This. I'm tired of so many "it's over, shocking, game changer, it's so over, we're so back" announcements that turn out to be just gpt-wrappers or resume-builder projects.
Very few papers that actually say something meaningful are left unnoticed, but as soon as you say something generic like "language models can do this", it gets featured in "AI influencer" posts.
This might be helpful: I haven't implemented it in the UI, but from the API response you can see what the word definitions are, both for the input and the output. If the output has homographs, likeliness is split per definition, but the UI only shows the best one.
Also, if it gets buried in comments, proper nouns need to be capitalized (Paris-France+Germany).
I am planning on patching up the UI based on your feedback.
Thank you! I actually had a hard time finding prior work on this, so I appreciate the references.
The dictionary is based on https://wordnet.princeton.edu/, no word2vec. It's just a plain lookup among precomputed embeddings (with mxbai-embed-large). And yes, I'm excluding words that are present in the query because.
It would be interesting to see how other models perform. I tried one (forgot the name) that was focused on coding, and it didn't perform nearly as well (in terms of human joy from the results).
The processing that goes into the phosphor and waterfall displays is usually overlapped FFTs, which is also the processing for a lot of common channelization techniques. So yes, probably not so much in the commercial space, but certainly very useful. In fact I'm aware of platforms that don't even store/manipulate I/Q data, but the overlapped FFT data since it's usually much more useful as a starting point.
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