🥳 2023 has been the year of Generative AI, marking an era of significant technological advancements. I am incredibly grateful to be part of the talented and passionate team at Microsoft. A huge thank you to all my colleagues, partners, and customers for their trust and fantastic collaboration throughout the year.
🤖 This year, we’ve seen remarkable milestones, including the launch of Copilot Studio. This easy-to-use IDE enables the creation of custom AI assistants – making AI accessible and functional! Imagine creating your own apps accessing all kinds of data without the need to write code!
🌎 Our memorable team events in Redmond, Dublin, and Munich will stay with me. Working with people who not only see AI as a value add but are intrinsically motivated to use it for global betterment has been incredibly inspiring.
🙏 Personally, my ongoing dedication to using AI to combat fake news has been particularly pertinent, especially in light of the upcoming US elections. Moreover, I’m thrilled to share that some of the AI/ethics ideas will flow into a book on AI, set to be published in a few weeks.
AGI is envisioned as an entity akin to human intelligence, exhibiting cognition, common sense, and knowledge. It is characterized by its human-like ability to comprehend, analyze, and engage in multi-step instructions and display apparent goals and pseudo-emotions. AGI spans a spectrum, ranging from ‚error-prone‘ or ’savant-like‘ sub-human intelligence to super-intelligence.
GPT-4: A Proto-AGI Precursor
The release of GPT-4 by OpenAI marked a significant milestone. It demonstrated vision capabilities and code interpretation, inching closer to higher-level cognitive abilities. Rumors of experiments with long-term memory suggest that integrating these components could result in a proto-AGI – an entity that meets some AGI criteria but lacks human precision and speed.
Predictions for 2024: The AI Landscape
OpenAI’s Next Leap: OpenAI is poised to unveil a more agent-like model. Anticipated to feature long-term memory and task-execution capabilities, this model – possibly named distinctively from the GPT lineage – might represent a nascent form of AGI.
Industrial Humanoid Robots: Beta deployments of humanoid robots in industrial settings will augment or replace human labor in specific tasks.
Text-to-Video Evolution: Expect breakthroughs in text-to-video technology, though generalization remains a challenge.
Synthetic Dataset Proliferation: AI training relying heavily on synthetic datasets could introduce hard-to-detect biases.
Medical AI Breakthrough: AI’s contribution to a major medical discovery is highly likely.
Public Sentiment and AI: Public opinion on AI will become increasingly polarized, with anti-AI sentiments emerging alongside widespread adoption.
Ethical, Financial, and Hardware Barriers to True AGI
While the path to AGI seems more tangible, ethical dilemmas, financial constraints, and hardware limitations remain formidable barriers. The upcoming elections will likely witness a surge in Generative AI for Fake News production, demanding AI-driven countermeasures.
Conclusion: Preparing for AI’s Leap Forward
2024 stands as a pivotal year in AI development, potentially heralding even more radical transformations. While absolute predictability is unattainable, rational analysis of existing trends can help us prepare for the likely scenarios. If 2024 aligns with these expectations, the journey to true AGI could be closer than we imagine, constrained predominantly by ethical, financial, and hardware limitations.
Data Smart (Amazon) is an exceptional guide that creatively uses Microsoft Excel to teach data science, making complex concepts accessible to business professionals. This 2nd edition, masterfully updated by Jordan Goldmeier, arrives a decade after John Foreman’s highly acclaimed original version, bringing fresh perspectives and contemporary insights to the renowned first edition.
Whether you’re a novice or a seasoned analyst, this book provides valuable insight and skill enhancement without requiring extensive programming knowledge. The practical, problem-solving approach ensures that you not only understand the theory, but also how to apply it in real-world scenarios. That’s why I’ve chosen Data Smart as our latest pick for the #datamustread book club.
Why „Data Smart“ is a #datamustread
Data Smart stands out in the realm of data science literature. Its approachable and practical methodology is a breath of fresh air for business professionals and data enthusiasts alike. Here’s why this book is an indispensable resource:
1. Excel as Your Data Science Laboratory: The use of Excel, a tool many of us are familiar with, to unravel data science concepts is nothing short of brilliant. This approach significantly flattens the learning curve, making complex techniques more digestible.
2. Practical Learning through Real Business Problems: Each chapter of the book introduces a different data science technique via a relatable business scenario. This context-driven approach makes the learning experience tangible and immediately applicable.
3. No Programming, No Problem: The author’s method of teaching data science without delving into programming languages makes the content accessible to a broader audience.
4. Excel Skills Elevated: In addition to data science concepts, readers will enhance their Excel prowess with advanced tools like Power Query and Excel Tables.
5. A Spectrum of Techniques: From cluster analysis to forecasting, the book covers a wide array of methods, making it a comprehensive toolkit for any aspiring data scientist.
6. Fresh Perspectives in the Second Edition: Goldmeier’s updates are not just cosmetic; they incorporate the latest Excel features, ensuring the content remains relevant in today’s fast-paced tech landscape.
Bridging the Gap with „Teach Yourself VISUALLY Power BI“
While exploring Data Smart, you’ll find parallels with the insights shared in my own book, Teach Yourself VISUALLY Power BI. Both texts aim to make data analytics accessible and actionable, providing a solid foundation for anyone looking to make informed decisions based on data.
Your Journey into Data Science Awaits
Data Smart is a gateway to understanding data science through a familiar and powerful tool: Excel. Whether you’re a beginner or a seasoned analyst, this book will enhance your analytical skills and expand your understanding of data in the business world.
Order Data Smart today and support both the authors and my endeavors in bringing such valuable resources to our community. Let’s dive into this journey of discovery together, transforming data into actionable insights.
The journey of AI from a niche concept in the labs of computer scientists to a ubiquitous force in society mirrors the trajectory of many revolutionary technologies. In its infancy, AI was a subject of academic curiosity, often confined to theoretical discussions and small-scale applications.
However, as technology advanced, we witnessed the birth of practical AI applications. From rudimentary chatbots to sophisticated machine learning algorithms, AI began to find its footing in the real world. These applications, although impressive, were just the tip of the iceberg.
Today, AI is not just a tool but a companion in our digital journey. Platforms like ChatGPT have democratized AI, making it accessible to everyone. They’ve turned AI from a mere concept into a daily utility, reshaping how we interact with technology and each other.
AI’s Impact on Society
AI is redefining job roles and industries. It’s automating mundane tasks, enhancing productivity, and even creating new career paths. While there are concerns about AI displacing jobs, it’s also undeniable that AI is creating opportunities for more creative and strategic roles.
As AI becomes more ingrained in our lives, ethical and societal considerations come to the forefront. Issues like privacy, bias in AI algorithms, and the digital divide need urgent attention to ensure that AI benefits society as a whole.
From personalized recommendations on streaming services to AI-assisted medical diagnostics, AI’s presence in our daily lives is growing. It’s not just a corporate or academic tool; it’s becoming a personal assistant, a healthcare advisor, and much more.
The Future of AI and Society
The future of AI is not just about technological advancements but also about how we, as a society, adapt to these changes. It’s about ensuring that AI grows in a way that is ethical, equitable, and beneficial to all.
As AI continues to evolve, it’s essential for individuals and organizations to stay informed and adapt to these changes. Embracing AI doesn’t mean blind adoption; it means understanding its capabilities and limitations and using it to enhance our lives and work.
AI is a catalyst for innovation, driving advancements in various fields. Its potential to solve complex problems and create new opportunities is immense. We are just scratching the surface of what AI can achieve.
The growth of AI, much like the discovery of the Higgs boson, is a breakthrough moment in our history. It’s a testament to human ingenuity and a reminder of the rapid pace of technological change. As we stand at this crossroads, it’s crucial to ponder how AI will shape our society and how we, in turn, will shape AI. The journey of AI is not just about technological development; it’s about the evolution of our society as a whole.
Power BI DAX (Data Analysis Expressions) is at the core of Microsoft’s Power BI and offers incredible capabilities for data manipulation and insights. In this post, we’ll explore 20 ultimate DAX tricks to elevate your Power BI reports. Whether you’re a beginner or an expert, these tips will help you unlock the full potential of Power BI and Microsoft Fabric.
20 Ultimate DAX Tricks – Simply Explained
Use CALCULATE for Context Modification 🛠️ CALCULATE is a powerful function that changes the context in which data is analyzed. Example:CALCULATE(SUM('Sales'[Sales Amount]), 'Sales'[Region] = "West") This calculates the sum of sales in the West region.
Use RELATED for Accessing Data from Related Tables 🔄 RELATED function allows you to access data from a table related to the current table. Example: RELATED('Product'[Product Name]) This fetches the product name related to the current row.
Use EARLIER for Row Context 🕰️ EARLIER is a useful function when you want to access data from an earlier row context. Example: CALCULATE(SUM('Sales'[Sales Amount]), FILTER('Sales', 'Sales'[Sales ID] = EARLIER('Sales'[Sales ID])))
Use RANKX for Ranking 🏅 RANKX function allows you to rank values in a column. Example: RANKX(ALL('Sales'), 'Sales'[Sales Amount], , DESC) This ranks sales amounts in descending order.
Use DIVIDE for Safe Division 🧮 DIVIDE function performs division and handles division by zero. Example: DIVIDE([Total Sales], [Total Units]) This divides total sales by total units and returns BLANK() for division by zero.
Use SWITCH for Multiple Conditions 🔄 SWITCH function is a better alternative to nested IFs. Example: SWITCH([Rating], 1, "Poor", 2, "Average", 3, "Good", "Unknown") This assigns a label based on the rating.
Use ALL for Removing Filters 🚫 ALL function removes filters from a column or table. Example: CALCULATE(SUM('Sales'[Sales Amount]), ALL('Sales')) This calculates the total sales, ignoring any filters.
Use CONCATENATEX for String Aggregation 🧵 CONCATENATEX function concatenates a column of strings. Example: CONCATENATEX('Sales', 'Sales'[Product], ", ") This concatenates product names with a comma separator.
Use USERELATIONSHIP for Inactive Relationships 🔄 USERELATIONSHIP function allows you to use inactive relationships. Example: CALCULATE(SUM('Sales'[Sales Amount]), USERELATIONSHIP('Sales'[Date], 'Calendar'[Date])) This calculates sales using an inactive relationship.
Use SAMEPERIODLASTYEAR for Year-Over-Year Comparisons 📆 SAMEPERIODLASTYEAR function calculates the same period in the previous year. Example: CALCULATE(SUM('Sales'[Sales Amount]), SAMEPERIODLASTYEAR('Calendar'[Date])) This calculates sales for the same period last year.
Use BLANK for Missing Data 🕳️ BLANK function returns a blank. Example: IF('Sales'[Sales Amount] = 0, BLANK(), 'Sales'[Sales Amount]) This returns a blank if the sales amount is zero.
Use FORMAT for Custom Formatting 🎨 FORMAT function formats a value based on a custom format string. Example: FORMAT('Sales'[Sales Date], "MMM-YYYY") This formats the sales date as „MMM-YYYY“.
Use HASONEVALUE for Single Value Validation 🎯 HASONEVALUE function checks if a column has only one distinct value. Example: IF(HASONEVALUE('Sales'[Region]), VALUES('Sales'[Region]), "Multiple Regions") This checks if there is only one region.
Use ISFILTERED for Filter Detection 🕵️♀️ ISFILTERED function checks if a column is filtered. Example: IF(ISFILTERED('Sales'[Region]), "Filtered", "Not Filtered") This checks if the region column is filtered.
Use MAXX for Maximum Values in a Table 📈 MAXX function returns the maximum value in a table. Example: MAXX('Sales', 'Sales'[Sales Amount]) This returns the maximum sales amount.
Use MINX for Minimum Values in a Table 📉 MINX function returns the minimum value in a table. Example: MINX('Sales', 'Sales'[Sales Amount]) This returns the minimum sales amount.
Use COUNTROWS for Counting Rows in a Table 🧮 COUNTROWS function counts the number of rows in a table. Example: COUNTROWS('Sales') This counts the number of rows in the Sales table.
Use DISTINCTCOUNT for Counting Unique Values 🎲 DISTINCTCOUNT function counts the number of distinct values in a column. Example: DISTINCTCOUNT('Sales'[Product]) This counts the number of distinct products.
Use CONTAINS for Lookup Scenarios 🔍 CONTAINS function checks if a table contains a row with certain values. Example: CONTAINS('Sales', 'Sales'[Product], "Product A") This checks if „Product A“ exists in the Sales table.
Use GENERATESERIES for Creating a Series of Numbers 📊 GENERATESERIES function generates a series of numbers. Example: GENERATESERIES(1, 10, 1) This generates a series of numbers from 1 to 10 with a step of 1.
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