Emerging technology refers to new technologies that are currently developing or expected within 5-10 years that will have significant social or economic effects. Examples include educational technology, information technology, nanotechnology, biotechnology, cognitive science, artificial intelligence, machine learning, deep learning, data science, natural language processing, 5G, blockchain, quantum computing, and serverless computing. AI means creating machines that can behave, think, and make decisions like humans through technologies like machine learning, deep learning, and data science. AI is growing rapidly and creating intelligent machines that can solve real-world problems accurately. Machine learning, deep learning, and data science are subsets of AI. Machine learning enables computers to learn from data through supervised, unsupervised,

1. What do you mean by Emerging Technology?
Emerging technology is a term generally used to describe a new technology, but it
may also refer to the continuing development of an existing technology; The term
commonly refers to technologies that are currently developing, or that are expected
to be available within the next five to ten years, and is usually reserved for
technologies that are creating, or are expected to create, significant social or
economic effects.
Emerging technologies include a variety of technologies such as
 Educational technology
 Information technology
 Nanotechnology
 Biotechnology
 Cognitive science
 Artificial Intelligence(AI)
 Machine Learning(ML)
 Deep Learning(DL)
 Data Science(DS)
 Natural Language Processing.(NLP)
 5G and the Internet of Things.
 Block chain.
 Quantum Computing
 Server less Computing
What is AI ?
AI is composed of two words: Artificial and Intelligence where “Artificial “
defines as “ Man-Made” and “Intelligence” defines as “Thinking Power”. Hence
AI means “ a man made thinking power”.

2. AI can be define as :
 It is a branch of Computer science by which we can create intelligent
machines which can behave like a human,
think like a human
and able to make decisions.
 AI suggest that machines can mimic humans in
Talking
Thinking
Learning
Planning
Understanding.
 AI is a system that can solve a problem optimally. This means that the
system can figure out alone which is the best action to take.
Why AI?
In today’s world, technology is growing very fast and we are getting in touch
with different new technologies day by day. So AI is the one of the blooming
technology in computer science which is ready to create a new revolution in the
world by making intelligent machines. The AI is now all around us. It is
recurrently working with a variety of subfields, ranging from general to specific
such as self driving cars, playing chess, playing music, printing etc.
Using AI:
1. We can create software or devices which can solve real world
problems very easily and with accuracy such as health issues,
marketing analysis, traffic issues.
2. We can create our personal virtual assistant such as Google Assistant,
Siri and many mores.
3. We can build such Robots which can work in an environment where
survival of humans can be at risk.

3. AI vs ML vs DL vs DS
AI, Machine learning, and Deep learning - these terms overlap with each other.
Let us consider a Venn Diagram :
In the above diagram, Outer big circle represents AI. This is the final goal and
you are creating an AI application. E.g- Self driving car that is an AI
application.

4. It helps us to enable the computer /machine to think (that basically means
without any human intervention, the machine will be taken own decision.
AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable
computers to figure things out from the data and deliver AI applications.
Supervised Machine learning :
It is ML method in which we provide sample labeled data to the machine
learning system in order to train it, and on that basis, it predicts the output.
Example: Spam filtering
Unsupervised learning:
It is a learning method in which a machine learns without any supervision.
The training is provided to the machine with the set of data that has not been
labeled
Example:
K-means clustering
KNN (k-nearest neighbors)
Reinforcement learning:
It is a feedback-based learning method, in which a learning agent gets a
reward for each right action and gets a penalty for each wrong action.
Example: robotic dog, which automatically learns the movement of his arms, is an
example of Reinforcement learning.
In a nutshell, supervised learning is when a model learns from a labeled dataset
with guidance. And, unsupervised learning is where the machine is given training
based on unlabeled data without any guidance. Whereas reinforcement learning is
when a machine or an agent interacts with its environment, performs actions, and
learns by a trial-and-error method.
Deep learning(DL), meanwhile, is a subset of machine learning that enables
computers to solve more complex problems.

5. It is the multi Neural N/W Architecture
Adjective “Deep” refers to multiple layers. It is also known as Deep Structured
Learning.
Different types of Neural Networks in Deep Learning:
 Artificial Neural Networks (ANN)
 Convolution Neural Networks (CNN)
 Recurrent Neural Networks (RNN)
Data Science(DS) works on AI,ML,DL by using some mathematical
techniques like statistics, probability, linear algebra, Differential calculus
and many more.
To be precise, Data Science covers AI, which includes machine learning.
However, machine learning itself covers another sub-technology — Deep
Learning.
Data Science, Artificial Intelligence and Machine Learning are lucrative
career options. However, truth is neither of the fields are mutually
exclusive. There’s often an overlap when it comes to the skill set required
for jobs in these domains.
Data Science roles such as Data Analyst, Data Science Engineer, and
Data Scientist are trending for quite some time.
Data Science is in the future:
No businesses or industries for that matter will be able to keep up without
data science. A large number of transitions have already happened
worldwide where businesses are seeking more data-driven decisions, more
is to follow suit. Data science quite rightly has been dubbed as the oil of the
21st century which can mean endless possibilities across industries.