Lesson 1: What is AI?

Lesson 1: What is AI?

Module 1: Essential AI

Did you know...?

A recent survey by KMPG on AI Literacy in Germany found that: Less than half of users (45%) feel able to evaluate AI applications appropriately or use them correctly. Many use AI tools without questioning the results (43%). This puts Germany in second-last place of the 47 countries surveyed in terms of AI literacy, behind many comparable economies. 12 May 2025, Source :

Where do you want to be in those statistics?

In this lesson we will look at:

Definition of AI

AI vs. Regular Software

Brief History of AI

How You Define AI

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A simple definition of AI: "AI is when computers can do tasks that usually need human thinking, like reasoning, learning, sensing or acting. It aims to help technologies to solve problems and make decisions like humans do."

Some familiar examples:

• Voice assistants (Siri, Alexa tell you the weather).

• Recommendation systems (Netflix suggestions, YouTube recommendations).

• Navigation apps (Google Maps finding the best route).

Can you think of other AI technologies around you?

AI Definition: learn more

If you want to know more about what AI is, you can watch the short videos below. Choose between the English or German videos, or watch both if you prefer. We recommend selecting the option to 'Watch on YouTube' so you can see it full screen and add subtitles if needed.

AI can handle situations it hasn't been specifically programmed for (or at least try to).

AI vs. Regular Software: What's the difference between regular software and AI?

Regular computer programs follow exact step-by-step instructions (like a recipe).

AI programs can learn and adapt from experience (like how humans get better at tasks with practice).

A calculator always adds 2+2=4 (regular program), but AI can learn that a photo shows a dog even if it has never seen that exact dog before.

AI Vs. Regular Software:

Brief History of AI

1990-2000sAI Gets Connected

1950Turing Test

2016-nowAI Everywhere

1980-90sAI Winter & Recovery

1960-70sEarly AI Programs

Brief History of AI: learn more

If you want to know more about the history of AI, you can watch the short videos below. Choose between the English or German videos, or watch both if you prefer. We recommend selecting the option to 'Watch on YouTube so you can see it full screen and add subtitles if needed.

How I define AI:

Before adding your own definition to our course document, practice below by completing the gaps with the options (drag and drop).

How I define AI:

Now click on the link below and write your own AI definition. This will be great practice for our upcoming live session.You can write it in English or German. You can add your name next to your definition, if you like.

Click here to open the document

Lesson 1 Summary: What is AI?

Click on the boxes below to reveal a summary of each of the points covered in this lesson.

Definition of AI

AI vs. Regular Software

Brief History of AI

How You Define AI

Click here to download the lesson summary

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Next Steps:

Lesson 5:AI Limitations & Considerations

Lesson 4:Basic Prompting

Lesson 1: What is AI?

Lesson 3:Types of AI

Lesson 2: How AI works

Click here to add your reflection, comments and feedback on 'Lesson 1: What is AI?'

References

Click on the blue links to learn more.

• Video: What is AI? - Museum of Science

• Video: Künstliche Intelligenz einfach erklärt, explainity® Erklärvideo

• Article: AI defined in simple terms

• Wikipedia: Artificial Intelligence

Click here to return to your course page and start Lesson 2: How AI Works.

The Smartphone and Social Media Era

• AI became part of daily life: voice assistants (Siri, Alexa), photo tagging, Netflix recommendations.
• 2016: AI beat human champions at complex games like Go.
• 2020s: ChatGPT and similar AI can write, answer questions, and help with creative tasks.
• AI is now in our phones, cars, homes, and workplaces - helping with everything from navigation to medical diagnosis.

Alan Turing's Big Question

• British mathematician Alan Turing asked: "Can machines think?"
• Created the "Turing Test": if a computer can fool a human into thinking it's human during a conversation, it might be considered intelligent.
• This became the starting point for thinking about artificial intelligence
• Simple idea: If it acts smart, maybe it is smart.

The Internet Changes Everything

• AI programs could now access vast amounts of information online.
• Search engines like Google used AI to find relevant web pages.
• Machine learning improved - AI got better at recognizing patterns in large amounts of data.
• AI became part of everyday life without people realizing it (email spam filters, recommendation systems).

The Reality Check Period

• Early AI promises didn't come true - computers weren't as smart as people hoped.
• This period called "AI Winter" - less funding and interest in AI research.
• But then AI found practical uses: expert systems helped doctors diagnose diseases and companies make decisions.
• AI started becoming useful in the real world, just not in the dramatic ways people first imagined.

AI Definition:

• AI enables computers to perform tasks that typically require human intelligence, such as reasoning, learning, sensing, and acting.
• The goal of AI is to empower technology to solve problems and make decisions in a human-like way.
• Common examples of AI in action include voice assistants (like Siri and Alexa), recommendation systems (like Netflix suggestions), and navigation apps (like Google Maps).
• AI enhances computer learning, reasoning, and perception, with applications spanning various industries like finance and healthcare.
• While there are concerns about its societal impact, AI has demonstrated significant benefits, such as improving healthcare treatments and diagnoses, and detecting fraud in finance.

Defining AI: Key Considerations

• Simulation of Human Intelligence: AI is fundamentally about machines performing tasks that typically require human cognitive functions like learning, reasoning, and decision-making.
• Learning and Adaptability: A core aspect of AI is its ability to learn from data and experience, improving its performance and making predictions without explicit programming for every scenario.
• Problem-Solving and Decision-Making: AI systems are designed to analyse complex information and make informed choices or inferences to solve problems.
• Autonomy (with varying degrees): AI often implies systems that can operate and adapt to new situations without constant human intervention.
• Perception and Interaction: AI enables machines to "perceive" their environment through various inputs (like vision or language) and interact accordingly.

First AI Success Stories

• Scientists created the first programs that could solve problems and play games.
• ELIZA (1966) - a computer program that could have simple conversations, like an early chatbot.
• Programs that could play checkers and solve math problems.
• People got very excited and thought AI would solve everything quickly (they were too optimistic!).

How AI works:

AI vs. Traditional Programs:

• Traditional computer programs operate based on rigid, step-by-step instructions, similar to following a precise recipe. They execute pre-defined commands.
• AI programs, in contrast, possess the ability to learn and adapt from experience, much like humans improve with practice. This allows them to evolve their capabilities over time.

Learning and Adaptability:

• A regular program, like a calculator, will always produce the same output for the same input (e.g., 2+2=4).
• AI can go beyond pre-programmed responses. For example, it can identify a dog in a photo even if it hasn't encountered that specific image before, demonstrating its capacity for generalization and pattern recognition.
• This enables AI to effectively handle novel situations or data that it hasn't been explicitly programmed to address, showcasing its flexibility and problem-solving abilities.

Brief history of AI:

• 1950s - The Turing Test: Alan Turing proposed a test to determine if a machine could exhibit intelligent behavior indistinguishable from a human, laying the groundwork for AI concepts.
• 1960s-1970s - Early AI Programs: Initial AI programs emerged, capable of simple conversations (like ELIZA) and playing games, sparking significant, though overly optimistic, excitement.
• 1980s-1990s - AI Winter & Recovery: A period of reduced interest and funding ("AI Winter") occurred as early promises went unfulfilled, but AI found practical applications in areas like expert systems.
• 1990s-2000s - AI Gets Connected: The internet revolutionized AI, enabling access to vast data for search engines and improving machine learning, subtly integrating AI into daily life.
• 2010s-Present - AI Everywhere: AI became pervasive in daily life through voice assistants, recommendations, and advancements in complex tasks like beating human champions in games, leading to powerful AI models like ChatGPT.

AI Definition:

• AI enables computers to perform tasks that typically require human intelligence, such as reasoning, learning, sensing, and acting.
• The goal of AI is to empower technology to solve problems and make decisions in a human-like way.
• Common examples of AI in action include voice assistants (like Siri and Alexa), recommendation systems (like Netflix suggestions), and navigation apps (like Google Maps).
• AI enhances computer learning, reasoning, and perception, with applications spanning various industries like finance and healthcare.
• While there are concerns about its societal impact, AI has demonstrated significant benefits, such as improving healthcare treatments and diagnoses, and detecting fraud in finance.

How AI works:

AI vs. Traditional Programs:

• Traditional computer programs operate based on rigid, step-by-step instructions, similar to following a precise recipe. They execute pre-defined commands.
• AI programs, in contrast, possess the ability to learn and adapt from experience, much like humans improve with practice. This allows them to evolve their capabilities over time.

Learning and Adaptability:

• A regular program, like a calculator, will always produce the same output for the same input (e.g., 2+2=4).
• AI can go beyond pre-programmed responses. For example, it can identify a dog in a photo even if it hasn't encountered that specific image before, demonstrating its capacity for generalization and pattern recognition.
• This enables AI to effectively handle novel situations or data that it hasn't been explicitly programmed to address, showcasing its flexibility and problem-solving abilities.

Brief history of AI:

• 1950s - The Turing Test: Alan Turing proposed a test to determine if a machine could exhibit intelligent behavior indistinguishable from a human, laying the groundwork for AI concepts.
• 1960s-1970s - Early AI Programs: Initial AI programs emerged, capable of simple conversations (like ELIZA) and playing games, sparking significant, though overly optimistic, excitement.
• 1980s-1990s - AI Winter & Recovery: A period of reduced interest and funding ("AI Winter") occurred as early promises went unfulfilled, but AI found practical applications in areas like expert systems.
• 1990s-2000s - AI Gets Connected: The internet revolutionized AI, enabling access to vast data for search engines and improving machine learning, subtly integrating AI into daily life.
• 2010s-Present - AI Everywhere: AI became pervasive in daily life through voice assistants, recommendations, and advancements in complex tasks like beating human champions in games, leading to powerful AI models like ChatGPT.

Defining AI: Key Considerations

• Simulation of Human Intelligence: AI is fundamentally about machines performing tasks that typically require human cognitive functions like learning, reasoning, and decision-making.
• Learning and Adaptability: A core aspect of AI is its ability to learn from data and experience, improving its performance and making predictions without explicit programming for every scenario.
• Problem-Solving and Decision-Making: AI systems are designed to analyse complex information and make informed choices or inferences to solve problems.
• Autonomy (with varying degrees): AI often implies systems that can operate and adapt to new situations without constant human intervention.
• Perception and Interaction: AI enables machines to "perceive" their environment through various inputs (like vision or language) and interact accordingly.