Ok I’ll try it out. Might be a few days as I am out of town ATM.

It is legal until a court says otherwise.

Where I'm at 'statistics' is what me and my co-workers call what we do, and 'machine learning' is what goes in the grant application. I'm sure this differs across regions/faculty/industry/whatever.

Not really hard when the model is spitting out watermarked images.

But anyway, is hard to demonstrate which is the dataset of a model right? in the case of Getty you can probably get images that look like Getty image dataset, but for a predictor? and if this case for example where "there wasn't any law" or predecessor case can lose the lawsuit having to pay?

We're going to find out soon with the Getty lawsuit. Until then, gray area.

Pyttsx, mbrola, mimic 3. I like the mimic 3. Which is lightweight. And can run on docker or just native.

I started out with mycroft which has mimic 3 build in. But you can run it just stand alone as well and quite easy to set up. https://mycroft.ai/mimic-3/

If you want to go down the rabbithole of speech synthesis and analsys check out praat praat.org it's a quiet impressive piece of software.

Try TortoiseTTS on the highest quality setting

ah my bad then, must have misread. i'll take another look

It does both buddy

Yeah that's speech to text. I want text to speech. Thanks tho

https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API

I would really appreciate it if you can try to finetune a T5-XXL Flan model from Huggingface on your hardware. I am curious if it works and if there is a big bottleneck. Thank you

google search didn't get me much, can you be more specific?

Speech WebKit

Link(s)? I can find more from examples, just need a thread to pull. Thanks!

Different goals and different tools

I have this exact setup (7700k & 3090). If you want me to try something out, I am happy to try running it.

There's a bunch of cool work on using constrained optimization as a layer in neural nets, differentiation through argmin. I'm not sure if this answers your question.

The whole landscape of ML research is a hunt to chase SOTA by tweaking an architecture here or using a different optimizer there and then squeezing out 0.2% accuracy on some well known imaging dataset in an attempt to churn out papers. That’s not science if you ask me.

Just communicating what I've heard. Nevertheless, I think the whole interpretable ML community (at the very least) would disagree with you on this one :). Reducing ML to "plug and chug" is well... Speaks for itself :D

this is great. thanks

My guess is ML scientists generally care less about statistical rigor which can lead to poor outcomes due to not properly understanding the data, assumptions, risk involved, etc

Ex: Zillow

People should be more skeptical of "emergent abilities" in big models: 1) Papers claiming such abilities generally use undertrained small models as per chinchilla scaling (compute is not controlled + suboptimal hyperparam choices for small models) and 2) these papers generally use a semilogx plot to demonstrate "emergence" but even a linear relationship will look exponential in such a plot. I'm not sure if I'd want to call a simple linear relationship "emergent".

Stats is finding interpretable ways to look at and mode data that ML plug and chug cs people don’t do