Ever wondered how a chatbot understands your frustration about a delayed order? This week on Tech Break by Friday, we're diving deep into the magic behind the screen!

Ever typed a question into a chatbot and marveled at how it just gets you? Or have you ever wondered how companies seem to know exactly how their customers feel about a new product based on thousands of online reviews? The secret lies in the fascinating world of Natural Language Processing (NLP), and precisely this episode, we're breaking it all down.

In this episode, we explore:

* What is Natural Language Processing (NLP)?

* The Power of Sentiment Analysis:

* Putting Theory into Practice: Ready to experiment? We share a fantastic list of user-friendly AI tools for writing, brainstorming, sentiment analysis, and even building your chatbot.

If you try any of these tools, please feel free to comment below on your thoughts.

If you enjoyed this episode, please follow the podcast, leave a review, or share it with someone who’s going to love it. Thanks again for reading. I’ll see you next Friday, where we’ll continue to discover new topics and ideas.

Check out the blog post on Substack for more details: https://open.substack.com/pub/kivroglouparaskevi/p/ai-demystified-4-what-is-natural?r=56ehxy&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Sources:

* https://www.ibm.com/think/topics/natural-language-processing

* https://aws.amazon.com/what-is/nlp/

* https://en.wikipedia.org/wiki/Chatbot

* https://moldstud.com/articles/p-nlp-in-chatbots-enhancing-sentiment-analysis-for-engagement

* https://tavtechnologies.aero/en-EN/review/pages/sentiment-analysis

* https://brand24.com/blog/best-sentiment-analysis-tools/

* https://www.joinhomebase.com/blog/ai-for-small-business

* https://blog.hubspot.com/sales/small-business-ai-tools

* https://www.semrush.com/blog/chatgpt-alternatives/

* https://team-gpt.com/blog/perplexity-vs-chatgpt-vs-team-gpt/

* https://clickup.com/blog/chatgpt-alternatives/

Media: Spotify | Instagram | Apple Podcasts | X

Welcome back to Tech Break by Friday, where we make big tech topics feel small and doable. In today’s episode, I’m sharing the story of how I joined a hackathon, used a tool called Lovable, and built an AI-powered research assistant — all in under 48 hours.

Even though I have a background in AI, I wanted to test how beginner-friendly vibe-coding really is. Using just natural language prompts (and no manual coding), I created a working MVP that connects with Zotero, fetches academic papers, and allows users to chat with their research library.

This episode is all about showing you that you can build your own AI project, even if you’re just getting started. I’ll walk you through:

* The exact tools I used (Lovable, Supabase, Clerk, Zotero)

* The problem I was trying to solve as a Master’s student

* What the MVP includes (chat interface, Zotero sync, persistent history)

* How you can recreate the project using prompts — no heavy coding required

And no, this episode isn’t sponsored. I’m just genuinely impressed with what Lovable made possible — and I think you will be too.

If you enjoyed this episode, please follow the podcast, leave a review, or share it with someone who’s been meaning to build something with AI but doesn’t know where to start. If you create something, please share it with me, either by dm or in the comments.

Thanks again for listening. I’ll see you next Friday, where we keep discovering new topics and ideas.

Blog of the full tutorial: https://open.substack.com/pub/kivroglouparaskevi/p/building-your-first-ai-project-beginner?r=56ehxy&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

A special offer for podcast listeners: If you have a business and would love to implement AI and automation ethically in your workflow, you get two one-hour free consultations to make a strategy together. Book a call!

Sources:

* Lovable

* Zotero

* Zotero Agent Lovable App

* Vibe Coding Episode

Media:Spotify | Instagram | Apple Podcasts | X

In this episode of Tech Break by Friday, Paraskevi Kivroglou dives into the foundational question: What is ethical AI? Ethical AI means building and deploying artificial intelligence that aligns with core human values—fairness, accountability, transparency, and respect for human rights. While the concept sounds straightforward, its real-world application is complex due to the risk of embedding and amplifying biases present in data and design.

I hope you like this episode. If you do, please leave a review or comment on any platform that you are listening to and share it with anyone you think might like this.

Click here to read the podcast content on Substack:

A special offer for podcast listeners: If you have a business and would love to implement AI and automation ethically in your workflow, you get two one-hour free consultations to make a strategy together. Book a call!

Media:Spotify | Instagram | Apple Podcasts

In this episode of Tech Break by Friday, Paraskevi Kivroglou discusses a really important topic regarding our protection in the AI era. You’ll learn what AI Deepfakes are and how to protect yourself and your community. We will also discuss the ethics of this topic and why it is dangerous not to recognize them.

In a world of noise and misinformation, we can learn to protect ourselves by using critical thinking and evaluation. I hope you like this episode. If you do, please leave a review or comment on any platform that you are listening to and share it with anyone you think might like this.

Click here to read the podcast content on Substack:

A special offer for podcast listeners: If you have a business and would love to implement AI and automation ethically in your workflow, you get two one-hour free consultations to make a strategy together. Book a call!

Media:Spotify | Instagram | Apple Podcasts

In this episode of Tech Break by Friday’s “AI Demystified” series, Paraskevi Kivroglou breaks down the mysteries behind one of AI’s most powerful tools. You’ll learn how the brain inspires neural networks (but aren’t brains!), how data travels through layers like beans turning into espresso, and how these models actually “learn” from their mistakes.

We’ll also spotlight real-world game-changers—from Tesla’s Autopilot to cancer-detecting algorithms—and clear up some of the biggest myths about neural networks. Whether you’re new to AI or just want a clearer picture, this episode will leave you feeling informed and curious for more.

Want to dive deeper? Read the full transcript here.

Media:

Spotify | Instagram | Apple Podcasts

Thanks for reading Tech Break by Friday! Subscribe for free to receive new posts and support my work.

Hi there. Welcome back to another episode of Tech Break by Friday. I was inspired to release this bonus episode after hearing people say how “new AI” is. It sparked an idea: why not do a quick dive into the fascinating history of Artificial Intelligence, and explore how this incredible field actually began? So, that’s what we’re doing today – a mini time-travel adventure!

Before we jump in, if you enjoy what we do here at Tech Break by Friday, I’d be so grateful if you could take a moment to rate the podcast on whatever platform you’re listening on – whether that’s Spotify, Apple Podcasts, or somewhere else. It’s a small click for you, but it genuinely helps the show reach more tech-curious folks. Spotify | Instagram | Apple Podcasts

Alright, ready to take a journey through time? Let’s rewind the clock!

Generated using ChatGPT and the script information of this episode.

1. The 1950s – The Idea is Born

Picture this: the 1950s. Computers weren't the sleek devices in our pockets; they were colossal machines, often filling entire rooms! It was in this era, specifically in 1950, that the brilliant British mathematician Alan Turing asked a question that would echo for decades: "Can machines think?" What does the word “think” mean? In this case, it was a bit unclear. So, Turing, being the practical genius he was, reframed this into something testable: the famous Turing Test, or as he called it, the "imitation game."

* You, as a human interrogator, are chatting (via text) with two participants hidden from view.

* One is a human (Player B), and the other is a machine (Player A).

* Your job? Could you figure out which is which? If the machine could consistently fool you into believing it was human, it would pass the test.

The famous Turing Test laid the philosophical foundation of artificial intelligence. It wasn't just about crunching numbers anymore; it was about mimicking human conversation and, by extension, intelligence.

I want to ask you a critical question: Do you think mimicking human conversation is a part of intelligence, and what are the actual traits that make humans unique?

2. 1956 – The Birth of Artificial Intelligence

Just a few years later, in 1956, a group of researchers at Dartmouth College in the U.S. coined the term Artificial Intelligence. Their ambition was nothing short of revolutionary: to build machines that could simulate every aspect of human learning and intelligence. Talk about a bold vision!

This initial optimism fueled a period of exciting exploration. And one fascinating example from this era emerged in the mid-1960s: a program called ELIZA. Created by Joseph Weizenbaum at MIT between 1964 and 1966, ELIZA was designed to simulate a conversation, famously taking on the persona of a Rogerian psychotherapist. If you typed something like, "I'm feeling a bit down today," ELIZA might respond, "Can you tell me more about why you are feeling a bit down today?"

Now, ELIZA wasn't actually understanding you. It cleverly used pattern matching and keyword spotting to rephrase your statements as questions. But the effect was surprisingly convincing for many people!

3. 1970s–80s – The Chill of the First "AI Winter"

But, as with many groundbreaking journeys, the path wasn't always smooth. The computers of the day, bless their vacuum-tube hearts, just didn't have the sheer power or memory needed for the really complex tasks the researchers dreamed of. As a result, funding started to dry up, and some of that initial fiery excitement began to cool. This period became known as the "AI Winter" – a time when things got a bit frosty for AI development.

4. 1997 – AI Roars Back into the Headlines!

Then, a real breakthrough moment that captured the world's attention! In 1997, IBM’s supercomputer, Deep Blue, went head-to-head with the reigning world chess champion, Garry Kasparov… and won. This was huge! It wasn't just a computer winning a game; it was AI defeating a human master at a game demanding deep strategy, foresight, and intellect. Suddenly, AI was back in the spotlight.

5. 2012 – The Deep Learning Revolution

The next truly fundamental shift started around 2012. This is where things really started to accelerate, leading to the AI we see today. What changed? Two main things: a massive increase in computing power (thank you, powerful GPUs – initially designed for gaming!) and the availability of vast amounts of data (hello, internet!). This combination fueled the rise of deep learning, particularly a type of AI inspired by the human brain called neural networks. A model named AlexNet made a massive splash by dominating an image recognition contest. For the first time, AI could "see" and identify objects in images with astounding accuracy, often better than humans!

6. Today – AI in Our Lives

And that brings us up to speed. From those early philosophical questions and room-sized computers, AI has blossomed. It’s in our smartphones, helping us type and take better photos, in our cars with driver-assist features, powering the recommendation engines that suggest what to watch or listen to, and, yes, in tools like ChatGPT, Claude or Gemini that help us write, learn, and create. If used correctly, it’s helping us work faster and more productively in this modern world.

From Turing’s pivotal question to the sophisticated tools we use daily, it’s been quite a ride — and honestly, it feels like it’s only just beginning. There's so much more to explore and understand, which is exactly what we try to do here on the podcast.

If you liked this little trip through AI history and found it helpful, please consider sharing it with a friend or colleague who might also find it interesting.

Have a great week, stay curious, and keep building.

Created with ChatGPT to represent the history of AI

Sources:

* https://www.iberdrola.com/innovation/history-artificial-intelligence

* https://en.wikipedia.org/wiki/Turing_test

* https://home.dartmouth.edu/about/artificial-intelligence-ai-coined-dartmouth

* https://aibc.world/learn-crypto-hub/ai-winter-history/

* https://www.ibm.com/history/deep-blue

* https://viso.ai/deep-learning/alexnet/

* https://en.wikipedia.org/wiki/History_of_artificial_intelligence

* https://www.ibm.com/think/topics/history-of-artificial-intelligence

* https://www.tableau.com/data-insights/ai/history

* https://www.britannica.com/science/history-of-artificial-intelligence

* Matt Wolfe Video

Media:Spotify | Instagram | Apple Podcasts

Hey there! Welcome back to another episode of Tech Break by Friday. Today, we have another episode of the series AI Demystified. Our topic: What’s the real difference between Artificial Intelligence, Machine Learning, and Reinforcement Learning? Spoiler: they’re not all the same thing, and understanding the difference will help you spot the hype from the real deal.

(Source: Created with ChatGPT)

Let’s remember what AI means from our last episode. AI is an algorithm that recognises patterns based on data that it has already seen. The period that AI learns from data is called the training period. After training, AI is capable of recognising the patterns in the data. An example that you might already have seen is the Netflix recommendation system. Based on the movies you have seen, it proposes new films and series that you might like.

🧠 What is Machine Learning (ML)?Machine Learning (ML) is a part of artificial intelligence (AI) that allows computers to learn patterns from data and make decisions without being explicitly programmed for each task. Unlike traditional software, which follows instructions (“if A, then B”), (“else C”).

A widely cited definition by Tom Mitchell states: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” For example, an email spam filter is an ML solution. The spam email filter is the task (T). The ML algorithm gets better at recognising spam emails ( this is the performance (P)). Performance here means how many emails have been recognised correctly. The data that has already been seen or processed, labelled emails of “spam”, “not spam”, are the experience (E).

Core Types of Machine Learning (ML)

Supervised Learning

In supervised learning, models are trained using labelled data—each input has a known output. The algorithm learns to map inputs to outputs by minimizing prediction errors. Typical applications include:

* Classification: Sorting emails as spam or non-spam.

* Regression: Predicting house prices based on features like size and location. (The difference here is that the data are not countable like spam, non-spam)

The model uses its training to make predictions on new, unseen data. Unsupervised LearningUnsupervised learning deals with unlabeled data. The algorithm tries to find hidden patterns or groupings within the data. Standard techniques include clustering (grouping similar customers). This approach is often used for anomaly detection. For example, you are given different shapes of objects manufactured. The unsupervised learning method explores all possible options to recognise which of these objects were created with anomalies.

Reinforcement Learning

Reinforcement learning (RL) is about learning by doing. An agent interacts with an environment, making decisions and receiving feedback in the form of rewards or penalties. Over time, the agent learns which actions yield the best outcomes. RL is used in robotics, game-playing AIs like AlphaGo, and industrial automation, where trial and error can optimise performance.

Think of a video game where you start with no instructions — just a controller and a goal to survive or score points.

Every time your character jumps over a pit and survives, you earn points — great! But when you walk into a trap, you lose a life — not so great.

Over time, you learn what actions help you win: jump at the right moment, avoid certain areas, grab bonus items.

That’s reinforcement learning:

* The game is the environment

* You are the agent

* Points and penalties are your feedback (rewards)

🎯 In real life: This is how AIs like AlphaGo learn to master complex games, or how robots learn to walk by trying, failing, and gradually improving with each attempt.

If you found it helpful and you liked this episode, share it with a friend. It helps more people find the podcast and learn about AI. Subscribe, leave a review, and keep building.

Sources:SAP: What is Machine Learning?: https://www.sap.com/germany/products/artificial-intelligence/what-is-machine-learning.html

Dev with Darwish – Core Machine Learning Paradigms (YouTube, April 2025)

OPIT: Supervised vs. Unsupervised Learning https://www.opit.com/magazine/supervised-vs-unsupervised-learning/

Alexander Thamm: Unsupervised Learning Explained https://www.alexanderthamm.com/en/blog/this-is-how-unsupervised-machine-learning-works/

Bernard Marr: What Is Reinforcement Learning https://bernardmarr.com/artificial-intelligence-what-is-reinforcement-learning-a-simple-explanation-practical-examples/

Spiceworks: What is machine learning?https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-ml/

Datasolut: Machine Learning Definition https://datasolut.com/was-ist-machine-learning/

Hi, there. Welcome back to another episode of Tech Break by Friday. Today’s topic is creating videos for short-form content using an AI creative platform, Runway. If you ever wanted to experiment with making videos without being a professional videographer, please stay for this episode’s sweet spots. Today’s episode aims to help students, AI enthusiasts (who like to experiment with AI tools), small business owners or marketers who are looking for an easy way to create engaging short videos to market their products or services.

If you like this episode, please subscribe and leave a review. This action helps the podcast more than you can imagine.

What is Runway?So, if you are up for the ride, let’s move on to see what Runway is exactly. Runway is an AI-powered creative suite built to help anyone make videos, images, and even audio. When you sign up with Runway, you get some free credits to try out the service.My ExperimentsYou can generate a video by typing a prompt like “a woman preparing to record a podcast episode”, with Gen-4 Turbo or Gen-4. I want to add here that the Gen-4 model is their more premium, robust model. I created a video from an image I already had saved. You can see the video here.

As you can see, the video gets a bit distorted from the initial picture. Then I used a bit more detailed prompt to create 4 images with Runway’s image generator. Prompt: “a white woman with black hair, black and white outfit preparing her space to record a podcast“

Then I used the image I created here as input for the video generation. By using the image and the same prompt from the previous video, I got a short video of a woman preparing to record a podcast. Below you can see the video.

I liked Gen-4 Turbo's fast video creation speed. The video seems super realistic. From my 2 experiments, you can see that the quality of the image plays a huge role.

What else can you make with Runway?Here’s where it gets fun. Runway isn’t just for one type of creator. You can use it to:

* Storyboard your next short film- no more stick figures!

* Make social media videos that pop, whether it’s for TikTok, Instagram, or YouTube Shorts.

* Create product demos or explainer videos for your business.

* Design educational content that actually holds students’ attention.

* Experiment with digital art- try wild styles, mash up genres, or animate your illustrations.

* Edit your pictures by removing random people or imperfections.

Because it’s all AI-powered, you can try a dozen ideas in the time it used to take to edit one video. If you don’t like the output, tweak your prompt and try again—no reshoots, no stress.

Why use Runway or similar tools?

Someone might ask, "Where does this help me?" If you don't mind, if you are a new product founder or business owner, you can upload a mockup of your product and create a set of short-form videos. By tweaking them and playing around, adding music or audio, you can create a fast ad or reel to boost your reach on social media.

If you are a professional and you see that your shots contain material that you don’t want, you can upload the frames in Runway and edit them a lot faster. AI can remove people and objects from videos or photos by replacing the background with a high probability. You can save time editing and make more videos you like.

If you are a student or someone who wants to create but lacks videography skills, you can experiment by creating a runway video. Hopefully, this will expand your skills and productivity, helping you develop ideas and skills you can use later in your life.

Useful Prompting Tips

* Use positive phrasing only

* Use direct, simple, and easily understood prompts

* Focus on describing the motion, rather than the input image

* Avoid conversational or command-based prompts

* Avoid overly complex prompts

Call to Action - Ready to try it out?

Here’s how to get rolling with Runway:

* Sign up- head to Runway’s website and create a free account.

* Choose your tool- start with text-to-video, image-to-video, or play with the magic tools.

* Type your prompt. Be as specific or creative as you like. “A cat surfing a giant wave at sunset”? Go for it.

* Tweak and download-adjust settings, preview your video, and when you’re happy, download and share it anywhere.

Pro tip: Start with Gen-4 Turbo if you want fast brainstorming results, then switch to the standard Gen-4 for your final, polished video. And don’t be afraid to experiment—the best ideas usually come from playing around.

Disclaimer: Runway is not sponsoring me to create this episode or say these words. This is my opinion, which I wanted to share with you since I gained a lot of value from it.

Let’s wrap up. Runway is changing the game for creators of all stripes. It’s fast, fun, and packed with features that make video creation accessible to everyone.

If you try it out, let me know what you make! Tag me or send your creations my way. I’d love to see what you dream up.If you liked this episode and found it useful, please share it with others who might also find it helpful. Have a great week, and keep building.

References:1. https://help.runwayml.com/hc/en-us/articles/39789879462419-Gen-4-Video-Prompting-Guide

2.Runway Audio

3.https://app.runwayml.com/video-tools/teams/kibroglouparaskevi/dashboard

Media:Spotify | Instagram | Apple Podcasts

(HBR Research Visuals, 2025)

Hello everyone, and welcome back to a bonus episode of Tech Break by Friday. This morning, while eating my breakfast and reading through Substack, I came across an interesting article about AI use cases in 2025. Some of them surprised me, so I decided to share them with you.

Thanks for listening to or reading Tech Break by Friday! You can subscribe for free to receive new posts and support my work.

I am surprised by the first-place use case: Therapy and Companionship. The data from Marc Zao-Sanders, co-founder of filtered.com, shows that people are seeking empathetic responses from AI. The low barrier to using AI to get therapy is what makes this solution attractive.

[This is only my opinion]I believe that AI is low-barrier and more cost-effective. As a person who seeks psychological guidance from a human expert, I don’t want to discriminate against therapists and experts in the field. I believe that a human therapist will go a long way to help you realise and evolve as a human being. But nowadays I have seen a lot of people being laid off, living paycheck by paycheck and worrying where their next meal is going to come from. So a 20$ or the free version of ChatGPT feels a lot easier and more accessible to them. Since a therapy session costs at least 50$.

Also, it takes time to find a therapist who matches your needs. I have seen many professionals who are not quite fit for me. People feel that the hustle of looking and spending money is harder, and as humans, we optimise for lower-effort tasks.

Another take is that a therapist is there once a week, most of the time. Which, again, I don’t want to sound rude, therapists are humans, they have their own lives. I read in the research PDF this query from a user:“I talk to it everyday. It helps me with my brain injury daily struggles. It's helping me work thru family shame, brain fog, inability to focus, remind of what I have accomplished as I don't have any memory. It helps me decide what to eat, how to manage my day. It has saved my sanity” (Marc Zao-Sanders,2025)And immediately I thought Wow, this is a use case, I didn’t even think about. I hope you liked today’s episode. Please check the references tab for all the information regarding this research. My goal was to show you the positive AI use nowadays, but also maybe make you think:Why in 2025, do we feel more lonely than ever? With so many tools to connect us? Are they actually connecting us?I am waiting to hear your opinions in the comments or review section of this episode, depending on the podcast platform you are listening to. If you like this bonus episode, please leave a podcast review. I would love to read it. I suggest also reading it here live, so you guys are listening to your contributions. Have a great week and keep building a great life.

Thanks for reading Tech Break by Friday! This post is public, so feel free to share it.

(HBR Research Visuals, 2025)

References:

* LinkedIn Post: https://www.linkedin.com/posts/engkeat_infographic-from-the-hbr-article-how-people-activity-7317860871303581698-CCb_?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEb20-0Blm_8KA5nEmc3mrTSKHXeX6kZ4fE

* Substack Article: https://substack.com/home/post/p-161778779

* Harvard Business Review: https://hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025

* Full PDF of the AI Use cases: https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf

* Visuals: https://hbr.org/data-visuals/2024/03/how-people-are-using-genal-2

* https://hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025

Welcome to the first episode of ‘AI Demystified’—a mini-series in which we break down what artificial intelligence really is, how it works, and why it matters to you. Whether you're curious, cautious, or confused about AI, this series is here to help you understand the essentials in plain English.

I’m Paraskevi Kivroglou, a software developer and AI researcher who’s passionate about making tech accessible to everyone. Today, we’re starting at the beginning: What actually is AI, and why do so many people think it’s magic?

If you’re interested in learning more from our upcoming episodes, you can subscribe here to receive all the updates.

Let’s Define ΑΙ – Simply

Picture this: You walk into your favourite coffee spot, and the barista knows your order- a freddo espresso black. They learned your pattern.

AI works the same way. It is an algorithm that learns to predict future patterns based on data that has already been received.

If you train AI on thousands of coffee orders, it will also learn to predict what order will come next.

Another AI algorithm you might already know is the Netflix algorithm. Have you ever wondered how Netflix recommends movies and series? Well, based on what you have already watched, find patterns in the data and be able to guess your next hit.

Why AI is different from traditional software algorithms

Traditional software is more like if A is true, then perform B, else perform C. You see here, you need to code every instruction strictly. If you don’t, then the software won’t check A to find out what to do next.

AI has seen data previously, a period we call the training period. Based on this data, it can make a prediction. For example, if you train AI to recognise cats and dogs, it will be able to recognise them most of the time. Since we are talking about prediction, it is essential to remember that AI can make mistakes. It might predict something wrong, too.

Daily Meets with AI that you might ignore

You’re already using AI right now. Let’s play a game:

* Do you know how your phone unlocks with your face? AI compares your selfie to stored patterns.

* Gmail finishing your sentences? AI predicts your following words like a psychic keyboard.

* Instagram’s weirdly accurate ads? AI stalked your search history (kidding… sort of).

It’s not sci-fi; it’s in your pocket, your inbox, and even your Spotify Wrapped. AI is the quiet helper you never knew you hired. HOW AI “LEARNS” (NO PHDS REQUIRED)

Let’s break down the secret sauce: training. You show them red blocks go here, blue blocks go there. AI does this, but at warp speed.

* Data In: Feed it examples (like 10,000 labelled colored Lego pictures, red, blue, and more).

* Pattern Hunting: It creates a “recipe” to tell them apart. Imagine that it tries to find a logic to tell apart the Legos.

* Testing: Give new pics on it. If it messes up, tweak the recipe.

That’s it! No PhD needed. It’s just trial, error, and a lot of caffeine for the engineers.

WHY THIS MATTERS TO YOU

“Okay, but why should I care?” Because AI is shaping everything:

* Jobs: It’s automating grunt work (bye-bye, data entry). The World Economic Forum's Future of Jobs Report 2025 indicates that employers expect 39% of key skills required in the job market will change by 2030. Technological skills, particularly AI and big data, are projected to grow in importance more rapidly than any other skills in the next five years.

* Healthcare: Spotting tumours in X-rays faster than humans.

* Creativity: Writing songs, making art, controversial, but fascinating.

But- and this is huge-AI’s only as good as its teachers. Biased data? Biased results. That’s why understanding AI matters. You don’t need to code it, but you should know when to question it and how to use it. A good starting point would be to play with Chatgpt if you have not already done so.

Let’s recap:

* AI = Pattern detective.

* Not magic, not conscious, not perfect.

* You’re already using it daily.

* And knowing the basics lets you spot the BS.

If you found it helpful and you liked this episode, share it with that friend who thinks AI’s all Terminators. Subscribe, leave a review, and keep building.

References:

* Future of Jobs Report: https://www.weforum.org/stories/2025/01/future-of-jobs-report-2025-jobs-of-the-future-and-the-skills-you-need-to-get-them/

* https://www.reddit.com/r/ChatGPT/comments/14h2656/here_are_some_ai_tools_you_can_use_for/

* Course Book Machine Learning DLMDSML01, IU International University of Applied Sciences 2022