Kategorie: Technology

Welcome to the tech section of my blog, where I share my thoughts and insights on the latest developments in the world of digital, data, and AI. As someone who is passionate about the transformative power of technology, I believe that staying up to date with the latest trends and innovations is crucial to success in this field.

In this section, you’ll find a wide range of articles and posts on topics such as digital transformation, data privacy, artificial intelligence, and more. I’ll share my perspectives on current events, emerging trends, and the future of technology, with a focus on how these developments will impact businesses, society, and individuals.

Whether you’re a technology professional, business leader, or just someone interested in the world of tech, you’ll find plenty of valuable insights and knowledge in these posts. My goal is to make complex topics accessible and easy to understand so that everyone can benefit from the incredible advances happening in the tech world today.

You can also find a lot of technical product reviews on my Amazon reviews page. These are mostly in German.

So, join me on this journey as we explore the cutting-edge world of digital, data, and AI together, and discover new ways to harness the power of technology for a better tomorrow.

KI-generierte Bilder Erkennen: Wie man Deepfakes wie den Balenciaga-Papst Identifiziert

Deepfakes erkennen: Dieses KI-generierte Bild zeigt Papst Franziskus untypisch gekleidet in einer Balenciaga-Daunenjacke. Beachten Sie die surreal anmutenden Details wie den schwebenden Kreuzanhänger und die ungreifbare Kaffeetasse – subtile Hinweise auf die digitale Herkunft des Bildes. (Bildquelle: Instagram @art_is_2_inspire)
Deepfakes erkennen: Dieses KI-generierte Bild zeigt Papst Franziskus untypisch gekleidet in einer Balenciaga-Daunenjacke. Beachten Sie die surreal anmutenden Details wie den schwebenden Kreuzanhänger und die ungreifbare Kaffeetasse – subtile Hinweise auf die digitale Herkunft des Bildes. (Bildquelle: Instagram @art_is_2_inspire)

Die Bedrohung durch KI-generierte Bilder im Kontext von Fake News, besser bekannt als Deepfakes, ist seit Jahren ein Thema in der öffentlichen Diskussion. Bis vor kurzem war es jedoch relativ einfach, ein KI-generiertes Bild von einem echten Foto zu unterscheiden. Doch diese Zeiten sind vorbei. Am vergangenen Wochenende ging ein KI-generiertes Bild von Papst Franziskus in einer Balenciaga-Daunenjacke viral und führte viele Internetnutzer in die Irre.

Die rasante Entwicklung der KI-generierten Bilder

In nur wenigen Monaten haben öffentlich zugängliche KI-Tools zur Bildgenerierung einen Grad an Fotorealität erreicht, der beeindruckend ist. Obwohl das Bild des Papstes einige verräterische Anzeichen einer Fälschung aufwies, war es überzeugend genug, um viele zu täuschen. Dieses Ereignis könnte als die erste wirklich virale Desinformation in die Geschichte eingehen, die durch Deepfake-Technologie angetrieben wurde.

Die Gefahren von Deepfakes

Opfer von Deepfakes, insbesondere Frauen, die Opfer von nicht einvernehmlicher Deepfake-Pornografie geworden sind, warnen seit Jahren vor den Risiken dieser Technologie. In den letzten Monaten sind Bildgenerierungstools noch zugänglicher und leistungsfähiger geworden, was zu qualitativ hochwertigeren gefälschten Bildern geführt hat. Mit dem raschen Fortschritt der künstlichen Intelligenz wird es noch schwieriger, echte von gefälschten Bildern zu unterscheiden. Dies könnte erhebliche Auswirkungen auf die Anfälligkeit der Öffentlichkeit für ausländische Einflussnahme, gezielte Belästigung und das Vertrauen in Nachrichten haben.

Wie lassen sich KI-generierte Bilder und Deepfakes erkennen?

Es gibt einige Tipps, wie Sie KI-generierte Bilder und Deepfakes erkennen können:

  1. Trügerische Details: Betrachtet man das Bild des Balenciaga-Papstes aus der Nähe, lassen sich einige verräterische Hinweise auf seine Herkunft aus der KI erkennen. Das Kreuz, das scheinbar ohne Kette in der Luft hängt, oder die Kaffeetasse, die ohne erkennbaren Henkel in seiner Hand steht, sind solche Hinweise.
  2. Unnatürliche Physik: KI-Generatoren verstehen oft nicht, wie Objekte in der realen Welt interagieren. Unlogische Elemente wie schwebende Objekte oder ungewöhnlich geformte Körperteile können ein Indikator für KI-Generierung sein.
  3. Detailgenauigkeit: KI-Bildgeneratoren sind im Wesentlichen Musterreplikatoren. Sie haben gelernt, wie der Papst aussieht und wie eine Daunenjacke von Balenciaga aussehen könnte, und können beides erstaunlich gut kombinieren. Aber die Gesetze der Physik verstehen sie (noch) nicht. So wird der scheinbar schwebende Kreuzanhänger oder die unlogische Verschmelzung von Brillengläsern und deren Schatten zum verräterischen Detail.
  4. Fehlende Logik: In den Randbereichen eines Bildes können Menschen intuitiv Unstimmigkeiten erkennen, die KI nicht versteht. Diese Unstimmigkeiten können ein Hinweis auf eine KI-Generierung sein.
  5. Technische Grenzen: KI-Generatoren haben Schwierigkeiten, komplexe und detaillierte Szenen fehlerfrei zu reproduzieren. Achten Sie auf Anomalien in Texturen oder ungewöhnliche Muster.
  6. Inkonsistente Beleuchtung: Beleuchtung und Schatten sind für KI-Generatoren oft schwierig korrekt darzustellen. Achten Sie auf inkonsistente Lichtquellen, unstimmige Reflexionen in den Pupillen oder unnatürlich wirkende Schatten.
  7. Unnatürliche Proportionen: KI-Generatoren können Schwierigkeiten haben, die richtigen Proportionen von Gesichtern oder Körpern zu reproduzieren. Achten Sie auf ungewöhnliche oder verzerrte Proportionen als Hinweis auf eine KI-Erstellung.

Wie wir uns in Zukunft nicht durch Deepfakes täuschen lassen

Im Moment sind Medienkompetenztechniken vielleicht Ihre beste Möglichkeit, um mit KI-generierten Bildern Schritt zu halten. Fragen Sie sich: Woher kommt dieses Bild? Wer teilt es und warum? Wird es durch andere verlässliche Informationen widerlegt?

Suchmaschinen wie Google bieten ein Tool für die umgekehrte Bildsuche an, mit dem Sie überprüfen können, wo ein Bild bereits im Internet geteilt wurde und was darüber gesagt wird. Dieses Tool kann Ihnen dabei helfen herauszufinden, ob Experten oder vertrauenswürdige Publikationen ein Bild als Fälschung eingestuft haben.

Dieser Artikel ist ein Auszug aus dem Buch KI für Content Creation von Alexander Loth. Alle Infos zum Buch und eine kostenlose Leseprobe findet ihr bei Amazon.

Diskutieren Sie mit auf LinkedIn, wie man Deepfakes erkennt und was das für unsere digitale Zukunft bedeutet:

„KI-generierte Bilder Erkennen: Wie man Deepfakes wie den Balenciaga-Papst Identifiziert“ weiterlesen

GPT-4 Launches Today: The Rise of Generative AI from Neural Networks to DeepMind and OpenAI

OpenAI GPT-4 launch illustrated with Stable Diffusion (CC BY-SA 4.0)
OpenAI GPT-4 launch illustrated with Stable Diffusion (CC BY-SA 4.0)

With today’s launch of OpenAI’s GPT-4, the next generation of its Large Language Model (LLM), generative AI has entered a new era. This latest model is more advanced and multimodal, meaning GPT-4 can understand and generate responses based on image input as well as traditional text input (see GPT-4 launch livestream).

Generative AI has rapidly gained popularity and awareness in the last few months, making it crucial for businesses to evaluate and implement strategies across a wide range of industries, including e-commerce and healthcare. By automating tasks and creating personalized experiences for users, companies can increase efficiency and productivity in various areas of value creation. Despite being in development for decades, it’s high time for businesses to apply generative AI to their workflows and reap its benefits.

Before you dive into OpenAI GPT-4, let’s take a quick look back at the evolution of generative AI…

The history of generative AI begins in the late 1970s and early 1980s when researchers began developing neural networks that mimicked the structure of the human brain. The idea behind this technology was to assemble a set of neurons that could pass information from one to another with some basic logic, and together the network of neurons could perform complicated tasks. While minimal advances were made in the field, it remained largely dormant until 2010, when Google pioneered deep neural networks that added more data, hardware, and computing resources.

In 2011, Apple launched Siri, the first mass-market speech recognition application. In 2012, Google used the technology to identify cats in YouTube videos, finally reviving the field of neural networks and AI. Both Google and NVIDIA invested heavily in specialized hardware to support neural networks. In 2014, Google acquired DeepMind, which built neural networks for gaming. DeepMind built AlphaGo, which went on to defeat all the top Go players, a pivotal moment because it was one of the first industrial applications of generative AI, which uses computers to generate human-like candidate moves.

OpenAI was founded to democratize AI as a non-profit organization

In 2015, OpenAI was founded to democratize AI and was established as a non-profit organization. In 2019, OpenAI released GPT-2, a large-scale language model capable of producing human-like text. However, GPT-2 sparked controversy because it could produce fake news and disinformation, raising concerns about the ethics of generative AI.

In 2021, OpenAI launched DALL-E, a neural network that can create original, realistic images and art from textual description. It can combine concepts, attributes, and styles in novel ways. A year later, Midjourney was launched by the independent research lab Midjourney. Also in 2022, Stable Diffusion, an open-source machine learning model developed by LMU Munich, was released that can generate images from text, modify images based on text, or fill in details in low-resolution or low-detail images.

OpenAI launched ChatGPT in November 2022 as a fine-tuned version of the GPT-3.5 model. It was developed with a focus on enhancing the model’s ability to process natural language queries and generate relevant responses. The result is an AI-powered chatbot that can engage in meaningful conversations with users, providing information and assistance in real-time. One of the key advantages of ChatGPT is its ability to handle complex queries and provide accurate responses. The model has been trained on a vast corpus of data, allowing it to understand the nuances of natural language and provide contextually relevant responses.

Today’s launch of OpenAI GPT-4 marks a significant milestone in the evolution of generative AI!

This latest model, GPT-4, is capable of answering user queries via text and image input. The multimodal model demonstrates remarkable human-level performance on various professional and academic benchmarks, indicating the potential for widespread adoption and use. One of the most significant features of OpenAI GPT-4 is its ability to understand and process image inputs, providing users with a more interactive and engaging experience.

Users can now receive responses in the form of text output based on image inputs, which is a massive step forward in the evolution of AI. Depending on the model used, a request can use up to 32,768 tokens shared between prompt and completion, which is the equivalent of about 49 pages. If your prompt is 30,000 tokens, your completion can be a maximum of 2,768 tokens.

Token limitations of GPT models with real-world scenarios
Token limitations of GPT models with real-world scenarios

Bing has already integrated GPT-4 and offers both, chat and compose modes for users to interact with the model. With the integration of GPT-4, Bing has significantly enhanced its capabilities to provide users with more accurate and personalized search results, making it easier for them to find what they are looking for.

The disruptive potential of generative AI is enormous, particularly in the retail industry. The technology can create personalized product recommendations and content, and even generate leads, saving sales teams time and increasing productivity. However, the ethical implications of generative AI cannot be ignored, particularly in the creation of disinformation and fake news.

To sum up, generative AI is here to stay, and companies must evaluate and implement strategies swiftly. As generative AI technology advances, so do the ethical concerns surrounding its use. Therefore, it is critical for companies to proceed with caution and consider the potential consequences of implementing generative AI into their operations.

Are you already using generative AI for a more productive workflow?

What improvement do you expect from OpenAI GPT-4 in this regard? I look forward to reading your ideas in the comments to this LinkedIn post:

„GPT-4 Launches Today: The Rise of Generative AI from Neural Networks to DeepMind and OpenAI“ weiterlesen

Authenticity in Photography: Samsung’s Moon Shots Controversy and the Ethics of Synthetic Media

Side-by-side comparison of the original capture and the synthesized version
Side-by-side comparison of the original capture and the synthesized version: Generative AI technology adds texture and details on moon shots, blurring the line between real and synthesized images.

Generative AI has made waves around the world with its ability to create images, videos, and music that are indistinguishable from human-made content. But what happens when this technology is applied to photography, and the images we capture on our devices are no longer entirely real?

While Samsung claims that no overlays or texture effects are applied, a recent Reddit post suggests otherwise. The post provides evidence that Samsung’s moon shots are „fake“ and that the camera actually uses AI/ML to recover/add the texture of the moon to the images.

The use of AI in photography is not new, as many devices already use machine learning to improve image quality. But the use of generative AI to create entirely new images raises ethical questions about the authenticity of the content we capture and share – especially when the photographer is unaware that their images are being augmented with synthesized content.

What do you think about the use of generative AI in photography? Is it okay for a phone to use this technology to synthesize a photo, or is it crossing a line?

Join the conversation on LinkedIn:

Storytelling with Data: A Data Visualization Guide for Business Professionals

Storytelling with Data: A Data Visualization Guide for Business Professionals
Storytelling with Data: A Data Visualization Guide for Business Professionals

We know that data can be a powerful tool for decision-making, but it can also be overwhelming and difficult to communicate to others in a meaningful way. This is where Storytelling with Data by Cole Nussbaumer Knaflic comes in. I chose this book for our #datamustread book club because it offers a practical guide to creating compelling data stories that resonate with your audience. Today, as we celebrate International Women’s Day, I’m excited to share this book written by a talented female author.

The Power of Storytelling with Data

In this book, we’ll explore the importance of storytelling in data visualization and how it can make your data more impactful. Cole emphasizes the need to understand your audience and context before creating a data story. Using real-world examples, she will show you how to go beyond the traditional tools to create a narrative that is engaging, informative, and compelling.

Data Visualization Best Practices

The book covers the key principles of effective data visualization. From choosing the right type of graph to eliminating clutter, Cole provides practical tips and tools for making your data visually appealing and easy to understand. She also shows how to use design concepts to make your data more engaging.

The Art of Data Storytelling

We’ll also learn how to craft a data story that resonates with your audience. Cole explains how to use data to create a compelling narrative that leads your audience to the key insights. By harnessing the power of storytelling, you can create visual stories that stick with your audience.

Throughout the book, Cole uses numerous real-world examples to illustrate her points. From the good to the bad, she provides insightful commentary on a variety of data stories. She also shares anecdotes and personal experiences that demonstrate the power of data storytelling.

Order Storytelling with Data Today

This guide is essential reading for anyone who wants to improve their data visualization skills. By following the lessons in this book, you can turn your data into powerful visual stories that resonate with your audience. If you’re looking to make your data more compelling and actionable, I highly recommend this book.

You can order Storytelling with Data using this link, which supports both me and the author. If you want to practice more, consider reading one of the other books written by the same author: Storytelling with Data: Let’s Practice!

Follow me on Twitter and LinkedIn for more insights. Also, be sure to check out my new book Teach Yourself Visually Power BI, a comprehensive guide to mastering Power BI.

The Big Book of Dashboards: A Comprehensive Guide to Building Effective Business Dashboards

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios
The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

As a data enthusiast, you know that creating an effective dashboard is not just about presenting data, it’s about communicating insights and making data-driven decisions. That’s why I’ve chosen The Big Book of Dashboards (BBoD) for our #datamustread book club. Written by Andy Cotgreave, Steve Wexler, and Jeffrey Shaffer, this book is a comprehensive reference that provides real-world solutions for building effective business dashboards.

Let’s dive into what makes this book so valuable for data enthusiasts.

Real-world solutions for building effective business dashboards

The BBoD covers dozens of examples that address different industries and departments, such as healthcare, transportation, finance, human resources, marketing, customer service, and sports, among others. It also covers different platforms, including print, desktop, tablet, smartphone, and conference room displays. With this book as your guide, you’ll be able to match great dashboards with real-world business scenarios.

Practical and effective visualization examples in The Big Book of Dashboards

The book is organized based on scenarios and offers practical and effective visualization examples. You’ll find an entire section of the book devoted to addressing many practical and psychological factors you will encounter in your work. The expert authors have combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They bring uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.

Tools, guidance, and models to produce great dashboards

The BBoD gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage. A well-designed dashboard can point out risks, opportunities, and more, but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. This book will help you avoid those challenges and produce dashboards that truly make a difference.

Examples of modern, effective designs

What sets The Big Book of Dashboards apart from its peers is that it contains a number of examples of modern, effective designs. The examples are of good visualizations, rather than bad, and this focus means that the reader is exposed to a vast range of quality content, rather than endless examples of what „not to do.“

Order The Big Book of Dashboards Today

Once you’ve read the BBoD, you’ll want to keep it nearby as a guide for your data visualization projects. The book provides 20+ high-quality dashboards based on all kinds of data from different industries, with detailed explanations of why certain chart types, layouts, and colors were used, why they work well, why other choices were not good enough, and what could be changed to make the dashboard even better.

Order your copy of The Big Book of Dashboards (BBoD) today with this link that supports both me and the authors. Also, comment on my LinkedIn post below for a chance to enter our book giveaway. Happy reading!

Follow me on Twitter and LinkedIn for more insights, and don’t forget to check out my new book Decisively Digital: From Creating a Culture to Designing Strategy, which dives deep into digital transformation.