCTURE
Core Technologies
AUTONOMOUS SYSTEMS
Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn
Additions or corrections? Email us at [email protected]

View Slide

The ‘UK 500’: A map of early stage UK AI companies
65
ANGEL / SEED
Ai Build Flexciton
Materialize.X
Thingtrax
ANGEL / SEED
Mouldbox Houseprice.AI PerchPeek
ANGEL / SEED
AskPorter
Skyscape
Ecosync
CrowdEmotion DAM Good Media
AI Music Antix
Let's Enhance
Lobster Logically
infloAi Kompas
DeepAR
Deep Innovations
Gameway
Factmata
Pimloc Thingthing
Snaptivity
Cybertonica
Fraugster
Ravelin
Featurespace
ANGEL / SEED
Fractal Labs
Aire
Rimilia
Pace
Fluidly
ANGEL / SEED
FRAUD DETECTION
FINANCE HUMAN RESOURCES
PredictiveHire
hackajob
GoSay Grad DNA
Filtered Headstart App
Beamery Qlearsite Rotageek
Saberr StatusToday
ANGEL / SEED
Potentially
Metaview MeVitae
Stitched TalentHunter.AI Wevolve
JamieAi
InteriMarket
Human
Darktrace RepKnight
Senseon
Alchemy Data Cyberlytic
Barac
Encode
Corax
CyberSparta Elemendar
ANGEL / SEED
Honeycomb Technologies
VChain Technology
Cybershield
CYBERSECURITY
Aistemos Amplyfi
Black Swan Data Cazana
Bird.i Gyana Logical Glue Callsign
Behavox
Audit XPRT Berry Technologies CoVi Analytics
Exonar Eyn
AimBrain Hazy Onfido
Tessian
Traydstream Limited
Sum&Substance
ANGEL / SEED
WaymarkTech
BotsAndUs
DigitalGenius Gluru
Hutoma
ANGEL / SEED
Action.AI
Enterprise Bot
True AI
Synthetix
Constellation AI
Humley
Sentient Machines
Samim.ai
Massive Analytic Peak Quorso Rezatec Ripjar Semantic Evolution Signal Media Simudyne
ANGEL / SEED
10x Airfinity causaLens
Analytics Intelligence Chorus Intelligence Data quarks Flumes Hertzian
Policy Radar
Reportbrain SeeQuestor Sensing Feeling
Satalia Singular Intelligence Terrabotics
illumr Krzana
Kite Edge
illumr Ohalo
Migacore Technologies Oxford Semantic Technologies
Hello Soda
CUSTOMER SERVICE
BI & ANALYTICS COMPLIANCE
Artlimes Aura Vision Boldmind
ANGEL / SEED
Hoxton Analytics JCC Bowers Measmerize
Cadouu CartMe
Orpiva
Pasabi Proximus See Fashion ThirdEye
Save your wardrobe
Olvin
Decibel Insight Fresh Relevance
Brandwatch
LoopMe MiQ
Buzz Radar
Admedo Fospha
ADTYPE adverttu
Artios
ANGEL / SEED
Carsift Chattermill
Concured Creative AI Crystal Apps CustomSell Digital MR
DaVinci11
MediaGamma Perfect Channel
Storystream
Realeyes
Qubit
Jampp
FindTheRipple Firedrop Mercanto Metageni Mobile Acuity
Nudgr
Phrasee
Ignition Ai Maybe*
Advizzo ArtuData
Aiden Bibblio
rais
Platform360 Vaix
Swogo
MARKETING
&
ADVERTISING
ANALYTICS/OPTIMIZATION TARGETING
BoomApp
ANGEL / SEED
Selerio
Echobox
Adoreboard Colourtext
ANGEL / SEED
Genus SentiSum
Big Data for Humans Codec
ANGEL / SEED
AshTV
Popcorn Metrics
Otus Labs Personalyze
Idio
iotec Pixoneye
Permutive
DataSine
Visii
ANGEL / SEED
Donaco
SENTIMENT ANALYSIS
AUGMENTED CONTENT PURCHASE DISCOVERY/
RECOMMENDATION
Function
IT
Cardinality
4th Office Mettrr Rainbird Technologies
Global App Testing re:infer Recordsure
Redsift
Automorph
Ampliphae
ANGEL / SEED
Aria Networks
BigHand
Autto Beneficiary.io CompareSoft
Automated Intelligence
Diffblue
Trint Vivid-q
Context Scout
Retechnica
Nexus Frontier Tech
Cyanapse Digital Taxonomy jClarity
Fantoo
Fedr8 Fraim Linguamatics Mudano Rossum
Skipjaq Thingful Unity {Cloud}Ware
TextRazor
Synthesized
Spot Intelligence Spotlight Data
PROCUREMENT
Previse
keelvar
Matchdeck
ANGEL / SEED
SALES
R&D
FeedStock
Klydo
Sparrho
Patsnap
GlassAI
ANGEL / SEED
DueDil
Cognism
GrowthIntel
Artesian Solutions
Conversity Kluster Intelligence
SalesSift
Synoptic Technologies
ANGEL / SEED
Netz
EnigmaMS
ANGEL / SEED
Skim Technologies
Instadeep Memgraph
Seldon
Invacio
Grakn Labs Hadean
Graphcore
Fetch.AI
ANGEL / SEED
FiveAI
Oxbotica
Latent Logic
Sky-Futures
ANGEL / SEED
SLAMcore
MicroBlink
Spectral Edge WeSee
Blue Vision Labs
Emteq
TouchByte
Vize.ai
Visio Ingenii
EA
A
P
Prowler.io
Brytlyt Hivemind
A
C
P
B2B
Angel/Seed: <$2m
Funding category
B2C
KEY
L
COMPUTER VISION
AI INFRASTRUCTURE
Core Technologies
AUTONOMOUS SYSTEMS
Sector
FINANCIAL SERVICES
AGRICULTURE EDUCATION
Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn
Additions or corrections? Email us at [email protected]

View Slide

France and Germany are increasing their share
of European AI entrepreneurship
The dynamics European AI entrepreneurship are in flux
66
UK
France
Germany
Spain
Ireland
Netherlands
Sweden
Italy
Finland
Portugal
Austria
Denmark
Norway
0%
5%
-5%
-10%
10%
15%
20%
25%
30%
35%
40%
Fig. X: France and Germany are increasing their share of European AI entrepreneurship
Share of European AI startups
Change in share over the last six years (% points)
• While the UK remains the powerhouse of European AI,
its share of Europe’s AI startups (by volume) has
slightly decreased in recent years.
• Brexit could accelerate this dynamic. If free
movement of workers between the EU and UK ends,
visas are unforthcoming or rhetoric is unwelcoming,
the UK’s access to talent could be inhibited.
• France, Germany and other hubs may extend their
influence in the decade ahead, spreading the
benefits of entrepreneurship more evenly 
across Europe.

View Slide

Italy, Sweden and Germany ‘punch above their weight’ 
in core technology
• Two thirds of of Europe’s ‘core technology’ 
(sector-agnostic) AI startups are located in the 
UK, Germany and France.
• However, adjusting for countries’ ‘size’ – their
number of AI startups – Italy, Sweden and
Germany ‘punch above their weight’ and 
are core technology hubs.
The European core AI technology hubs
67
Italy
Sweden
Germany
Finland
Austria
Denmark
Norway
Spain
UK
France
Netherlands
Ireland
Portugal
0
5%
10%
15%
20%
25%
30%
Fig. X: Core technology startups as % of AI startups
Core technology startups as % of AI startups
Average across all countries
% of Europe’s Core Tech
25%
30%
Fig. X: Core technology startups as % of AI startups
Core technology startups as % of AI startups
Average across all countries
% of Europe’s Core Tech

View Slide

• Nine in ten of Europe’s 1,600 AI startups sell to other businesses (B2B).
• Just one in ten AI startups sells directly to consumers (B2C).
• But B2C AI is on the rise. In 2018, a quarter of new AI startups were B2C.
• B2C entrepreneurs are mitigating or circumventing the ‘cold start’ data challenge.
Nine in ten AI startups are B2B
B2C AI is on the rise – a quarter of new AI companies are B2C
Nine in ten AI startups are B2B, but B2C AI is on the rise
68
Fig. X: Nine in ten of the AI startups in our universe are B2B
B2B
B2C
87%
13%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011
2010
pre
2010
2012 2013 2014 2015 2016 2017 2018*
Fig. X: The share of new AI startups focusing on B2C is increasing
B2C B2B
90%
90%
94% 90% 87% 87% 90% 87% 83% 75%
10%
10%
6% 10% 13% 13% 10% 13% 17% 25%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011
2010
pre
2010
2012 2013 2014 2015 2016
Fig. X: The share of new AI startups focusing on B2C is increasing
B2C B2B
90%
90%
94% 90% 87% 87% 90% 87%
10%
10%
6% 10% 13% 13% 10% 13%

View Slide

• There is a higher proportion of B2C AI companies in sectors where data is readily available.
• The sectors attracting the highest proportion of new AI startups have been: finance; 
health & wellbeing; and media & entertainment.
Media, finance, and health attract a higher percentage of B2C companies
Half of new AI startups target the finance, health or media sectors
Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn , Startups founded in the last 24 months.
B2C AI is growing in sectors with available data
69
Finance
Infrastructure & Utilities
Health & Wellbeing
Media & Entertainment
Retail
Transport & Travel
0% 20% 40% 60% 80% 100%
Fig. X: B2B/C dynamics by sector
79%
69%
53%
86%
73%
74%
21%
31%
47%
14%
27%
26%
B2C
B2B
5%
10%
15%
20%
25%
0%
Finance Health &
Wellbeing
Media &
Entertainment
Retail Transport
& Travel
Fig. 5: Almost one in four of the sector AI startups founded
in 2017/8 targets the financial services space
23%
17%
10%
10% 9%

View Slide

• Nine in ten AI startups address a need in a speciﬁc ‘vertical’ (business function or sector).
• Just one in ten is developing a core, sector-agnostic ‘horizontal’ AI technology.
• The proportion of core technology providers will remain modest as global technology
companies oﬀer an extensive and expanding suite of core AI technologies.
One in ten AI startups focuses on core tech
The proportion of ‘horizontal’ core technology providers has remained consistent over time
Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn , Startups founded in the last 24 months.
AI entrepreneurship remains vertically-focused
70
Fig. X: One in ten AI startups focuses on core tech
Core technologies
Vertical (Sector/Function)
88%
12%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 2017
Fig. X: Vertical/Horizontal dynamics of new AI startups
43% 41% 44% 44% 37%
11% 15% 11% 10% 11%
45% 44% 45% 46% 52%
Sectors Functions Core Technologies
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 2017
43% 41% 44% 44% 37%
11% 15% 11% 10% 11%
45% 44% 45% 46% 52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 2017
43% 41% 44% 44% 37%
11% 15% 11% 10% 11%
45% 44% 45% 46% 52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 2017
43% 41% 44% 44% 37%
11% 15% 11% 10% 11%
45% 44% 45% 46% 52%

View Slide

• More AI startups – one in five – serve the health & wellbeing sector than any other. Healthcare is a focal
point for AI entrepreneurship.
• In the coming decade, developers will have a greater impact on the future of healthcare than doctors.
• Activity is thriving given profound new opportunities for process automation and cost reduction and
a tipping point in stakeholders’ openness to innovation.
More AI startups – one in
five – serve the health &
wellbeing sector than any
other
Source: MMC
Ventures, Beauhurst,
Crunchbase, Tracxn
Health & wellbeing is a focal point for AI entrepreneurship
71
21%
Health & Wellbeing
18%
Finance
12%
Media & Entertainment
6%
Education
5%
Agriculture
11%
Retail
8%
Transport & Travel
8%
Infrastructure
& Utilities
Fig. X: One in five AI sector startups in our universe focuses on Health & Wellbeing

View Slide

The UK is the heartland of European healthcare AI
• The UK is home to a third of Europe’s health & wellbeing AI startups.
• UK entrepreneurs beneﬁt from: leading universities for medicine and high quality teaching hospitals;
successful healthcare scale-ups stimulating talent; and increasing expenditure and openness to
innovation within the National Health Service (NHS).
The UK is the heartland of European healthcare AI
72
Fig. X: Share of European health & well being startups
35%
1%

View Slide

More startups – one in four – serve the marketing function than any other
• Among AI startups serving a business function, more – a quarter – serve the marketing function 
than any other.
• Customer service and IT departments also enjoy a rich ecosystem of suppliers.
Marketing and customer service teams are well served
73
23%
Marketing
16%
IT
16%
Customer Service
9%
Operations
7%
Sales
4%
Compliance
8%
BI & Analytics
8%
HR
Fig. X: Marketing accounts for one in four AI function startups

View Slide

The UK has four in ten of Europe’s AI marketing startups (% of European AI marketing startups)
France is Europe’s hub for AI Customer Service (% of European AI Customer service startups)
The UK leads in marketing AI, France for customer service
74
Fig. X: Share of European customer service startups
22%
1%
Fig. X: Share of European marketing startups
39%
1%
• The UK contributes half of Europe’s AI marketing startups.
• France is Europe’s hub for customer service AI with a ﬁfth of Europe’s customer service AI startups.

View Slide

• While currently underserved, the operations  
function is benefitting from an influx of new, 
AI-led startups in the last 24 months.
• AI is expanding the ‘envelope’ of automation –  
the breadth and value of processes susceptible 
to digital mechanisation and robotic process  
automation (RPA).
• Over time, domain-focused vendors may build  
defensibility through domain expertise, workflow  
integrations, data network effects and referenceability.
An influx of AI startups is driving process automation
75
Among new startups addressing a business function, one in seven
serve operations teams
5%
10%
15%
20%
25%
0%
Customer service Marketing Operations IT HR
Fig. X: Almost one in five of the function AI startups
founded in 2017/8 focuses on customer service
% AI start-ups founded since 2017 serving a business function
20%
15% 15%
13% 13%

View Slide

Since 2015, AI companies have raised larger investment rounds
Source: Crunchbase, MMC Ventures
AI companies are raising larger rounds at all stages of maturity
Source: Crunchbase, MMC Ventures
AI companies raise larger amounts of capital
76
• AI companies raise larger volumes of capital than traditional software companies across 
all stages of maturity.
• Additional capital may be required for the longer journey to ‘minimum viable product’ and 
the high cost of AI talent.
• An imbalance between supply and demand for capital (many venture capital ﬁrms chasing  
limited high quality opportunities) is also inﬂating round sizes.
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
2012 2013 2014 2015 2016 2017 2018
Fig. X: Since 2015, AI companies have raised larger investment rounds
Median funding (US$m)
AI premium over software
Software ex AI AI AI premium
0
5
10
15
20
25
0%
10%
20%
30%
40%
50%
60%
70%
80%
Seed Series A Series B Series C
Fig. X: AI companies are raising larger rounds at all stages of maturity
Median funding (US$m)
AI premium over software
Software ex AI AI AI premium

View Slide

Core technology companies attract a disproportionate share of funding
• While core (‘deep tech’, sector-agnostic AI) technology companies comprise a tenth of AI startups,  
they attract a ﬁfth of venture capital investment.
• The cost of world-class talent, extended time to achieve a minimum viable product, and alternative  
revenue models (e.g. licensing agreements) can increase cash requirements.
Core tech attracts a disproportionate share of funding
77
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0%
Core Technology Function Sector
Fig. X: Covers companies founded since 2014
Share of funding
19%
12%
34%
41%
47% 47%
Share of total (number)

View Slide

A greater proportion of AI startups are highly valued
Source: Dealroom.co, MMC Ventures
A greater proportion of AI startups are highly valued (detail – lower values)
Source: Dealroom.co, MMC Ventures
AI companies attract premium valuations
78
• AI companies are securing higher valuations as well as more capital.
• Beyond investment mechanics (larger rounds require higher valuations to avoid excessive dilution), and
industry fundamentals, an imbalance in demand for capital and its supply is increasing valuations.
• With AI entrepreneurship becoming mainstream, this valuation tailwind is likely to reduce.
AI startups Software (ex AI) startups
Proportion of companies
Valuation bracket (€m)
0%
10%
20%
30%
40%
50%
60%
70%
AI startups valuation data 1
0–5m 5m–10m 10m–15m 15m–20m 20m–25mm 25m–50m 50m–100m
AI startups Software (ex AI) startups
Proportion of companies
AI startups valuation data 2
0–1.0m 1.1m–2.0m 2.1m–3.0m 3.1m–4.0m 4.1m–5.0m
0%
10%
20%
30%
40%
50%
60%
Valuation bracket (€m)

View Slide

79
• Recruiting AI talent is challenging. Startups compete with multiple categories of competitors (large
technology companies, banks, professional service ﬁrms and other early stage companies) for a limited
pool of AI practitioners.
Data, talent and productising AI are key challenges
Competition for AI talent, the limited availability of training data, and the diﬃculty
of moving AI from ‘lab to live’ are entrepreneurs’ key challenges.
“Access to talent, and its competitiveness is the 
biggest challenge.”
David Benigson, Signal Media
“London has one of the best pools of talent 
in the world – and is the main reason we’re here.”
Fabio Kuhn, Vortexa

View Slide

80
• Access to initial training data sets poses a challenge. Startups are mitigating the diﬃculty by drawing
on a diverse range of data sources, developing powerful use cases for access to client data, and by
implementing a data acquisition strategy from early in their lives.
“We started collecting data very early in our journey.”
Timo Boldt, Gousto
“It’s a classic chicken and egg problem. Early customers, and thus data,
are hard to get when you don’t have any existing reference clients.”
Tim Sadler, Tessian

View Slide

81
• Developing production-ready AI difficult. Entrepreneurs recommend moving ‘from lab to live’ as soon as
possible, testing development systems on low-risk real-world data. Cross-functional collaboration is key.
“Taking what works well in a lab and getting it to work in a diverse
and sick population is a big challenge.”
Chris McCann, Current Health
“The real world is full of black swans and exceptions. We’ve learned
to overcome them by getting great at cross-functional collaboration
and constant monitoring of risk.”
Timo Boldt, Gousto

View Slide

82
• Our AI Playbook is an accessible, step-by-step guide to taking advantage of AI in your company.
• A blueprint for success with best practices in AI: strategy; data; talent; development; production; 
and regulation & ethics.
Our AI Playbook: your guide to taking advantage of AI
Download the Playbook free at 
mmcventures.com/research

View Slide

5. The implications of AI

View Slide

The implications of AI
AI will have profound implications for companies and societies.
AI will reshape sector value chains, enable new business models
and accelerate cycles of creative destruction.
While oﬀering societies numerous beneﬁts, AI poses risks of 
job displacement, increased inequality and the erosion of trust.

View Slide

AI offers innovation, efficacy, velocity and scalability
Source: MMC Ventures
AI offers innovation, efficacy, velocity and scalability…
85

View Slide

AI will disrupt companies and markets by enabling:
• New market participants
• Shifts in sector value chains
• New business models
• New commercial success factors
• Changes in companies’ competitive positioning
• Shifts in skills and organisational design
• Accelerated cycles of innovation
86
…with signiﬁcant implications for markets

View Slide

• By automating capabilities previously delivered by human professionals, AI will 
reduce the cost and increase the scalability of services.
• Markets including healthcare and ﬁnancial services will see broader global participation.
87
AI will enable new market participants…
• Healthcare example: medical diagnosis is expensive for people in
developed nations and inaccessible to many in developing economies.
• AI-powered automated diagnosis available at negligible marginal cost,
coupled with extensive smartphone penetration, will reduce the cost and
increase access to primary care – enabling an inﬂux of new 
market participants.

View Slide

• AI will change where, and the extent to which, profits are available
within a value chain.
• Value chains in sectors including insurance, legal services and transport
will be reshaped.
88
…and drive shifts in sector value chains
• Transport example: 42% of revenue from global insurance premiums come
from car insurance. As AI-powered autonomous vehicles gain adoption, 
the frequency of car accidents will reduce.
• The profitability of car insurance policies will fall – by as much as 81% 
in the UK (Autonomous Research).
• How will insurers adjust to a collapse in profitability of much of their revenue?
• Multiple sector participants must plan for a profound shift in their sector’s
value chain.
 
42%
of the revenue from
global insurance
premiums comes  
from car insurance.
Source: 
Autonomous Research

View Slide

• AI, growth of ‘x-as-a-service’ consumption, and subscription payment
models will obviate select business models and enable new ones.
• Expect new business models in sectors ranging from healthcare to
transport and insurance.
89
AI will enable new business models…
Transport example: AI will transform the economic fabric of ownership 
and insurance.
• Historically, wasteful private vehicle ownership has been required to provide
spontaneity, convenience, security and privacy.
• On-demand access to a fleet of autonomous vehicles will provide the same 
at a disruptive low cost.
• Expect ‘transport-as-a-service’: pay a fixed, low monthly fee for all-you-can-eat
access to an autonomous fleet - and reduced private vehicle ownership.
• ‘Downstream’ models (of repair centres, charging centres) will also be transformed.
Cars are parked for an
average of
96%
of their lives.
Source: UITP Millennium 
Cities Database

View Slide

New technology paradigms offer significant benefits but demand new competencies.
Winners in the age of AI will develop the new competitive success factors required for AI.
Success factors for the age of AI include the ability to:
• possess the vision to embrace AI and the organisational changes it requires
• leverage large, non-public data sets to train market-leading algorithms
• evaluate the opportunities and risks of sharing training data with partners and competitors
• attract, develop, retain and integrate data scientists
• form effective partnerships with ‘best-of-breed’ third-party AI software providers
• diligence AI partners effectively
• understand and respond to regulatory challenges posed by AI
• effect a mindset shift to software that provides probabilistic instead of binary recommendations
• manage workflow changes resulting from AI implementations
• manage challenges of organisational design and culture as AI augments and replaces personnel
90
…and demand new commercial success factors

View Slide

New platforms, disruptors, leaders and laggards will emerge as AI disrupts companies’ competitive positioning:
Providers of AI:
• Platforms: With vast data sets, world-class AI teams and extensive resources, select GAIM vendors (Google, Amazon,
IBM and Microsoft) will accrue value as platforms that support the provision of AI. GAIM lack the strategic desire or
data advantage to address myriad domain-specific use cases, presenting opportunities for Disruptors.
• Disruptors (early stage, AI-first software companies tackling business problems in a novel way using AI): enable SMBs
and Enterprises who embrace them while eroding the value of those that do not. 
Buyers of AI (SMBs and Enterprises):
• Leaders will emerge in key industries by: anticipating the shifts in value chains and business models caused by AI;
taking advantage of large, proprietary data sets to train AI algorithms; having the organisational ability to deploy AI
effectively; and by having sufficient resources and reputation to attract high quality AI talent.
• Laggards lack the will or organisational ability to embrace AI. While some lack the foresight to adapt, more will falter
due to limited organisational capability.
91
AI will cause shifts in companies’ competitive positioning…

View Slide

• As AI gains adoption the skills that companies seek and companies’ organisational
structure will change.
• Currently, companies’ engagement with data scientists is limited; a mix shift to
employing data scientists is likely.
• Hiring for adaptability will be increasingly important, as the range of tasks supported
or undertaken by AI systems increases.
92
…and drive changes in skills and organisational design
• Professional services example: global professional services and consulting ﬁrms
average 5,000 to 15,000 in-house analytics professional; fewer than 8% are data
scientists (MMC Ventures) .
• Tomorrow’s leaders are aggressively expanding their data science teams; time to
market is key given competitive advantage through data network eﬀects.
41%
of companies are
considering the impact 
of AI on future skill
requirements.
Source: PWC

View Slide

Cycles of innovation, adoption and consumption are compressing
Source: European Environment Agency, based on Kurzweil
AI will accelerate cycles of innovation
93
• By reducing the time required for process-driven work, AI will accelerate the pace of innovation.
• This may compress cycles of creative destruction, reducing further the period of time for which all but a
select number of super-competitors maintain value.
Cycles of innovation, adoption and consumption are compressing
Time before mass use
Long Short
Black and white television 26
Invention available to the general public
1926
1873
1897
1876
1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
1983
1991
1991
1979
1975
1951
Computer 16
Mobile phone 13
Compact disc 12
7
10
World Wide Web
Smartphones
31
Radio
Telephone 35
Electricity 46
Colour television 18
Years neccessary for an invention
to be used by a quarter of the US population
33 years
In 1965, average company 
duration in S&P500.
14 years
By 2026, est. average company 
duration in S&P500.
Source: Insight

View Slide

In addition to numerous benefits, AI presents challenges and risks for societies:
• AI-powered automation may displace jobs
• Biased systems could increase inequality
• Artificial media will undermine trust
• AI offers states greater control and poses trade-offs between privacy and security
• Autonomous weapons may increase conflict between nations
94
To societies, AI presents risks as well as benefits

View Slide

• AI will directly enable the automation of select occupations that involve routine and repetition – from truck-driving
(3.6 million US jobs) to telemarketing.
• AI will augment and then displace workers in some more complex roles, while reducing the need for additional
workers to be hired as companies expand.
• However: analysis of UK census data since 1871 shows: historically, contracting employment in agriculture and
manufacturing (due, in part, to automation) have been more than oﬀset by rapid growth in alternative (caring,
creative, technology and business service) sectors (Deloitte).
• Greater automation of manual and business service roles may concentrate employment further in occupations
resistant to automation – including care work and teaching.
95
AI-powered automation may displace jobs
• Whether or not, over time, AI creates more jobs than it destroys, the short period in which select
workers could be displaced, with a reduction in the availability of similar roles, could prevent
those who lose their jobs being rapidly re-absorbed into the workforce. Social dislocation,
with political consequences, may result.

View Slide

AI-powered facial recognition systems misgender 1% of lighter-
skinned males but 35% of darker- skinned females
Source: J Buolamwini, M.I.T. Media Lab, via The New York Times
96
Biased AI systems could increase inequality
• Theoretically, AI can free decision-making from human bias 
by finding objective patterns in large data sets.
• In practice, AI systems learn by processing training data. 
Available training data reflects systemic historic biases,
particularly regarding gender and race.
• Incomplete or inadequate training data are causing AI systems to
behave problematically, particularly to minorities.
• Example – in a popular data set used to train facial recognition systems, 
75% of faces are male and 80% are white.
• As a result of poor training data, facial recognition systems that offer gender  
classification misgender 1% of lighter-skinned males but up to 7% of 
lighter-skinned females (Buolamwini, Gebru).

View Slide

• AI systems are being used to make a growing range of decisions that have a signiﬁcant impact 
on individuals’ lives (credit, recruitment).
•“If we fail to make ethical and inclusive AI, we risk losing gains made in civil rights and 
gender equity under the guise of machine neutrality.” Joy Buolamwini
There are
potential harms
from algorithmic
decision-making
Source: Megan Smith via gendershades.org
97
INDIVIDUAL HARMS
HIRING
EMPLOYMENT
INSURANCE & SOCIAL BENEFITS
HOUSING
EDUCATION
ILLEGAL DISCRIMINATION UNFAIR PRACTICES
COLLECTIVE
SOCIAL HARMS
CREDIT
DIFFERENTIAL PRICES OF GOODS
ECONOMIC LOSS
LOSS OF LIBERTY
INCREASED SURVEILLANCE
SOCIAL
STIGMATISATION
STEREOTYPE REINFORCEMENT
DIGNITARY HARMS
LOSS OF
OPPORTUNITY
Potential harms from algorithmic decision-making

View Slide

98
To avoid ‘automating inequality’ developers can:
• recognise the challenge, as a starting point for action;
• develop diverse teams that reﬂect the communities they serve;
• create balanced, representative data sets;
• deploy rigorous ethics and testing frameworks for system validation.
There is a battle going on for fairness, inclusion and justice in the digital world.” 
(Darren Walker, via The New York Times).

View Slide

Given video of former President Obama, researchers
synthesised photorealistic , new lip-synched video
Source: Suwajanakorn, Seitz and Kemelmacher-Shlizerman
• Generative Adversarial Networks (‘GANs’) are an emerging AI technique that enable the creation of 
artificial media, including pictures and video, impossible to differentiate from real content.
• GANs will democratise the creation of content, enabling the production of media at scale and low cost.
• GANs also enable the creation of damaging counterfeit media in which individuals appear to perform
actions they have not undertake or speak alternative dialogue.
99
Artificial media may undermine trust – ‘fake news 2.0’
• In addition to damaging individuals, artificial media
will undermine trust. If any media can be
counterfeit, all media is open to challenge.
• In the age of artificial media, society 
will grapple with challenges of truth and trust.
• Adversaries recognise that sowing doubt and
confusion to divide populations can be more powerful
than direct action.

View Slide

• AI-powered facial recognition has unprecedented capability. To what extent
will citizens and governments sacriﬁce anonymity to prevent crime?
• AI with real-time analytics are enabling the high-tech surveillance state
with greater capacity for social control. China intends to combine real-
time recognition with social scoring to inhibit ‘undesirable' behaviour.
100
AI presents new trade-offs and offers greater state control
“You don’t need people’s cooperation for us to be able to recognise 
their identity” (Huang Yongzhen, Watrix, via the Associated Press).
 
A citizen is
captured on 
CCTV an average of
68 times
per day.
Source: Cheshire
Constabulary Camera
Survey

View Slide

• AI-powered computer vision systems, AI-based decision-making algorithms and improved
robotics are enabling humanoid and aerial drones with greater capability and autonomy.
• While the risk of ‘killer robots’ turning against their masters is overstated, less considered is the
possibility that conflict between nations may increase if the human costs of war are lower.
101
Autonomous weapons may increase conflict
A country that thinks twice about sending young people into conflict may be
more adventurous if the only assets in harm’s way are equipment.

View Slide

MMC Ventures is a research-led venture capital ﬁrm.
We’ve backed over 60 early-stage, high growth
technology companies since 2000.
AI is a core area of research and investment. 
We’ve made 20 investments into many of the UK’s 
most promising AI companies.
If you’re an early stage AI company, get in touch 
to see how we can accelerate your journey.
www.stateofai2019.com
Get in touch
102
MMC Ventures Research
David Kelnar: Partner & Head of Research 
Asen Kostadinov, CFA: Research Manager
Explore MMC’s cutting-edge research at
mmcventures.com/research.

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The State of AI 2019: Divergence

The State of AI 2019: Divergence

David Kelnar

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