Artificial Intelligence.pdf nhbhbuhuuhjuj

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1. "ARTIFICIAL INTELLIGENCE”
(A.I)
"EXPLORINGTHEFUTUREOF
TECHNOLOGY"

2. Narrow AI
❑Artificial Intelligence (AI)
A simulates human intelligence in machines, enabling them to
think, learn, and solve problems.
❖Highlight of PPT
➢Impact of AI in today's world
➢Milestones in the development of AI
➢Types of AI
➢Machine Learning
➢AI in Industries
➢Ethical Considerations
➢Challenges and Limitations
➢AI and Society
➢Future Trends

3. ➢Automation and Efficiency
➢Data Analysis and Insights
➢Advancements in Research
➢Enhancing Education
➢Economic Impact
3
❖Impact of AI in Today’s World

4. ❖MILESTONES IN THE
DEVELOPMENT OF AI
1950:
Alan Turing’s
Turing proposes
the Turing Test
as a measure of
machine
intelligence, and
introduces key
concepts in AI.
1970s-
1980s:
AI Winter
Funding cuts
and unmet
expectations
lead to a
period of
reduced
interest and
progress in AI
research.
2000s:
Rise of Machine
Learning and
Big Data
Advances in
machine
learning, fueled
by large datasets
and
computational
power, drive AI
applications in
various
domains.
2010s:
Deep
Learning
Dominance
Deep
learning,
particularly
convolution
al neural
networks
and
recurrent
neural
networks
achieves
breakthroug
hs in
computer
vision, NLP,
and speech
recognition.
2020s:
Continued
Advancements
AI continues to
advance with
applications in
autonomous
vehicles,
healthcare
diagnostics,
natural
language
understanding,
and robotics.

5. ❖TYPES OF AI
Narrow AI General AI
➢AI systems designed to
perform specific tasks..
➢Limited to its
programming; cannot
perform tasks outside its
designated functions
➢Ex :- Siri, Alexa, Google
Translate, self-driving
cars.
➢Current State: Widely
used and implemented
in various industries.
➢Hypothetical AI with
human-like intelligence.
➢Understands, learns, and
applies knowledge across
a broad range of tasks..
➢Ex :- Currently non-
existent; theoretical
concept.
➢Future Potential:-
Transformative impact
with ethical
considerations.

6. ❖MACHINE LEARNING
6
❑Types of Machine
Learning
•Supervised Learning:
Trained on labeled data
(e.g., classification,
regression).
•Unsupervised
Learning: Finds
patterns in unlabeled
data (e.g., clustering,
association).
• Learning: Learns
through
rewards/punishments
from interactions.
❑Key Concepts
•Data: Foundation
for training models
(features, labels).
•Algorithms:
Decision trees,
neural networks,
support vector
machines.
•Training &
Testing: Building
and evaluating
model performance.
•Model Evaluation:
Metrics like
accuracy, precision,
recall, F1-score.
❑Applications
•NLP: Language
translation, sentiment
analysis, chatbots.
•Computer Vision:
Image recognition,
autonomous driving.
•Recommendation
Systems:
Product/content
suggestions.
•Healthcare:
Predictive diagnostics,
personalized
treatment.
❑Challenges
•Data Quality:
Requires high-
quality, relevant
data.
•Overfitting/Unde
rfitting: Balancing
model complexity.
•Ethics:
Addressing biases,
ensuring fairness
and transparency.

7. 7❖AI IN INDUSTRIES
➢Healthcare:-
Diagnosis, personalized medicine
➢Finance:-
Algorithmic trading, fraud detection
➢Transportation:-
Autonomous vehicles, logistics

8. 8
❖ETHICAL CONSIDERATIONS
➢Bias and Fairness:- Addressing and mitigating
biases in AI algorithms to ensure equitable outcomes.
➢Privacy Concerns and Data Security:
Protecting personal data and ensuring secure handling of
information.
➢Ethical Implications in Decision-Making:-
Ensuring transparency, accountability, and ethical
standards in AI-driven decisions.

9. 9
❖CHALLENGES AND LIMITATIONS
➢Technical Challenges:
Scalability, interpretability
➢Socio-Economic Impacts:
Job displacement, inequality
➢Regulatory Challenges:
AI governance and regulations

10. 10
❖AI AND SOCIETY
➢Impact of AI on Society and Everyday Life
•Automation and Employment: Job displacement and new job creation
•Healthcare: Improved diagnostics, personalized treatments, and telemedicine
•Education: Personalized learning and administrative efficiency
•Daily Life: Smart homes and virtual assistants
•Transportation: Autonomous vehicles and traffic management
•Finance: Fraud detection and data-driven investment decisions
➢Education and Skills Needed for an AI -Driven Future
•Technical Skills: Programming (Python, R, Java), Data Science, AI/ML concepts
•Soft Skills: Critical thinking, creativity, adaptability
•Ethical Awareness: Privacy, bias, societal impact, collaboration
•Lifelong Learning: Continuous education, online courses (Coursera, edX, Udacity)

11. 11❖FUTURE TRENDS
➢Autonomous Systems
•Self-driving vehicles, advanced robotics, and drones.
➢AI in Education
•Personalized learning and intelligent tutoring systems.
•Automated grading.
➢AI and AR/VR Integration
•Personalized, immersive AR/VR experiences.
•Advanced training and simulation capabilities.

12. THANK
YOU
- Arpit Dhagate-