5 terms to understand Artificial Intelligence-3
Knowledge Graph
Consider knowledge graphs, often referred to as semantic networks, as the architectural blueprints of knowledge, providing machines with the means to fathom the intricate web of concept interdependencies. Picture this: at the foundational stratum, within this cerebral structure, a cat and a dog stand shoulder to shoulder, their connection steadfast, rooted in their shared identity as domesticated mammals adorned with fur and the elegance of four limbs. In this same graph, the bald eagle, a majestic sentinel of the skies, occupies a distant node, its linkages frail and far-flung.
Now, as we delve into the domain of cutting-edge artificial intelligence, envisage the emergence of a remarkably sophisticated network of connections. This neural behemoth assembles itself using the fragments of multifaceted relationships, nuanced traits, and distinctive attributes that define the tapestry of human knowledge. It traverses the boundless expanse of terabytes of training data, sculpting an intricate labyrinth of connections that transcends the imagination, propelling the pursuit of artificial wisdom into uncharted realms.
LLM(Large Language Model)
In the realm of artificial intelligence, a large language model stands as a pinnacle of innovation, poised to grasp and emulate the intricacies of human speech. This technological marvel harnesses the power of a deep neural network, a vast construct adorned with millions, or perhaps billions, of parameters. These digital sinews grant it the ability to unravel the tapestry of linguistic nuances, from the subtle dance of patterns to the profound understanding of grammar and semantics. This knowledge is distilled from the boundless seas of textual data.
For a more accessible perspective, consider Bard, the brainchild of Google, as your guide. It defines a large language model as a versatile entity, a sentient creation that imbibes vast volumes of text and code. This deep immersion empowers LLMs with the gift of comprehending and generating text akin to human speech. They possess the prowess to translate languages, craft diverse and creative compositions, and respond informatively to your queries.
While these LLMs are still in their evolutionary stage, Bard envisions a transformative future. It foresees the dawn of an era where LLMs redefine our interaction with machines. Picture AI assistants seamlessly assisting with tasks, from composing emails to scheduling appointments. Imagine new realms of entertainment, where interactive novels and games come to life, guided by the genius of these evolving language models. The horizon of possibilities stretches wide, painted with the strokes of innovation.
Model Collapse
In the pursuit of cutting-edge artificial intelligences, often referred to as "models," researchers embark on a journey through the vast seas of data, using them as the crucible for AI's evolution (as detailed in "Training Data"). However, there comes a point in this progression when the AI-generated content begins to loop back into the very training data that birthed it.
Should errors find their way into this loop, their impact may grow exponentially over time, giving rise to a phenomenon known in academic circles as "model erosion." Ilia Shumailov, a distinguished researcher from Oxford University, offers insight into this process, describing it as "a degenerative metamorphosis where, with the passage of time, models lose their grasp on acquired knowledge." To envision it, one might draw a parallel to the notion of senility, where the brilliance of AI starts to fade, leaving behind fragments of its former capabilities.
Neural network
In the nascent stages of AI exploration, the foundation rested upon the meticulous crafting of logic and rigid rules. However, the landscape of artificial intelligence underwent a seismic shift with the advent of machine learning. This transformative juncture heralded a new era, where the most advanced AI entities embarked on a journey of self-discovery and autonomous learning.
This remarkable evolution has given birth to the paradigm of "neural networks," a formidable archetype within machine learning. These networks draw inspiration from the intricacies of the human brain, fashioning themselves as interconnected nodes, where the lines between artificial intelligence and biological cognition blur with subtle elegance.
Open-source
Decades ago, the biological community grasped the perilous notion of disclosing intricate blueprints of perilous pathogens on the vast canvas of the internet. The wisdom behind this cautionary stance lay in the fear that such revelations might inadvertently equip malevolent actors with the knowledge to craft devastating diseases. Despite the virtues of open science, the specter of risk loomed too large to ignore.
In contemporary times, AI researchers and corporate entities confront a strikingly similar quandary: the delicate question of how much openness should enshroud the realm of artificial intelligence. As the most advanced AI currently resides in the guardianship of a select few private conglomerates, voices advocating for heightened transparency and the democratization of these technologies have risen. Nevertheless, diverging perspectives persist, as the debate rages on regarding the optimal equilibrium between openness and safeguarding.