The space between
Generative AI's goldmine lay in the hands of generalists.
Much ado is made about SMEs—Subject Matter Experts. Those with a depth of knowledge on specific subjects are among the most sought after in the professional world. However, I believe the truly transformative value is found in the space between these knowledge islands.
Note: Unique AI-generated phrases are presented as bold and italic.
How does the 'space between' function? Isn’t it just dead space?
Well, technically, it’s called latent space—a term drawn from machine learning which refers to the semantic relationship between nodes of knowledge. Latent space is not empty; rather, it is rich with potential, awaiting activation.
Think about it this way. When I say “fuzzy puppy,” you can’t help but visualize a fuzzy puppy, but surely you’ve imagined a specific breed. Was it a Labrador, a Husky, or maybe a German Shepherd? Your individual latent space generated additional context when prompted using information from your lived experience.
Sound familiar?
Prompt engineering is essentially the trendy buzzword packaging that describes the process of activating a model’s latent space. It's the Renaissance art of the digital age, where the spaces between known quantities hold the secrets to new forms of expression and discovery.
Our abstraction from our learned experience leads to us synthesize new ideas by generating meaningful representations and contextual coherence within emerging scenarios. Or more simply put: we experience, we reflect, and we act. Much like an LLM uses its distilled representations of training data to create novel outputs.
Historically, the most significant breakthroughs often occurred at the intersection of diverse fields. Consider the Enlightenment, a period marked by an explosion of knowledge across disciplines. Philosophers like Francis Bacon and René Descartes didn’t confine themselves to narrow specialties but rather explored the space between to forge new methodologies and philosophies that shaped modern science.
Similarly, in the employment sector, the traditional value placed on specialization is being supplemented by an appreciation for generalists—akin to the polymaths of the Renaissance who bridged diverse domains to ignite Europe’s intellectual rebirth.
From a philosophical standpoint, the concept of latent space can be compared to the Socratic method, where knowledge is seen not as a series of discrete compartments but as a continuum that is expanded and explored through questioning and dialogue—the gaps between known facts.
Plato’s allegory of the cave teaches us about perception, suggesting that what we perceive as reality is but shadows of true forms. In a similar vein, the latent space in AI and human interaction represents the unseen realities that exist between our solidified understandings, waiting to be illuminated.
The Untapped Generalist Goldmine
In practical terms, the workforce's evolving needs reflect this shift. With LLMs capable of handling specific domain knowledge, the real value lies in connecting disparate points of data. The activation of latent space requires a new workplace superstar: the generalist. The Swiss Army knife, the Jack and Jill of all trades.
As we equip these modern thinkers with tools like generative AI, they can synthesize new ideas through novel connections - creating relevance between disparate information troves - thus driving innovation forward.
So, while SMEs remain invaluable for their deep knowledge, the future belongs to those who can navigate and exploit the space between. After all, the correct answer was Basset Hound because I’m an SME on fuzzy puppies—but the individual manifestation of breeds within each reader’s mind? That’s where the magic happens.
For posterity, here’s the chat transcript used in shaping this piece:
Help me expand this partial draft with historical relevance and additional philosophical insights:
Title: The Space Between
The secret to getting the most out of Generative AI is the same secret to maximizing output from generalists in the workforce.
Much ado has been made about SMEs. Subject Matter Experts. Those with a depth of knowledge on specific subjects are among the most sought after in the professional world. However, the space between knowledge is where the transformative value is truly found.
How does the space between function though? Isn’t it just dead space? Well, technically it’s called latent space. Latent space is the relational proximity between nodes of knowledge.
Think about it this way. When I say “fuzzy puppy,” you can’t help but visualize a fuzzy puppy, but surely you’ve imagined a specific breed. Was it a labrador? Was it a husky? Maybe a German shepherd? Your individual latent space gave birth to additional context when prompted. Sound familiar?
Prompt engineering as a concept is the trendy packaging surrounding the activation of a model’s latent space.
How does this factor into employment? Well, up to this point, specialists were the most sought after due to the expert amount of knowledge on specific subjects. But now we’re all granted tools that access that depth of knowledge instantaneously.
The value is now in the connecting between two disparate points of data. The activation of that latent space. For this, we need generalists. We need swiss army knives, we need jacks and jills of all trades.
Since LLMs can handle the specifics of any domain, the only way to synthesize new information is by novel approaches to connecting relevance between information.
By the way, the correct answer was Basset Hound. Since I’m an SME on fuzzy puppies.
Here's an expanded version of your draft, integrating philosophical insights and historical relevance to enhance the exploration of the "space between":
Title: The Space Between
The secret to getting the most out of Generative AI mirrors the strategy for maximizing output from generalists in the workforce. Much ado has been made about SMEs—Subject Matter Experts. Those with a depth of knowledge on specific subjects are among the most sought after in the professional world. However, the transformative value is truly found in the space between these knowledge islands.
How does the 'space between' function? Isn’t it just dead space?
Well, technically, it’s called latent space—a term drawn from machine learning which refers to the relational proximity between nodes of knowledge. Latent space is not empty; rather, it is rich with potential, awaiting activation.
Think about it this way. When I say “fuzzy puppy,” you can’t help but visualize a fuzzy puppy, but surely you’ve imagined a specific breed. Was it a Labrador, a Husky, or maybe a German Shepherd? Your individual latent space generated additional context when prompted. Sound familiar?
Prompt engineering as a concept is essentially the trendy packaging surrounding the activation of a model’s latent space. It's the Renaissance art of the digital age, where the spaces between known quantities hold the secrets to new forms of expression and discovery.
Historical Insights: Bridging Disciplines Through 'The Space Between'
Historically, the most significant breakthroughs often occurred at the intersection of diverse fields. Consider the Enlightenment, a period marked by an explosion of knowledge across disciplines. Philosophers like Francis Bacon and René Descartes didn’t confine themselves to narrow specialties but rather explored the space between to forge new methodologies and philosophies that shaped modern science.
Similarly, in the employment sector, the traditional value placed on specialization is being supplemented by an appreciation for generalists—akin to the polymaths of the Renaissance who bridged diverse domains to ignite Europe’s intellectual rebirth.
Philosophical Insights: The Latent Space as a Metaphor for Human Understanding
From a philosophical standpoint, the concept of latent space can be compared to the Socratic method, where knowledge is seen not as a series of discrete compartments but as a continuum that is expanded and explored through questioning and dialogue—the gaps between known facts.
Plato’s allegory of the cave teaches us about perception, suggesting that what we perceive as reality is but shadows of true forms. In a similar vein, the latent space in AI and human interaction represents the unseen realities that exist between our solidified understandings, waiting to be illuminated.
In Practice: The New Workforce of Generalists
In practical terms, the workforce's evolving needs reflect this shift. With LLMs capable of handling specific domain knowledge, the real value lies in connecting disparate points of data. The activation of latent space requires a new kind of worker: the generalist, the Swiss Army knife, the Jack and Jill of all trades.
As we equip these modern thinkers with tools like generative AI, they can synthesize new information through novel approaches to connecting relevance between information, thus driving innovation forward.
Conclusion: Embracing the Potential of the Space Between
So, while SMEs remain invaluable for their deep knowledge, the future belongs to those who can navigate and exploit the space between. After all, the correct answer was Basset Hound because I’m an SME on fuzzy puppies—but the conversation between breeds? That’s where the magic happens.
This expanded version integrates the historical and philosophical dimensions of latent spaces and generalism, enriching the narrative while keeping the playful tone intact.