Artifical intelligence

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
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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