Master Microsoft Power BI with My New Book: Teach Yourself VISUALLY Power BI

Alexander Loth holding his books Teach Yourself VISUALLY Power BI, Decisively Digital and Visual Analytics with Tableau
Alexander Loth holding his books Teach Yourself VISUALLY Power BI, Decisively Digital and Visual Analytics with Tableau

A Comprehensive, Fully Visual Guide to Data Visualization and Analytics

I am thrilled to announce the release of my latest book, Teach Yourself VISUALLY Power BI – a comprehensive and fully visual guide to mastering Microsoft Power BI. This book is designed to help both beginners and experienced users improve their skills in data visualization and analytics using Power BI, a powerful tool that has become an industry standard. In this blog post, I’ll give you a sneak peek into the contents of the book and how it can help you become a Power BI pro.

Why Teach Yourself VISUALLY Power BI?

Power BI is a robust data visualization software that enables users to transform raw data into meaningful insights through stunning visuals. However, learning Power BI can be challenging, especially for those who are new to the world of data visualization. That’s where Teach Yourself VISUALLY Power BI comes in. The book adopts a visual learning approach, combining step-by-step instructions with clear screenshots to walk readers through the basic and advanced functions of Power BI.

What’s Inside Teach Yourself VISUALLY Power BI?

This guide offers a comprehensive learning experience, covering a wide range of topics, including:

  1. Working with, transforming, and processing data sources: Learn how to connect to various data sources, clean and transform data, and create relationships between tables.
  2. Customizing data visualizations: Discover how to create informative and presentation-ready charts and graphs by customizing their appearance, colors, and interactivity.
  3. Advanced Power BI features: Explore app integrations, data access with DAX, and other advanced features that will help you get the most out of Power BI.
  4. Real-world examples and guidance: Apply your learnings to real-world scenarios with practical examples and expert tips throughout the book.

Who Can Benefit from Teach Yourself VISUALLY Power BI?

This book is suitable for a wide range of readers, including:

  1. Beginners: If you’re new to Power BI or data visualization, Teach Yourself VISUALLY Power BI provides an accessible introduction to help you get started.
  2. Intermediate users: For those with some experience in Power BI, the book offers valuable tips and advanced techniques to enhance your skills further.
  3. Professionals: Data analysts, business intelligence professionals, and managers can use this book to improve their data visualization and reporting capabilities using Power BI.

Get your copy today!

Teach Yourself VISUALLY Power BI is a must-have resource for anyone looking to master Microsoft Power BI. Whether you’re a beginner or an experienced user, this book offers valuable insights and guidance to help you harness the full potential of this powerful data visualization tool.

Don’t miss out – grab your copy today and embark on your journey toward becoming a Power BI pro! Also, follow me on Twitter and LinkedIn for more insights.

Discover the Power of Honest Data Visualization: Elevate Your Data Storytelling Skills with The Truthful Art

Truthful Art, The: Data, Charts, and Maps for Communication
Truthful Art, The: Data, Charts, and Maps for Communication

Welcome to the #datamustread book club, where we dive into the world of data with compelling and informative reads. This month, I’ve chosen a book that will revolutionize the way you approach data visualization: The Truthful Art by Alberto Cairo. In this blog post, I’ll provide you with an in-depth look at the book and its valuable lessons, as well as examples and anecdotes that demonstrate its relevance to data enthusiasts like you.

Unleashing the Storytelling Potential of Data

The Truthful Art delves into the principles and practices of data visualization, teaching you how to create graphical representations of information that are both effective and honest. By viewing data visualization as a tool for communication, the book emphasizes its potential to convey stories, arguments, and insights to a diverse audience.

A Comprehensive Guide to Data Visualization

This book provides readers with a deep understanding of data analysis, design, storytelling, and the ethical and practical challenges that accompany working with data. Throughout the book, Alberto draws from his experience as a journalist, professor, and consultant to offer practical advice and insights on creating and evaluating data visualizations.

Real-World Examples and Exercises with „The Truthful Art“

What sets The Truthful Art apart from other data visualization resources is its use of real-world examples and exercises. The book goes beyond theoretical concepts by demonstrating how to apply these ideas to your own projects. This hands-on approach ensures that you can readily implement the techniques you learn, making your data visualizations more effective and honest.

A Manifesto for the Truthful Art of Data Communication

The Truthful Art is more than just a guide to data visualization. It’s a call to action, urging readers to think critically and creatively about data and communicate their findings in a clear and compelling way. The book challenges you to reflect on your biases and assumptions and to consider the ethical implications of your work.

Don’t Miss This Essential Read for Data Enthusiasts

If you’re passionate about data visualization and want to take your skills to the next level, The Truthful Art is a must-read. By offering valuable insights, practical examples, and a focus on ethics, this book is an essential resource for anyone who wants to use data to inform, persuade, or inspire others.

Ready to dive into The Truthful Art and transform your approach to data visualization? Order your copy of The Truthful Art today and support both me and the author in our quest to spread the power of honest data visualization.

Feel free to share your thoughts in the comments section of this LinkedIn post or with a reply on this Twitter thread. Stay connected with me on Twitter and LinkedIn, and don’t forget to explore my new book, Teach Yourself Visually Power BI.

AI Insights and Inspiration: My Story of Becoming a LinkedIn Top Voice with 30,000 Followers

Screenshot of Alexander Loth's LinkedIn profile, displaying the LinkedIn Top Voice badge, alongside the milestone achievement of 30,000 followers.
My LinkedIn profile, displaying the LinkedIn Top Voice badge, alongside the milestone achievement of 30,000 followers. Thank you very much!

I’m overjoyed to share an exciting milestone in my journey as a digital strategist and data scientist: my LinkedIn account has now reached a milestone of 30,000 followers! This achievement is more than just a number – it symbolizes a thriving community of enthusiasts passionate about data, AI, and digital transformation. The icing on the cake? Being recognized as a LinkedIn Top Voice in AI, an accolade that underscores my commitment to this fascinating field.

The surge in followers came on the heels of my inspiring ACM talk on Generative AI—a topic that captivates and challenges the norms of technology and creativity. This talk was more than a presentation of the work of our Microsoft AI For Good Lab; it was an invitation to explore the endless possibilities that AI brings to our world.

I cannot express enough gratitude for the overwhelming response to my book 📘 Decisively Digital  (➡️ Amazon). Each page was crafted with the intent to guide, enlighten, and inspire. Your support and feedback have been pivotal in its success.

What makes this journey truly remarkable is you – my followers, peers, and fellow enthusiasts. Our shared passion for data, AI, and digital technology has created a unique and vibrant community. Engaging with you, sharing insights, and learning from your perspectives has been one of the most rewarding aspects of my career.

One of the most exciting initiatives has been the #datamustread book club. Witnessing your engagement, the dynamic discussions, and how we are collectively uncovering the potential of data and analytics to shape our world is nothing short of inspiring.

As LinkedIn’s Top Voice in AI, I look forward to continuing our journey of discovery and discussion. Your thoughts, ideas and contributions are the lifeblood of this community. Let’s keep the momentum going and dive deeper into the realms of AI and digital innovation.

Thank you, each and every one of you, for your unwavering support and enthusiasm. Together, let’s take the road to the next 30,000 followers and beyond, making every step a leap toward a more informed, innovative, and inspired digital world.

The Rise of Generative AI: Revolutionizing Innovation and Enhancing Human Collaboration

Alexander Loth speaking at German Chapter of the Association for Computing Machinery (ACM) on March 24, 2023. Title of the presentation: "The Rise of Generative AI".
The Rise of Generative AI (photo by Bernd Vellguth)

Last week I had the pleasure of presenting a talk for the German Chapter of the Association for Computing Machinery (ACM). The session sparked lively discussions and elicited numerous thought-provoking questions from our engaged audience.

Following the talk, I was inspired by a conversation to leverage the power of GPT-4 and create an automatically generated summary of the Microsoft Teams transcript. This approach not only streamlines information sharing but also showcases the practical applications of advanced AI technology.

Below, I will share the key insights generated by GPT-4 and also include some captivating images from the event:


Decisively Digital: AI’s Impact on Society

In my talk, I drew inspiration from my book Decisively Digital, which discusses the impact of AI on society. I shared about the innovative projects underway at Microsoft’s AI for Good Lab. In light of GPT-4’s recent launch, I also highlighted our mission to leverage technology to benefit humanity.

Alexander Loth presents his book Decisively Digital, which also discusses Generative AI.
AI’s impact on society is discussed in Decisively Digital: From Creating a Culture to Designing Strategy. John Wiley & Sons. (photo by Gerhard Müller)

By harnessing Generative AI, we can stimulate the creation of innovative ideas and accelerate the pace of advancement. This cutting-edge technology is already transforming industries by streamlining drug development, expediting material design, and inspiring novel hypotheses. AI’s ability to identify patterns in vast datasets empowers humans to uncover insights that might have gone unnoticed.

Generative AI can Augment our Thinking

For instance, researchers have employed machine learning to predict chemical combinations with the potential to improve car batteries, ultimately identifying promising candidates for real-world testing. AI can efficiently sift through and analyze extensive information from diverse sources, filtering, grouping, and prioritizing relevant data. It can also generate knowledge graphs that reveal associations between seemingly unrelated data points, which can be invaluable for drug research, discovering novel therapies, and minimizing side effects.

„Now is the time to explore how Generative AI can augment our thinking and facilitate more meaningful interactions with others.“

Alexander Loth

At the AI for Good Lab, we are currently employing satellite imagery and generative AI models for damage assessment in Ukraine, with similar initiatives taking place in Turkey and Syria for earthquake relief. In the United States, our focus is on healthcare, specifically addressing discrepancies and imbalances through AI-driven analysis.

Our commitment to diversity and inclusion centers on fostering digital equality by expanding broadband access, facilitating high-speed internet availability, and promoting digital skills development. Additionally, we are dedicated to reducing carbon footprints and preserving biodiversity. For example, we collaborate with the NOAH organization to identify whales using AI technology and have developed an election propaganda index to expose the influence of fake news. Promising initial experiments using GPT-4 showcase its potential for fake news detection.

Alexander Loth: "We live in a rapidly changing world, facing big challenges."
„We live in a rapidly changing world, facing big challenges.“ (photo by Gerhard Müller)

ChatGPT will be Empowered to Perform Real-time Website Crawling

While ChatGPT currently cannot crawl websites directly, it is built upon a training set of crawled data up to September 2021. In the near future, the integration of plugins will empower ChatGPT to perform real-time website crawling, enhancing its ability to deliver relevant, up-to-date information, and sophisticated mathematics. This same training set serves as the foundation for the GPT-4 model.

GPT-4 demonstrates remarkable reasoning capabilities, while Bing Chat offers valuable references for verifying news stories. AI encompasses various machine learning algorithms, including computer vision, statistical classifications, and even software that can generate source code. A notable example is the Codex model, a derivative of GPT-3, which excels at efficiently generating source code.

Microsoft has a long-standing interest in AI and is dedicated to making it accessible to a wider audience. The company’s partnership with OpenAI primarily focuses on the democratization of AI models, such as GPT and DALL-E. We have already integrated GPT-3 into Power BI and are actively developing integrations for Copilot across various products, such as Outlook, PowerPoint, Excel, Word, and Teams. Microsoft Graph is a versatile tool for accessing XML-based objects in documents and generating results using GPT algorithms.

Hardware, particularly GPUs, has played a pivotal role in the development of GPT-3. For those interested in experimenting with Generative AI on a very technical level, I recommend Stable Diffusion, which is developed by LMU Munich. GPT-3’s emergence created a buzz, quickly amassing a vast user base and surpassing the growth of services like Uber and TikTok. Sustainability remains a crucial concern, and Microsoft is striving to achieve a CO2-positive status.

Generative AI Models have garnered Criticism due to their Dual-use Nature

Despite its potential, Generative AI models such as GPT-3 have also garnered criticism due to their dual-use nature and potential negative societal repercussions. Some concerns include the possibility of automated hacking, photo manipulation and the spread of fake news (➡️ deepfake disussion on LinkedIn). To ensure responsible AI development, numerous efforts are being undertaken to minimize reported biases in the GPT models. By actively working on refining algorithms and incorporating feedback from users and experts, developers can mitigate potential risks and promote a more ethical and inclusive AI ecosystem.

Moving forward, it is essential to maintain open dialogue and collaboration between AI developers, researchers, policymakers, and users. This collaborative approach will enable us to strike a balance between harnessing the immense potential of AI technologies like GPT and ensuring the protection of society from unintended negative consequences.

Alexander Loth discusses Microsoft's responsible AI principles: fairness, reliability and safety, privacy and security, and inclusiveness, underpinned by transparency and accountability, which also apply to Generative AI.
Microsoft‘s responsible AI principles: fairness, reliability & safety, privacy & security, and inclusiveness, underpinned by transparency and accountability. (photo by Gerhard Müller)

GPT-3.5 closely mimics human cognition. However, GPT-4 transcends its forerunner with its remarkable reasoning capabilities and contextual understanding. GPT models leverage tokens to establish and maintain the context of the text, ensuring coherent and relevant output. The GPT-4-32K model boasts an impressive capacity to handle 32,000 tokens, allowing it to process extensive amounts of text efficiently. To preserve the context and ensure the continuity of the generated text, GPT-4 employs various strategies that adapt to different tasks and content types.

GPT-4 Features a Robust Foundation in Common Sense Reasoning

One of GPT-4’s defining features is its robust foundation in common sense reasoning. This attribute significantly contributes to its heightened intelligence, enabling the AI model to generate output that is not only coherent but also demonstrates a deep understanding of the subject matter. As GPT-4 continues to evolve and refine its capabilities, it promises to revolutionize the field of artificial intelligence, expanding the horizons of what AI models can achieve and paving the way for future breakthroughs in the realm of generative AI.

Comparisons between GPT-4 and GPT-3 show the superior performance of the former in various tasks. Finally, I will present the results of my live Twitter poll asking the audience about the feasibility of achieving Artificial General Intelligence (AGI). Nearly half of the respondents are of the opinion that AGI is achievable within the next five years.

Alexander Loth discussing the results of the live Twitter survey: 47.8% predict the emergence of AGI is possible within the next 5 years
Results of the live Twitter survey: 47.8% predict the emergence of AGI is possible within the next 5 years (photo by Bernd Vellguth)

In the near future, advanced tools like ChatGPT will elucidate intricate relationships without requiring us to sift through countless websites and articles, further amplifying the transformative impact of Generative AI.

I appreciate the opportunity to share my insights at the German Chapter of the ACM.


The slides of my talk are available on ResearchGate.

Did you enjoy this GPT-generated Summary of my Talk?

Leveraging GPT-4 to generate a summary of my talk was an exciting experiment, and I have to admit, the results are impressive. GPT was able to provide a brief overview of the key takeaways from my talk.

Now, I would love to hear about your experiences with GPT. What are your experiences with GPT so far? Feel free to share your thoughts in the comments section of this Twitter thread or this LinkedIn post:

„The Rise of Generative AI: Revolutionizing Innovation and Enhancing Human Collaboration“ 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