Title: AI Basics
Course: AI Guide Academy > Section: AI-Basics (AIB)

Module overview

🚀AI Basics

Welcome to this module! 🎉 By the end of this module, you'll understand why you should/ or shouldn't use LLMs, how to interact with them effectively, and what to watch out for when you are evaluating AI tools. You will not only actively work on your AI skills but also work on a capstone project that you can use for your work.

🏆 Capstone Project: Your own AI Evaluation Workflow

Your overarching task for this module is to design a structured process for evaluating AI tools. You will undergo this process several times, so let everything you learn in this module influence your evaluation workflow!

At the end of this module, we ask you to:
✅ Define 3 very small real-world use cases where AI could help (e.g. searching for literature, summarizing a script, creating quiz questions).
✅ Select 2 AI tools for each use case.
✅ Compare & evaluate these tools using your workflow.

We require both the workflow and the results for the live training!

🔍 What will you learn in this module?

1️⃣ What Are LLMs & How Do They Work? 🤖

- What AI & LLMs are and how they differ from other technologies.
- How LLMs are trained, their core functions, and their limitations.

2️⃣ Prompting - Easy to learn, hard to master 🎯

- What prompting is and why you profit from good prompts.
- How to structure effective prompts to get the best results.
- Adopting the right mindset when working with AI.

3️⃣ Evaluating - Can AI do that? 📊

- Benchmarks & Metrics: Understanding which AI benchmarks are useful.
- How to realistically evaluate LLMs for your own needs.
- A quick and structured approach to deciding if a tool is right for you.

4️⃣ The Limits & Ethical Considerations of AI ⚖️

- Understanding what AI should and should not do today.
- Key ethical dilemmas and how to form your own stance.

If you are on the parrot track we have an extra task for you.

LLMs

First of all: What is AI?

Share your mindmap here!

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Written Version:

🎉 Hi there! Here's your chance to really wrap your head around the concept of Artificial Intelligence (or AI).

First off, it's clear that there's a lot of debate about the definition of AI. Some folks are convinced that everything is AI, whereas others argue that only specific use cases fit the bill. 🤷 It isn't very clear, we know! Even experts have a hard time finding a definition everyone can agree on.

This video tutorial aims to clear up that confusion, offering you a firm grasp of AI. In this Chapter, we will try to explore with you what AI really means and how it works. By the end of it, you'll be able to differentiate between different AI types and will be able to tell how they work. Pretty useful, right? 🙌 At least we hope so.

Before diving into the rest of the module, grab a piece of paper and sketch a mind map of everything you currently know about AI. As the videos play into even richer content, continue updating your map, adding or modifying parts where necessary. At the end, you will have made quite a journey and seen what you learned.

What are LLMs?

What is an LLM?

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What is AI, really?

Honestly, I don’t have a final answer to that. Basically, we’re trying to give human capabilities to machines (minus the coffee addiction and existential crises 🤖☕). And that covers a lot of ground. In short, AI covers anything that mimics human skills, involves some form of intelligence, and is done by a machine.

A smaller area within this is machine learning. Normally, when we code, we define each step clearly, first this, then that. But with machine learning, we let the machine figure it out. We give it inputs and a situation, like where it is in a room with someone else, and then let it learn how to act, for example, by playing a game like tag 🏃‍♀️🤖.

The machine learns by trying things out, it sees what works and what doesn’t. If it does well, we give it positive feedback 👍 if not, we don’t 👎. Over time, it learns how to succeed without us programming every step.

A powerful example of this is neural networks. These systems are built to mimic the way a brain works 🧠, with layers of decision-making. Input comes in, decisions are made at each layer, and those choices guide the next steps. It goes on like that until it reaches an output.

From this, we also get transformer models, ones that can pay attention to context and understand things like language. That’s where models like GPT-4 come in. They’re trained to recognize patterns, focus on what matters, and improve through human feedback.

With that in mind, let’s go explore! 🚀

Time for our task:

The task at hand is to discover an AI application that doesn't involve Generative AI. Search for fun stuff, be it videos, websites or papers!

Post it in the forum!

How does an LLM work?

How LLMs work

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Understanding how a language model (LLM) works may seem incredibly complex, but a few steps are quite clear and worthwhile to understand.

1️⃣ Tokenizing: Whatever text you input, be it a greeting or a question, the LLM breaks it down into smaller parts, or "tokens." These could be whole words, or even parts of words — the idea is to make it manageable for the computer.

2️⃣ Number Assignment: These tokens are transformed into unique numerical IDs as the AI can handle numbers better than text.

3️⃣ Embedding: The numbers then get context by being placed in a higher-dimensional "embedding" space. This is like a description of the word. Think of it as giving the word numerous attributes to better understand its meaning in a particular sentence — like the word "king" might be associated with "royal," "male," "rich," and so on.

4️⃣ Attention: Once the attributes are understood, the LLM assigns importance to them according to their relevance in the context of the sentence — this is called attention. It helps the model focus on what matters most, like understanding that 'Paris' was 'beautiful', not 'he'.

5️⃣ Context Window: The AI has a "context window" that limits how much it can hold in its 'short-term memory'. Depending on the model, this can range from 1k to 128k tokens.

6️⃣ Prediction: Then, the LLM tries to predict the next word based on given input. It doesn't just guess — it calculates probabilities and lists likely words that come after a given phrase or word.

7️⃣ Temperature and Sampling: Instead of always choosing the most probable word, the LLM introduces some randomness via temperature and sampling. Higher temperatures (like 0.8 or 0.9) make the model more creative, while lower ones (around 0) make it more predictable. So you could say it’s a bit random. 🎲

So, with a seemingly simple text input, an LLM runs through an intricate process to give you relevant output! 💬👩‍💻

Check your understanding:

Check your understanding of how LLM works with this short quiz:

How_LLM_works.h5p

Here is a little challenge for you:

Let's solve a little challenge. You heard that LLMs do quite a few interesting things before answering. So choose one of these challenges and explain why LLMs behave the way they do:

1️⃣ Consistent Answers: AI might struggle to give consistent answers to the same question, such as "How do I look today?". This becomes particularly complicated when used in contexts like exams.

2️⃣ Citing Sources: This section explores why AI often falls short in correctly citing sources. It is an important skill in many academic and professional environments, yet AI systems seem to struggle with it. What's behind this?

3️⃣ Reading Lengthy Texts: Many have noticed that AI's answers tend to deteriorate when it's asked to comprehend and respond to questions about a whole book. Is the length of the text the reason? Why do the responses improve when the text is shorter?

4️⃣ Counting Items: Most AI systems (except a few models like ChatGPT's o1 struggle to correctly count "r"s, such as the ones in "strawberry". Why is this seemingly straightforward task so hard for AI?

Answer one of these problems here!

Using LLMs efficiently

What we will see in this chapter:

In this chapter, we talk about how to treat genAI and how we can ensure that we get the most from our prompts. But before we start: How do you treat AI? Do you see it as a person or just a tool you use? Do you address it as "he" or "she," 🚻 or simply as "it?" Could it have a personality in your perception, or does it seem plain and boring? 💭🤖 How do you picture AI? How to best treat LLMs and how to prompt?

Update your mindmap and share it!

How to talk to AI

The Parrot Trainee

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Written Version:

Let's dive into the picture of the 'Parrot Trainee' to explain the concept of how to work with generative AI. The 'Parrot' signifies the AI’s ability to mimic human language perception but without a full understanding — much like a parrot repeating words without comprehending them. The speaker stresses that the AI 'parrot' relies heavily on the quality of the data we feed it; high-quality data will yield better responses, while "feeding it trash will result in trash out". 🦜➡️🗑️

Moving on to the second part, 'Trainee' depicts a zealous worker — quite clever, eager to assist, but who can sometimes overstep bounds due to their enthusiasm and missing knowledge of your problem. In order to get the best out of the trainee, clear instructions and regular evaluations of their output are required. You should be able — and willing — to assess the parrot trainee’s performance. Only then can you be sure to produce a good result.

In conclusion, generative AI is like a 'Parrot Trainee'. It is capable, but requires careful data input, ongoing assessment of its output, and regular feedback for optimum performance. That is our picture of generative AI.

The question is: What is your picture?

Your turn!

We want you to create your own illustration of generative AI. How is your relationship with genAI? Please write a paragraph or two about why you think your picture of AI is helpful and create a picture with whatever tool you deem worth to illustrate your picture. We recommend Firefly, DALL-E, or Midjourney to generate pictures, but there are other generators out there. If you don't feel comfortable, the text is enough.

Share your picture here.

Prompting

How to Prompt like a Pro

👋Hello everyone! Get ready to go on a journey to becoming a millionaire! 💰 Just kidding, but we are diving deep into the concept of "prompt engineering" today. It actually can be as simple or as complex as you make it. First:

Create a simple prompt to receive a summary of this text.

Share it here.

COFEE

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Written Version:

Let's explore an easy way to develop effective prompts using the COFEE framework! Broken down, the acronym COFEE stands for Context, Order, Format, Example, and Evaluation.

☕ Context first — if, for instance, you are a professor needing a point-by-point summary of your slides, be sure to mention that. Different situational contexts call for varied approaches in generating content.

☕ Order (clear directions) — Specify what exactly you need. If you're requesting a special kind of summary, say so. If you want to execute multiple steps, like asking for quality criteria first and the text that you want to summarize as a second step: Do it! 🙂

☕ Format — the structure of what you want; it could be a PDF, a Word document, a markdown file, or a specific arrangement. You can also ask for a short summary, then bullet points, and finally, a structured summary.

☕ Example — Define your quality criteria such as: a good summary only covers main points, it's short, trimmed to only necessary words and sentences. Or go the extra mile and search for good examples of summaries you like and place them in the prompt.

☕ Evaluation — It’s crucial to assess the resultant piece. If the response doesn’t fit the bill, fine-tune your prompt based on your assessment.

The main point is to stay flexible in adapting your COFEE blueprint. Iterate, re-evaluate, and tailor your prompt until the generated responses meet your needs optimally. For instance, you can command the AI first to deliver the main points, upon which you give feedback, and then generate the summary.

Perfecting your prompt is about refining it to meet your needs and expectations. So go ahead, sip your COFEE, and get those effective prompts flowing! 🔥👩‍💻👨‍💻☕🚀

Check your understanding

Check your understanding of the COFEE framework with the following short quiz:

COFEE.h5p

Time for the task:

Let's refine your prompt with coffee!

You will create a summary for a university grade presentation on a topic you currently study. Assume that you will do a presentation in two weeks and are low on time. What do you need to create a good presentation?
Should your summary have bullet points on your slides or, instead, concise summary texts to guide your understanding? Subsequently, would it help to add relevant examples, images, or any other visual aids that could enhance your presentation? Think about what you want and put it all in the prompt.

Also, ensure that it sounds like you sound. What is your style when doing presentations? Make it fit for you! You want to throw the information and get something you like and can work with.

Upload it here.

Mega-Prompts

The Prompt of Prompts

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Written Version:

So we don't want to throw away high quality prompts, but make them reusable. Whether it's a class discussion preparation or creating PowerPoint slides — automation can be a game-changer. But how do we make a prompt reusable and not tailored to one specific scenario?

1️⃣ Input: The starting point of any task is defining inputs and their relevance in the given context. For instance, when creating a text document, you need to feed in your requirements to the AI and expect the machine to act upon it. Let the user (of the prompt) be asked for it 🖥
Example: "Ask me for any relevant text documents before you do anything else!"

2️⃣ Decisions: This phase involves refining the inputs and setting clear guidelines for the AI to follow. If the outcome of the task is mission-critical, take the reins here and make necessary decisions about the task execution process! 📝
Example: "Give me the main points of the summary and let me evaluate them before you proceed!"

3️⃣ Output: This stage is all about defining the final product, be it a filled out Word template or a PowerPoint presentation. Is there a format you have to adhere to? 💼
Example: "The result should look like this:

# Bulletpoints
Write 5 bullet points about the input
# Short summary
Summarize the article in 3 sentences here
# Structural summary
For every headline in the text give a short summary per headline here."

By following these steps, you can create a prompt that you can reuse, saving time and effort. It's highly advantageous for tasks that require similar processes again and again. Now you try it!

Here is an example of how a mega-prompt could be written:
"I'm a professor and want to create a presentation for university students in their master's. First, ask me for the input material, then summarize it in a bullet point list. Separate the important parts from the unimportant parts, and let me give feedback. When I have given feedback. Create the summary like this:

# Bulletpoints
Write 5 bullet points about the input
# Short summary
Summarize the article in 3 sentences here
# Structural summary
For every headline in the text, give a short summary of the headline here.
Please export it in a docx file."

Can you mega prompt?

I want you to create your first mega-prompt. We adhere to the example before and you should create a mega-prompt that you can always use to create material for a presentation.

1️⃣ Start simple: Don't feel the need to go big immediately. Writing a single, concise sentence can get a lot accomplished.

2️⃣ Quality Assurance: If you are aiming for better quality, think of coffee! ☕ By ensuring all elements are included from the get-go, you can continually refine and perfect your prompt.

3️⃣ Reusing and Streamlining: Embrace the Input, Decision, and Output routine. Incorporating this structure into your workflow can really enhance your results and working time.

Experiment with your methods, iterate on your results and share them in the forum. We are excited to see your progress! Get into that engineering mindset and try out our tools!

Let's see those mega prompts! 😀 Share them here.

Deciding on LLMs

Before you start working with LLMs, it helps to know which one fits your needs best. In this video, Ben walks through the strengths and limitations of popular tools such as ChatGPT, Claude, and Gemini, comparing their features in everyday study and research tasks. Watch the video to get a practical overview and tips for making smart choices when it comes to LLMs. ☺️

Here is an activity for you. Match the correct tool and LLM with certain situations.

Evaluating LLM (Tools)

What is success for you?

👋 Hey there! This video unravels a very intriguing topic - 🧐 "What is Success?".

So, 🤔 what is success? Some might say it's about acing all benchmarks, but are these benchmarks set within any context or reality? Is success merely just going off without any guideposts? Are you more than your exam results? I surely hope so. But we are in a pickle here, especially considering genAI. Therefore, Let's explore how we can judge if an LLM is any good... or better: If AI can help you with any given task.

In this chapter, we discuss 'Why Benchmarks Matter', how we can approximate if LLM can do something and we also walk you through creating your very own benchmarks. Before you dive in, take a moment to think - "What genAI benchmarks do I know? What do they measure? What even is a benchmark?"

Write it down and explore what you already know. Share your initial thoughts here.

Benchmarks

What are benchmarks? How can we utilize them?

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Let's face it, LLMs are pretty good at almost every cognitive task. But, when everything is good, how can we distinguish the good from the excellent? This is where 'benchmarks' come in!

Benchmarks are like sophisticated scales that help us evaluate and compare large language models, identify their strengths, and areas for improvement. They help us see if the LLMs improve (or stagnate) and what they can and can't do. These days, we see nearly as many benchmarks as new models — so how do you find benchmarks that actually tell you what you need to know?

Well, Google is your friend — but the interesting part starts after you’ve found a benchmark worth looking at. You should ask yourself:

- What does the benchmark focus on, and is this relevant for me? → If it's not relevant, you may be wasting your time or misjudging the LLM’s competencies.
- Who are the evaluators — subject matter experts or other language models? → Can you trust that the benchmark is measuring what it claims to?
- Is the test data public, and could the models have been trained on it? 🧐 → If so, it’s like handing out the exam two weeks before the test! 😉

These questions help us unveil whether the benchmarks actually suit our needs. They ensure we’re evaluating LLMs fairly and meaningfully.

Let's make an example:

We use HumanEval to explore what a benchmark can look like. Here is a short article if you are interested in exploring this benchmark in-depth: Link

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Written Version:

The process begins by setting up different challenges that language models have to overcome — in this case, it's programming exercises. The model then attempts to provide suitable solutions in response to these tests.

The primary purpose is to verify if the solutions produced work. 📝 If they work after a certain number of tries, we consider the LLM capable of fulfilling the job. So if we have pass@5, we try it 5 times, and if one of the solutions is working, we consider it successful. If it's pass@1, we only try once, and it has to work instantly to get a score point.

The tests were created by industry experts who wrote the tests by hand, which is a good thing. However, there's a weak point in this process: the data utilized for these benchmark tests is public, leading to the risk of the models already being comprehensively trained with it. Since we don't know if the model was trained on this data, we have to be a bit more careful when interpreting this test.❗

Now it's your turn:

Now, you have seen a benchmark in action. Find a benchmark yourself and try to answer our questions!

First off, I encourage you to take some time to hunt for an LLM Benchmark that resonates with you. With so many options out there, finding the right one is like searching for a hidden treasure! 🗺️💎 Once you've located "the one", it's time to see if it already exists in our forum. If yes, fantastic! Make it even better by enhancing the article with your fresh insights. If not, don't hesitate to create a new article!

We are really curious about what you will choose! See you soon.📝

Here is the link to the forum.

Problems and favourite benchmarks

How to Pick a Benchmark

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👋 We think benchmarks are helpful in general. However, there are a few problems that we need to address so we don't blindly trust them:

The first problem is 🔬 contamination — the data used for tests (like with HumanEval) might already be available, and hence, accessible to the LLM. This makes it a bit too easy for the model, potentially skewing the results.

The next hurdle is that benchmarks can quickly become outdated 💾. (Have you seen one of those in your life?) Benchmarks are not only costly but also time-consuming to create. Researchers usually conduct them once every year or two — but that’s a problem, given how fast AI evolves.

Furthermore, some benchmarks rely on bold evaluation strategy choices 📝. For instance, some contain a massive number of questions that can’t be reviewed in detail, which makes it hard to judge whether the results or the questions themselves are even accurate.

Now, let’s talk about my favorite benchmark — LLM Arena! 🏟 It’s kind of like a chess match leaderboard 🎯. Two models go head-to-head in a blind test. You input a prompt, get two responses, and rate the better one — without knowing which model wrote which. 💭 That way, you're judging only the quality of the output, leading to more unbiased results.

There’s even a leaderboard that tracks model performance. If a “weak” model beats a “strong” one, it gains a lot of Elo points. If it loses, it only loses a few. This makes the competition fair — just like in chess or other ranked games.

But enough talking... You guessed it: Try it out yourself!

Try it yourself:

Have fun, try a few blind prompt comparisons yourself 🎮, and see what the best models for your area could be in the leaderboard.

Here is the link to the arena: Chatbot Arena

You can also find the leaderboard in a tab on the same page.

All in all...

Rule of thumb

How to decide on whether to use an LLM: Rule of thumb

Let's keep it short in this chapter. When you use genAI, you should always consider if an LLM is really the right tool for the job. Therefore, we have a short checklist for you. You should be good to go if you can answer all these questions with a "Yes!". If not, you might need to explore other ways to get to the result you wish for.

Checklist

Training data

- Is the knowledge required accessible (preferably) on the internet?

LLMs are unlikely to be helpful when working with undigitized literature or content behind paywalls, such as academic research, or legal texts, because they haven’t been trained on this data. This limitation poses a significant challenge when addressing specific, specialized questions.

- Is the knowledge required time-stable?

LLMs also struggle with time-sensitive data because they are trained at a fixed point in time and don’t automatically reflect ongoing changes. This poses challenges in rapidly evolving fields like law, software engineering, or startups. However, there are solutions for handling such dynamic contexts.

Task Performance

- Was the kind of task performed often (preferably on the internet)?

LLMs can be used for both knowledge-based and competency-based tasks, and it's helpful to distinguish between the two. Even if an LLM lacks specific knowledge in its training data, it can still perform tasks, such as answering questions, summarizing, or writing emails, if the necessary information is provided in the prompt. The key strength of LLMs lies in their ability to carry out cognitive tasks, not just store knowledge.

So, in a nutshell, here is a decision tree for you:

Time to test your knowledge

Let's see if you've got it. Read each case and the sentence below it, then mark whether the sentence is true or false. (Leave the mouse on the image for extra information)

true-false-question.h5p

True or false Case 3.h5p

True or false case 4.h5p

true or false case 4.h5p

To sum it up

If you only take one thing with you from this chapter: When you assume (or better tested) that the required knowledge isn't available through the model: You have to deliver it somehow.

Your turn. Share two examples with us of your field:

What can LLMs do good and what can't they do?

Build a benchmark

Creating your own benchmark

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This chapter is a cornerstone for your capstone project, so maybe enjoy it twice and think about how you want to incorporate it into your cornerstone project. 😉

🔍 Let us walk you through creating your own benchmark for evaluating AI tools. The focus is on achieving clear, measurable outcomes based on unique use cases.

Use cases
💡 First, let's emphasize the importance of starting with a use case — something you want to achieve — and defining what good results really mean to you. If you don't know what you want, then really nobody can help you. Search for products or results you think are great. What makes them so good? What is likeable about them? It could be the speed at which a task is accomplished, the quality of the output, or simply reducing extra work on your part. Whatever it is, write it down and be clear about what you expect the LLM tool to achieve.

Example: When I want to have a good summary, I want to get the most important content presented in an easily readable way — fast.

Metrics
⚖️ Next, it's about choosing the right measurement for evaluation. It could be quantifiable figures, or “vibes,” referring to your subjective feeling when using a tool.

Example:
The most important content: Summarize a text manually and list the key points. Then, test the LLM and compare using 3 metrics:
- Success: Every point the LLM gets right earns a score.
- Correctness: Each nonsensical output is a point (the fewer, the better).
- Focus: Each unimportant bit is a point (again, fewer is better).

Readability: Give it a vibe check — how much do you like the text on a scale from 1–10?
Speed: Measure the time it takes to summarize manually vs. the time the LLM takes.

Let’s assume we test every LLM three times and take the average of your metrics. Now we have a solid benchmark for summarizing — one we can trust to help pick the best tool. Maybe some tasks are better done manually, while others benefit from LLM support.

Failure analysis
🧐 Sometimes the LLM output will be garbage... and that's okay! If an LLM doesn't help with your task, analyze the problem. If you’ve tried honestly and it still fails — great! Send us an email, post it in the forums, let us know. We love to document what LLMs can’t do — that’s science! 😄

Dirty work
🔄 Lastly, a prediction: when you benchmark anything, it’ll feel like a “working but dirty” solution. That’s totally fine. Trying something new often means working without a polished outcome. Embrace the mess and give it your best try. And don’t forget — share what feels off with others!

So... ready to roll up those sleeves and have fun benchmarking? 😊💪🎯💻

Are you ready for some benchmarking?

So, let's work on the capstone project. Choose three use cases and two different tools for each of these cases. 🧪📈🛠️

Why is this so important? Well, it's a practical and systematic method to evaluate and compare tool performance. It's a solid foundation on which to make informed decisions about different tools' efficacy. The last thing you want is spending money and time on something that is just not worth it. 🙌📚

When you are done with this task: Great! This exercise was not just an academic task—it elevates your ability to critically assess and validate genAI tools, putting you well ahead of others!👩‍🔬📝

Share your results with us.

Bonus Subchapter

If you want to go the extra mile: Here is your chance! Update your initial answer what you know about Benchmarks and answer again: What did you learn, were you wrong about something? Did your perspective change somehow?

Answer here!

Limitations of genAI

What are LLMs' limits?

It's not all sunshine in the world of genAI. We have quite a few legal and ethical dilemmas and problems we have to address. We will explore some of these cases and you will explore your value system.

⚖️Let's discuss! ⚖️

Can you imagine any case where we should not use AI, or LLMs?

Write them down here!

Legal limits of AI use

Legal Limits

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Written Version:

Keep in mind, we are no lawyers, but we know quite a few that will help us out. Stay tuned for new videos on the topic of AI law in education! Nevertheless, we have a few topics that are fairly strict and clear. So let's dive into the main issues we'll be covering today: Copyright issues, generative AI-based decisions, and cheating.

Firstly, on the hot seat is copyright 📚! We all know that the internet is a playground for creativity. Nevertheless, you own your work — be it text, pictures, or other forms of content. Well... unless a big AI company decides to ignore your rights. Currently, we see ongoing legal disputes between major companies. It doesn’t seem like users of generative AI are being targeted, but it still raises the question: Is it okay to just use someone else’s work?

On a positive note, Adobe Firefly sets a good example — it was trained only on Adobe's own material. That’s great from a legal perspective, but the results don’t quite match up to tools like Midjourney. So we’re in a bit of a pickle: obey the law, and get a worse product. Copyright in the age of AI remains a complex issue — let’s see how it plays out.

The second key topic is the ethical use of generative AI 🤖. AI should not replace human judgment in decision-making that affects people or communities. Generative AI is excellent for support tasks, but the responsibility and liability must stay human. Relying on AI for general help? Fine. Letting it make critical decisions? Not so much.

Just imagine your academic work being judged by an AI. What if the training data had gender bias? A male psychologist or a female engineer could be unfairly evaluated. That would not only be unjust but also very hard to detect. For this reason, important decisions must always involve human oversight.

Lastly, let’s talk about cheating 😱. Accusations of AI-assisted cheating are serious. You should always check with your supervisors before using AI. The real twist? It's currently almost impossible to reliably detect whether a text was written with generative AI. This makes fairness and enforcement tricky — especially with bachelor's or master's theses. Is it fair to use AI as long as everyone does? Now that’s a conversation to have with your lecturer. 😉

So, in conclusion: No decisions by AI, no cheating, and apparently no copyright for anyone till further notice. Weird times ahead. But that’s just my two cents — now it’s time for a little reflection. 🧠✨

AI-Made Decisions

Try to think of a use case where LLMs or AI is making big decisions. Who would profit from LLM-based decisions, and who would suffer? Share your thought experiment here.

Ethical considerations

Let's talk about our values. Don't worry, the aim here isn't to force any ready-made decisions upon you. In fact, we don't want to make any decisions for you at all. What we do want to encourage is thoughtful reflection, meaningful decision-making, and an ability to articulate those decisions.

We have chosen a few dilemmas and problems we see with AI. Pick one of the three subjects we will provide. After this, your mission is to formulate a stance on the chosen topic. ✍️📚 Go deep, question your viewpoint, and take time to develop your position. There are no wrong answers here. The important thing is to reflect on why you made these decisions 🤔. Try to come up with arguments for all sides and finally justify your stance. Put it all in one page and share it with us all.

Remember, ethics is all about the lens we use to view the world and our decisions. Enjoy exploring yours. We are very excited to read about it!🧭

Student learning

Just picture this - a new student steps into a university, an institution with little guidance and brimming with knowledge. They have likely used AI in their high school years to help with their homework. Now, this student has learned that Generative AI is a credible source of truth that can help them with all tasks. But here's the rub. This reliance on AI might make learning quite easy, and as a result, students might shy away from grappling with the more challenging tasks. This could lead to lesser cognitive strain tolerance, therefore less cognitive strain and learning results.

On the flip side, Generative AI smartly distills key takeaways for learners. It cuts down the noise and pulls out relevant insights that students need to understand their course material better. 🎓

So, here's a question for you: As a student yourself, what advice would you give to a new student? Should they use Generative AI to assist with their courses at all, or should they exercise a bit of caution given its pros and cons? 💭 Maybe they should just embrace it and do everything with AI? How would they know if they are doing it "right"? What do you tell them?

Write down what you would tell them.

A burning world

🌍 Let's talk about the intersection of genAI and climate change 🌡️. Most likely, we've already surpassed the critical 1.5 degrees Celsius threshold, hinting at a critical level for our planet. We made huge progress in transitioning to renewable energy and alternative forms of transport... and then comes genAI...

We could reap numerous benefits from Generative AI on one side of the spectrum. Let's think about advancing research in new climate tech or better predication models. On the other side, this technological giant has a massive energy appetite 🍽️. Private companies such as Google and Microsoft are even considering nuclear energy supplies to meet the demands of AI, underlining how energy-hungry these technologies are 🏭.

Should we continue using Generative AI, should we abandon it, or should we advocate for a 'greener' Generative AI with more eco-friendly infrastructure 🌿? If you were a technology leader and could have a say in how we should build and use AI in Germany, what would your goals be? Should we make it more efficient and faster? Should we ban it? Only build it with renewable energies or only use nuclear? Or is it not important for climate change? Research how much of a threat AI is for our climate goals and write your leadership goals. Share them here.

Information as a weapon

Let's talk about mass misinformation in today's digital era, brought about by the lack of gatekeepers and the rise of generative AI. 🌐 This enables anyone, particularly those engaged in conflicts or those looking to destabilize democracies, to churn out misinformation at a dizzying scale. 🔄

Worryingly, this issue extends beyond media to even newer AI technologies. There's a danger of 'bad actors' intentionally introducing biases to the data that feeds our AI systems, subsequently skewing the results in dangerous ways. 📊👥

Let's make a very practical example. Should you use Grok?

Your first, but hopefully not only source, are two (biased) YouTube videos in German

 or another one in English
They are a bit different, but the task is the same: What ethical problem could you see when we would give this model to university students? What if people used it en masse? What problems could you see?

Share them here.

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