This document provides an overview of artificial intelligence (AI), including definitions, a history of AI, current applications and challenges. It begins with definitions of AI and discusses early milestones like the first AI conference in 1956 and Deep Blue defeating a chess champion in 1997. It outlines current progress in deep learning, natural language processing, robotics and autonomous vehicles. Key challenges are biases in data, lack of transparency in models, issues with data availability and security/privacy. The future of AI is discussed in terms of advancements in deep learning, increased healthcare applications, and focus on ethics.

1. ARTIFICIAL INTELLIGENCE

2. CONTENTS
 DEFINATION
 EARLY HISTORY
 TYPES OF AI
 CURRENT STATUS OF AI
 CHALLENGES OF AI
 FUTURE OF AI
 CONCLUSION

3. DEFINATIOIN
 The study of computer system that attempt to model
and apply the intelligence of human mind
 A branch of computer science dealing with the
simulation of intelligent behaviour in computers.
 The capability of a machine to imitate intelligent
human behaviour.

4. EARLY HISTORY OF AI
 1956 - John McCarthy coined the term ‘artificial intelligence’ and had the first
AI conference.
 1969 - Shakey was the first general-purpose mobile robot built. It is now able to
do things with a purpose vs. just a list of instructions.
 1997 - Supercomputer ‘Deep Blue was designed, and it defeated the world
champion chess player in a match. It was a massive milestone by IBM to create
this large computer.
 2002 - The first commercially successful robotic vacuum cleaner was created.
 2005 - 2019 - Today, we have speech recognition, robotic process automation
(RPA), a dancing robot, smart homes, and other innovations make their debut.
 2020 - Baidu releases the LinearFold AI algorithm to medical and scientific and
medical teams developing a vaccine during the early stages of the SARS-CoV-2
(COVID-19) pandemic. The algorithm can predict the RNA sequence of the
virus in only 27 seconds, which is 120 times faster than other methods.

5. TYPES OF AI
1 Purely Reactive
2 Limited Memory
3 . Theory of Mind
4 Self-Aware

6. CURRENT STATUS OF AI
Deep Learning:
Natural Language Processing (NLP):
Robotics:
Autonomous Vehicles
Healthcare.

7. CHALLENGES OF AI

8. AI models rely on large amounts of data to learn and make
decisions. However, if the data used to train these models
is biased or of poor quality, it can lead to inaccurate results
and reinforce existing biases.

9. : Many AI models are complex and opaque, making it difficult to
understand how they make decisions. This lack of transparency
can lead to mistrust and hinder adoption of AI systems.

10. AI models rely heavily on data to learn and make predictions.
Ensuring that the data is accurate, representative, and available
can be a significant challenge, particularly in industries or
applications where data is scarce or difficult to collect.

11. As AI becomes more prevalent, the risk of cyberattacks and data
breaches increases. Ensuring the security and privacy of data is
crucial to protecting individuals and organizations from harm.

12. AI has the potential to significantly impact society, and its
development and deployment must consider ethical implications. Key
considerations include issues such as accountability, responsibility,
and the potential impact of AI on employment and the economy.

13. FUTURE OF AI
 Advancements in deep learning and neural networks
 Increased use of AI in healthcare
 Greater focus on ethical considerations:
 Advancements in natural language processing:
 Continued growth in robotics: :

14. In conclusion, artificial intelligence (AI) is a rapidly growing
field with tremendous potential for transforming many aspects
of our lives. AI-powered applications are already being used in
industries such as healthcare, finance, and transportation, and
we can expect to see continued growth and innovation in the
future.

15. THANKYOU