For a long time, AI was a bad word that stood for unfulfilled promise. See AI Winter. Hence, researchers strictly avoided the term while still working on learning algorithms, the same that power LLM training.
There’s a bit of a grey area, for example speech recognition. Would you classify that as analytic or generative? Whisper and speech LLMs work pretty well, but can completely make up stuff that wasn’t in the audio at all (see e.g. “thank you for watching” transcribed during silence). Other approaches are closer to the acoustic evidence but may make other mistakes (especially wrongly transcribing long tail, low frequency terms). Pick your poison.
HF is notorious for making it difficult to work offline (or at least not waste time trying to connect when everything needed is offline) and is constantly changing how it is being handled. Previously, there was TRANSFORMERS_OFFLINE, HF_DATASETS_OFFLINE, etc.
This will limit the agent in what it can do in the system and what IPs/domains it can reach. This requires a lot of customization to your specific framework/environment. Note that this can reduce the agent’s effectiveness, as it will have to “work around” some of the limitations. This isn’t foolproof either, and the agent could exfiltrate data e.g. via DNS requests.
It highly depends on the sort of data you’re processing (phone calls, podcasts, meetings of more people recorded using single channel?). For NVIDIA/NeMo, check out their softformer diarization models (also streaming).
It’s a distinction that IMHO likely doesn’t make much difference, at least for the mostly automated/non-interactive coding agent use case. What matters more is how well the post-training on synthetic harness traces works.
There are still opportunities, but they aren’t paid nearly as well as less researchy positions in industry. US post-doc salaries at state universities aren’t that high.