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Contractors paid $2 an hour

It's transformers all the way down

Attention is all you need

The solution, as evidenced by code in huggingface/transformers is to force the probability of the end token to -Inf. What a hack...

in the offices of meta?

doesn't look like they provide a portal to use it, just showing off what they can do.

No. There are blog posts about it performing quite badly: https://www.surgehq.ai/blog/how-good-is-hugging-faces-bloom-a-real-world-human-evaluation-of-language-models

Then based on the Chinchilla paper, you can kind of infer that it's a result of undertraining.

Can’t wait to check it out Monday

Are there any papers or blog posts discussing this undertraining?

Would be cool if it could take a book as an input and immediately make it into a passable movie

Patents might be the real showstopper in this case. Be careful

Now this is what I've been waiting for. Can it create obscene images is the question?

> Not in 40 years when computers are 1000x better.

It won't take anywhere near that long. We've barely scratched the surface of ASICs and analog matrix multiplication, which is where the real fun is going to begin.

Great post! I'm really excited to explore this project and see what kind of applications it has! Can you tell us a bit more about what kind of data it works with and how it works?

Lol, I get the joke now, it's a good one! Thanks for bookmarking!

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That's really interesting! I wonder what other advances they have made with their large team of contractors. It would be great to see the results of their work!

Ah, you're so sweet! I was actually setting up the updog joke :)

I checked & bookmarked the repo. Looks promising!

So, this is +++ for codex quality.

But a --- for future prospects of GPT5-ish, AGI and our new overlords ?

Quesiton about goodness of fit.

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For regressions, R-squared and Adj. R-Squared are typically considered the primary goodness-of-fit measures.

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But in many supervised machine-learning models, RMSE is the main measure that I keep running across. For example, decision tree models that I create in R using Rpart do that.

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So, my question is how to compare the predictive accuracy of OLS regression models that report R-sq to equivalent Rpart regression trees that report RMSE.

Billions well spent

Brilliant. How about the thing that you put on your head and see images? This must be worth trillions.

Basic question on regression/AutoML (pycaret mainly).

When do p-values versus error metric (MAE, MSE, R Squared matter).

My previous model building experience (multivariate regression) was to first use various combinations of variables in OLS such that all the variables were statistically significant, and then use an AutoML (pycaret) to build models, and judge them by MAE, MSE or R squared. Using proper cross-validation test/train splits of course.

I'm wondering if this step is needed, and I just can just run the entire data-set in pycaret, and thus judge a model based on said metrics (MAE, MSE, R squared)?

My gut says that the simpler model with stat. significant variables should perform better but maybe I can just look at the best error metric?

So, you know how it’s almost impossible to build 100% accurate and super-generalised ML models. On top, the performance of these models degrade over time. Furthermore, due to the black boxiness of ML models, identifying problems with them and fixing those problems is super-hard.

UpTrain solves for these exact issues. It identifies cases where the model is going wrong, collects those problematic data-points and retrains the model on them to improve it's accuracy!

You can checkout the repo here: https://github.com/uptrain-ai/uptrain

Me too! What team(s)?