DEEP LEARNING
As the world settles into the era of AI , industries are lining up to harvest the fruit of its labour . Powering everything from chatbots , to document summarisers to code generators , its effects in a mere two years have been monumental for both a company ’ s internal operations and offerings it gives to its customers .
Yet behind these marvels lies the systems in place working away to make this all happen . Deep learning . The very complexity that grants LLMs their power to create such applications like ChatGPT also shrouds them in mystery , earning them an ominous moniker of ‘ black boxes ’.
What this means is visibility is limited into the machinations of just how everything gets done . Like how do we get from this point A to B . For the casual consumer , they don ’ t need all this added information , they just want the end product .
But for the enterprise user , operating AI in an age of regulation and business intelligence , understanding what LLMs are doing to arrive at their conclusion has become essential .
The evolution of LLMs The development of LLMs has been marked by significant milestones in recent years . Yet , it is the introduction of a few key advancements that has been the game-changer .
“ LLMs have undergone rapid evolution in recent years , with three primary branches emerging on their evolutionary tree : Encoder-only , Encoder-Decoder ,
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