THIS ABOVE IS A PROJECT ON ARTIFICIAL INTTELEGENCE. IT CAN ALSO BE USED TO TEACH CHILDREN ABOT ARTIFICIAL INTELLEGENCE IN FUN WAY. ALL THE INFORMATION GIVEN ABOVE ARE GIVEN IN A SYNCHRONISED WAY TO UNDERSTAND ABOUT ARTIFICIAL INTELLEGENCE IN FASTER WAY. MANY EXAMPLES ARE TAKEN FROM OUR DAILY LIFE WHERE ARTIFICIAL INTELLEGENCE ARE USED. HOW ARTIFICIAL INTELLENGENCE IS USED IN SPACE, DEFENCE ,MANUFACTURING, CARS, ROBOTS etc. ARTIFICIAL INTELLENCE USED IN VARIOUS SPACE PROGRAMS AND THE PLACES WHERE ARTIFICIA INTELLENGENCE PLAYS A PIVOITAL ROLE.

1. ARTIFICIAL
INTELLEGENCE

3. ARTIFICIAL INTELLEGENCE WAS FOUNDED AS AN
ACADEMIC DISCIPLINE IN 1955,AND IN THE YEARS
SINCE HAS EXPERIENCED SEVERAL WAVES OF
OPTIMISM,FOLLOWED BY DISAPPOINTMENT AND
THE LOSS OF FUNDING FOLLOWOED BY NEW
APPROACHES,SUCCESS AND RENEWED FUNDING.
FOR MOST OF ITS RESEARCH HAS BEEN DIVIDED
INTO SUB-FIELDS THAT OFTEN FAIL TO
COMMUNICATE WITH EACH OTHER . THESE SUB-
FIELDS ARE BASED ON TECHNICAL
CONSIDERATIONS,SUCH AS PARTICULAR GOALS
E.G-MACHINE LEARNING, THE USE OF
PARTICULAR TOOLS E.G LOGIC OR DEEP
PSYCHOLOGICAL DIFFERENCES.

7. ARTIFICIAL
INTELLEGENCE
TREE

8. USE OF AI IN
DIFFERENT
FIELDS
Use of AI in Following Things/Fields/Areas:
• Virtual Assistant or Chatbots.
• Agriculture and Farming.
• Autonomous Flying.
• Security and Surveillance.
• Sports Analytics and Activities.
• Manufacturing and Production.
• Transportation

9. ARTIFIAL INTELLEGENCE
USED IN TRANSPORTATION
AI IS USED TO ENABLE THE CARS TO NAVIGATE THROUGH
THE TRAFFIC AND HANDLE COMPLEX SITUATIONS. ALSO,
WITH A COMBINED AI SOFTWARE AND OTHER LOT
SENSORS,SUCH AS CAMERAS, IT BECOMES EASIER TO
ENSURE PROPER AND SAFE DRIVING.
IT CAN BE BEST SEEN IN THE TESLA CARS SYTEM WHICH
CONSISTS OF TWO AI CHIPS IN ORDER TO SUPPORT IT FOR
BETTER ROAD PERFORMANCE. EACH OF THE AI CHIPS
MAKES A SEPEATE ASSESSMENT OF THE TRAFFIC SITUATION
FOR GUIDING THE CAR ACCORDINGLY. THE ASSESSMENT OF
BOTH THE CHIPS IS THEN MATCHED BY THE SYSTEM AND
FOLLOWED IF THE INPUT FROM BOTH IS THE SAME

10. A.I USED IN HEALTH CARE
INDUSTRIES
• Radiology. AI is being studied within the radiology field to
detect and diagnose diseases within patients through
Computerized Tomography (CT) and Magnetic Resonance (MR)
Imaging. ...
• Screening. ...
• Psychiatry. ...
• Primary care. ...
• Disease diagnosis. ...
• Telemedicine. ...
• Electronic health records. ...
• Drug Interactions.

11. AI USED IN DRDO
BHARAT DRONES
THE DRDO BHARAT IS A LIGHT SURVEILLANCE QUAD COPTER UNMANED
AIRRIAL VEHICLE DEVELOP FOR THE INDIAN ARMY BY THE DEFENCE
RESEARCH AND DEVELOPMENT ORGINISATION FOR THE INDIAN ARMY.
IT IS EQUIPPED WITH NIGHT VISION CAPABILITIES AND STEALTHY
DESIGN ENSURES THAT ITS SIGNATURE REMAINS UNDETECTED FROM
RADARS IT IS INTEGRATED WITH ARTIFICIAL INTELLEGENCE TO DETECT
FRIENDS AND FOES ANS ACT ACCORDINGLY. IT IS THE WORLD'S MOST
AGILE AND LIHTEST SURVILLENCE DRONE AND IS CAPABLE OF
SURVIVING IN EXTREME WEATHER CONDITIONS ALONG THE LAC THE
UNIBODY BIOMIMETIC DESIGN WITH AVANCE TECHNOLOGY IS A
LEATHAL COMBINATION FOR SURVEILLANCE MISSIONS . THE DRONE
ALSO PROVIDES REAL-TIME VIDEO TRANSMISSION AND IS CAPABLE OF
DETECTING HUMANS UNDER DEEP FOREST COVERS.

15. 1:-Boston Dynamics.
2:-Canvas Technology.
3:-DroneSense.
4:-iRobot.
5:-Miso Robotics.
6:-Neurala.
7:-Rethink Robotics.
8:-Sea Machines.
BEST ROBOTS IN THE WORLD

16. BENEFITS OF ARTIFICIAL
INTELLIGENCE
1:-Reduction in Human Error: The phrase
human error” was born because humans
make mistakes from time to time
2:-Takes risks instead of Humans
3:-Available 24x7: ...
4:-Helping in Repetitive Jobs
5:-Digital Assistance
6:-Faster Decisions
7:-Daily Applications
8:-New Inventions

17. TYPES OF ARTIFICIAL INTELLIGENGE
1:-REACTIVE MACHINES
2:-LIMITED MEMORY
3:-THEORY OF MIND
4:-SELF-AWARENESS

18. MACHINE LEARNING
Machine learning is a sub-field of artificial
intelligence (AI) that provides systems the
ability to automatically learn and improve
from experience without being explicitly
programmed

19. TYPES OF MACHINE LEARNING
1:-SUPERVISED LEARNING
2:-UNSUPERVISED LEARNING
3:-REINFORCMENT LEARNING

20. SUPERVISED LEARNING
To predict future events. For the training procedure,
the input is a known training data set with its
corresponding labels, and the learning algorithm
produces an inferred function to finally make
predictions about some new unseen observations that
one can give to the model. The model is able to provide
targets for any new input after sufficient training. The
learning algorithm can also compare its output with
the correct intended output

21. For this family of models, the research needs to have at hand a dataset with some observations and the labels/classes
of the observations. For example, the observations could be images of animals and the labels the name of the animal
(e.g. cat, dog etc).

22. UNSUPERVISED LEARNING
Unsupervised learning studies how systems
can infer a function to describe a hidden
structure from unlabeled data. The system
doesn’t predict the right output, but instead, it
explores the data and can draw inferences
from datasets to describe hidden structures
from unlabeled data.

23. For this family of models, the research needs to have at hand a dataset with some
observations without the need of having also the labels/classes of the observations.

24. REINFORCMENT LEARNING
• Trial error search and delayed reward are the
most relevant characteristics of reinforcement
learning. This family of models allows the
automatic determination of the ideal behavior
within a specific context in order to maximize
the desired performance
• Reward feedback is required for the model to
learn which action is best and this is known as
“the reinforcement signal”.

27. CHALLENGESS
1:-Bias is one of the biggest
challenges facing AI.
2:-Computing Power. The tech industry
has faced computing power challenges
in the past.
3:-Integrating AI.
4:-Collecting and Utilizing Relevant
Data.
5:-Man Power.
6:-Implementation Strategies.
7:-Legal Issues.

28. THANKS FOR
WATCHING
BY -
RISHIK AND HARMAN