The document discusses various AI technologies including machine learning, deep learning, robotic process automation, virtual agents, speech recognition, AI-optimized hardware, natural language generation, decision management, biometrics, and text analytics. For each technology, it provides a definition, example use cases, and benefits. It also discusses the differences between machine learning and deep learning, as well as RPA and AI. Finally, it poses a question about which technology could help a business be more efficient and includes a quote from Bill Gates on automating efficient versus inefficient operations.

1. • Day 1: Definition, Concept
• Day 2: Components
• Day 3: Technologies
• Day 4: Business Cases
• Day 5: Transformation
Step by Step AI
Martin Polozadeh

2. AI Technologies
Day 3
Which technology can help you?

3. Machine learning
Definition
The basic premise of
machine learning is to build
algorithms that can receive
input data and
use statistical analysis to
predict an output while
updating outputs as new
data becomes available.
Use case
• Adjusting insurance
premiums based on
publicly available risk
factors
• Weather prediction
Benefits
Ability to estimate what
would happen in the
event of a change, or
what would have
happened if the change
hadn’t taken place

4. Definition
Deep Learning is a subfield
of machine learning
concerned with algorithms
inspired by the structure and
function of the brain called
artificial neural networks.
Deep Learning
Use case
• Automatic Translation
• Self-service solutions
Benefits
Possibility to use a
model to discover and
learn from user
activities and take an
action based on that
knowledge

5. In machine learning
algorithms that parse data,
learn from that data, and then
apply what they’ve learned to
make informed decisions
Machine Learning vs. Deep Learning
A deep learning model is designed to
continually analyze data with a logic
structure similar to how a human would
draw conclusions. To achieve this, deep
learning uses a layered structure of
algorithms called an artificial neural
network (ANN).
If an ML algorithm returns an inaccurate prediction, then an engineer needs
to step in and make adjustments. But with a deep learning model, the
algorithms can determine on their own if a prediction is accurate or not.

6. Definition
RPA is an application of
technology, governed by
business logic and
structured inputs, aimed
at automating business
processes.
Robotic Process Automation
Benefits
Possibility to replace
repetitive, manual
tasks by automation
and create
opportunity to have
employees working
with tasks that
require height
cognitive capability
Use case
• Regular diagnostics
• Track Profit and Loss
• Onboarding process

7. RPA is about
performing task
automatically and
without mimic human
thinking
RPA vs. AI
AI is about performing
task automatically by
mimic human thinking

8. Definition
software that can simulate a
conversation (or a chat) with a
user in natural language
through messaging
applications, websites, mobile
apps or through the
telephone.
Virtual Agents
Benefits
Delivers an unified,
high available and
scalable assistant to
end user
Use case
• Chatbot
• Internal and external help
desk
• Collecting customer
feedback

9. Definition
The ability of a machine
or program to identify
words and phrases in
spoken language and
convert them to a
machine-readable format.
Speech Recognition
Benefits
Delivers an unified, high
available and scalable
assistants to end user
Use case
• Customer service
• Voice-controlled
automotive environment

10. Definition
processing units and
appliances that are
specially designed for
AI-oriented tasks
AI-Optimized Hardware
Benefits
Enables more local
autonomous AI
processing and
reducing the need to
communicate with the
cloud for AI processing
Use case
Companies investing
heavily in machine
learning and AI

11. Natural Language Generation
Definition
Natural language
generation (NLG) is a
software process that
automatically
transforms data into
written narrative.
Benefits
Ability to generate
summary report using
the customer’s unique
set of information and
speak directly to the
individual
Use case
• Written analysis for business
intelligence dashboards
• Personalized customer
communications via email
and in-app messaging
• IoT device status and
maintenance reporting

12. Decision Management
Definition
Decision management
is a process or set of
processes for improving
and streamlining action
items.
Benefits
Increasing the
precision, consistency
and agility of decisions
and making good
choices taking known
risks and time
constraints into
consideration
Use case
• Automated operation
decision process

13. Biometrics
Definition
Enables more natural
interactions between
humans and machines,
including but not
limited to image and
touch recognition,
speech, and body
language.
Benefits
Ability to remove
identification from
something you know
(like a password) and
augment it with who
the actual user is.Use case
Biometric facial
recognition

14. Text Analytics and NLP
Definition
NLP is the scientific
discipline concerned
with making natural
language accessible to
machines
Benefits
harness unstructured
data and make it more
meaningful to a
machine
Use case
• Fraud detection and
security
• Text analyses
automated assistants

15. Questions
Which technology will help your business to be
efficient?
“The first rule of any technology used in a business is that
automation applied to an efficient operation will magnify the
efficiency. The second is that automation applied to an
inefficient operation will magnify the inefficiency.”
- Bill Gates (Microsoft CO-founders)

16. Next
Business cases

17. References
• Machine Learning What it is and why it matters: https://www.sas.com/en_hk/insights/analytics/machine-learning.html
• AI and decision management: https://www.allerin.com/blog/ai-and-decision-management
• Automating Operational Business Decisions Using Artificial Intelligence: an Industrial Case Study: http://publications.lib.chalmers.se/records/fulltext/184462/184462.pdf
• Machine Learning Development Environment: https://machinelearningmastery.com/machine-learning-development-environment/
• 8 Fun Machine Learning Projects for Beginners: https://elitedatascience.com/machine-learning-projects-for-beginners#health-care
• 8 Inspirational Applications of Deep Learning: https://machinelearningmastery.com/inspirational-applications-deep-learning/
• An easy introduction to Natural Language Processing: https://towardsdatascience.com/an-easy-introduction-to-natural-language-processing-b1e2801291c1
• Goal of the speech recognition system: http://shodhganga.inflibnet.ac.in/bitstream/10603/40667/7/07_chpater2.pdf
• Top 10 Hot Artificial Intelligence (AI) Technologies: https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/#44ca04461928
• What Will The Impact Of Machine Learning Be On Economics: https://www.forbes.com/sites/quora/2016/01/27/what-will-the-impact-of-machine-learning-be-on-
economics/#69785e2b600f
• New AI systems on a chip …. https://siliconangle.com/2018/04/13/new-ai-systems-chip-will-spark-explosion-even-smarter-devices/
• The Ultimate Guide to Natural Language Generation: https://medium.com/@AutomatedInsights/the-ultimate-guide-to-natural-language-generation-bdcb457423d6
• Decision management: https://whatis.techtarget.com/definition/decision-management
• The Evolution Of Natural Language Processing And Its Impact On AI: https://www.forbes.com/sites/forbestechcouncil/2018/11/06/the-evolution-of-natural-language-
processing-and-its-impact-on-ai/#1f62472a1119
• Technology Quotes: ://www.brainyquote.com/quotes/bill_gates_104353?src=t_technology
• A simple way to understand machine learning vs deep learning: https://www.zendesk.com/blog/machine-learning-and-deep-learning/