Artificial Intelligence (AI) is like giving machines a mind of their own! When a machine can think, learn, and make decisions just like a human, it’s considered artificially intelligent. 

Here’s how it works:

• Mimicking Human Intelligence - Imagine a computer that can understand language, recognize faces, or even play chess better than any grandmaster.

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• Solving Real-World Problems - AI is not just about theoretical smarts; it’s about solving practical problems. Think about self-driving cars that navigate traffic safely, medical diagnosis systems that identify diseases from X-rays more accurately than doctors, or financial algorithms that detect fraudulent transactions in real time. 

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• Learning from Past Experiences - A fascinating feature of AI is its ability to learn and improve over time. This is called machine learning. For example, email spam filters get better at detecting unwanted messages the more emails they process. 

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• Predicting and Decision Making - AI systems can analyze massive amounts of data to make predictions and decisions on their own. For instance, weather forecasting models use AI to predict future weather patterns with high accuracy.​​​

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Examples of AI in action

1. Voice Assistants: Siri, Alexa, and Google Assistant can understand and respond to spoken language.

2. Self-Driving Cars: Tesla’s Autopilot system uses AI to navigate roads safely.

3. Healthcare: AI-powered diagnostic tools can identify diseases from medical scans.

4. Finance: Fraud detection systems use AI to monitor and identify suspicious activities.

5. Entertainment: Netflix’s recommendation engine suggests shows and movies based on your viewing history

 

Activity Time:

Can you think of a few more examples of AI in action around you? Write them down:​

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Advantages of AI

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1. Efficiency

   AI can perform tasks faster and more accurately than humans, increasing productivity. For example, automated systems in factories work continuously without breaks.

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2.Improved Healthcare​

   AI helps doctors diagnose diseases more accurately and quickly. AI algorithms can analyze medical images and detect issues that might be missed by humans.

 

3. Personalized Experiences

   AI personalizes our experiences, like how Netflix recommends movies based on your viewing history.

 

4. Enhanced Safety

   AI improves safety, such as in self-driving cars, which use AI to navigate roads and avoid accidents.

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Disadvantages of AI

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1. Job Loss

   AI can replace human jobs, leading to unemployment in sectors like manufacturing and customer service.

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2. Lack of Human Touch

   AI lacks emotional understanding, so it can't provide the empathy and personal connection a human can.

 

3. High Cost

   Developing and implementing AI technology can be very expensive, requiring advanced hardware, software, and skilled personnel.

 

4. Privacy Concerns

   AI systems often need large amounts of data, raising privacy concerns about how personal information is collected and used.

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Brief History of Artificial Intelligence

 

1950s: Birth of AI

• 1950: Alan Turing publishes "Computing Machinery and Intelligence," proposing the Turing Test to measure a machine's ability to exhibit intelligent behavior.

 

• 1956: The Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, officially coins the term "artificial intelligence" and sets the stage for AI research.

 

1960s-1970s: Early Development

• Researchers develop basic AI programs, including early natural language processing systems and simple game-playing programs like IBM's chess-playing computer.

 

• 1966: ELIZA, an early natural language processing computer program, is created by Joseph Weizenbaum.

 

1980s: AI Winter and Expert Systems

• AI experiences a period of reduced funding and interest, known as the "AI winter," due to unmet expectations.

 

• Despite setbacks, expert systems, which simulate the decision-making ability of a human expert, gain popularity in industries like medicine and finance.

 

1990s: Renewed Interest and Advances

• Advances in machine learning and data mining rekindle interest in AI.

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• 1997: IBM's Deep Blue defeats world chess champion Garry Kasparov, demonstrating AI's potential in complex tasks.

 

2000s: Machine Learning and Big Data

• AI research shifts focus to machine learning, enabling systems to learn from data and improve over time.

 

• 2011: IBM's Watson wins the quiz show "Jeopardy!" against human champions, showcasing advancements in natural language processing and information retrieval.

 

2010s: Deep Learning and AI Integration

• Deep learning, a subset of machine learning involving neural networks with many layers, leads to breakthroughs in image and speech recognition.

 

• AI becomes integrated into everyday applications like virtual assistants (Siri, Alexa), recommendation systems (Netflix, Amazon), and autonomous vehicles (Tesla).

 

2020s: AI in Society

• AI continues to evolve, impacting healthcare, finance, transportation, and more.

 

• Ethical considerations and regulations around AI become increasingly important, addressing issues like bias, privacy, and job displacement.

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Activity Time:

Write down your favourite era from the history of AI and why?

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​Types of AI

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  1. Broad AI:

Broad AI systems are capable of executing various tasks across various fields. Broad AI
would truly replicate human intelligence and help us leverage true power of AI.
Examples of broad AI:

• Generalize knowledge and apply it as applicable to different circumstances

• Use knowledge and experience acquired to plan for the future

• Alter and adapt to circumstances as things shift

• Ability to reason

 

  2. ​Narrow/ weak AI:

Narrow AI is an application of artificial intelligence technologies to enable a high-
functioning system that replicates -- and perhaps surpasses -- human intelligence for a

dedicated purpose.
Examples of narrow AI:

• Siri by Apple, Alexa by Amazon, Cortana by Microsoft and other virtual assistants

• IBM’s Watson

• Image / facial recognition software

• Disease mapping and prediction tools

• Manufacturing and drone robots

• Email spam filters / social media monitoring tools for dangerous content

• Entertainment or marketing content recommendations based on watch/listen/purchase behaviour

 

What is Turing Test?

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 The Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being. The test is named after Alan Turing, the founder of the Turing Test and an English computer scientist, cryptanalyst, mathematician and theoretical biologist.

 
Experiencing AI in real life - Chatbots

1966, ELIZA was the first natural language programming(NLP)- based conversation program described by Joseph Weizenbaum.​

Depending on the how they are programmed, they can be categorized as:

•  Simple Chatbot- Simple chatbots have limited capabilities, and are usually called rule-based bots. They are task-specific. The chatbot will not make any inferences from its previous interactions. These chatbots are best suited for straightforward dialogues. They are very simple to build and train.

• Example: Ordering Pizza - When a customer interacts with a chatbot to order pizza.

•  Smart Chatbot- AI-enabled smart chatbots are designed to simulate near-human interactions with customers. They can have free-flowing conversations and understand intent, language, and sentiment. These chatbots require programming to help it understand the context of interactions. They are much harder to implement and execute and need a lot of data to learn.

       Example: Virtual Assistants.

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Domains of Artificial Intelligence

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