2024 AI Predictions: Artificial General Intelligence and the Road to Proto-AGI

2024 AI Predictions: Artificial General Intelligence and the Road to Proto-AGI
2024 AI Predictions: Artificial General Intelligence and the Road to Proto-AGI

As 2023 draws to a close, the field of Artificial Intelligence (AI) stands on the cusp of a transformative leap. With GPT-4 setting a precedent in multimodal and code interpretation capabilities, we edge closer to what many term as Artificial General Intelligence (AGI). This post delves into the probable trajectory AI may take in 2024, especially in the context of AGI.

Defining AGI and Its Emerging Spectrum

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.

Unlocking the Power of Data Science with Excel: Discover the Book „Data Smart“

Exploring the depths of Data Science with Excel: A glimpse into 'Data Smart' by Jordan Goldmeier, a must-read for data enthusiasts.
Exploring the depths of Data Science with Excel: A glimpse into ‚Data Smart‘ by Jordan Goldmeier, a must-read for data enthusiasts.

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.

Join the Conversation

After delving into Data Smart, I’d love to hear your thoughts and takeaways. Share your insights and join the discussion in our vibrant #datamustread community on LinkedIn and Twitter:

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AI & Society: Charting the Unprecedented Growth of Artificial Intelligence with Decisively Digital

Alexander Loth presenting his book Decisively Digital
AI & Society: AI’s impact on society discussed in Decisively Digital: From Creating a Culture to Designing Strategy. John Wiley & Sons. (photo by Gerhard Müller)

Three years ago, while drafting my book Decisively Digital (Amazon), I believed that the peak popularity of artificial intelligence (AI) was nearing its zenith. Little did I realize then how profoundly and rapidly the landscape of AI would transform, much like the unexpected and remarkable discovery of the Higgs boson in particle physics. The current surge in AI interest, fueled by innovations like ChatGPT and Copilot, has catapulted AI into a realm of unprecedented growth and societal impact.

AI’s Journey: From Concept to Ubiquity

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.

Conclusion

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.


Explore more about the interplay of AI and society in my book Decisively Digital.

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Power BI Tricks: 20 Essential DAX Tricks for Your Power BI Reports – A Comprehensive Guide to Power BI DAX

Even more Power BI DAX tricks in these books: "Datenvisualisierung mit Power BI" and "Teach Yourself Visually Power BI"
Even more Power BI DAX tricks in these books: „Datenvisualisierung mit Power BI“ and „Teach Yourself Visually Power BI“

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

  1. 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.
  2. 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.
  3. 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])))
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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“.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.

Even more Power BI DAX Tricks

📚 If you want to dive even deeper into the world of Power BI, check out my Power BI books 🔗 Teach Yourself Visually Power BI (Amazon) and 🔗 Datenvisualisierung mit Power BI (Amazon)! These books are packed with even more tips, tricks, and tutorials to help you master Power BI. Don’t miss out on these invaluable resources!

Want to stay updated with the latest Power BI insights? Follow me on Twitter and LinkedIn. Share your thoughts, ask questions, and engage with a community of Power BI enthusiasts like yourself.

Feel free to leave a comment, ask questions, or share my Power BI DAX tweets:

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Newsletter: Data & AI Digest #3

Generated with DALL-E

Welcome to the latest edition of the ‚Data & AI Digest‘, where we voyage through the cascading waves of data and AI innovations. This edition is brimming with fresh advancements, critical discussions, and a sprinkle of controversy that showcases the dynamic nature of our field. Let’s delve into the highlights:

  1. [Generative AI] OpenAI’s DALL-E 3 Revolution: OpenAI unveils the third iteration of its acclaimed DALL-E visual art platform. Experience enhanced contextual understanding, seamless ChatGPT integration, and bolstered security in this generative marvel: Dive deeper.
  2. [Microsoft] AI Integration in Windows 11: Microsoft introduces Copilot, bringing the prowess of GPT-4 to Windows 11. Engage with this new AI assistant across various applications and discover Bing’s support for DALL.E 3: Explore Copilot.
  3. [ChatGPT] Internet-Savvy ChatGPT: OpenAI supercharges ChatGPT with real-time internet scanning capabilities, ensuring your interactions are backed by the most recent information. Discover the new browsing rules ensuring respectful web interaction: Unveil the update.
  4. [Finance AI] Morgan Stanley’s Wealth Management AI: In collaboration with OpenAI, Morgan Stanley is launching a generative AI chatbot aimed at revolutionizing wealth management. Explore this new virtual assistant’s journey from conception to deployment: Read more.
  5. [Meta] AI-Powered Creativity Unleashed: Meta unfolds new AI experiences across its app ecosystem. Dive into the AI-powered assistants, characters, and creative tools enriching digital interactions: Discover more.
  6. [Artistic Stand] AI Image Generation Sparks Debate: Chinese artists rally against a major social media platform over AI-generated imagery concerns. Uncover the discourse between creativity and AI: Join the discussion.
  7. [Google] Privacy-Forward AI Training: Google unveils an opt-out feature for publishers wary of contributing to AI training datasets. Explore the implications for data privacy and AI development: Learn more.
  8. [Amazon] Generative AI on Amazon Bedrock: AWS heralds a new era of generative AI innovation with the rollout of Amazon Bedrock. Uncover the powerful new offerings accelerating the AI frontier: Explore Bedrock.

If you found value in this edition, share the knowledge with colleagues and friends. For those keen on diving deeper into discussions and networking, the LinkedIn Data & AI Hub awaits your insights. Until the next issue, where we’ll venture further into the data and AI cosmos, thank you for your continued support and curiosity.

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