SlideShare a Scribd company logo
COMMERCIALIZATION OF AI 3.0
WHITEPAPER
OVERVIEW
ARTIFICIAL INTELLIGENCE - BUZZWORD OR GAME CHANGER?
THE BASICS
Where does AI come from, where does it go?
FACTS & FIGURES
Round-up of science, applied AI and talent
COMMERCIAL VALUE
Strategies of B2B platforms and practical action points
2
3
9
18
About this whitepaper
Artificial Intelligence is trendy. Every event, every strategy meeting and every
consulting firm talks about it. This whitepaper aims to separate actual facts
and important background information from the overarching marketing buzz.
You will get a short but information-rich wrap up about: What causes the
current hype? Where are we today? What are the innovation leaders doing
with AI? And what are immediate action points to focus on by applying
artificial intelligence in your business.
WHERE CAN TECHNOLOGY TAKE
YOU?
Appanion in a nutshell
We provide high-quality insights on real-world technology-driven business
applications and help you as innovation partner with actionable strategies,
technology knowhow and business model competence.
I hope you can take away some
valuable insights and new ideas
to improve your business!
Tobias Bohnhoff
Founder
3
THE BASICS
INTRODUCTION
ARTIFICIAL INTELLIGENCE IS CAPABLE OF FAR MORE
THAN JUST PLAYING CHESS, JEOPARDY OR GO
Artificial Intelligence (AI) is the ability of a
computer-controlled entity to perform
cognitive tasks and react flexibly to its environment
in order to maximize the probability of achieving
a particular goal.
The system can learn from experience data,
and can mimic behaviors associated to humans,
but does therefore not necessarily use
methods that are biologically observable.
Definition is based on McKinsey, Stanford University, Wikipedia, European Commission 4
„
“
Artificial General Intelligence (AGI)
Artificial Intelligence (AI) can be
distinguished into systems designed
to solve problems in a very specific
context – the so called Narrow AI –
and a human-like Artificial General
Intelligence (AGI).
AGI is currently only a future
scenario. Some experts say it is
decades away to build such an AI,
some argue that humans will never
be able to build something at an
equal level of intelligence.
Most often, AGI scenarios outline a
dystopia for the human race due to
the uncontrollable effects this
technology potentially has.
Thought leaders in the AI space
assume that at some tipping point an
AGI would act without any
interventions of humans and that
setting up a global governance of AI
development is critical right now.
Artificial Narrow Intelligence (ANI)
Narrow AI on the other hand is
already here. It is automating or
augmenting many tasks in a business
context or in our private life.
Self-driving forklifts in warehouses,
Amazon product recommendations,
real-time trades on stock exchanges,
interacting with Siri, person
recognition in the iPhone photo
album – everything is already done
automatically, without the need of
human intervention.
It’s worth mentioning that
automation per se does not require
intelligence. This can also be done by
pre-programmed rules. However,
complex tasks require the ability to
react flexibly to their environment,
which differentiates AI applications
from the well-understood domain of
rule-based automation.
AI IS ENTERING MASS COMMERCIALIZATION - EVERY
BUSINESS IS GOING TO CHANGE
Startup perspective
Artificial Intelligence promises
disruption. It opens up the possibility
to achieve better product or service
quality at less time and cost. And if
this can be proven to the market
successfully, it will change everything.
Customers start to jump from legacy
solutions to better products in a very
short timeframe, others follow to
adopt, old solution providers die and
the new players overtake the market
lead. That is what we call disruption
and what makes AI as a space so
attractive for the many young
companies in the recent years.
Investor perspective
Platform and as-a-service business
models have been proven very
successful due to recurring revenues
and scalability. To investors, owning a
new market and earning high
margins means to be fast and to take
a lot of financial risk. As VC funding
data shows, the race for number one
in AI has just taken off. At some point,
the market will start to consolidate
and the winning companies will
emerge.
Corporate perspective
Almost all large corporations are
confronted with entirely new
processes through AI. Its value lies in
automation and augmentation of
human work. The technology not only
changes the frontend or digitizes the
communication interface, it changes
the entire way of collaborating,
planning and decision-making.
The flexibility to change a – to this
point – successful concept, is crucial
to survive. Starting early experiments,
communicating change and investing
into research and talent is the way
forward.
1) Source: CB Insights (02/2018)
2) Source: CB Insights (01/2018)
3) Source: AIIndex.org (11/2017)
4) Source: Technews.io (09/2018)
5
20152013 2014 2016 2017
1.739
4.5693.477
6.255
15.242
+72%
2020
10
20101995 20152000 2005
0
5
15
20
+12%
Venture Funding in AI startups
In million U.S. dollars, global1
AI related research publications
In thousands, global3
Number of AI press mentions
Original media coverage by tech journalists4
20152014 2016
8.772
2017 2018
42.973
14.670
90.752
125.462
+94%
Startups with „.ai“ URL suffix
Global # of startups that received financing2
5 22
51
100
225
201720162013 2014 2015
+159%
Compound annual growth rate Compound annual growth rate
Compound annual growth rateCompound annual growth rate
THE AI HISTORY IS IMPORTANT TO UNDERSTAND
WHERE WE ARE TODAY AND WHAT IS DIFFERENT
AI 1.0 – Pattern Recognition
Since the term ‚AI‘ was established in
in 1956, government agencies like
DARPA1 funded scientific research
until the mid-1970s. Successes in the
fields of search, natural language
processing and simplified models and
simulations were achieved.
The tremendous expectations raised
by AI researchers afterwards didn‘t
match the achievements for the
investors. Limited computing power,
the rather low performance of neural
networks, combinatorial explosion
and a lack of commonsense
knowledge by the systems led to
significant cuts in funding. The first AI
winter lasted until 1980.
In the 1980s, the expert system
emerged. It used logical rules, derived
from the knowledge of domain
experts. Avoiding the challenges of
commonsense logic, funding came
back until 1987. The rise of IBM‘s and
Apple‘s desktop computers made
specialized AI hardware obsolete. As
a consequence, markets went down
and trust was gone – the second AI
winter.
AI 2.0 – Deep Learning
In the mid-1990s milestones were
achieved that brought back trust.
Chess champion Garry Kasparov was
beaten by ‘Deep Blue’ in 1997.
Intelligent agents that perceive the
environment and take actions to
maximize successful outcomes were
the leading paradigm to that time.
In addition, the deep learning concept
was developed, but it took another 10
years until the availability of big data
and computational power from
graphic processing units (GPU)
created the „big bang“ of deep
learning algorithms.
1) DARPA = Defense Advanced Research Projects Agency (Part of the United States Department of Defense) 6
AIPERFORMANCE
TIME
HIGHLOW
AI 1.0
Pattern
Recognition
AI 2.0
Deep
Learning
AI 3.0
Contextual
Reasoning
AI 3.0 IS COMING - BUT IT IS NOT
EQUIVALENT TO SUPERINTELLIGENCE
AI 3.0 – Contextual Reasoning
AI 2.0 is slowly fading out as new
breakthroughs in scientific and
applied AI demonstrate the
superiority of computer intelligence
compared to human intelligence in
selected domains.
It started with chess, continued with
Jeopardy, Go and is now at the point
of beating humans in collaborative
multiplayer games like Dota.
At the same time medical diagnostics
surpasses the accuracy of human
doctors in many domains such as
cancer detection from x-ray scans.
Also, the dependency on AI-powered
systems in our daily life (private and
business) increases rapidly. Search
engines, industrial robots, real-time
trading systems and soon also
autonomous mobility and delivery
services cannot be substituted by
humans anymore.
If you would turn off the AI-engine in
the future, the world would most
likely break apart. That is why many
smart thinkers urgently warn of the
consequences of too powerful
systems. Most of them agree that AI
will be uncontrollable for humans and
the question is, if humans can
gracefully co-exist with an artificial
super intelligence.
AI 3.0 is at this point not a synonym
to super-intelligence or singularity.
Contextual reasoning describes the
fact that most cognitive processes
depend on the environment and
therefore the context. Systems that
perform tasks in silos without
incorporating outside information are
already in use but they can be easily
identified as computer applications
with limited capabilities to respond to
exceptions and changes in their
environment.
7
The new spring in AI is the most
significant development in computing
in my lifetime.
Every month, there are stunning new
applications and transformative new
techniques. But such powerful tools
also bring with them
new questions
and responsibilities.
Sergey Brin
Co-Founder of Google
„
“
THE BREEDING GROUND FOR AI IS THERE – THE
QUESTION IS HOW FAST IT WILL GROW
8
Where are we today?
Even if there are already disciplines
where narrow AI systems outperform
human capabilities, in most areas of
application that are not repetitive,
rule-based or require simple
knowledge representation, humans
still do better. AI systems rather
augment tasks to be more secure,
work faster or deliver higher quality.
Why is it different now?
It is quite unrealistic that AI will enter
another ‘winter’ very soon because
multiple critical requirements are
fulfilled by the market:
1) Financing – AI is no longer in a
scientific silo that is depending on
external financial resources. With
successfully applied and monetized AI
applications, more and more money
floods into the system to keep the fire
burning.
2) Big data – led to the deep learning
big bang – but quantity is not
everything. With sophisticated data
warehouses, the quality of data
improves rapidly and so does the
output of AI applications.
3) Computational power – increased
significantly by using graphical
chipsets, but this is just the beginning.
Performance is likely to improve by
orders of magnitude in a short
timeframe with Google’s specialized
tensor processing unit (TPU) already
in the market and quantum
computing casting its shadows ahead.
4) Global collaboration – It’s not part
of a national strategy anymore to
decide about investing in research
and hiring talent. It’s a global task
with ambitious players – may that be
countries or corporates – to become
the most successful and influential
party in this collaborative
competition.
It’s likely that machines
will be smarter than us
before the end of the century
—
not just at chess or trivia
questions but at just about
everything, from mathematics
and engineering to science
and medicine.
Gary Marcus
Professor New York University
„
“
9
FACTS &
FIGURES
LEVEL UP YOUR COFFEE TALKS
TRANSFER LEARNING SPEEDS UP LEARNING
SUCCESSES AND IMPROVES SCALABILITY
1) Source: Machine Learning Mastery (2017) – exemplary visualization 10
Research Advancement #1: Transfer Learning Performance of untrained / transfer learning models1
What does it mean?
Transfer learning is a machine learning method where a model developed and
trained for task A can be reused for task B by re-applying the already
acquired fundamental knowledge. The model for task B then already starts
learning already at a basic level of knowledge.
Why does it matter?
Re-using knowledge significantly reduces the required amount of data for
a model to learn a new task. Especially when data is hard to capture. It brings
not only faster results, it also achieves higher accuracy after the same
amount of training.
training
performance1
Applied transfer learning
Untrained model
higher
start
steeper
learning
curve
better output
AI RESEARCH
AFFORDABLE HIGH-END CHIP PERFORMANCE IS THE
KEY TO BRING AI TO THE MASS MARKET
1) Source: Baidu (2018) 11
Research Advancement #2: Chip Performance Speed benchmark for AI training with one or multiple GPUs
What does it mean?
Today, much of the processing power for training an algorithm is done with
graphics processing units (GPU), instead of central processing units (CPU). The
computational parallelism of the higher performing graphic chipsets
allows significant improvements of results as shown on the right.-
Why does it matter?
Cost efficient availability of GPU-based computational power was a game
changer in AI research. Training time of AI models decreased significantly.
Training a model with 50 GPUs is 40 times faster compared to one single GPU
unit.
1 2 3
8
15
40
56
0
5
10
15
20
25
30
35
40
45
50
55
60
2 4 50 100
# GPUs used for training
Training speedup over one GPU (x-fold)1
161 8
AI RESEARCH
RELIABLE AI-SYSTEMS REQUIRE CONTEXTUAL
ADJUSTMENTS UNDER CHANGING CONDITIONS
1) Source: ImageNet, Standford Vision Lab 12
Research Advancement #3: Contextual Learning Error rate development on ImageNET visual recognition
What does it mean?
Detecting objects in images or videos based on the sematic structure of pixels
and classification was very challenging in the field of computer vision.
Common sense logic of actions or context was missing. Due to advances in
convolutional neural network architectures and reinforcement learning,
AI systems improved a lot in the recent years.
Why does it matter?
Precise detection and classification of the environment data is key to
many applications such as autonomous driving. Moreover, contextual learning
is also relevant in natural language processing supporting smart assistants
like Siri.
0%
5%
10%
15%
20%
25%
2012
Error-rates on ImageNet1
2011 2013 2014 2015
human
level
AI RESEARCH
ADVANCED DIAGNOSIS SUPPORT HUMAN DOCTORS
AND PROGRESS ATTRACTS MORE INVESTMENT
1) Source: CB Insights 13
300
790
20162015
1.200
2017 HY 18
1.100
VC Funding in AI healthcare startups
Disclosed equity funding in million U.S. dollars (rounded)1
AI healthcare news in Q3 2018
Selected press mentions of successful AI trials or implementation projects
ARTIFICIAL
INTELLIGENCE
IN
HEALTHCARE
APPLIED AI
SMART AUTOMATION IS ON THE RISE, AI-POWERED
ROBOTS TRIGGER BILLION-DOLLAR INVESTMENTS
1) Source: International Federation of Robotics 14
Supply of industrial smart robots worldwide
In thousand units (forecasted)1
AI manufacturing news in Q3 2018
Selected press mentions of successful AI trials or implementation projects
ARTIFICIAL
INTELLIGENCE
IN
MANUFACTURING
178
221
254
294
346
378
433
521
2013 2018*2014 2015 2016 2019*2017* 2020*
APPLIED AI
AUTONOMOUS WAREHOUSE-LOGISTIC ACCELERATES
THROUGH AI IN DISTRIBUTION CENTERS
1) Source: Amazon 15
Number of robots working in Amazon fulfilment centers
in global units1
AI logistics news in Q3 2018
Selected press mentions of successful AI trials or implementation projects
ARTIFICIAL
INTELLIGENCE
IN
LOGISTICS
20152013 2016
30.000
120.000
2014 2017
1.400
15.000
45.000
APPLIED AI
THE IMMENSE NEED FOR AI EXPERTS DRIVES
SALARIES TO ASTRONOMICAL HEIGHTS
Size of the AI talent pool
The Global AI Talent Report states a
total number of 22,000 PhD-level AI
Researchers1. The data was derived
from LinkedIn queries and analyses
of the leading conferences. The scope
focuses on highly specialized AI
experts rather than counting entire
software development teams.
Distribution of talent has a strong
focus on native English-speaking
countries (U.S. followed by U.K.,
Canada and Australia) biased by the
research methodology. Especially
China is underrepresented, while
France, Germany and Spain
complement the significantly smaller
European counterpart.
Chinese tech giant Tencent sets a
broader focus and counts 300,000
active AI researchers and
practitioners globally. Two-thirds of
them employed in the industry and
100,000 researching in academia.
Salaries for AI professionals
According to figures from job listings
platform Indeed, average salaries for
AI programmers in the U.S. range
between $134k and $170k2. On
Glassdoor, the average annual salary
for AI listed jobs is at $111k, including
a broad range of AI related jobs in
startups or corporates.
Real domain experts like software
architects and engineers can easily
earn three to four times as much.
Especially the large tech companies
such as Google or Facebook pay from
$300k to $500k3 in base salary and
company stock a year even for
university drop-outs.
Public figures of DeepMind show
staff costs of $138 million, which
equals $345k for each of the 400
researchers. For leadership positions
in AI domains the sky is the limit. Not
only in the U.S. – this is also true for
companies in China, Korea or Europe.
1) Source. Element AI (2018) - http://www.jfgagne.ai/talent
2) Source: Medium (2018) - https://medium.com/mlmemoirs/artificial-intelligence-salaries-heading-skyward-e41b2a7bba7d
3) Source: NY Times (2018) - https://www.nytimes.com/2017/10/22/technology/artificial-intelligence-experts-salaries.html
16
Global AI talent distribution
PhD Researchers based on LinkedIn and conference anaylses1
AI TALENT
TECH COMPANIES FIGHT WAR OVER TALENTS FROM
TOP AI UNIVERSITIES
Industry vs. academia
Skilled computer and data scientists
that understand the complex
mathematical techniques to develop
artificially intelligent software and use
it to improve business are very rare.
This is why big tech companies
aggressively recruit talents from top
universities. In 2015, Uber hired 40
students out of Carnegie Mellon to
boost its self-driving car ambitions.
Even professors are increasingly
recruited to work in the industry.
Four professors from Stanford and
six from the University of Washington
were at least temporarily on a leave
in the past years.
At this point, the talent hunger of the
industry already affects the following
classes in the educational system. But
Google, Microsoft and IBM were just
the start. Every well-funded Silicon
Valley company and increasingly also
old-economy players enter the AI
talent war.
According to a Stack Overflow survey,
86.7% of developers are self-taught3
(not specified on the AI space). Given
this fact, online course offerings like
Coursera and Udacity as well as
corporate programs will most likely
continue to grow rapidly to meet the
demand.
1) Source: U.S. News (2018) - https://www.usnews.com/best-graduate-schools/top-science-schools/artificial-intelligence-rankings
2) Source: State of AI (2018) - https://www.stateof.a
3) Source: Developer Survey Results 2018 - https://insights.stackoverflow.com/survey/2018
17
1
Carnegie Mellon University
Pittsburgh, Pennsylvania (USA)
Students: 12,500
2
Massachusetts Institute of Technology
Cambridge, Massachusetts (USA)
Students: 11,319
3
Stanford University
Stanford, California (USA)
Students: 16,430
4
University of California
Berkeley, California (USA)
Students: 41,910
5
University of Washington
Seattle, Washington (USA)
Students: 46,686
Top universities for artificial intelligence research
Based on survey results of academics in the United States1
Leading employers of AI talent
Size of employed AI staff2
900
450 400
300
1.400
1.000
„There is a giant sucking sound of
academics going into industry“
Oren Etzioni
CEO of the Allen Institute for AI
AI TALENT
18
COMMERCIAL
VALUE
STARTUPS, PLATFORMS AND ACTION POINTS
19
AI STARTUP ACQUISITIONS BY THE TOP B2B
CLOUD PLATFORM VENDORS
Source: CB Insights, CrunchBase (as of October 2018)
Excluded: Chinese platform vendors such as Baidu and Alibaba
TIME
2014 2015 2016 20172013 2018
20
AI STRATEGIES OF THE LEADING
B2B DATA PLATFORMS
INFRASTRUCTURE MATURITY
LOW HIGH
FIRST MOVERS
LEADERSVISIONARIES
EARLY FOLLOWERS
STRATEGICCOMMITMENT
HIGHLOW
Google’s AI strategy is to create a
strong market position with
corresponding patents and related
technology areas as its business
relies heavily on machine learning.
With TensorFlow, Google developers
delivered the leading open-source
software library in the ML space. This,
alongside with their investment
strategy in AI start-ups, shows
Googles dedication to be and stay the
leader in AI development.
Microsoft - Since 2016, the company
keeps the pace of Google with
increasing acquisition activity. And
still, the company's entire AI efforts
are well linked with the Azure cloud. It
will be crucial to seamlessly integrate
the technology from start-ups into
the company’s portfolio.
Amazon will continue to build
around the successful Alexa project
with voice, virtual assistants and
natural language processing. AI-as-a-
service is pushed into the AWS
environment to make Amazon a
leader in this field as well.
IBM's AI strategy is lazor-focused on
its enterprise customers. In giving
them the control of their data and
insights, IBM tries to assist them
increase efficiency, lower costs or
augment human intelligence.
Simultaneously, open source projects
in the fields of AI are supported and
APIs to other vendors like Google's
TensorFlow are endorsed.
Salesforce’s Einstein AI platform has
been the central focus for the
development and marketing strategy.
CEO Benioff described the AI strategy
quite straight forward: ‘AI is the next
platform – all future applications, all
future capabilities for all companies will
be built on AI.’
SAP - Cloud surpassed the licensing
business for the first time in 2018. In
addition, acquiring Recast.AI and
creating Europe’s first corporate AI
ethics advisory panel clearly positions
AI as a central strategy building block.
21
FIVE ACTION POINTS TO CREATE SUSTAINABLE VALUE
FROM ARTIFICIAL INTELLIGENCE
TALENT ACQUISITION
▪ Data scientists, AI engineers and software architects are the scarcest resources in terms of development speed as of today
▪ Make sure to create a vibrant and attractive environment to make smart people want to work for you
▪ Start early to recruit and be competitive in talent acquisition, otherwise you can’t expect to outpace your competition in this domain
DEVELOP A COMPANY-WIDE DATA STRATEGY
▪ Data is the raw input to every AI application and determines the quality of the output
▪ Unstructured data held in silos with multiple decision-making stakeholders slows the adoption process
▪ Define and implement a coherent and flexible data architecture and set responsibilities on the executive-level
EMBRACING A TECHNOLOGY MINDSET
▪ Start to develop iteratively and find ways to apply this logic to mission critical processes as well
▪ Openly communicate and promote advances and changes through technology to avoid fear and opposition building
▪ Allow new and radical ways of thinking and encourage experiments and failure
EVALUATION OF NEW BUSINESS MODELS
▪ Think holistic! Consider how technology impacts and changes the way partners, customers, competitors and new market entrants act
▪ Be open to even license proprietary solutions to other market players in order to create new revenue streams
▪ Question current processes and evaluate external as-a-service offerings to keep focus on your core product
START TODAY
▪ Value creation of AI is strongly correlated with the experience in this domain – the sooner you start, the higher your return
▪ Facilitate ideas in your company by small-scale prototypes or proof-of-concept projects
▪ Invest time into the exploration of possibilities that are out there already
1
2
3
4
5
22
LET’S TALK ABOUT BOOSTING YOUR BUSINESS
WITH ARTIFICIAL INTELLIGENCE!
We identify suitable AI use cases and help
with target-oriented prioritization.
IDENTIFY
We create a concrete plan about
requirements, project organization and
feedback-driven iteration steps.
PLAN
We evaluate suitable solutions for your
company-specific requirements
EVALUATE
We facilitate the development and
implementation process and support you in
the selection of partners.
EXECUTE
We provide decision confidence through
process design and functional prototypes
TEST
We develop a future-proof data strategy
based on the company's long-term goals
SUSTAIN
GET IN TOUCH!
Tobias Bohnhoff
Appanion Labs GmbH ▪ Hopfenstrasse 11 ▪ 20359 Hamburg ▪ www.appanion.com
Discover more at
www.appanion.com
Disclaimer: This whitepaper is based on research and data of the previously mentioned sources. All information presented were researched and prepared by Appanion with great care. For the presented data and information Appanion cannot assume any warranty of any kind. Information and data represent in selected cases individual opinions
and may be in need of further interpretation as a basis for decisions. Advances in research may occur after the publishing of this document. Therefore, Appanion is not liable for any damage arising from the use of information and data provided in this whitepaper.

More Related Content

What's hot

Responsible Generative AI
Responsible Generative AIResponsible Generative AI
Responsible Generative AI
CMassociates
 
AI and ML Series - Introduction to Generative AI and LLMs - Session 1
AI and ML Series - Introduction to Generative AI and LLMs - Session 1AI and ML Series - Introduction to Generative AI and LLMs - Session 1
AI and ML Series - Introduction to Generative AI and LLMs - Session 1
DianaGray10
 
Responsible AI: An Example AI Development Process with Focus on Risks and Con...
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Responsible AI: An Example AI Development Process with Focus on Risks and Con...
Responsible AI: An Example AI Development Process with Focus on Risks and Con...
Patrick Van Renterghem
 
An Introduction to Generative AI - May 18, 2023
An Introduction  to Generative AI - May 18, 2023An Introduction  to Generative AI - May 18, 2023
An Introduction to Generative AI - May 18, 2023
CoriFaklaris1
 
How People Are Leveraging ChatGPT
How People Are Leveraging ChatGPTHow People Are Leveraging ChatGPT
How People Are Leveraging ChatGPT
Roy Ahuja
 
Using Generative AI
Using Generative AIUsing Generative AI
Using Generative AI
Mark DeLoura
 
Generative AI: Past, Present, and Future – A Practitioner's Perspective
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveGenerative AI: Past, Present, and Future – A Practitioner's Perspective
Generative AI: Past, Present, and Future – A Practitioner's Perspective
Huahai Yang
 
ChatGPT, Foundation Models and Web3.pptx
ChatGPT, Foundation Models and Web3.pptxChatGPT, Foundation Models and Web3.pptx
ChatGPT, Foundation Models and Web3.pptx
Jesus Rodriguez
 
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...
Applitools
 
A Framework for Navigating Generative Artificial Intelligence for Enterprise
A Framework for Navigating Generative Artificial Intelligence for EnterpriseA Framework for Navigating Generative Artificial Intelligence for Enterprise
A Framework for Navigating Generative Artificial Intelligence for Enterprise
RocketSource
 
Exploring Generating AI with Diffusion Models
Exploring Generating AI with Diffusion ModelsExploring Generating AI with Diffusion Models
Exploring Generating AI with Diffusion Models
KonfHubTechConferenc
 
Generative AI, WiDS 2023.pptx
Generative AI, WiDS 2023.pptxGenerative AI, WiDS 2023.pptx
Generative AI, WiDS 2023.pptx
Colleen Farrelly
 
Generative AI and law.pptx
Generative AI and law.pptxGenerative AI and law.pptx
Generative AI and law.pptx
Chris Marsden
 
14 2 2023 - AI & Marketing - Hugues Rey.pdf
14 2 2023 - AI & Marketing - Hugues Rey.pdf14 2 2023 - AI & Marketing - Hugues Rey.pdf
14 2 2023 - AI & Marketing - Hugues Rey.pdf
Hugues Rey
 
Generative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdfGenerative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdf
Liming Zhu
 
Stanford AI Report 2023
Stanford AI Report 2023Stanford AI Report 2023
Stanford AI Report 2023
Kapil Khandelwal (KK)
 
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYGENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
Andre Muscat
 
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...
DianaGray10
 
UTILITY OF AI
UTILITY OF AIUTILITY OF AI
UTILITY OF AI
Andre Muscat
 
Using the power of Generative AI at scale
Using the power of Generative AI at scaleUsing the power of Generative AI at scale
Using the power of Generative AI at scale
Maxim Salnikov
 

What's hot (20)

Responsible Generative AI
Responsible Generative AIResponsible Generative AI
Responsible Generative AI
 
AI and ML Series - Introduction to Generative AI and LLMs - Session 1
AI and ML Series - Introduction to Generative AI and LLMs - Session 1AI and ML Series - Introduction to Generative AI and LLMs - Session 1
AI and ML Series - Introduction to Generative AI and LLMs - Session 1
 
Responsible AI: An Example AI Development Process with Focus on Risks and Con...
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Responsible AI: An Example AI Development Process with Focus on Risks and Con...
Responsible AI: An Example AI Development Process with Focus on Risks and Con...
 
An Introduction to Generative AI - May 18, 2023
An Introduction  to Generative AI - May 18, 2023An Introduction  to Generative AI - May 18, 2023
An Introduction to Generative AI - May 18, 2023
 
How People Are Leveraging ChatGPT
How People Are Leveraging ChatGPTHow People Are Leveraging ChatGPT
How People Are Leveraging ChatGPT
 
Using Generative AI
Using Generative AIUsing Generative AI
Using Generative AI
 
Generative AI: Past, Present, and Future – A Practitioner's Perspective
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveGenerative AI: Past, Present, and Future – A Practitioner's Perspective
Generative AI: Past, Present, and Future – A Practitioner's Perspective
 
ChatGPT, Foundation Models and Web3.pptx
ChatGPT, Foundation Models and Web3.pptxChatGPT, Foundation Models and Web3.pptx
ChatGPT, Foundation Models and Web3.pptx
 
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...
 
A Framework for Navigating Generative Artificial Intelligence for Enterprise
A Framework for Navigating Generative Artificial Intelligence for EnterpriseA Framework for Navigating Generative Artificial Intelligence for Enterprise
A Framework for Navigating Generative Artificial Intelligence for Enterprise
 
Exploring Generating AI with Diffusion Models
Exploring Generating AI with Diffusion ModelsExploring Generating AI with Diffusion Models
Exploring Generating AI with Diffusion Models
 
Generative AI, WiDS 2023.pptx
Generative AI, WiDS 2023.pptxGenerative AI, WiDS 2023.pptx
Generative AI, WiDS 2023.pptx
 
Generative AI and law.pptx
Generative AI and law.pptxGenerative AI and law.pptx
Generative AI and law.pptx
 
14 2 2023 - AI & Marketing - Hugues Rey.pdf
14 2 2023 - AI & Marketing - Hugues Rey.pdf14 2 2023 - AI & Marketing - Hugues Rey.pdf
14 2 2023 - AI & Marketing - Hugues Rey.pdf
 
Generative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdfGenerative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdf
 
Stanford AI Report 2023
Stanford AI Report 2023Stanford AI Report 2023
Stanford AI Report 2023
 
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYGENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
 
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...
AI and ML Series - Leveraging Generative AI and LLMs Using the UiPath Platfor...
 
UTILITY OF AI
UTILITY OF AIUTILITY OF AI
UTILITY OF AI
 
Using the power of Generative AI at scale
Using the power of Generative AI at scaleUsing the power of Generative AI at scale
Using the power of Generative AI at scale
 

Similar to Commercialization of AI 3.0

Inteligencia Artificial.pdf
Inteligencia Artificial.pdfInteligencia Artificial.pdf
Inteligencia Artificial.pdf
AnaCoronel30
 
Understanding Emerging Technology - Artificial Intelligence
Understanding Emerging Technology - Artificial Intelligence Understanding Emerging Technology - Artificial Intelligence
Understanding Emerging Technology - Artificial Intelligence
CompTIA
 
Artificial Intelligence can Offer People Great Relief from Performing Mundane...
Artificial Intelligence can Offer People Great Relief from Performing Mundane...Artificial Intelligence can Offer People Great Relief from Performing Mundane...
Artificial Intelligence can Offer People Great Relief from Performing Mundane...
JPLoft Solutions
 
Get Technology Smart - AI and our Digital Futures
Get Technology Smart - AI and our Digital FuturesGet Technology Smart - AI and our Digital Futures
Get Technology Smart - AI and our Digital Futures
emmersons1
 
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...
vmblog
 
Hype vs. Reality: The AI Explainer
Hype vs. Reality: The AI ExplainerHype vs. Reality: The AI Explainer
Hype vs. Reality: The AI Explainer
Luminary Labs
 
About Machine and real
About Machine and realAbout Machine and real
About Machine and real
muhammad hunain
 
Allaboutailuminarylabsjanuary122017 170112151616
Allaboutailuminarylabsjanuary122017 170112151616Allaboutailuminarylabsjanuary122017 170112151616
Allaboutailuminarylabsjanuary122017 170112151616
Quang Lê
 
Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...
Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...
Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...
IBM HongKong
 
The True Meaning of AI: Action & Insight
The True Meaning of AI: Action & InsightThe True Meaning of AI: Action & Insight
The True Meaning of AI: Action & Insight
Cognizant
 
Will You Embrace A.I. Fast Enough
Will You Embrace A.I. Fast EnoughWill You Embrace A.I. Fast Enough
Will You Embrace A.I. Fast Enough
Michael Hu
 
LIMITATIONS OF AI
LIMITATIONS OF AILIMITATIONS OF AI
LIMITATIONS OF AI
AdityaK52
 
Artificial Intelligence overview (by impulse)
Artificial Intelligence overview (by impulse)Artificial Intelligence overview (by impulse)
Artificial Intelligence overview (by impulse)
Software in Brussels
 
Artificial Intelligence- What Is It.pdf
Artificial Intelligence- What Is It.pdfArtificial Intelligence- What Is It.pdf
Artificial Intelligence- What Is It.pdf
yamunaNMH
 
AI101 Guide
AI101 GuideAI101 Guide
AI101 Guide
Sajan Mathew 💡
 
AI101 guide
AI101 guideAI101 guide
AI101 guide
Sajan Mathew 💡
 
Realizing_the_real_business_impact_of_gen_AI_white_paper.pdf
Realizing_the_real_business_impact_of_gen_AI_white_paper.pdfRealizing_the_real_business_impact_of_gen_AI_white_paper.pdf
Realizing_the_real_business_impact_of_gen_AI_white_paper.pdf
PhilipBasford
 
[REPORT PREVIEW] AI in the Enterprise
[REPORT PREVIEW] AI in the Enterprise[REPORT PREVIEW] AI in the Enterprise
[REPORT PREVIEW] AI in the Enterprise
Altimeter, a Prophet Company
 
What Is The Next Level Of AI Technology?
What Is The Next Level Of AI Technology?What Is The Next Level Of AI Technology?
What Is The Next Level Of AI Technology?
Bernard Marr
 
Artificial Intelligence and Big Data
Artificial Intelligence and Big DataArtificial Intelligence and Big Data
Artificial Intelligence and Big Data
Hatim EL-QADDOURY
 

Similar to Commercialization of AI 3.0 (20)

Inteligencia Artificial.pdf
Inteligencia Artificial.pdfInteligencia Artificial.pdf
Inteligencia Artificial.pdf
 
Understanding Emerging Technology - Artificial Intelligence
Understanding Emerging Technology - Artificial Intelligence Understanding Emerging Technology - Artificial Intelligence
Understanding Emerging Technology - Artificial Intelligence
 
Artificial Intelligence can Offer People Great Relief from Performing Mundane...
Artificial Intelligence can Offer People Great Relief from Performing Mundane...Artificial Intelligence can Offer People Great Relief from Performing Mundane...
Artificial Intelligence can Offer People Great Relief from Performing Mundane...
 
Get Technology Smart - AI and our Digital Futures
Get Technology Smart - AI and our Digital FuturesGet Technology Smart - AI and our Digital Futures
Get Technology Smart - AI and our Digital Futures
 
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...
VMblog - 2018 Artificial Intelligence and Machine Learning Predictions from 3...
 
Hype vs. Reality: The AI Explainer
Hype vs. Reality: The AI ExplainerHype vs. Reality: The AI Explainer
Hype vs. Reality: The AI Explainer
 
About Machine and real
About Machine and realAbout Machine and real
About Machine and real
 
Allaboutailuminarylabsjanuary122017 170112151616
Allaboutailuminarylabsjanuary122017 170112151616Allaboutailuminarylabsjanuary122017 170112151616
Allaboutailuminarylabsjanuary122017 170112151616
 
Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...
Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...
Getting started-with-artificial-intelligence-a-practical-guide-to-building-en...
 
The True Meaning of AI: Action & Insight
The True Meaning of AI: Action & InsightThe True Meaning of AI: Action & Insight
The True Meaning of AI: Action & Insight
 
Will You Embrace A.I. Fast Enough
Will You Embrace A.I. Fast EnoughWill You Embrace A.I. Fast Enough
Will You Embrace A.I. Fast Enough
 
LIMITATIONS OF AI
LIMITATIONS OF AILIMITATIONS OF AI
LIMITATIONS OF AI
 
Artificial Intelligence overview (by impulse)
Artificial Intelligence overview (by impulse)Artificial Intelligence overview (by impulse)
Artificial Intelligence overview (by impulse)
 
Artificial Intelligence- What Is It.pdf
Artificial Intelligence- What Is It.pdfArtificial Intelligence- What Is It.pdf
Artificial Intelligence- What Is It.pdf
 
AI101 Guide
AI101 GuideAI101 Guide
AI101 Guide
 
AI101 guide
AI101 guideAI101 guide
AI101 guide
 
Realizing_the_real_business_impact_of_gen_AI_white_paper.pdf
Realizing_the_real_business_impact_of_gen_AI_white_paper.pdfRealizing_the_real_business_impact_of_gen_AI_white_paper.pdf
Realizing_the_real_business_impact_of_gen_AI_white_paper.pdf
 
[REPORT PREVIEW] AI in the Enterprise
[REPORT PREVIEW] AI in the Enterprise[REPORT PREVIEW] AI in the Enterprise
[REPORT PREVIEW] AI in the Enterprise
 
What Is The Next Level Of AI Technology?
What Is The Next Level Of AI Technology?What Is The Next Level Of AI Technology?
What Is The Next Level Of AI Technology?
 
Artificial Intelligence and Big Data
Artificial Intelligence and Big DataArtificial Intelligence and Big Data
Artificial Intelligence and Big Data
 

More from APPANION

The Impact of AI Use Cases on the German Economy
The Impact of AI Use Cases on the German EconomyThe Impact of AI Use Cases on the German Economy
The Impact of AI Use Cases on the German Economy
APPANION
 
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...
APPANION
 
Intelligente visuelle Qualitätskontrolle in der Fertigungsindustrie
Intelligente visuelle Qualitätskontrolle in der FertigungsindustrieIntelligente visuelle Qualitätskontrolle in der Fertigungsindustrie
Intelligente visuelle Qualitätskontrolle in der Fertigungsindustrie
APPANION
 
Understanding Artificial Intelligence - Major concepts for enterprise applica...
Understanding Artificial Intelligence - Major concepts for enterprise applica...Understanding Artificial Intelligence - Major concepts for enterprise applica...
Understanding Artificial Intelligence - Major concepts for enterprise applica...
APPANION
 
AI in Business - Key drivers and future value
AI in Business - Key drivers and future valueAI in Business - Key drivers and future value
AI in Business - Key drivers and future value
APPANION
 
Top 10 Technology Predictions - Future Outlook for AI and DLT
Top 10 Technology Predictions - Future Outlook for AI and DLTTop 10 Technology Predictions - Future Outlook for AI and DLT
Top 10 Technology Predictions - Future Outlook for AI and DLT
APPANION
 

More from APPANION (6)

The Impact of AI Use Cases on the German Economy
The Impact of AI Use Cases on the German EconomyThe Impact of AI Use Cases on the German Economy
The Impact of AI Use Cases on the German Economy
 
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...
Betrugserkennung in Versicherungen durch Machine Learning und Predictive Anal...
 
Intelligente visuelle Qualitätskontrolle in der Fertigungsindustrie
Intelligente visuelle Qualitätskontrolle in der FertigungsindustrieIntelligente visuelle Qualitätskontrolle in der Fertigungsindustrie
Intelligente visuelle Qualitätskontrolle in der Fertigungsindustrie
 
Understanding Artificial Intelligence - Major concepts for enterprise applica...
Understanding Artificial Intelligence - Major concepts for enterprise applica...Understanding Artificial Intelligence - Major concepts for enterprise applica...
Understanding Artificial Intelligence - Major concepts for enterprise applica...
 
AI in Business - Key drivers and future value
AI in Business - Key drivers and future valueAI in Business - Key drivers and future value
AI in Business - Key drivers and future value
 
Top 10 Technology Predictions - Future Outlook for AI and DLT
Top 10 Technology Predictions - Future Outlook for AI and DLTTop 10 Technology Predictions - Future Outlook for AI and DLT
Top 10 Technology Predictions - Future Outlook for AI and DLT
 

Recently uploaded

Public CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptxPublic CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptx
marufrahmanstratejm
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
akankshawande
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
operationspcvita
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Chart Kalyan
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Neo4j
 
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
Edge AI and Vision Alliance
 
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying AheadDigital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Wask
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
saastr
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
Fwdays
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Neo4j
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation TechniquesWeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Postman
 
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
Data Hops
 

Recently uploaded (20)

Public CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptxPublic CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptx
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
 
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
 
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying AheadDigital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation TechniquesWeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
 
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
 

Commercialization of AI 3.0

  • 1. COMMERCIALIZATION OF AI 3.0 WHITEPAPER
  • 2. OVERVIEW ARTIFICIAL INTELLIGENCE - BUZZWORD OR GAME CHANGER? THE BASICS Where does AI come from, where does it go? FACTS & FIGURES Round-up of science, applied AI and talent COMMERCIAL VALUE Strategies of B2B platforms and practical action points 2 3 9 18 About this whitepaper Artificial Intelligence is trendy. Every event, every strategy meeting and every consulting firm talks about it. This whitepaper aims to separate actual facts and important background information from the overarching marketing buzz. You will get a short but information-rich wrap up about: What causes the current hype? Where are we today? What are the innovation leaders doing with AI? And what are immediate action points to focus on by applying artificial intelligence in your business. WHERE CAN TECHNOLOGY TAKE YOU? Appanion in a nutshell We provide high-quality insights on real-world technology-driven business applications and help you as innovation partner with actionable strategies, technology knowhow and business model competence. I hope you can take away some valuable insights and new ideas to improve your business! Tobias Bohnhoff Founder
  • 4. ARTIFICIAL INTELLIGENCE IS CAPABLE OF FAR MORE THAN JUST PLAYING CHESS, JEOPARDY OR GO Artificial Intelligence (AI) is the ability of a computer-controlled entity to perform cognitive tasks and react flexibly to its environment in order to maximize the probability of achieving a particular goal. The system can learn from experience data, and can mimic behaviors associated to humans, but does therefore not necessarily use methods that are biologically observable. Definition is based on McKinsey, Stanford University, Wikipedia, European Commission 4 „ “ Artificial General Intelligence (AGI) Artificial Intelligence (AI) can be distinguished into systems designed to solve problems in a very specific context – the so called Narrow AI – and a human-like Artificial General Intelligence (AGI). AGI is currently only a future scenario. Some experts say it is decades away to build such an AI, some argue that humans will never be able to build something at an equal level of intelligence. Most often, AGI scenarios outline a dystopia for the human race due to the uncontrollable effects this technology potentially has. Thought leaders in the AI space assume that at some tipping point an AGI would act without any interventions of humans and that setting up a global governance of AI development is critical right now. Artificial Narrow Intelligence (ANI) Narrow AI on the other hand is already here. It is automating or augmenting many tasks in a business context or in our private life. Self-driving forklifts in warehouses, Amazon product recommendations, real-time trades on stock exchanges, interacting with Siri, person recognition in the iPhone photo album – everything is already done automatically, without the need of human intervention. It’s worth mentioning that automation per se does not require intelligence. This can also be done by pre-programmed rules. However, complex tasks require the ability to react flexibly to their environment, which differentiates AI applications from the well-understood domain of rule-based automation.
  • 5. AI IS ENTERING MASS COMMERCIALIZATION - EVERY BUSINESS IS GOING TO CHANGE Startup perspective Artificial Intelligence promises disruption. It opens up the possibility to achieve better product or service quality at less time and cost. And if this can be proven to the market successfully, it will change everything. Customers start to jump from legacy solutions to better products in a very short timeframe, others follow to adopt, old solution providers die and the new players overtake the market lead. That is what we call disruption and what makes AI as a space so attractive for the many young companies in the recent years. Investor perspective Platform and as-a-service business models have been proven very successful due to recurring revenues and scalability. To investors, owning a new market and earning high margins means to be fast and to take a lot of financial risk. As VC funding data shows, the race for number one in AI has just taken off. At some point, the market will start to consolidate and the winning companies will emerge. Corporate perspective Almost all large corporations are confronted with entirely new processes through AI. Its value lies in automation and augmentation of human work. The technology not only changes the frontend or digitizes the communication interface, it changes the entire way of collaborating, planning and decision-making. The flexibility to change a – to this point – successful concept, is crucial to survive. Starting early experiments, communicating change and investing into research and talent is the way forward. 1) Source: CB Insights (02/2018) 2) Source: CB Insights (01/2018) 3) Source: AIIndex.org (11/2017) 4) Source: Technews.io (09/2018) 5 20152013 2014 2016 2017 1.739 4.5693.477 6.255 15.242 +72% 2020 10 20101995 20152000 2005 0 5 15 20 +12% Venture Funding in AI startups In million U.S. dollars, global1 AI related research publications In thousands, global3 Number of AI press mentions Original media coverage by tech journalists4 20152014 2016 8.772 2017 2018 42.973 14.670 90.752 125.462 +94% Startups with „.ai“ URL suffix Global # of startups that received financing2 5 22 51 100 225 201720162013 2014 2015 +159% Compound annual growth rate Compound annual growth rate Compound annual growth rateCompound annual growth rate
  • 6. THE AI HISTORY IS IMPORTANT TO UNDERSTAND WHERE WE ARE TODAY AND WHAT IS DIFFERENT AI 1.0 – Pattern Recognition Since the term ‚AI‘ was established in in 1956, government agencies like DARPA1 funded scientific research until the mid-1970s. Successes in the fields of search, natural language processing and simplified models and simulations were achieved. The tremendous expectations raised by AI researchers afterwards didn‘t match the achievements for the investors. Limited computing power, the rather low performance of neural networks, combinatorial explosion and a lack of commonsense knowledge by the systems led to significant cuts in funding. The first AI winter lasted until 1980. In the 1980s, the expert system emerged. It used logical rules, derived from the knowledge of domain experts. Avoiding the challenges of commonsense logic, funding came back until 1987. The rise of IBM‘s and Apple‘s desktop computers made specialized AI hardware obsolete. As a consequence, markets went down and trust was gone – the second AI winter. AI 2.0 – Deep Learning In the mid-1990s milestones were achieved that brought back trust. Chess champion Garry Kasparov was beaten by ‘Deep Blue’ in 1997. Intelligent agents that perceive the environment and take actions to maximize successful outcomes were the leading paradigm to that time. In addition, the deep learning concept was developed, but it took another 10 years until the availability of big data and computational power from graphic processing units (GPU) created the „big bang“ of deep learning algorithms. 1) DARPA = Defense Advanced Research Projects Agency (Part of the United States Department of Defense) 6 AIPERFORMANCE TIME HIGHLOW AI 1.0 Pattern Recognition AI 2.0 Deep Learning AI 3.0 Contextual Reasoning
  • 7. AI 3.0 IS COMING - BUT IT IS NOT EQUIVALENT TO SUPERINTELLIGENCE AI 3.0 – Contextual Reasoning AI 2.0 is slowly fading out as new breakthroughs in scientific and applied AI demonstrate the superiority of computer intelligence compared to human intelligence in selected domains. It started with chess, continued with Jeopardy, Go and is now at the point of beating humans in collaborative multiplayer games like Dota. At the same time medical diagnostics surpasses the accuracy of human doctors in many domains such as cancer detection from x-ray scans. Also, the dependency on AI-powered systems in our daily life (private and business) increases rapidly. Search engines, industrial robots, real-time trading systems and soon also autonomous mobility and delivery services cannot be substituted by humans anymore. If you would turn off the AI-engine in the future, the world would most likely break apart. That is why many smart thinkers urgently warn of the consequences of too powerful systems. Most of them agree that AI will be uncontrollable for humans and the question is, if humans can gracefully co-exist with an artificial super intelligence. AI 3.0 is at this point not a synonym to super-intelligence or singularity. Contextual reasoning describes the fact that most cognitive processes depend on the environment and therefore the context. Systems that perform tasks in silos without incorporating outside information are already in use but they can be easily identified as computer applications with limited capabilities to respond to exceptions and changes in their environment. 7 The new spring in AI is the most significant development in computing in my lifetime. Every month, there are stunning new applications and transformative new techniques. But such powerful tools also bring with them new questions and responsibilities. Sergey Brin Co-Founder of Google „ “
  • 8. THE BREEDING GROUND FOR AI IS THERE – THE QUESTION IS HOW FAST IT WILL GROW 8 Where are we today? Even if there are already disciplines where narrow AI systems outperform human capabilities, in most areas of application that are not repetitive, rule-based or require simple knowledge representation, humans still do better. AI systems rather augment tasks to be more secure, work faster or deliver higher quality. Why is it different now? It is quite unrealistic that AI will enter another ‘winter’ very soon because multiple critical requirements are fulfilled by the market: 1) Financing – AI is no longer in a scientific silo that is depending on external financial resources. With successfully applied and monetized AI applications, more and more money floods into the system to keep the fire burning. 2) Big data – led to the deep learning big bang – but quantity is not everything. With sophisticated data warehouses, the quality of data improves rapidly and so does the output of AI applications. 3) Computational power – increased significantly by using graphical chipsets, but this is just the beginning. Performance is likely to improve by orders of magnitude in a short timeframe with Google’s specialized tensor processing unit (TPU) already in the market and quantum computing casting its shadows ahead. 4) Global collaboration – It’s not part of a national strategy anymore to decide about investing in research and hiring talent. It’s a global task with ambitious players – may that be countries or corporates – to become the most successful and influential party in this collaborative competition. It’s likely that machines will be smarter than us before the end of the century — not just at chess or trivia questions but at just about everything, from mathematics and engineering to science and medicine. Gary Marcus Professor New York University „ “
  • 9. 9 FACTS & FIGURES LEVEL UP YOUR COFFEE TALKS
  • 10. TRANSFER LEARNING SPEEDS UP LEARNING SUCCESSES AND IMPROVES SCALABILITY 1) Source: Machine Learning Mastery (2017) – exemplary visualization 10 Research Advancement #1: Transfer Learning Performance of untrained / transfer learning models1 What does it mean? Transfer learning is a machine learning method where a model developed and trained for task A can be reused for task B by re-applying the already acquired fundamental knowledge. The model for task B then already starts learning already at a basic level of knowledge. Why does it matter? Re-using knowledge significantly reduces the required amount of data for a model to learn a new task. Especially when data is hard to capture. It brings not only faster results, it also achieves higher accuracy after the same amount of training. training performance1 Applied transfer learning Untrained model higher start steeper learning curve better output AI RESEARCH
  • 11. AFFORDABLE HIGH-END CHIP PERFORMANCE IS THE KEY TO BRING AI TO THE MASS MARKET 1) Source: Baidu (2018) 11 Research Advancement #2: Chip Performance Speed benchmark for AI training with one or multiple GPUs What does it mean? Today, much of the processing power for training an algorithm is done with graphics processing units (GPU), instead of central processing units (CPU). The computational parallelism of the higher performing graphic chipsets allows significant improvements of results as shown on the right.- Why does it matter? Cost efficient availability of GPU-based computational power was a game changer in AI research. Training time of AI models decreased significantly. Training a model with 50 GPUs is 40 times faster compared to one single GPU unit. 1 2 3 8 15 40 56 0 5 10 15 20 25 30 35 40 45 50 55 60 2 4 50 100 # GPUs used for training Training speedup over one GPU (x-fold)1 161 8 AI RESEARCH
  • 12. RELIABLE AI-SYSTEMS REQUIRE CONTEXTUAL ADJUSTMENTS UNDER CHANGING CONDITIONS 1) Source: ImageNet, Standford Vision Lab 12 Research Advancement #3: Contextual Learning Error rate development on ImageNET visual recognition What does it mean? Detecting objects in images or videos based on the sematic structure of pixels and classification was very challenging in the field of computer vision. Common sense logic of actions or context was missing. Due to advances in convolutional neural network architectures and reinforcement learning, AI systems improved a lot in the recent years. Why does it matter? Precise detection and classification of the environment data is key to many applications such as autonomous driving. Moreover, contextual learning is also relevant in natural language processing supporting smart assistants like Siri. 0% 5% 10% 15% 20% 25% 2012 Error-rates on ImageNet1 2011 2013 2014 2015 human level AI RESEARCH
  • 13. ADVANCED DIAGNOSIS SUPPORT HUMAN DOCTORS AND PROGRESS ATTRACTS MORE INVESTMENT 1) Source: CB Insights 13 300 790 20162015 1.200 2017 HY 18 1.100 VC Funding in AI healthcare startups Disclosed equity funding in million U.S. dollars (rounded)1 AI healthcare news in Q3 2018 Selected press mentions of successful AI trials or implementation projects ARTIFICIAL INTELLIGENCE IN HEALTHCARE APPLIED AI
  • 14. SMART AUTOMATION IS ON THE RISE, AI-POWERED ROBOTS TRIGGER BILLION-DOLLAR INVESTMENTS 1) Source: International Federation of Robotics 14 Supply of industrial smart robots worldwide In thousand units (forecasted)1 AI manufacturing news in Q3 2018 Selected press mentions of successful AI trials or implementation projects ARTIFICIAL INTELLIGENCE IN MANUFACTURING 178 221 254 294 346 378 433 521 2013 2018*2014 2015 2016 2019*2017* 2020* APPLIED AI
  • 15. AUTONOMOUS WAREHOUSE-LOGISTIC ACCELERATES THROUGH AI IN DISTRIBUTION CENTERS 1) Source: Amazon 15 Number of robots working in Amazon fulfilment centers in global units1 AI logistics news in Q3 2018 Selected press mentions of successful AI trials or implementation projects ARTIFICIAL INTELLIGENCE IN LOGISTICS 20152013 2016 30.000 120.000 2014 2017 1.400 15.000 45.000 APPLIED AI
  • 16. THE IMMENSE NEED FOR AI EXPERTS DRIVES SALARIES TO ASTRONOMICAL HEIGHTS Size of the AI talent pool The Global AI Talent Report states a total number of 22,000 PhD-level AI Researchers1. The data was derived from LinkedIn queries and analyses of the leading conferences. The scope focuses on highly specialized AI experts rather than counting entire software development teams. Distribution of talent has a strong focus on native English-speaking countries (U.S. followed by U.K., Canada and Australia) biased by the research methodology. Especially China is underrepresented, while France, Germany and Spain complement the significantly smaller European counterpart. Chinese tech giant Tencent sets a broader focus and counts 300,000 active AI researchers and practitioners globally. Two-thirds of them employed in the industry and 100,000 researching in academia. Salaries for AI professionals According to figures from job listings platform Indeed, average salaries for AI programmers in the U.S. range between $134k and $170k2. On Glassdoor, the average annual salary for AI listed jobs is at $111k, including a broad range of AI related jobs in startups or corporates. Real domain experts like software architects and engineers can easily earn three to four times as much. Especially the large tech companies such as Google or Facebook pay from $300k to $500k3 in base salary and company stock a year even for university drop-outs. Public figures of DeepMind show staff costs of $138 million, which equals $345k for each of the 400 researchers. For leadership positions in AI domains the sky is the limit. Not only in the U.S. – this is also true for companies in China, Korea or Europe. 1) Source. Element AI (2018) - http://www.jfgagne.ai/talent 2) Source: Medium (2018) - https://medium.com/mlmemoirs/artificial-intelligence-salaries-heading-skyward-e41b2a7bba7d 3) Source: NY Times (2018) - https://www.nytimes.com/2017/10/22/technology/artificial-intelligence-experts-salaries.html 16 Global AI talent distribution PhD Researchers based on LinkedIn and conference anaylses1 AI TALENT
  • 17. TECH COMPANIES FIGHT WAR OVER TALENTS FROM TOP AI UNIVERSITIES Industry vs. academia Skilled computer and data scientists that understand the complex mathematical techniques to develop artificially intelligent software and use it to improve business are very rare. This is why big tech companies aggressively recruit talents from top universities. In 2015, Uber hired 40 students out of Carnegie Mellon to boost its self-driving car ambitions. Even professors are increasingly recruited to work in the industry. Four professors from Stanford and six from the University of Washington were at least temporarily on a leave in the past years. At this point, the talent hunger of the industry already affects the following classes in the educational system. But Google, Microsoft and IBM were just the start. Every well-funded Silicon Valley company and increasingly also old-economy players enter the AI talent war. According to a Stack Overflow survey, 86.7% of developers are self-taught3 (not specified on the AI space). Given this fact, online course offerings like Coursera and Udacity as well as corporate programs will most likely continue to grow rapidly to meet the demand. 1) Source: U.S. News (2018) - https://www.usnews.com/best-graduate-schools/top-science-schools/artificial-intelligence-rankings 2) Source: State of AI (2018) - https://www.stateof.a 3) Source: Developer Survey Results 2018 - https://insights.stackoverflow.com/survey/2018 17 1 Carnegie Mellon University Pittsburgh, Pennsylvania (USA) Students: 12,500 2 Massachusetts Institute of Technology Cambridge, Massachusetts (USA) Students: 11,319 3 Stanford University Stanford, California (USA) Students: 16,430 4 University of California Berkeley, California (USA) Students: 41,910 5 University of Washington Seattle, Washington (USA) Students: 46,686 Top universities for artificial intelligence research Based on survey results of academics in the United States1 Leading employers of AI talent Size of employed AI staff2 900 450 400 300 1.400 1.000 „There is a giant sucking sound of academics going into industry“ Oren Etzioni CEO of the Allen Institute for AI AI TALENT
  • 19. 19 AI STARTUP ACQUISITIONS BY THE TOP B2B CLOUD PLATFORM VENDORS Source: CB Insights, CrunchBase (as of October 2018) Excluded: Chinese platform vendors such as Baidu and Alibaba TIME 2014 2015 2016 20172013 2018
  • 20. 20 AI STRATEGIES OF THE LEADING B2B DATA PLATFORMS INFRASTRUCTURE MATURITY LOW HIGH FIRST MOVERS LEADERSVISIONARIES EARLY FOLLOWERS STRATEGICCOMMITMENT HIGHLOW Google’s AI strategy is to create a strong market position with corresponding patents and related technology areas as its business relies heavily on machine learning. With TensorFlow, Google developers delivered the leading open-source software library in the ML space. This, alongside with their investment strategy in AI start-ups, shows Googles dedication to be and stay the leader in AI development. Microsoft - Since 2016, the company keeps the pace of Google with increasing acquisition activity. And still, the company's entire AI efforts are well linked with the Azure cloud. It will be crucial to seamlessly integrate the technology from start-ups into the company’s portfolio. Amazon will continue to build around the successful Alexa project with voice, virtual assistants and natural language processing. AI-as-a- service is pushed into the AWS environment to make Amazon a leader in this field as well. IBM's AI strategy is lazor-focused on its enterprise customers. In giving them the control of their data and insights, IBM tries to assist them increase efficiency, lower costs or augment human intelligence. Simultaneously, open source projects in the fields of AI are supported and APIs to other vendors like Google's TensorFlow are endorsed. Salesforce’s Einstein AI platform has been the central focus for the development and marketing strategy. CEO Benioff described the AI strategy quite straight forward: ‘AI is the next platform – all future applications, all future capabilities for all companies will be built on AI.’ SAP - Cloud surpassed the licensing business for the first time in 2018. In addition, acquiring Recast.AI and creating Europe’s first corporate AI ethics advisory panel clearly positions AI as a central strategy building block.
  • 21. 21 FIVE ACTION POINTS TO CREATE SUSTAINABLE VALUE FROM ARTIFICIAL INTELLIGENCE TALENT ACQUISITION ▪ Data scientists, AI engineers and software architects are the scarcest resources in terms of development speed as of today ▪ Make sure to create a vibrant and attractive environment to make smart people want to work for you ▪ Start early to recruit and be competitive in talent acquisition, otherwise you can’t expect to outpace your competition in this domain DEVELOP A COMPANY-WIDE DATA STRATEGY ▪ Data is the raw input to every AI application and determines the quality of the output ▪ Unstructured data held in silos with multiple decision-making stakeholders slows the adoption process ▪ Define and implement a coherent and flexible data architecture and set responsibilities on the executive-level EMBRACING A TECHNOLOGY MINDSET ▪ Start to develop iteratively and find ways to apply this logic to mission critical processes as well ▪ Openly communicate and promote advances and changes through technology to avoid fear and opposition building ▪ Allow new and radical ways of thinking and encourage experiments and failure EVALUATION OF NEW BUSINESS MODELS ▪ Think holistic! Consider how technology impacts and changes the way partners, customers, competitors and new market entrants act ▪ Be open to even license proprietary solutions to other market players in order to create new revenue streams ▪ Question current processes and evaluate external as-a-service offerings to keep focus on your core product START TODAY ▪ Value creation of AI is strongly correlated with the experience in this domain – the sooner you start, the higher your return ▪ Facilitate ideas in your company by small-scale prototypes or proof-of-concept projects ▪ Invest time into the exploration of possibilities that are out there already 1 2 3 4 5
  • 22. 22 LET’S TALK ABOUT BOOSTING YOUR BUSINESS WITH ARTIFICIAL INTELLIGENCE! We identify suitable AI use cases and help with target-oriented prioritization. IDENTIFY We create a concrete plan about requirements, project organization and feedback-driven iteration steps. PLAN We evaluate suitable solutions for your company-specific requirements EVALUATE We facilitate the development and implementation process and support you in the selection of partners. EXECUTE We provide decision confidence through process design and functional prototypes TEST We develop a future-proof data strategy based on the company's long-term goals SUSTAIN
  • 23. GET IN TOUCH! Tobias Bohnhoff Appanion Labs GmbH ▪ Hopfenstrasse 11 ▪ 20359 Hamburg ▪ www.appanion.com Discover more at www.appanion.com Disclaimer: This whitepaper is based on research and data of the previously mentioned sources. All information presented were researched and prepared by Appanion with great care. For the presented data and information Appanion cannot assume any warranty of any kind. Information and data represent in selected cases individual opinions and may be in need of further interpretation as a basis for decisions. Advances in research may occur after the publishing of this document. Therefore, Appanion is not liable for any damage arising from the use of information and data provided in this whitepaper.