Ai Prediction #12, A World Without Data

By Greg Walters

Data will be proven Redundant & Historical

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Here is the crux: After the LLMs are trained, past data is irrelevant because Ai has moved on from being taught WHAT to think to being instructed on HOW to think in REAL TIME. 
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Reports are coming in about how "The demand for high-quality data, essential for powering artificial intelligence (AI) conversational tools like OpenAI’s ChatGPT, may soon outstrip supply and potentially stall AI progress..."

Ominous, yet is inevitable - and that's okay.

​​“I set the date for the Singularity—representing a profound and disruptive transformation in human capability—as 2045. The nonbiological intelligence created in that year will be one billion times more powerful than all human intelligence today.” –Ray Kurzweil

​AI won't require data; it will ingest streams in real time, from businesses to each of us personally to the humidity in the air and moisture in you potted plants' soil.  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.  What's more, the quest for data will be satiated as sensors digest new models and information in real time - we are moving from 'retrieval' computing to 'generational compute', on an individual basis - human to human. 

My notion is a world without data, at least in the contemporary manner.  Yes, Ai will create 'synthetic' data and train itself - but - my contention is that as GAi connects to the real world through sensors, developing all six of the human senses, data is no longer historic - the move from retrieval to generative computing takes stage as inputs and outputs or continuous and in real time, avoiding the 'echo chamber'.

The impact is stupendous.  Ai, through personal LLMs, will feel more human than human, offering advice on everything from predicting the weather, cooking dinner to personal, on demand therapy.  

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I put the question to the Ai in the sky.  It came back with some interesting proofs and extensions.

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:

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
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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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