Artificial intelligence refers to systems that mimic human intelligence by learning, reasoning, and solving problems. Examples include medical diagnosis, voice recognition, search engines, and self-driving cars. Machine learning is a branch of AI that allows systems to learn from data without being explicitly programmed. Key techniques in machine learning include supervised learning, unsupervised learning, and reinforcement learning. AI and machine learning are integrated by using machine learning algorithms to power data mapping, processing, and analysis to derive business intelligence and insights from large datasets.

1. Artificial
Intelligence
intelligence by machines
Submitted by Group-3

3. Artificial intelligence
Artificial intelligence refers to systems or machines that mimic
human intelligence to perform tasks and can iteratively improve
themselves based on the information they collect.
Examples
Medical diagnosis
Voice or Handwriting recognition
Chatbox
Computer Search Engines
Self-driving Cars
Automated Financial Assistant

4. Components of AI
Learning
Reasoning
Problem Solving
Perception
Language

6. MACHINE LEARNING
It is branch of artificial intelligence that enables
computer to "self learn" from training data &
improves over time, without being explicitly
programmed
Learning
Predicts
Ordinary
system
Machine
Learning
With AI
Improves

7. Techniques in M L
Supervised Learning Reinforcement Learning
This type of machine learning
comprises three major
In this type labeled data is used
for the training data. Supervised
learning technique helps us to
predict future events with the
help of past experience
components: the agent, the
environment, and the actions.
Unsupervised Learning
A machine is trained with some
input samples, while output is
not known. Thus machine may
not always provide correct
output

8. How AI & ML are Integrated
Data mapping powered by AI can automate
data transformation mapping by providing
advanced features and agile data mapping
predictions with machine learning algorithms.
01
02 The autonomous learning capability of AI and
machine learning enable enterprises to learn
more about hidden patterns and trends from
large datasets, so that accurate business
intelligence and insights are derived from them
through applying statistical models.
Data processing with machine learning-powered data
integration tools can parse big data generating precise
data models that require less human intervention
03

9. Tesla cars come standard with advanced hardware capable of providing
Autopilot features, and full self-driving capabilities—through software updates
designed to improve functionality over time
Tesla's Autopilot AI enables your car to steer, accelerate and brake.
Navigate on Autopilot - All you will need to do is get in and tell your car where to go. Don’t
say anything, your car will look at your calendar and take you there as the assumed
destination.
Standard Safety Features like Automatic Emergency Braking, Front Collision Warning, Side
Collision Warning, Auto High Beams
Eight cameras and powerful vision processing provide 360
degrees of visibility at up to 250 meters of range.
Each Tesla car has a powerful set of vision processing tools
developed by Tesla. Built on a deep neural network, Tesla Vision
deconstructs the car's environment at greater levels of
reliability than those achievable with classical vision processing
techniques

11. Apple stands out compared to other tech companies, given its family of proprietary softwares,
i.e., the iOS operating system in-built with Safari, Maps, Photos, iMessage, FaceTime and more.
Apple’s services go beyond these to leverage AI, ML, deep learning and neural networks to provide
you with the smart tech you didn’t even know you needed.
FaceID uses facial recognition patterns to generate a specific model for your face. This
way, it’s easier to identify your face so you can unlock or perform functions on your device.
Siri - Siri is built on large-scale machine learning algorithms that combine speech
recognition and natural language processing (NLP)
Apple watch - Detecting sleep patterns, Heartbeat, irregular
movements
Apple TV - Suggests content based on user preferences.
Smart HDR photo mode - like Automatic Emergency Braking, Front
Collision Warning, Side Collision Warning, Auto High Beams

12. SOPHIA -THE AI ROBOT
Social human like AI Capable of displaying
humanlike expressions
and interacting with
people.
robot developed by
Hong Kong based
company Hanson
Robotics
Designed for
World 1st robot
citizen & 1st robot
Innovation
research,
education, and
entertainment,
promote discussion
about AI ethics and
future of robotics
Ambassador for UN
Develpment
Programme

13. Health-Care
Detecting Skin Cancer
Recognise Depression
Reading CT scan
Drug development

14. This technology provides
the surgeon with a 10x
magnified, high-definition,
3D-image of the body’s
intricate anatomy.
Less Tissue damage
Minimal blood loss
Reduce risk of infection

15. Associated Risks
Unclear liability for
Action
Humans can be prosecuted for
their mistakes but in the case of
machines, it will be termed an
accident.
Threat to Jobs:
AI itself is a big threat to
traditional jobs.
Transparency is
not Ensured
Dangerous to
privacy
Many AI systems were
created using neural networks
that are less capable of
showing the intentions for its
decision making them less
transparent.
AI recognizes our personal
data, and it can be a threat to
our privacy and personal data

16. Future of AI and ML
Creating the Future
The Future of Neural technology Auto-ML- Facilitate end-to-end
01
02
03 model development
04 Health care and pharma
Quantum machine learning

17. Thank You