Who’s Going to Make Money in AI and Machine Learning? We’re currently experiencing an AI gold rush. Billions are being invested. AI startups abound. Google, Amazon, and Microsoft are duking it out for AI supremacy. Corporations are scrambling to ensure they adopt AI ahead of their competitors while looking over their should at startups. The winners will be the picks and shovels of this gold rush. They are the tech giants that have the scale of data, talent, capital and distribution to power the AI revolution. But enterprises stand to create nearly $4 trillion in value from AI by 2022. But to get AI and data science projects out of experimentation and into use across the organisation requires mobilising and engaging the executive team. AI startups that are looking to scale also need to cross the commercial divide from technology to the enterprise.
Decoding Movie Sentiments: Analyzing Reviews with Data Analysis model
2019 London Data Science Festival. Making Money in AI, Machine Learning and Data Science.
1. Who’s going to make
money in AI?
The secrets of successful AI and
data science in the real world
Simon Greenman
Partner, Best Practice AI
@simongreenman
www.bestpractice.ai
2. (c) 2019 Best Practice Artificial Intelligence Ltd
Simon Greenman, Partner Best Practice AI Ltd
2
• BA in Computing & Artificial Intelligence from the University of
Sussex
• Co-founder of the early AI-enabled mapping service
MapQuest.com
• Chief Digital Officer, GM, CEO with over twenty years of
leading digital transformations internationally through
technology, data science and AI
• Highly active in the early stage eco-system as Co-President
of the Harvard Business School Alumni Angels of London, an
advisor at DN Capital, and AI Expert in Residence at
Seedcamp
• Board advisor at Seldon, a ML deployment company
• Member World Economic Forum Center for 4th Industrial
Revolution - AI.
S I M O N G R E E N M A N
Partner, Best Practice AI Ltd
AI Management Consultancy
www.bestpractice.ai
Create Competitive Advantage with AI
2
3. (c) 2019 Best Practice Artificial Intelligence Ltd 3
https://www.forbes.com/sites/artimanmanage
ment/2014/10/28/drone-technology-
investment-bet-on-the-picks-and-
shovels/#7bb7b4cc5313
4. (c) 2019 Best Practice Artificial Intelligence Ltd 4
Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
5. (c) 2019 Best Practice Artificial Intelligence Ltd 5
So. Are you ready for another AI gold rush?
https://www.forbes.com/sites/artimanmanage
ment/2014/10/28/drone-technology-
investment-bet-on-the-picks-and-
shovels/#7bb7b4cc5313
6. (c) 2019 Best Practice Artificial Intelligence Ltd 6
AI is a story of many boom and busts
Within a generation…I am
convinced…the problems of
creating artificial intelligence
will be substantially solved.
“
M a r v i n M i n s k y
M I T
Within our lifetime machines
may surpass humans in
general intelligence
“
1961 1967
7. (c) 2019 Best Practice Artificial Intelligence Ltd 7
But is it different now? The power of exponential!
ESTIMATE OF AMOUNT OF DATA BYTES
THAT WILL BE CREATED EVERY SECOND
BY EVERY PERSON ON THE PLANET IN 2020
1 , 7 0 0 , 0 0 0
B Y T E S / S E C
1. Data
THE SPEED OF A CAR TODAY IF ITS
SPEED HAD IMPROVED AT THE SAME
RATE AS COMPUTATIONAL CHIPS
SINCE 1971
4 2 0 , 0 0 0 , 0 0 0
M P H
2. Comp Power
THE COST OF MANY OF THE AI
FRAMEWORKS, LIBRARY AND
TOOLS
$ 0
3. Software
THE COST OF MANY OF THE AI
FRAMEWORKS, LIBRARY AND
TOOLS
$ 0
8. (c) 2019 Best Practice Artificial Intelligence Ltd
Your are the champions of change
Bringing AI and data science to the enterprise
and society
8
10. (c) 2019 Best Practice Artificial Intelligence Ltd 10
Enterprise
Solutions
4
Chips &
hardware
1
Platform &
infrastructure
2
Vertical
Industry
Solutions
5
Corporates6
Healthcare & Life
Sciences
Finance &
Insurance
Agriculture Automotive Legal &
Compliance
Industrials, Robotics
& Logistics
Frameworks &
algorithms
3
Nations7
* Excludes SMB sectors. The companies noted are
representative of larger players in each category but in no
way is this list intended to be comprehensive or predictive.
** Acquired by Cisco and Google respectively.
Conversational
agents**
VisionCore
Algorithms
NLP &
Semantics
Speech
There is a huge race going on in AI from the chip makers to enterprise
software providers to corporates to countries with $billions being invested
AutomotiveFinance &
Insurance
Healthcare Agriculture Legal &
Compliance
Industrials, Retail, media,
other
Tech & Telco
Customer
Management
Intelligence &
Analytics
CybersecurityMarketing &
Sales
HR & Talent RPA,
Other
ConsultantsTools
Sources: CBInsights,
Crunch Base, and
misc. others
AI is going to be ubiquitous and woven into the fabric of society and organisations globally
11. (c) 2019 Best Practice Artificial Intelligence Ltd 11
Chips &
hardware
1
Platform &
infrastructure
2
* Excludes SMB sectors. The companies noted are
representative of larger players in each category but in no
way is this list intended to be comprehensive or predictive.
** Acquired by Cisco and Google respectively.
Frameworks &
algorithms
3
Conversational
agents**
VisionCore
Algorithms
NLP &
Semantics
Speech
The picks and shovels of this gold rush will be the tech giants
Sources: CBInsights,
Crunch Base, and
misc. others
“This Tech Company May Be Near a
‘Tipping Point’ in Dominating Artificial
Intelligence.” Barron’s on Nvidia Sep 30th
2018
1 2
“There are 1.2 million
developers using our cognitive
services while 300,000 use
conversational AI.”
Microsoft’s GM of AI, David
Carmona
3
The providers of chips, platform and the frameworks / algorithms will power everyone who is
looking to find gold with AI services regardless of who finds it
12. (c) 2019 Best Practice Artificial Intelligence Ltd 12
* Excludes SMB sectors. The companies noted are
representative of larger players in each category but in no
way is this list intended to be comprehensive or predictive.
** Acquired by Cisco and Google respectively.
The AI enterprise & vertical solution provider race is intense
Sources: CBInsights,
Crunch Base, and
misc. others
Enterprise
Solutions
4
Vertical
Industry
solutions
5
Healthcare & Life
Sciences
Finance &
Insurance
Agriculture Automotive Legal &
Compliance
Industrials, Robotics
& Logistics
$156M
$25M
$93M
$137M
$448M
$104M
$230M
$108M
<
$202M
$1000M
$217M
$95M
$77M
$182M
“Salesforce Strengthens Its
AI Capabilities With an $800
Million Purchase [of
Datorama],” July 16th 2018
“SAP Acquires Recast.AI
to Accelerate Natural
Language Processing
Capabilities,” 22nd Jan
2018.
$557M
Customer
Management
Intelligence &
Analytics
CybersecurityMarketing &
Sales
HR & Talent RPA,
Other
ConsultantsTools
$147M
The winners will be the incumbents and those start-ups who gain category leadership with scale
of customers, data, capital and talent
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* https://towardsdatascience.com/the-secrets-of-successful-
ai-startups-whos-making-money-in-ai-part-ii-207fea92a8d5
But watch out for fool’s gold
• MMC Ventures noted 40% of 2,830 so
called AI startups in Europe are not really
using AI
“Tech companies benefit from the
perception that they’ve built sophisticated
automation and A.I., rather than a system
that relies on manual labor.
Nearly every startup today claims to be an AI powered company
15. (c) 2019 Best Practice Artificial Intelligence Ltd 15
https://www.forbes.com/sites/alexknapp/2018/04/25/gart
ner-estimates-ai-business-value-to-reach-nearly-4-
Gartner Research predicts AI-derived business value will reach up to $3.9
trillion by 2022
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Source: Capgemini Digital Transformation Institute, State of AI survey, N=993 companies that are implementing AI, June 2017
https://www.capgemini.com/gb-en/wp-content/uploads/sites/3/2017/09/dti-ai-report_final1-1.pdf
Revenue
75% of
organisations
implementing AI
increase sales of
products and
services by more
than 10%
Deeper
Insights
79% of
organisations
implementing AI
generate new
insights and
better analysis
There are four main business benefits of AI
Customer
Engagement
75% of
organisations
using AI enhance
customer
satisfaction by
more than 10%
Automation
& Operational
Efficiency
78% of
organisations
implementing AI
increase
operational
efficiency by more
than 10%
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To help leaders better understand the value of AI, Best Practice AI
created the world’s largest AI use case and case study library
Free at www.bestpractice.ai
6 0 0 +
Use Cases
1 0 0 0 +
Case Studies
6 0 +
Territories
3 0 0 0 +
Vendors
U s e
c a s e
KPIs
Technology
Data
Vendors
C a s e
s t u d y
Geography
Players
Impact
Data
Vendors
M e m b e r o f
W o r l d E c o n o m i c F o r u m A I p r o g r a m &
U K A l l P a r l i a m e n t a r y G r o u p o n A I
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In node graph format - functions and use cases
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Most case studies contain details on the reported project details,
ROI, data and technology
20. (c) 2019 Best Practice Artificial Intelligence Ltd
We created a heat map of where AI technology can be used to
create value today
Predict future customer demand - Help screen CVs - Optimise supply
chain purchasing - Reduce cyber risks - Improve customer service -
Automate data entry with RPA - Better market and engage prospective
customers - Improve product offerings - Predict customer churn -
Score top customer prospects
20
21. (c) 2019 Best Practice Artificial Intelligence Ltd
There is also an AI arms race
across nations
21https://www.gartner.com/technology/pressRoom.do?id=3872933
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Countries are vying for AI supremacy
https://www.prnewswire.com/news-releases/london-tech-week---london-is-named-artificial-intelligence-ai-capital-of-europe-by-new-report-
685101231.html
https://medium.com/creation-being-human/who-will-win-the-ai-arms-race-187ac2ae8927
• France announces a $1.85B AI investment over the next five and that foreign take over
of AI companies will require government approval
• UK announces a $1.3B corporate and government AI investment
• Europe announces a $22B AI investment
• And then the Chinese city of Tianjin announces it is setting up a $16B AI fund
• China has developed the ‘Big Fund’ estimated total investment at ~$140B to grow their
semiconductor industry.
China has an explicit goal developed at the highest level of government to make itself
the global leader in AI by the year 2030 with more favourable structural environment
including looser data and privacy regulation
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“China is the Saudi Arabia in data” Kai Fu-Lee
https://www.slideshare.net/AIFrontiers/kaifu-lee-at-ai-frontiers-the-era-of-artificial-intelligence?from_action=save
Watch China closely in the battle for AI supremacy
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To learn more search for this article
25. (c) 2019 Best Practice Artificial Intelligence Ltd 25
Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
26. (c) 2019 Best Practice Artificial Intelligence Ltd
Achieving real business outcomes
from AI and data science
26https://www.gartner.com/technology/pressRoom.do?id=3872933
27. (c) 2019 Best Practice Artificial Intelligence Ltd 27
Getting to POC is the tip of the AI transformation iceberg
Above the waterline proving AI works requires:
• AI talent
• Data wrangling and training set labelling
• Frameworks and algorithms
• Compute access
• Project support management including subject
matter expertise.
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Below the waterline much more needs to be done to capture value
Biggest challenge faced by organisations operationalising AI:
1.Lack of results and efficacy - ROI
2.Lack of technology maturity especially chatbots
3.Change management – getting humans workers
comfortable and educated on AI solutions
4.Business process redesign - how is AI integrated with
humans in business processes
5.Technology integration and scale-up – difficult to
integrate cognitive projects with existing processes and
systems
6.Technology deployment - difficult to deploy and update
the technology in production.
h t t p s : / / b i t . l y / 2 F F G F y T
If the business is not bought in then technology will
struggle to deliver
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People challenges are reported as the greatest barrier to becoming data-
driven organisation
2018 New Vantage Partners survey of executives including Bank of America, Goldman Sachs, Capital
One, Morgan Stanley, UBS, Travelers
2 9
h t t p : / / n e w v a n t a g e . c o m / w p - c o n t e n t / u p l o a d s / 2 0 1 8 / 0 2 / B i g - D a t a - E x e c u t i v e - S u r v e y - 2 0 1 8 - F i n d i n g s . p d f
“58% of executives cite insufficient organisational
alignment or cultural resistance as the biggest barrier
to business adoption of data driven organisation
AI and data science is a team sport
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Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Agree a Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
31. (c) 2019 Best Practice Artificial Intelligence Ltd 31
Find strong use cases that will resonate with executives
For example fraudulent activity using unusual payment transaction patterns and other
data
HSBC automated its anti-money
laundering (AML) investigations to
increase efficiency and
effectiveness in its regulatory
compliance. Historically AML was
carried out by humans but the bank
has turned to Ayasdi and its
machine learning software to
monitor transactions and automate
identification of potential criminal
activity.
https://www.bestpractice.ai/studies/hsbc_reduces_false_pos
itives_for_money_laundering_detection_by_20_using_ai_to
_automate_the_system_rules
Danish Danske Bank had a problem
with false positives in its payment
fraud detection that was
approaching 99.5%. Using
advanced machine learning they
were able to increases fraud
detection in real-time by 60% and
reduce false positives by 50%.
https://www.bestpractice.ai/studies/danish_danske_bank_increases
_payment_fraud_detection_by_60_and_reduces_false_positives_b
y_50_with_machine_learning
OCBC bank of Singapore analyses
transaction activity to identify unusual
payments that might be financial
crime. In a pilot OCBC was able to
reduce the number of false positives
by 35% by using machine learning.
They were also able to classify
transaction alerts into 48 unique risk
clusters allowing the compliance
team to better prioritise based on risk.
https://www.bestpractice.ai/studies/ocbc_bank_reduces_nu
mber_of_false_positive_financial_transaction_alerts_by_35_
with_machine_learning
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Find out what your competition is doing
McKinsey found the largest motivator for a European company to adopt AI is competition
McKinsey Global Institute - Notes from the AI frontier
Tackling Europe’s gap
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Ensure you quantify the potential business benefits
AI delivers different potential benefits by function. Manufacturing, Operations and Supply
Chain offer significant cost saving potential, Marketing and sales revenue.
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Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science project success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
35. (c) 2019 Best Practice Artificial Intelligence Ltd 35
The challenge of senior management understanding and buy-in
Source:McKinsey, June 2017;Deloitte, May 2018
“41 percent of the 3,000
executives surveyed in a
recent McKinsey Global
Institute study admitted
they’ve yet to adopt AI
because they’re unsure about
how it can help their
organisation.”
“Half of respondents do not
believe their organisation’s
leadership has a clear
understanding of AI”
Deloitte UK survey 2018
36. (c) 2019 Best Practice Artificial Intelligence Ltd
But what is AI?
36
You need to educate, excite but set expectations
with your executives
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Deep neural networks are being used for unfathomably complex statistical pattern recognition
The big breakthrough in AI - deep learning (ML)
https://www.youtube.com/watch?v=3JQ3hYko51Y
#1 AI is Data Science on steroids
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#2 AI is Sensing and Cognition
* Thomas Davenport, AI Advantage,
https://mitpress.mit.edu/books/ai-advantage**
Diagnose diseases from MRIs
Identify persons with facial recognition
Recognise spoken words
Analyse text of legal documents
Identify defects on production line
Improve customer services with bots
Seeing, Hearing, Reading, Understanding, Reasoning
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#3 AI is Creativity
* Thomas Davenport, AI Advantage,
https://mitpress.mit.edu/books/ai-advantage**
https://www.theverge.com/2017/10/30/16569402/ai-generate-fake-faces-celebs-nvidia-gan
The era of automated content generation…and deep fakes
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#4 AI is Robotics
* https://www.youtube.com/watch?v=g0TaYhjpOfo
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#5 AI is Robotic Process Automation (RPA)*
https://youtu.be/FV8lM9SIFQ8
* maybe
RPA allows anyone today to configure computer software, or a “robot” to emulate and integrate the
actions of a human interacting within digital systems to execute a business process.
42. (c) 2019 Best Practice Artificial Intelligence Ltd 42
Management can set frothy expectations…
"I would actually welcome a correction in public
opinion about what AI can and cannot do. This
has happened to me multiple times, where I
would listen to a CEO on stage make an
announcement about what their company is
doing with AI, and then 20 minutes later I’d talk
to one of their engineers, and they’d say, “No,
we’re not doing that, and we have no idea how
to do it.”
Andrew Ng, ex-Google Brain, ex-Baidu
quoted in the WSJ
43. (c) 2019 Best Practice Artificial Intelligence LtdSource: https://arxiv.org/pdf/1811.11553.pdf
Set expectations - AI is far from “solved”
School Bus
100%
Garbage Truck
99%
Punching Bag
100%
Snow Plough
92%
What happens when AI fails?
There are many legal and ethical issues around AI failures, such as autonomous vehicle crashes
43
For example deep learning has no human like concept of objects
44. (c) 2019 Best Practice Artificial Intelligence Ltd 44
Skills remain a key challenge for projects
Survey of O’Reilly subscribers
45. (c) 2019 Best Practice Artificial Intelligence Ltd 45
Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
46. (c) 2019 Best Practice Artificial Intelligence Ltd 4 6
Big trends we are seeing in ML - data science tech
1. Cloud-native - Serverless, containerisation
and microservices
2. Automation of data science tasks with
Gartner Research estimating that by 2020
more than 40% of data science tasks will be
automated
3. Rise of ML as a platform and service model
4. The introduction of AutoML - a system for
automatically searching and discovering model
configurations (algorithm, feature sets, hyper-
parameter values, etc.) and automatically build
the production data pipelines to generate the
features and labels
5. NLP and conversational analytics
6. Explainable AI
7. Increasing control and empowerment of
data science with the business.
h t t p s : / / a i - a u d i t i n g f r a m e w o r k . b l o g s p o t . c o m / 2 0 1 9 / 0 3 / a n - o v e r v i e w - o f - a u d i t i n g - f r a m e w o r k - f o r _ 2 6 . h t m l
Example of an ML platform to
help manage end-to-end
workflow (Algorithmia)
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Start-up experience of enterprises can be challenging
Start-ups often underestimate corporate complexity
h t t p s : / / a i - a u d i t i n g f r a m e w o r k . b l o g s p o t . c o m / 2 0 1 9 / 0 3 / a n - o v e r v i e w - o f - a u d i t i n g - f r a m e w o r k - f o r _ 2 6 . h t m l
Speed is an issue
• Why cannot they move as fast as us?
• How long to get sign-off? Contract finalized? Payments made?
Decisions feel needlessly complicated
• Why cannot things be agreed on the spot?
• How many committees / stakeholders do we need to respond to?
• Did we not answer all those questions from compliance 3 months ago?
Corporate ability to implement a challenge
• How many systems do we need to interface with?
• Do we need to work through these internal resources (who are over-worked already)?
• How much communication do we need to do?
Resource imbalance
• There is only so long that the CEO / CTO / key data scientist can be focused in one place
• One-off implementation costs (e.g. legal fees) can be a real challenge
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Procurement frameworks are being created to help
ensure effective and responsible AI supplier solutions
For example the WEF is working with UK Government on a new framework
Example: The Institute for Ethical AI & Machine Learning
49. (c) 2019 Best Practice Artificial Intelligence Ltd 49
Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
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Scaling and managing ML models in production is really hard
Data scientists are facing many roadblocks spending much of their time on infrastructure,
deployment and data engineering across the ML end-to-end workflow
h t t p s : / / b i t . l y / 2 F F G F y T
“38% report difficulty in deploying models to the
needed scale as result of resource challenges in
DevOps, lack of infrastructure, etc
“30% report challenges in supporting
different programming languages and
frameworks”
“30% report challenges in model management
tasks such as versioning and reproducibility”
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Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
52. (c) 2019 Best Practice Artificial Intelligence Ltd 52
Much of AI Ethics is not really about the AI
Data issues reflecting broader societal issues such as mirroring our language
Engineering
IncentivesStakeholders
Bluff / crooks
Ethics
Mirror
Uber crash
Tesla automation
IBM Watson healthcare
Boston bus
Job automation
Google Duplex calls
Admiral Insurance
Cadillac Fairview
facial recognition
FB newsfeeds
You Tube video
ranking
Amazon HR
Microsoft’s Tay
US judicial system
Cambridge Analytica
“20% of AI start-ups
have no AI”
Did the
technology
go wrong?
Yes
No User issues
Builder
issues
Were
Human
motives
wrong?
No
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Multiple Ethical AI frameworks being created
Over 30 such examples by our recent count
Example: The Institute for Ethical AI & Machine Learning
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And the regulators are stepping into the fray
The ICO just announced a draft AI audit framework for comment
Under GDPR today there are clauses that are
relevant to the use of AI. For example:
• Article 22(1) of the GDPR limits the
circumstances in which you can make solely
automated decisions, including those based
on profiling, that have a legal or similarly
significant effect on individuals. (e.g.
employment and loan decisions)
• Decisions need to be explainable and
individuals have the right to opt out.
The ICO has just released this AI audit
framework for comment. Designed to:
• Support the work of the ICO investigation
and assurance teams to assess data
controller compliance
• Help guide organisations on the
management of data protection risks arising
from AI applications
h t t p s : / / a i - a u d i t i n g f r a m e w o r k . b l o g s p o t . c o m / 2 0 1 9 / 0 3 / a n - o v e r v i e w - o f - a u d i t i n g - f r a m e w o r k - f o r _ 2 6 . h t m l
h t t p s : / / i c o . o r g . u k / f o r - o r g a n i s a t i o n s / g u i d e - t o - d a t a - p r o t e c t i o n / g u i d e - t o - t h e - g e n e r a l - d a t a - p r o t e c t i o n -
r e g u l a t i o n - g d p r / a u t o m a t e d - d e c i s i o n - m a k i n g - a n d - p r o f i l i n g / w h a t - d o e s - t h e - g d p r - s a y - a b o u t -
a u t o m a t e d - d e c i s i o n - m a k i n g - a n d - p r o f i l i n g /
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Successful AI-driven projects needs more than just data science
AI & data science requires alignment of six factors to ensure it realises
its value across an operation
Have the
right plan
Mobilise
the right
people
Have the
data
Deploy
the right
tech
Make it
operat-
ional
Manage
the risks
Agree project
ownership +
governance /
oversight
Ensure domain
expertise and
cross-functional
buy-in in place
Ensure skilled
team in place –
whether
internal or
vendor
Plan how to get
the right data at
right stage of
preparation
(e.g. labeled
and complete)
Consider what
bias issues
potentially
faced
Ensure relevant
regulations
complied with
Plan approach,
testing and
refinement of
key
algorithm(s)
Ensure right IT
architectural
stack in place
Check impact of
tech choices on
broader tech
stack
Decide how
prediction will
lead to action
(degree of
automation)
Ensure
measurement
and feedback
loops in place
Map and
manage
barriers to
deployment
Ensure key
staff bought in
Identify
potential
regulatory /
legal issues (eg
GDPR)
Map
stakeholder set
and prepare
clear comms
plan
Ensure
alignment and
information to
empowered
’ethics’
governance
Be very clear
what you want
to achieve
Understand
what you need
to predict /
optimise – and
levels of
confidence
required
Agreed metrics
that measure
success
1 2 3 4 5 6
56. (c) 2019 Best Practice Artificial Intelligence Ltd 56
Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science
57. Can your startup cross the commercial divide from
technology to the enterprise?
58. (c) 2019 Best Practice Artificial Intelligence Ltd 58
Raising money - AI is a different beast for most UK investors
VCs are not that comfortable evaluating AI opportunities but are getting better
The past five years of VC investing has really been focused on B2C marketplaces, commence and mobile
AI investment opportunities are harder to evaluate. Why?
• They are often based on academics and their research which is a DNA anathema to VCs
• They are often“deep tech” and it is hard to find real experts to opine
• Go-to-Market (GTM) is enterprise and not consumer digital marketing funnel
• The market dynamics are evolving very quickly
• Massive talent war
• Scale is critical to AI startups and data scale is often with incumbent corporates
• Long product development cycles with hard to measure progress.
59. (c) 2019 Best Practice Artificial Intelligence Ltd 59
Make it easier for VCs to evaluate your AI startup
1.What is the business use case you are solving?
2.Is it a valuable use case?
3.Can your AI company be a global leader and not be squashed by tech giants?
4.Do you have unique domain knowledge?
5.Do you have access to unique, large and quality data sets?
6.Do you have access to AI talent?
7.Do you have a unique and proprietary technology? Why is it good?
But make sure your VCs have patience and are smart about deep tech investment profiles.
60. (c) 2019 Best Practice Artificial Intelligence Ltd 60
The foundations of most AI startups
(1)
Get Data
(2)
Get
Talent
(3)
Get
Domain
Expertise
61. (c) 2019 Best Practice Artificial Intelligence Ltd 61
Cross the AI commercial divide from technology to the enterprise
Six key factors for success:
1. Don’t move too fast and break things; embrace responsible AI
2. Solve really high value use cases, not nice to haves
3. Master B2B enterprise sales and learn calculated patience
4. Translate AI for the real world
5. Lower the barriers to a trial
6. Technical founders need to hire business people.
62. (c) 2019 Best Practice Artificial Intelligence Ltd 62
To learn more search for this article
63. (c) 2019 Best Practice Artificial Intelligence Ltd
AI Management Consultants
Helping you create competitive
advantage with AI
Simon @ bestpractice. Ai
+44 7824 557979
63
www.bestpractice.ai
@simongreenman
Notas do Editor
1960s mathematical symbolic AI hype followed by 1970s bust
1980s expert systems hype followed by 1990s and 2000s bust
2010s AI is back!
AI - no one is quite sure what is AI. How real is it?
They are painting their own picture on blank canvas
From the media we have a dystopian view - 30-40% job loss, existential threat, biased AI hiring,
Others a more utopian view that AI will free us from all the mundane and boring in the job; and we will no longer need to drive as autonomous vehicles will free our time and clear the city streets
CEOs say their companies will transform from AI, then you talk to the practioncers and they say “no idea”
So what is AI. AI has been around for a long time.
So where is the value being created with AI?
As I started thinking about who was going to make money in AI I ended up with seven questions. Who will make money across the (1) chip makers, (2) platform and infrastructure providers, (3) enabling models and algorithm providers, (4) enterprise solution providers, (5) industry vertical solution providers, (6) corporate users of AI and (7) nations? While there are many ways to skin the cat of the AI landscape, hopefully below provides a useful explanatory framework — a value chain of sorts. The companies noted are representative of larger players in each category but in no way is this list intended to be comprehensive or predictive.
So where is the value being created with AI?
As I started thinking about who was going to make money in AI I ended up with seven questions. Who will make money across the (1) chip makers, (2) platform and infrastructure providers, (3) enabling models and algorithm providers, (4) enterprise solution providers, (5) industry vertical solution providers, (6) corporate users of AI and (7) nations? While there are many ways to skin the cat of the AI landscape, hopefully below provides a useful explanatory framework — a value chain of sorts. The companies noted are representative of larger players in each category but in no way is this list intended to be comprehensive or predictive.
“ThDigitalGenius raised $25M including from Salesforce
Ziprecruiters raises $156M to build AI and ML tools for recruitment
HireVue raised $93M to accelerate video interviewing of candidatesUiPath, a RPA, has raised $400M to automate many data entry tasks
DigitalReasoning has raised $104M to provide corporate intelligence
DarkTrace has raised $230M to help guard against cyber threats
Tools company Petuum has raised $100M to accelerate enterprise AI
And consultancies proliferate
ZestFinance has raised nearly $217M to help improve credit decision making that will provide fair and transparent credit to everyone
Affirm, offers loans to consumers at the point of sale, and has raised $720M
Babylon health has raised over $57M
Drive.ai has raised over $77M
Benson-Hill has raised over $95M
Anki has raised over $182M
And the cheque books go on and on…
“Announcing new AI and mixed reality business applications for Microsoft Dynamics [Sales and Customer Service]”, Microsoft Sep 18th, 2018
“Salesforce Strengthens Its AI Capabilities With an $800 Million Purchase [of Datorama],” July 16th 2018
“SAP Acquires Recast.AI to Accelerate Natural Language Processing Capabilities,” 22nd Jan 2018.
“Google announces a suite of updates to its contact center tools,” July 2018.
“Google acquires AI customer service startup Onward,” October 2nd 2018.
“ThDigitalGenius raised $25M including from Salesforce
Ziprecruiters raises $156M to build AI and ML tools for recruitment
HireVue raised $93M to accelerate video interviewing of candidatesUiPath, a RPA, has raised $400M to automate many data entry tasks
DigitalReasoning has raised $104M to provide corporate intelligence
DarkTrace has raised $230M to help guard against cyber threats
Tools company Petuum has raised $100M to accelerate enterprise AI
And consultancies proliferate
ZestFinance has raised nearly $217M to help improve credit decision making that will provide fair and transparent credit to everyone
Affirm, offers loans to consumers at the point of sale, and has raised $720M
Babylon health has raised over $57M
Drive.ai has raised over $77M
Benson-Hill has raised over $95M
Anki has raised over $182M
And the cheque books go on and on…
“Announcing new AI and mixed reality business applications for Microsoft Dynamics [Sales and Customer Service]”, Microsoft Sep 18th, 2018
“Salesforce Strengthens Its AI Capabilities With an $800 Million Purchase [of Datorama],” July 16th 2018
“SAP Acquires Recast.AI to Accelerate Natural Language Processing Capabilities,” 22nd Jan 2018.
“Google announces a suite of updates to its contact center tools,” July 2018.
“Google acquires AI customer service startup Onward,” October 2nd 2018.
AI - no one is quite sure what is AI. How real is it?
They are painting their own picture on blank canvas
From the media we have a dystopian view - 30-40% job loss, existential threat, biased AI hiring,
Others a more utopian view that AI will free us from all the mundane and boring in the job; and we will no longer need to drive as autonomous vehicles will free our time and clear the city streets
CEOs say their companies will transform from AI, then you talk to the practioncers and they say “no idea”
So what is AI. AI has been around for a long time.
But the big breakthrough over the past five years has been deep learning where machines teach themselves based on training set
Deep neural networks are being used for unfathomably complex statistical pattern recognition
This is a beautiful illustration of that - you SHOW as INPUT an object as a digit 0 - 9
And then inside the machine there are layers upon layers of neurons / nodes that are interconnected (like the brain)
As it sees the digit activates different neurons and connections that fire together resulting in the OUTPUT a recognition of a digit
Now imagine this in thousands, 10Ks, 100Ks, 1M, 1B interconnections and something magic happens
So really what AI has become is DATA SCIENCE on steroids - statistics and pattern recognition on steroids
And it being used to PREDICT customer churn, OPTIMISE ad portoflio, IDENTIFY ad types, ANALYSE ROI, EVALUATE presence
AI is also Sensing and Cognition
These are CAPABILITIES often associated with humans and animals - seeing, hearing, reading, understanding and reasoning
Look at this autonomous vehicle - as drive down streets it needs to “see” the objects around it - trees, sidewalk, center of the road, other cars, pedestrations, stop signs
It has to talk all of this information and decide what to do - speed up, break, stop, turn left, avoid hitting a dog…
No wonder Tim Cook, Apple CEO, calls this the “mother of AI problems”
Don’t believe the hype that are cars are going anywhere soon
Another set of AI technologies give rise to creativity
How about automatic generation of ad copy, banner ads, content marketing, etc
But this is also giving rise to deep fakes
This is a GAN trying to create life-like faces - it is actually in a massive battle over time between two deep nerual networks. One that generates a face and one that tries to determine if it is fake or not. Do this millions of times and you come up with some pretty realistic faces.
Lots of big societal issues to come here.
And another set of AI technologies is robotics…
I’ll let the video speak for itself…
And another set of AI technologies is robotics…
I’ll let the video speak for itself…
However don’t let anyone tell you that all the problems have been solved - we are so far from it
There is a school of thought that we need to start again on deep neural networks -