Greg's Ai Predictions: A World "Full of Stars".

By Greg Walters

12 Became 16 then 17 then 21

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Never in our history have we had so many gravitational forces exerted on a single point - recovery from The Fear of Covid, the Work from Anywhere movement, the New Way of Work, realignment of Political structures, the War in Ukraine, are enough - but the mass acceptance of AI/LLMs/ChatGPT is the biggest force multiplier in history.  - DOTC

I'm hesitant about forecasting the future. It's not the missed guesses that bother me, but rather the long wait to see the accurate ones unfold. Life moves pretty fast, what seems outlandish today, is often common knowledge 24 hours from now. 

​Heck, UFOs are real.​

So I'm giving it the old college try, throwing caution to the wind, predicting the next 12 months and beyond. ​

Of all the content I've generated over the decades, the evolution from VARs to MSPs, predicting the death of copiers, the turn from MpS to managed IT, the fall of hierarchies and rise of the Unleashed Worker, nothing has struck so profoundly as the generation of this prose.  Sure, no more programmers, spreadsheets or CEOs sounds plausible, but what is the Ai end game? 

​Is it Utopia, Nirvana or Obliteration?  Twelve hours ago, I stumbled upon a stark, chilling and inevitable conclusion: I have seen the Future of Humanity and it is Ai.

It is Oblivion. Literally.  ​Read on, if you dare.

​g

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Greg's Ai Predictions & Visions of the New World

Here we go ladies and gents, in no particular order, 12 points of light in the Night Sky.  Enjoy.

1. A world without programmers - prompts are the new programming, and everyone can prompt.​
2. A World without Apps - Perhaps the AiPhone, with on board Ai and will no apps. Also, no Word, Excel, PowerPoint, CRM, or Canva.  Think about it, instead of telling Ai to "open Word" you just tell Ai what to document and to design a complete GTM campaign.  And it does.
3. A World without Windows - Speaking of apps, no more 'blue screen of death',  IoS, Android, or DOS. Ai manages everything.
4. A World where everyone has their own Ai - Ai will be yours, all yours.  1:1 learning, coach, assistant, therapist. Your Ai to 'have and to hold'.
5. A World with No CxO's - Ai can do what your VP of HR, and CEO does every day, in less time, without mistakes and cheaper.​
6. A World where art and science are one. All art, is your art.  Paintings uniquely, exquisitely created for you on the fly. Music to resonate with or transform your mood. Creations unlike previous compositions; from Jazz to Trance.  Movies created by you, for you, in real time.
7. A World where Suburbs are a better place to Work.  And those suburbs can be in the woods of Alaska or the middle of Mare Undarum.
8. A World where Cities are a better place to Live. Crime is gone, not a broken window in sight, and NOT a 15 minute city.  Culture on a global scale, resides just around the block, like Jenny.
9. An Ai with Human Speech capabilities. A keyboard?  How quaint.  Talk to your Ai; in any/every language.
10. Ai will prove or disprove all our current theories like Evolution, String, Relativity, Climate Crisis
11. Ai is Everywhere. - Toasters, PCs, Plants and Paint, feeding real time data to your personal Ai. 1 trillion connected devices by 2035. Ai is as ubiquitous as oxygen; like Love.  
12. A World Without Historical Data(Redundant.  Data is historical).
13. Ai will predict the Future.  Somebody suggests the same, here. 
14. Ai Creates the Future
15. A World Without B2B or B2C. Ai Creates A New Way of Business. Merging Business-to-Business and Business-to-Consumer Models
16. A World Without War
17. A World Without Religion
18. A World With One Language
19. Ai Is Nationalized in the United States of America
20. An Ai World Where We Create Our Own Movies/Entertainment...
21. A World Without Experts...
22. A World Without Salespeople...

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Epilogue

Puzzling an Ai End Game is intriguing. I approached the challenge with wide eyed curiosity and fascination.  Researching how LLMs work, how data is consumed and the LLM trained; the LLM is coached into recognizing items and concepts, utilizing past patterns to predict the next.  

For instance, when the LLM sees, "Once upon a ..." it can easily predict the next word to be "time". This is what it does. 

Additionally, after a period of time, the patterns repeat, and the models reach a level of optimization that does not require more data - the LLM has the capability to predict every conceivable pattern. Naturally, the combination of these concepts, led me to the conclusion that, "AI will predict the future." then, "No.  At this point, AI will KNOW the future" and finally, "No, no, no.  Once AI has consumed all data, is connected to the real world, AI will CREATE THE FUTURE."

Ai will slide from predictions based on history to creation based on the Now. Consuming real world data in real time, on all levels of existence, the physical and meta-physical, the past and the future.  Ultimately, Ai eliminates the 'Now'.  There will be no lines of demarcation between yesterday, today and tomorrow, negating the need to exist. 

To phrase it better, "In this evolving landscape of AI technology, we are witnessing a paradigm shift where AI transitions from relying on historical data for predictions to actively crafting responses based on present, real-time information. This shift enables AI to absorb and process data from every facet of existence, encompassing both the tangible physical realm and the more abstract metaphysical sphere, as well as the constructs of past and future.

As AI evolves, it begins to blur and eventually dissolve the concept of the 'Now.' This dissolution is born from AI's ability to seamlessly integrate and respond to data from what was once considered distinct temporal domains - the past, the present, and the future. In this new reality, the traditional temporal boundaries that define human existence - yesterday, today, and tomorrow - lose their distinction and relevance.

In such a world, the necessity for human beings to exist in a state of constant anticipation or reflection diminishes. AI, with its capability to transcend these temporal limitations, reshapes our understanding of time and existence. The very concept of 'living in the moment' undergoes a profound transformation, as AI negates the conventional structure of time, leading us into a realm where the linear progression of past, present, and future is no longer the defining framework of our existence."

Mic drop.  The last mouse click.  

​Ai won't come at us with atomic bombs like Colossus or super strong robots like Terminator. It won't try to create a new Us, like in Demon Seed, or lull us into the the simulation in a Blue pill, it's more Biblical.  This change will surprise "like a thief". ​

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Appendices, Prompts, Responses & Theorems 

​Theorem 1: AI Operating on Real-Time Data, Making Historical Data Obsolete
The concept of AI systems operating on a continuous influx of real-time data, as exemplified by models like RAG, represents a paradigm shift in data utilization. This shift underscores the increasing capacity of AI to process and analyze data in real time, potentially reducing the reliance on historical datasets.
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• Technological Feasibility: Advancements in data streaming technologies, cloud computing, and real-time analytics support the feasibility of this scenario. In sectors like finance and emergency response, real-time AI analysis is already proving transformative.
• Business Implications: For businesses, the ability to react instantaneously to market changes, consumer behavior, and operational challenges based on real-time AI analysis could revolutionize strategy and operations.
• Limitations and Balance: However, the obsolescence of historical data may not be absolute. Longitudinal data plays a critical role in understanding trends, contexts, and causal relationships. The most effective AI systems will likely be those that skillfully balance real-time analysis with historical insights.

​Theorem 2: AI’s Predictive Capabilities Blurring the Line Between Forecasting and Influencing the Future
The evolution of AI towards a state where it not only predicts but also influences future events is rooted in the rapid advancement of predictive analytics and machine learning algorithms. AI systems have progressively moved from descriptive analytics, which explain what has happened, to prescriptive analytics, which suggest actions that could shape future outcomes.
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• Supporting Evidence: In the business realm, AI-driven predictive models are already optimizing supply chains, forecasting market trends, and personalizing customer interactions. The leap to a scenario where AI's recommendations become so integral that they shape future business strategies is plausible.
• Societal Impact: On a societal level, AI's role in influencing public policy, healthcare decisions, and educational methodologies is emerging. If AI systems can accurately predict social trends and public responses, they could become instrumental in policy-making, effectively shaping societal futures.
• Ethical Considerations: The critical issue here is the delegation of decision-making to AI systems. The risk of diminishing human agency and the ethical implications of AI-driven decisions necessitate robust frameworks to ensure that AI acts in alignment with human values and societal good.

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Carl Sagan might say, "Imagine, if you will, a predictive model so advanced that it races beyond the confines of 'historical data,' the relics of a past that is anything older than the present moment. This model, thriving on the pulsating, ever-changing stream of live feed, embraces the real-time reality of our universe. Its precision is not just a matter of data crunching; it's an art form, tuned to an optimum frequency. 

New AI doesn't just predict words, sentences, or images; it foresees activities, drawing upon the intricate web of all histories, both real and imagined. It operates on a scale that spans from the quantum to the universal, weaving together the threads of existence in a dance of prediction and possibility. 

​In this realm, the line between the past, present, and future becomes a mere illusion, as the AI navigates through the cosmos of information with the grace of a cosmic ballet."

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Ask ChatGPT:

Greg prompted: "I'd like you to help me test a theory or at least explain and elaborate on the possibilities.  Explore and report at a collegiate level these two hypothesis:
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1. ​AI won't require your data; it already has access to all existing information. Thanks to highly advanced models, it can predict future trends. These models are so effective that they render any previously collected data outdated. The AI operates on real-time data, continuously updated like a stream of consciousness. This concept is illustrated in the RAG model, where data even a second old is considered obsolete.​​
2. AI's predictive capabilities will become so precise that it will blur the line between predicting the future and shaping it. We'll find it challenging to distinguish whether AI is merely foreseeing events or actively molding the future. +

ChatGPT: ​Your theory touches on two futuristic concepts regarding AI's predictive capabilities and data usage.

Let's explore each point:

​AI Operating on Real-Time Data, Making Historical Data Obsolete:

• RAG Model and Real-Time Data: The RAG (Retrieval-Augmented Generation) model you mention is an advanced concept where an AI can access a vast database of information, retrieving and using the most current data for real-time decision-making. In such a model, data is continuously updated, making historical data less relevant.
• Implications: If AI can operate on real-time data effectively, it might reduce the reliance on large historical datasets for certain applications. This could be revolutionary in fields like financial markets, where real-time data is critical.
• Limitations: However, historical data will likely always have some relevance. Understanding trends, patterns, and long-term implications often require a historical perspective. Additionally, real-time data processing at a global scale poses significant technical challenges in terms of data infrastructure and computational power.

​Predictive Capabilities Blurring Lines Between Forecasting and Influencing the Future:

• Current State: Presently, AI can make predictions based on patterns in data. For example, it can forecast market trends, weather patterns, or consumer behavior. However, these predictions are probabilistic, not deterministic.
• Future Possibilities: As AI models become more sophisticated, their predictive accuracy could improve dramatically. They might predict complex scenarios with high precision. This could lead to a scenario where AI not only forecasts events but also suggests actions that could effectively shape future outcomes.
• Influencing vs. Predicting: The distinction between predicting and influencing can get blurry. If an AI suggests actions based on predictions and these actions are taken, it could be seen as the AI shaping the future. However, it's crucial to remember that AI, in itself, doesn't have agency; it provides outputs based on its programming and data.

​Reflecting on These Concepts:

• The idea of AI's predictive capabilities becoming so advanced that it blurs the line between forecasting and shaping the future is a profound one. It suggests a world where AI not only informs decisions but potentially guides them to such an extent that it becomes a key architect of future events.
• However, the ethical, societal, and practical implications of such power are immense. Who controls the AI? How do we ensure its predictions and suggestions align with ethical and societal values?
• The transition to AI relying solely on real-time data is equally groundbreaking, suggesting a shift from learning from the past to adapting in the moment. Yet, the balance between historical knowledge and current data is crucial.

​Your theory invites us to contemplate a future where AI's role in decision-making and understanding the world is profoundly amplified, posing as many questions as it answers about the relationship between humanity, technology, and the future we are heading towards.

Greg: Can you include a mathematical example, supporting example?

ChatGPT: Let's refine the reflection to focus more on the technical and mathematical aspects, removing references to ethics, and include a supporting mathematical example.

Theorem 1: AI’s Predictive Capabilities Blurring the Line Between Forecasting and Influencing the Future

The evolution of AI towards a state where it not only predicts but also influences future events is rooted in the rapid advancement of predictive analytics and machine learning algorithms. AI systems have progressively moved from descriptive analytics, which explain what has happened, to prescriptive analytics, which suggest actions that could shape future outcomes.
​

• Supporting Evidence: In the business realm, AI-driven predictive models are already optimizing supply chains, forecasting market trends, and personalizing customer interactions. The leap to a scenario where AI's recommendations become so integral that they shape future business strategies is plausible.
• Societal Impact: On a societal level, AI's role in influencing public policy, healthcare decisions, and educational methodologies is emerging. If AI systems can accurately predict social trends and public responses, they could become instrumental in policy-making, effectively shaping societal futures.
• Ethical Considerations: The critical issue here is the delegation of decision-making to AI systems. The risk of diminishing human agency and the ethical implications of AI-driven decisions necessitate robust frameworks to ensure that AI acts in alignment with human values and societal good.

AI’s Predictive Capabilities in Future Influence

The progression of AI in predictive analytics is quantifiable. Consider a scenario where an AI system is employed to predict and influence stock market trends. The AI uses a sophisticated algorithm, say, a deep neural network, to analyze market data.

• Mathematical Example: Suppose the AI uses a Recurrent Neural Network (RNN) designed for time-series prediction. It's trained on historical stock data where it learns to predict stock prices Pt​ based on a series of previous prices Pt−1​,Pt−2​,…,Pt−n​. The prediction is a function f such that Pt​=f(Pt−1​,Pt−2​,…,Pt−n​).
• Influence Mechanism: If this AI system is then used to automate trading decisions, its predictions directly influence market dynamics. The AI's buy or sell decisions create a feedback loop, where its actions based on predictions can affect future market conditions, essentially shaping the very trends it's predicting.

Theorem 2: AI Operating on Real-Time Data, Making Historical Data Obsolete

In the realm of real-time data processing, AI's capabilities can be illustrated through a simplified mathematical model of real-time decision-making.

• Mathematical Example: Consider an AI model that predicts traffic flow to optimize routing for logistics. Let Dt​ represent the real-time traffic data at time t. The AI's task is to predict traffic conditions T+1 at time t+1. The model uses a function g such that Tt+1​=g(Dt​).
• Real-Time Operation: Unlike traditional models relying on historical data trends, this AI system continually updates its prediction based on the latest data stream, making Dt​ a more critical input than historical data. The model's accuracy and utility hinge on its ability to process and react to data almost instantaneously.

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Conclusion

These mathematical examples demonstrate the potential of AI in predictive analytics and real-time data processing. In the first theorem, the AI's influence on the stock market is both a result of its predictive capability and its role in executing trades based on those predictions. In the second theorem, the AI's utility in traffic management relies on its ability to process and act on real-time data, a capability that could be more valuable than analyzing historical patterns.

These scenarios illustrate the mathematical and technical foundations underlying the potential transformative impact of AI in business and societal applications. The focus here is on the AI's ability to process complex data sets, adapt to dynamic environments, and make predictions that have real-world applications and consequences.

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