CBSE Syllabus for Class 9 Artificial Intelligence (AI) - Eduique

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Artificial Intelligence Class 9 Edunique
Artificial intelligence is the simulation of
human intelligence processes by machines,
especially computer systems. Specific
applications of AI include expert systems,
natural language processing, speech recognition,
and computer vision. How does AI work? As the
hype around AI has picked up, vendors have
struggled to promote the way their products and
services use AI. Often times what they call AI is
just a component of AI, like machine learning.
AI requires a base of specialized hardware and
software to write and train machine learning
algorithms. No programming language is synonymous
with AI, but some, including Python, R, and
Java, are popular. In general, AI systems work
by ingesting large amounts of labeled training
data, analyzing the data for correlations and
models, and using those models to make
predictions about future states. This way, a
chatbot that receives text chat examples can
learn how to produce realistic exchanges with
people, or an image recognition tool can learn to
identify and describe objects in pictures by
browsing millions of images. Examples. AI
programming focuses on three cognitive skills
learning, reasoning, and self-correction. Learnin
g process. This aspect of AI programming focuses
on acquiring data and creating rules on how to
turn data into actionable insights. Rules, called
algorithms, provide computing devices with
step-by-step instructions on how to accomplish a
specific task. Reasoning process. This aspect of
AI programming focuses on choosing the right
algorithm to achieve the desired
result. Self-correction process. This aspect of
AI programming is designed to continually tune
algorithms and ensure that they deliver the most
accurate results possible. Why is artificial
intelligence important? Artificial intelligence
is important because it can provide companies
with information about their operations that
they may not have known before, and because in
some cases artificial intelligence can perform
tasks better than humans. . Especially when it
comes to repetitive and detailed tasks, such as
analyzing a large number of legal documents to
ensure that the relevant fields are filled in
correctly, artificial intelligence tools often
finish the work quickly. and with relatively few
errors. This has contributed to an explosion in
efficiency and opened the door to whole new
business opportunities for some large companies.
Before the current wave of AI, it would have
been hard to imagine using computer software to
connect passengers to taxis, but today Uber has
grown into one of the biggest companies in the
world doing just that. It uses sophisticated
machine learning algorithms to predict when
people are likely to need trips in
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• certain areas, helping drivers proactively start
  before they need it. As another example, Google
  has become a major player in a variety of online
  services by using machine learning to understand
  how people use its services and then improve
  them. In 2017, the company's CEO, Sundar Pichai,
  said Google would operate as an AI first
  company.
• Today's largest and most successful companies
  have used artificial intelligence Class 9 to
  improve their operations and gain advantages over
  their competitors.
• What are the advantages and disadvantages of
  artificial intelligence?
• Artificial neural networks and deep learning
  artificial intelligence technologies are evolving
  rapidly, mainly because AI processes large
  amounts of data much faster and makes
  predictions with greater accuracy than is humanly
  possible. .
• While the sheer volume of data created daily
  would bury a human researcher, artificial
  intelligence applications using machine learning
  can take that data and quickly turn it into
  actionable information. At the time of this
  writing, the main downside to using AI is that it
  is expensive to process the large amounts of
  data required by AI programming.
• Advantage
• Good at detail-oriented work
• Reduced time for data-intensive tasks
• Offers consistent results and
• Virtual agents with artificial intelligence
  technology are always available.
• Disadvantages
• This requires a great deal of technical
  experience

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into four types, starting with task-specific
intelligent systems that are widely used today
and progressing to sensitive systems. . , which
do not yet exist. The categories are as
follows Type 1 Reactive machines. These AI
systems have no memory and are task specific. An
example is Deep Blue, the IBM chess program that
defeated Garry Kasparov in the 1990s. Deep Blue
can identify pieces on the chessboard and make
predictions, but since it has no memory it can
cannot use past experiences to inform future
ones. Type 2 limited memory. These artificial
intelligence systems have memory, so they can
use past experiences to inform future decisions.
Some of the decision-making functions in
autonomous vehicles are designed this way. Type
3 Theory of the mind. Theory of mind is a term
of psychology. Applied to AI, this means that
the system would have the social intelligence to
understand emotions. This type of AI will be
able to infer human intentions and predict
behavior, a skill necessary for AI systems to
become full members of human teams. Type 4
Self-awareness. In this category, AI systems have
a sense of themselves, which gives them
awareness. Self-conscious machines understand
their own present state. This type of AI does
not yet exist. What are the examples of AI
technology and how is it used today? AI is
embedded in various types of technologies. Here
are six examples Automating. When combined with
artificial intelligence technologies, automation
tools can increase the volume and types of tasks
performed. One example is robotic process
automation (RPA), a type of software that
automates repetitive rule-based data processing
tasks traditionally performed by humans. When
paired with machine learning and emerging AI
tools, RPA can automate larger parts of business
work, enabling RPA tactical bots to spread AI
intelligence and respond to process
changes. Machine learning. It's the science of
running a computer without programming. Deep
learning is a subset of machine learning which,
in very simple terms, can be thought of as the
automation of predictive analytics. There are
three types of machine learning algorithms
Supervised teaching. Data sets are labeled so
that patterns can be detected and used to label
new data sets. Unsupervised learning. Data sets
are not labeled and are ranked based on
similarities or differences. Reinforced
learning. The datasets are not labeled, but after
performing one or more actions, the AI system
receives feedback. Industrial vision. This
technology gives a machine the ability to see.
Machine vision captures and analyzes visual
information using a camera, analog-to-digital
conversion, and digital signal processing. It is
often compared to human sight, but computer
vision is not limited by biology and can be
programmed to see through walls, for example. It
is used in a variety of applications, from
signature identification to medical image
analysis. Computer vision, which focuses on
computer image processing, is often associated
with machine vision. Natural language processing
(NLP). It is the processing of human language by
a computer program. One of the oldest and most
well-known examples of NLP is spam detection,
which examines the subject line and text of an
email and decides whether it is spam. Current
approaches to NLP are based on machine learning.
NLP tasks include text translation, sentiment
analysis, and speech recognition.
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Robotics This area of engineering focuses on the
design and manufacture of robots. Robots are
often used to perform tasks that are difficult
for humans to perform or consistently perform.
For example, robots are used on assembly lines
for automobile production or at NASA to move
large objects in space. Researchers are also
using machine learning to build robots capable
of interacting in social environments. Autonomous
cars. Autonomous vehicles use a combination of
computer vision, image recognition, and deep
learning to develop automated skills to steer a
vehicle while staying in a certain lane and
avoiding unexpected obstacles, such as
pedestrians. What are the applications of
AI? Artificial intelligence has found its place
in a wide variety of markets. Here are nine
examples. AI in health. The biggest bets are on
improving patient outcomes and reducing costs.
Businesses apply machine learning to perform
better and faster diagnostics than humans. One
of the most well-known health technologies is IBM
Watson. Understands natural language and can
answer questions asked. The system extracts
patient data and other available data sources to
form a hypothesis, which it then presents with a
confidence scoring scheme. Other AI applications
include the use of virtual health assistants and
online chatbots to help patients and health care
clients find medical information, schedule
appointments, understand the process. billing and
other administrative processes. Various
artificial intelligence technologies are also
used to predict, combat and understand pandemics
like COVID-19. AI in business. Machine learning
algorithms are built into customer relationship
management (CRM) and analytics platforms to
discover how to better serve customers. Chatbots
have been integrated into websites to provide
immediate service to customers. Task automation
has also become a topic of conversation among
academics and IT analysts. AI in education. AI
can automate grading, giving teachers more time.
You can assess students and adapt to their
needs, helping them to work at their own pace. AI
tutors can provide additional support to
students, making sure they stay on track. And it
could change where and how students learn,
perhaps even replacing some teachers. AI in
finance. AI in personal finance apps like Intuit
Mint or TurboTax is disrupting financial
institutions. Apps like these collect personal
data and provide financial advice. Other
programs, like IBM Watson, have been applied to
the home buying process. Today, artificial
intelligence software conducts a large portion of
transactions on Wall Street. AI in law. The
discovery process (document review) in law is
often overwhelming for humans. Using artificial
intelligence Class 9 to help automate
labor-intensive processes in the legal industry
saves time and improves customer service. Law
firms use machine learning to describe data and
predict outcomes, computer vision to classify and
extract information from documents, and natural
language processing to interpret requests for
information.
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AI in manufacturing. Manufacturing has been at
the forefront of integrating robots into the
workflow. For example, industrial robots that
were once programmed to perform individual tasks
distinct from human workers increasingly function
as cobots smaller, multitasking robots that
collaborate with humans and take responsibility
for more of the work. warehouse work. ,
factories. and other workspaces. AI in banking.
Banks successfully use chatbots to keep their
customers up to date with services and offers
and to manage transactions that do not require
human intervention. AI virtual assistants are
used to improve and reduce the costs of banking
regulatory compliance. Banking organizations are
also using artificial intelligence to improve
their lending decision-making, set credit
limits, and identify investment
opportunities. AI in transport. In addition to
AI's essential role in the functioning of
autonomous vehicles, AI technologies are used in
transportation to manage traffic, predict flight
delays, and make shipping safer and more
efficient.
Security. Artificial intelligence and machine
learning are at the top of the list of buzzwords
that security providers use today to
differentiate their offerings. These terms also
represent truly viable technologies.
Organizations use machine learning in security
information and event management (SIEM) software
and related fields to detect anomalies and
identify suspicious activity that indicates
threats. By analyzing data and using logic to
identify similarities to known malicious code,
AI can provide alerts on new and emerging attacks
long before human employees and previous
iterations of technology. Mature technology plays
an important role in helping organizations fight
cyberattacks. Augmented intelligence versus
artificial intelligence Some industry experts
believe the term artificial intelligence is tied
too closely to popular culture, which has led
the general public to have unlikely expectations
of how AI will change the workplace and life in
general. . Intelligence increased. Some
researchers and marketers are hoping that the
augmented intelligence label, which has a more
neutral connotation, will help people understand
that most AI implementations will be weak and
simply improve products and services. Examples
include automatically displaying important
information in business intelligence reports or
highlighting important information in legal
presentations. Artificial intelligence. True AI,
or general artificial intelligence, is closely
associated with the concept of technological
singularity a future ruled by an artificial
superintelligence that far exceeds the ability
of the human brain to understand it or the way it
shapes our reality. This remains in the realm of
science fiction, although some developers are
working on the issue. Many believe that
technologies like quantum computing could play an
important role in realizing AGI and that we
should reserve the use of the term AI for this
type of general intelligence. Ethical use of
artificial intelligence
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While artificial intelligence tools present an
array of new features for businesses, the use of
artificial intelligence also raises ethical
questions because, for better or worse, an
artificial intelligence system will enhance what
it is. he has already learned. This can be
problematic because the machine learning
algorithms, which underpin many of the more
advanced artificial intelligence Class 9 tools,
are only as smart as the data provided during
training. Since a human selects the data used to
train an AI program, the potential for machine
learning bias is inherent and should be closely
monitored. Anyone looking to use machine
learning in real production systems should
consider ethics in their AI training processes
and strive to avoid bias. This is especially true
when using AI algorithms that are inherently
inexplicable in deep learning applications and
contradictory generative networks (GANs). The
explanation is a potential barrier to the use of
AI in industries that operate under strict
regulatory compliance requirements. For example,
financial institutions in the United States
operate under regulations that require them to
explain their credit issuance decisions. However,
when the decision to deny credit is made using AI
programming, it can be difficult to explain how
the decision was made because the AI tools used
to make such decisions work by revealing subtle
correlations between thousands of variables. When
the decision-making process cannot be explained,
the program can be called AI black box.