rontoes

Interesting idea - I would like to try it out.

Thanks for sharing TLDV. Would love to hear more about your approach, if you're willing to share?

Interesting - I wonder what's the ratio of true vs. false knowledge in the corpus that GPT-3 is trained on. Perhaps someone has performed a study on this for a specific domain?

Would love to get feedback on how to make this more interesting, any technical issues, etc.

This is an incredibly poor take. It's well known that academic software doesn't follow great software engineering practices. The model is an incredibly complex piece of software that attempts to model stochastic behaviour. That it isn't fully deterministic (with a fixed random seed) doesn't make thee research invalid and shouldn't discredit research that builds on the model.

On a personal level, I’d go further and suggest that all academic epidemiology be defunded. This sort of work is best done by the insurance sector.

This is another level of crazy.

Will this break a lot of systems?

This is currently the minimal (maybe even less) viable project. Would welcome feedback on:

- Is this useful?

- Killer features

- Bugs

Would appreciate any feedback, particularly on the price point. Happy to provide free CV/resume feedback to the first 5 people that request it here.

Similar approach, although Subtle Medical are not using adversarial training, just plain old conv-nets with a non-adversarial loss.

Will try to reduce the download size. Unfortunately I don't have much control over the embedded map but I assume Google has optimised this to a decent extent

Which browser/device are you using?

Do you have any experience with the Google Maps API? I would love to implement this.

I will add this feature!

That's right. Honestly, web development is far from my expertise, I'm more of an AI guy. Data is mainly from http://volcano.oregonstate.edu/volcano_table and a few other sources.

Thank you for the feedback! I am going to collate the inaccuracies and update the volcano database. I am also going to try to fix the errors a few of you have experienced.