Machine learning systems have now excelled at tasks they are trained for by using a combination of large datasets and high-capacity models. They are capable of performing a variety of functions, from completing a code to generating recipes. Perhaps the most popular one is the generation of novel text – a content apocalypse – that writes no differently than a human.
In 2018, the BERT model (Bidirectional Encoder Representations from Transformers) sparked discussion around how ML models were learning to read and speak. Today, LLMs or logic learning machines are rapidly developing and mastering a wide range of applications.
In a generation of text-to-anything with incredible AI-ML models, it’s important to remember that more than understanding the language, the systems are fine-tuned to make it appear like they do. Speaking of the language domain, the correlation between the number of parameters and sophistication has held up remarkably well.
Race of parameters
Parameters are crucial to machine learning algorithms – a part of the model trained using historical data. OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) – trained at 175 billion parameters, is an autoregressive language model that uses deep learning to produce human-like text. According to OpenAI, the model can be applied “to any language task, including semantic search, summarization, sentiment analysis, content generation, translation, with only a few examples or by specifying your task in English.”
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