Download an analyst report about AI for business: https://ap.pn/2IZktjG For science fiction fans, Artificial Intelligence (AI) has always fired up the imagination. As a field of study, AI has been a part of academia since the mid-1950s. Since then, AI has been hyped as the key to our civilization’s future, and panned as nothing more than entertainment for nerds. However, over the past few years, AI has started to gain real traction. A lot of this has to do with the availability of powerful, cheaper and faster computing capability, the emergence of the Internet of Things, and the explosion of data generated as images, text, messages, documents, transactions, mapping and other data. Many companies are aggressively adopting AI, for instance, to free up highly-skilled workers from routine, repetitive, low-skilled tasks. Learn more. Get a briefing on AI: https://ap.pn/2IZktjG

1. Artificial Intelligence:
From Science Fiction to Alexa

2. Artificial Intelligence (AI) has always
fired up the imagination. As a field of
study, AI has been a part of
academia since the mid-1950s.
Since then, AI has been hyped as the
key to our civilization’s future, and
panned as nothing more than
entertainment for nerds.
Artificial Intelligence

3. Many companies are aggressively adopting
AI, for instance, to free up highly-skilled
workers from routine, repetitive,
low-skilled tasks.
International Data Corporation is
forecasting spending on AI and machine
learning will grow from $8B in 2016 to
$47B by 2020.
Adopting AI

4. Back to the Basics –
What is Artificial Intelligence?
John McCarthy, one of the fathers of AI, defined AI as
In other words, it's a way of making either hardware or software think
intelligently, similar to the way humans think.
„The science and engineering of making intelligent
machines, especially intelligent computer programs”.

5. What is Intelligence?
According to Intelligent Systems, the definition of intelligence is:
“The ability of a system to calculate, reason, perceive
relationships and analogies, learn from experience, store
and retrieve information from memory, solve problems,
comprehend complex ideas, use natural language
fluently, classify, generalize, and adapt new situations”.

6. When we talk about intelligence in a very simplified form, we are actually
involving several very complex functions:
Functions
Learning Reasoning Problem Solving
PerceptionLinguistic Intelligence

7. Machine Learning in its most basic form
is about designing “models.”
Models are composed of algorithms
that use data, learn from it, and
produce an “inference” or prediction
about something.
Machine Learning

8. Neural Networks are based on our
interpretation of the connections
between neurons in our brains.
While real neurons can connect to any
other neuron close enough in any
direction, artificial neural networks
have specific layers, connections, and
directions in which data travels.
Deep Learning – Neurons in layers

9. It’s all about the data.
AI’s Deep Learning power comes from its
ability to learn patterns from large amounts
of data.
This makes understanding data sets critical.
AI runs on data. Lots of data

10. You can divide the typical AI process in a sequence of steps:
Getting your hands dirty
Data Collection Data Preparation Choosing the Model
Parameter tuningEvaluation
Training the model
Inference or prediction

11. TensorFlow is probably the most popular deep
learning framework today.
It is an open source library originally developed by the
Google Brain Team, and the TensorFlow team has
created a large number of models, many of which
include trained model weights.
Tools and Frameworks

12. There are other powerful frameworks, of course,
like Caffe, PyTorch, and BigDL.
Also, simulators, such as Digital Twins, allow
developers to accelerate development of AI systems
and the reinforcement learning libraries that integrate
with them.
Tools and Frameworks

13. In general terms, we can catalog the primary machine learning tasks in four groups:
Machine learning tasks
Supervised
Learning
Unsupervised
Learning
Semi-supervised
Learning
Reinforcement
Learning

14. Over the years, different algorithms have been developed to resolve
different types of use cases, including:
Algorithms
Decision Tree
Learning
Inductive Logic
Programming
and Many OthersBayesian Networks
and Clustering
Reinforced
Learning

15. AI is increasingly being integrated into business processes across a number
of different areas. To name just a few:
Applications of AI
Sales and CRM
Applications
Payments and
Payment Services
ManufacturingCustomer
Recommendations
Logistics and
Delivery

16. Companies in all industries are rapidly
finding use cases where AI can be
successfully applied.
In the short term though, it seems
applications and use cases that have the
highest rate of success and adoption are
those that have a direct, measurable impact
or return on investment.
What’s next

17. Learn More
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