AI is expanding with an edge on the mainstream breakthrough. AI will be involved in all spheres of our life in future. It is important for us to understand what AI is, what it’s terms means, and what are the AI terminologies. Below are some AI terms. We, NuAIg helps businesses to reap the benefit of AI for their revenue growth with cost reduction.

1. NUAIG PRESENTS
AI professional
vocabulary collection
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2. Why: Your Reason
To Understand AI
AI TERMINOLOGIES
The field of artificial intelligence continues to expand, standing on the
edge of the precipice of mainstream breakthroughs.AI will be more
involved in our day today life in the near future. So, it is important to
understand some basic AI terminologies. We will see AI in almost all
technical gadgets in the near future. Understand the below terms
before starting for AI.
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3. MACHINE LEARNING
(ML):
Machine learning belongs to a
branch of artificial intelligence.
Machine learning acts as a means to
solve artificial intelligence problems.
It is a process by which AI algorithms
use AI functions by applying rules to
create results. Machine learning
designs and analyzes AI algorithms
that helps a computer to “learn” the
same as humans. In general, machine
learning is an important collection of
methods to realize artificial
intelligence.One of the examples of
machine learning is image
recognition.
ARTIFICIAL
INTELLIGENCE (AI):
Artificial intelligence is an evolving
modern technology that automates
manual tasks, thus making machines
in an efficient manner. AI generates a
sense of intelligence in machines,
same as human intelligence works. AI
is an abbreviation used for Artificial
intelligence. AI is a field of computer
science, which works on data
sampling and making decisions thus
solving problems. AI is all about
training and testing a machine to
develop an intelligence power into
the machine.
SUPERVISED ML:
Supervised learning is a training
method/learning method in machine
learning. When you train an AI model
using supervised learning methods,
you provide the machine with the
correct answers in advance. Basically
AI knows the answer, it knows the
question. This is the most commonly
used training method because it
produces the most data: it defines
the pattern between questions and
answers. If you want to know why or
what happened, AI can look at the
data and use supervised learning
methods to determine connections.
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UNSUPERVISED
ML:
TURING TEST: NEURAL
NETWORK:
Unsupervised Learning is an artificial
intelligence networks algorithm.
Unsupervised learning does not know
the result prior like supervised learning,
here the system is unaware whether the
classification result is correct or not
when learning. In the case of
unsupervised learning, we will not give
AI an answer. Instead of finding
predefined patterns like “why people
choose one brand over another”, we
provide a machine with a bunch of data
so that it can find the pattern it needs.
Based on the input examples it finds the
potential rules and then applies the
classification. New cases can be applied
after learning and testing of the system.
The test was originally thought to be a
way to determine whether humans
might be fooled by dialogue, only in
the text display, the confusion
between human intelligence and
artificial intelligence, and then it has
become an abbreviation for any AI that
can deceive people into believing they
are watching to or interact with real
people. The field of artificial
intelligence research is not science
fiction, although it is exciting and
avant-garde.
The underlying model of artificial
intelligence is the “neural network” .
Applications such as pattern
recognition, automatic control and
advanced models such as deep
learning are based on neural
networks. Learning artificial
intelligence must start from it. Neural
networks try to imitate the human
brain or, as we now understand, the
process of simulating the human brain.
Third, the development of neural
networks can only be achieved with
high-end processors in the past few
years.Essentially, it means a large
number of layers.
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5. ROBOTIC PROCESS AUTOMATION (RPA):
Robotic Process Automation (RPA) is an emerging business process automation technology.
RPA allows configuration of computer software or robots to integrate and simulate human
interaction in any system. The RPA system captures data using the user interface and uses it
like humans. To perform a variety of repetitive tasks RPA interpret, trigger responses, and
communicate with other systems. An RPA software robot never sleeps, makes zero mistakes,
and its cost is much lower than an employee.In short, RPA as a set of applications that “enable
organizations to effectively automate tasks, simplify processes, increase employee
productivity, and ultimately provide a satisfying customer experience.”
NATURAL LANGUAGE PROCESSING (NLP):
Natural language processing is a branch of artificial intelligence and linguistics. This field
explores how to process large amounts of natural language data and train systems to use
them in computer programs .It requires advanced neural networks to parse human language.
When artificial intelligence is trained to explain human communication, it is called natural
language processing. This is very useful for chatbots and translation services, but it also
represents AI assistants like Alexa and Siri.
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6. REINFORCEMENT
LEARNING:
Compared with humans, artificial
intelligence is more like humans. We
learn almost the same way. One way
to teach a machine like a robot is to
use reinforcement learning. This
involves providing artificial
intelligence with a goal that is not
defined by specific metrics, such as
telling it to “improve efficiency” or
“find a solution.” Instead of finding a
specific answer, the AI will run the
plan and report the results, and then
people will evaluate and evaluate it.
AI accepts feedback and adjusts the
next scene for better results.
COMPUTER VISION:
The process by which a model
predicts which specific known group
or groups a new input belongs to.To
give a specific example: In order to
help keep the Gmail inbox clean and
data safe, the ML model runs in the
background and continuously
classifies each email as spam or non-
spam (if any questions arise in the
process, Gmail will ask you to verify
the email address of the unknown
sender). In this example, “spam” is
one group, and “non-spam” is
another group. This classification is
called “binary classification”.
DEEP LEARNING:
The concept of deep learning
originated from the research of
artificial neural networks, but it is not
completely equal to traditional neural
networks. Deep learning is an
algorithm in machine learning based
on characterization learning of data .
The goal of representation learning is
to find better representation methods
and build better models to learn
these representation methods from
large-scale unlabeled data. You may
teach your AI to understand cats, but
once it understands that AI can apply
this knowledge to different tasks.
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