Artificial intelligence leverages computer science and large datasets to enable problem-solving capabilities similar to the human mind. There are two main types: weak AI which is designed for specific tasks, and strong AI which can solve unfamiliar problems autonomously. AI has many applications including speech recognition, automated trading, customer service chatbots, and computer vision. Key developments in AI include Alan Turing's 1950 paper, the coining of the term in 1956, and recent advances in neural networks that have led to systems that can beat humans at games like Go. Potential issues with AI include bias, privacy concerns, job loss, and lack of generalizability.

1. Artificial Intelligence

2. AI -What is it?
 Artificial intelligence leverages computers and machines to
mimic the problem-solving and decision-making capabilities of
the human mind
 Combines computer science and robust datasets, to enable
problem-solving. It also encompasses sub-fields of machine
learning and deep learning, which are frequently mentioned in
conjunction with artificial intelligence


3. Strong Vs Weak AI
 Weak AI, also known as narrow AI, is designed and trained to
complete a specific task. Industrial robots and virtual personal
assistants, such as Apple's Siri, use weak AI
 Strong AI, also known as artificial general intelligence (AGI),
describes programming that can replicate the cognitive abilities
of the human brain. When presented with an unfamiliar task, a
strong AI system can use fuzzy logic to apply knowledge from
one domain to another and find a solution autonomously

4. Applications
 Speech recognition
 Automated trading
 Customer service (Chatbots etc...)
 Recommendation systems
 Computer vision

5. Evolution-Key Dates
 1950: Alan Turing publishes Computing Machinery and Intelligence.
 1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI
conference at Dartmouth College
 1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer
based on a neural network that 'learned' though trial and error.
 1980s: Neural networks which use a backpropagation algorithm to train itself
 1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in
a chess match

6. Evolution-Key Dates
 2011: IBM Watson beats champions Ken Jennings and Brad Rutter at
Jeopardy!
 2015: Baidu's Minwa supercomputer uses a special kind of deep
neural network called a convolutional neural network to identify and
categorize images with a higher rate of accuracy than the average
human.
 2016: DeepMind's AlphaGo program, powered by a deep neural
network, beats Lee Sodol, the world champion Go player
 2022: A rise in large language models, or LLMs, such as ChatGPT

7. Basic Components
 Learning
 Reasoning
 Problem solving
 Perception
 Language understanding

8. Pros
 Good at detail-oriented jobs
 Reduced time for data-heavy tasks
 Saves labor and increases productivity
 Delivers consistent results
 Can improve customer satisfaction through personalization
 Available 24*7

9. Cons
 Expensive.
 Requires deep technical expertise.
 Limited supply of qualified workers to build AI tools.
 Reflects the biases of its training data, at scale.
 Lack of ability to generalize from one task to another.
 Eliminates human jobs, increasing unemployment rates

10. Types
 Reactive machines -AI systems have no memory and are task-
specific (Chess)
 Limited memory -AI systems have memory, so they can use
past experiences to inform future decisions (Self driving car)
 Theory of mind - When applied to AI, it means the system would
have the social intelligence to understand emotions
 Self-awareness. In this category, AI systems have a sense of
self, which gives them consciousness.

12. Ethical Challenges
 Bias, due to improperly trained algorithms and human bias
 Misuse, due to deepfakes and phishing
 Legal concerns, including copyright issues
 Elimination of jobs
 Data privacy concerns, particularly in the banking, healthcare
and legal fields.

13. AI Technologies
 Automation
 Machine learning
 Machine vision
 Natural language processing
 Robotics
 Autonomous vehicles
 Generative AI

14. AI Decision making
 Speed and efficiency
 Automating workflows
 Doing complex problem solving
 Removing biases
 Outcome prediction
 Customer understanding

15. Deep Learning
 Deep Learning is a subfield of Machine Learning that involves the use of neural
networks to model and solve complex problems
 Neural network - method that teaches computers to process data in a way that is
inspired by the human brain.
 Neural networks (ANNs) are comprised of a node layers, containing an input
layer, one or more hidden layers, and an output layer.
 A neural network that consists of more than three layers—which would be
inclusive of the inputs and the output—can be considered a deep learning
algorithm.
 RNN, CNN

17. Types