4 ms·

Don't be so dismissive, this was an impossible goal for decades. It's extremely impressive that the same algorithm can handle many different types of data with

• uoaei 3y ago
  But that's the point. If you think they are "different types of data" you are confusing yourself. It's all just bits representing floating-point values. If they're ints you can trivially make them floating-point using routines that have existed since those data formats were invented. If they're discrete values you can encode them in ways that make them legible to the models. One of the more recent interesting developments was token embeddings, admittedly, but again this is just an example of taking slightly more abstract representations and turning them into bits representing floating-point values, which has been an established paradigm known as one of the pillars of "feature engineering" since the beginning of the ML field. One-hot encoding is just a special case of token embeddings. It's amazing that humans figured out how to store data in useful ways, not that models can "figure out" what to do with things they are already capable of ingesting and processing.