As AI takes hold, it fundamentally starts with data.
DATA, DATA, and more DATA!
For all the GPT models that seem like they can do anything, it is because humans have created the data that allowed the models to be trained on. Google used to have Captcha with images to show dogs or cars and users would select. Google also has Youtube which beyond the videos users watch has the title and description. Now when users want to create a video, well it already has a great matchup to what should be used for content. Users have even uploaded videos and applied timestamps to reference, giving creators even more refined data to use in creation.
Facebook has built years of shared posts by users and reactions. AI can craft posts to represent the right emotion to use. The web is over run with many sites with dedicated user bases tailored to specific specialties which discuss topics both granular and broad. With user responses like reddit or stackoverflow, algorithms can categorize information for their models to leverage at generation.
Business has evolved from using numbers to make the world run to now using text. Estimates of daily data generated are almost 400 million terabytes. To leverage the data, it needs the correct models run on it to be leveraged. From classification for subject, to analysis for tone, information is labeled to maximize its use in FPT models.
The classification of information helps GPT models distinguish between words with multiple contexts. By understanding its related groupings and categories, the models can then create information inline with user expectations.
Models are complex to build so they can be leveraged across domains. A robust classification engine is the first step in ensuring models ingest well labeled data to provide robust outputs.