Intelligent Technologies Behind  Artificial Intelligence
                     INTELLIGENT 
TECHNOLOGIES BEHIND 
ARTIFICIAL INTELLIGENCE
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THE SUPER 
TECHNOLOGIES 
01 Machine Learning Neural network 04
OF AI
02 Deep Learning Cognitive 05
computing 
Natural Language  
03
Processing (NLP) Computer vision 
06
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“You're only as good as the tools you use.”
A phrase that’s been echoed for many years highlighting the 
significance of using the right tool for the right job. 
Like a painter knows about selecting the right color for their painting, 
so does the technologists building the AI system. As much as we’ve 
been harkening buzzwords such as AI and machine learning, do we 
even know what AI truly is? Or are weall blindfolded by the trending 
technology that we barely know the logic that runs behind AI?
Therefore, it is certainly important for humans to understand AI and 
the mechanism behind it. Certain people still don’t know that we’re 
already incorporating AI in our lives one way or the other. Ironic, yet 
true. 
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The Ground Breaking Reality
Here’s what you need to know. Behind AI there’s always those 
intelligent technologies. Oh, wait, which technologies are you talking 
about? Well, there aren’t five but six technologies you need to be well 
acquainted in AI. 
Many of the AI jargon include machine learning, predictive analytics, 
and deep learning. These cutting-edge technologies are transforming 
the way humans interact with data. Not to mention, it has taken a toll 
on revolutionizing the jobs market.
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01 Machine 
Learning
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• MACHINE LEARNING
Machine learning, the first step most organizations are adding to their AI-related technologies within 
the IT portfolio. ML, a subset of AI gives the ability to the system to automatically learn and improve 
from experience without needing to be programmed. Machine learning’s focus is solely on developing 
algorithms that analyze data to make future predictions.
• Role of Machine Learning in AI
As mentioned, it is an AI technique that allows the computer system to learn 
without being explicitly programmed.
•  Used Cases
Netflix, you’ve probably heard this one – using machine learning technologies, 
Netflix generates multiple variations of high-probability click-through images 
(thumbnails) directly interacting with the user. Besides Netflix, Amazon, Spotify, and 
Facebook use the same AI-related solutions to interact with consumers daily.
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02 Deep 
Learning
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 2. DEEP LEARNING
Deep learning otherwise called self-educating machines employs artificial neural networks that learn by 
processing data. The artificial neural network helps mimic the biological neural networks that go on 
inside the human mind.
• Role of Deep Learning in AI
With the help of multipl he layers of the artificial neural network, single output from 
multiple inputs can easily be determined. For instance, it can identify the image of a 
face from a mosaic of tiles. These machines learn from negative and positive 
reinforcement of the functions they perform. Now, this requires constant processing 
and reinforcement for progression.
• Used Cases
Image recognition, speech recognition, recommendation systems in Netflix and 
Amazon,
and drug discovery and toxicology.
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03 Natural Language 
Processing (NLP)
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 3. NATURAL LANGUAGE PROCESSING (NLP)
NLP helps the computer system interpret, understand, and develop the human language. They 
do not just speak in zeros or ones; however, the goal of a machine learning is to understand 
what humans speak.
• Role of Natural Language Processing in AI
The core focus of NLP is to enable seamless interaction with machines by teaching 
the system how to recognize and comprehend human language and produce logical 
results.
• Used Cases
The best example is the Skype translator – it helps interpret the speech of 
multiple languages that too in real-time.
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04 Neural 
Network
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4. NEURAL NETWORK
The neural network acts as the enabler for deep learning. Simply said, neural networks are 
computer systems that have been modeled after the neural connections present in the brain.
• Role of neural network in AI
The neural network learns by processing the training examples. In a similar manner 
where neurons create neural networks inside the brain, there is apile of perceptron 
that helps develop an artificial neural network in the computer system.
• Used Cases
Imagine the dataset composes of thousands of cat photos. By processing multiple 
images (inputs) the machine can detect one single output, whether the photo is a cat 
or not.
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05 Cognitive 
Computing
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5. COGNITIVE COMPUTING
Another major component of AI that imitates and improves the interaction between both the 
machine and human. Cognitive modeling is here to recreate what humans are thinking and 
compile it in a computer model.
• Role of cognitive computing in AI
Cognitive computing helps in making inferences from the context by understanding 
the meaning of the images and understanding human language.
• Used Cases
With the help of data mining, cognitive computing can be used in identifying risk 
points such as fraud detection and chatbots. Industries like insurance, healthcare, and 
customer service have started adopting cognitive computing at every level.
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06 Computer Vision
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6. COMPUTER VISION
It is a unique technique that uses deep learning along with pattern identification to make interpretations 
of images – this includes images within PDFs, tables, graphs, texts, videos, and documents. It is all 
about recognizing images the way humans can, this includes not just the pixels but what the pixel 
represents.
• Role of computer vision in AI
Being an integral part of AI, computer visions helps the computers identify the data, 
process it, and finally make an interpretation of the data.
• Used Cases
The utilization of computer vision is already seen within industries such as healthcare 
and research and development.
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The impact of AI is already being felt across the industries. 
The technology is inevitably transforming lives today. 
AI IS HERE TO MAKE US MORE HUMAN, AND 
NOT LESS HUMAN.
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