Imo it's about the same. ChatGPT is just replacing the daily "do I need to know math, plz say no" post.

If you don't mind me asking, which school are you at that's paying less that CUNY?

Want to highlight this. It sounds a bit trollish but "academic Twitter" is a great repository for current research. It's also a cesspool where academics try to engage in social media discourse...

You can throw GasTs or use a speaker embedding to influence the energy/ pitch outputs. The sound is meh but it works.

Fast speech 2 would be your best bet.

Depends on your problem space. If you're talking about NLP/Speech applications, English is the most popular simply because it's the most resources language available and has a larger market application.

Even then, most models only show good performance with prestige dialects. Minority dialects such as AAVE notorious suffer with modern models.

Atm, my hard drive failed and SSD doesn't come until Tuesday.

In actuallity, I work in the space and the main limitation is hardware. Most small problems still require a ton of storage space and Google Collab ain't giving me a terribyte for audio until I start paying tiers.

Eh, depends on context. People forget that all the things that go into writing (drafting, rewriting, sounding words out to make sure they articulate what you mean), is a pedagogical act in itself. Assignments aren't supposed to be busy work, they're additional opportunities for learning in which students have to evaluate their own writing strategies. Using AI tools removes that element of metacognition and reduces assignments to just prompt tuning. If you're just filling out reports and are suffering writer's block, sure, why not. But other cases the writing process is the lesson.

It's not really a thing. Also kinda defeats the purpose of a PhD (research within an intellectual community of scholars). There's just too many variables across program needs and university funding limitations for it to be worth developing. Also, for a good number of people, you develop remote opportunities during the course of your PhD. Like mine is effectively remote in practice but that's just because of my research focus.

Technically, and I emphasize the technically, the set of function represented by a neural network require only one layer. However, there is little guarantee that you can feasibly find the proper configuration or train the network accurately.

By adding another layer, you can reduce the training burden by spreading it across layers. The extra dropout also allows more regularization.

This is the part of deep learning where it's less science and more, "eh, sounds like it works."

No, many hiring managers will use arbitrary criteria to reduce the number of applicants they need to evaluate for a job. Degree requirements are one of those. While yes, there probably are a few people who are in ML jobs without meeting degree requirements, in general, you're going to struggle without them.

Because thesis and dissertation work require some level of independent research to simply receive the degrees.

Yeah, I may have come off a bit too negative because of second hand responses. You'll still have good opportunities to acquire experience and build a starting resume. The glass ceiling just comes down hard for some people if interested in mainline ML.

It's basically masters/PhD. A lot of ML involves research work and those degrees are strong filtering mechanisms for selecting that skill. You caaaan do ML adjacent work but it's a lot of the grunt tasks and you'll grow frustrated in about a year.

Source: PhD student. Collaborate in NLP/CompLing lab. The recurring refrain mong the masters students in that lab is they wanted to kill themselves at Conversational AI jobs.

Trade secret for ML: your problem is always an alteration of preexisting cv/speech/NLP framework

Sounds like HuBERT and other MLMs used for ASR pretraining. Look for seq2seq work in the world of TTS and ASR.

These OG guys from the PDP days usually do that. I just take it as a bit of garnish for some fun hypotheticals.

Not to be rude, but you may need to reword this question since it's confusing to read and give advice on. What is the "engineering thing"? What do you mean you're "good at analysis"? Do you mean real analysis, being analytical, etc.?

Also, you may want to provide more detail. Such as: your current degree, why you're interested in AI...

That's nice, but you're posting on reddit with a throwaway account, so spam is going to happen. You're also asking for a specialized skillset in a niche area. The candidates you would be looking for already would be turned off by a generalized job posting. So the pool you would attract is somewhat desperate new grads with interests in the field. Hemce this has an exploitive vibe.

If good faith: just name your company so your hiring pool knows you're genuine. It'll make your life easier

If bad faith: just remove post before mods do it.

Disclaimer: I don't do graph networks so am uninterested in posting. I'm just over exploitation in tech.

If you want quality candidates you'll need to be forthcoming in your materials. Provide name of your company, links to your home page, and expected salary. Being vague just provides a sense that this is an exploitive venture.