From Layers to Language
It began in the ‘50s, a glimmer, a spark—
Perceptrons blinking in labs cold and dark.
A single-layered mind, small and neat,
not much power, but a mighty feat.
It saw shapes and shadows, it guessed at lines,
computing like clockwork, simple designs.
Jump to the ‘80s, and things got a boost—
backpropagation brought in, given new use.
Weights could shift, errors fixed on the fly,
layers stacking up, reaching for the sky.
They called them neural nets, these growing towers,
learning it all, bit by bit, hour by hour.
Then came the 2000s, a bigger need grew,
computers got faster, models did too.
Convolutions for vision, pooling to trim,
these nets took pictures and pulled out the whim.
From cats on YouTube to traffic signs red,
these networks saw shapes, knew where they led.
By 2014, RNNs took the stage—
sequential beasts, memories like a sage.
They looped through sentences, lines, and beats,
in poems, in music, they danced to repeats.
LSTM’s memory kept far-off cues,
made sense of long texts and language hues.
By 2017, the game had changed—
Transformers hit, and things got rearranged.
“Attention’s the trick,” they said, plain as day,
no more step-by-step, just meaning the way.
Language was handled all at one glance,
words woven together in a single dance.
Then came BERT and GPT, big and bold,
billions of parameters, stories to hold.
And GPT’s rise? A feat, to be sure—
trained on the web, billions strong, pure.
Through texts, through data, across time they roam,
each sentence, each story, a fragment of home.
They learn the structure, the quirks of the tongue,
from prompts they respond, in patterns unsung.
Parameters soared—10 billion, then more,
each weight a neuron, an intricate core.
GPT-3, with 175 billion strong,
it learned all our ways, the right and the wrong.
From essays to code, predictions that sing,
they write like we do, they think like they bring.
Now we stand here, on AI’s ledge,
peering down from tech’s razor edge.
The models, they dream, they calculate, spin—
a glimpse of the future, a neural kin.
GPT-4, even mightier still,
with language it answers, shapes what we will.
But what’s next to come, what do we fear?
Will they join our thoughts, or lend us an ear?
Supervised, unsupervised, their paths to refine—
learning, evolving, from data’s design.
Our hands shape these models, but their paths unroll,
from basements to headlines, reaching for goals.
Beyond conversation, creation, reply,
we wonder how deep they’ll reach, and why.
Will they feel the world, or still just compute?
An era emerges, mysterious, mute.
Yet here we are, training each sign,
pushing this field past every line.
From early neurons to networks bold,
a vision of future, technology’s hold.
An endless quest—of layers and lore,
these deep neural dreams, opening doors.
By: Om Sahu
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