AI is a camera, not an engine.

Yet another brilliant essay from Venkatest Rao on how modern AI puts us into an age of exploration of computational reality. I got halfway through my usual gig of extracting clips from the article to archive the main points in a post for later look up by myself or others. Halfway through this chore, I realized the material was so deep and dense that my clips were containing more than half of the original text. So, I have halted the editing down and suggest that readers have a go at the entire original article. I also presented the entire text to ChatGPT-4, preceded by the following prompt:

Intelligence as a Measure of Data. Could you condense, rephrase, extract the main messages of the following text to be more comprehensible to college educated non-specialists? Here is the text:

Here is ChatGPT-4's take on the article, which is a vastly better job than I could have done without spending many more hours on the material: 

The text you've provided is a deep and philosophical exploration of modern artificial intelligence (AI), contrasting it with earlier forms of AI and other technological advancements. Here's a more accessible summary:

1. Modern AI as a Discovery, Not an Invention: Unlike earlier AI (termed "Good Old-Fashioned AI"), modern AI is viewed not as a machine or an invention, but as a significant discovery. This new era of AI is compared to the James Webb Space Telescope, an instrument of exploration rather than a tool of production. Modern AI is seen as a way to delve deeply into vast amounts of data.

2. AI as a Data Camera: The author suggests thinking of AI as a "data camera" that reveals hidden layers and insights within large datasets, much like telescopes reveal distant galaxies. This analogy emphasizes AI's role in uncovering hidden patterns and information in data, rather than creating it.

3. The Nature of Intelligence in AI: The text challenges traditional views of intelligence, suggesting that what we consider intelligent behavior may arise from simple processes and large datasets. It proposes that AI is enabling us to understand the nature of intelligence itself, which is more about data interaction than complex processing.

4. AI's Role in Understanding Computational Reality: Modern AI fits within a series of discoveries about computational reality, which lies between objective (physical) and subjective (mental) realities. This journey of discovery includes various fields like information theory, computability theory, and now, AI.

5. The Broad Impact of AI: The author explores the potential of AI to transform our understanding of various domains, from language to natural phenomena. This includes using AI to interpret complex datasets in novel ways, such as understanding ocean currents or predicting solar flares.

6. Reconceptualizing Intelligence: The idea is proposed that intelligence might be a property of data processed in certain ways, rather than an attribute of the processing technology itself. This view opens up new ways of understanding and measuring intelligence based on data characteristics.

7. AI and the Universe's Memory: The author posits that AI reveals a natural, universal memory phenomenon inherent in all matter, suggesting a deep connection between information, space, time, and matter. This perspective considers AI as a tool that uncovers the universe's way of remembering and expressing itself.

8. The Human Perspective: Finally, the text touches on the human aspect in this technological evolution, pondering how our understanding of ourselves and our place in the universe might change in the face of these AI-driven discoveries.

This summary simplifies the original text's complex ideas, focusing on the main themes of AI as a tool of discovery, the redefinition of intelligence, and the broader implications for understanding reality and our place within it.