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I interviewed with them a few months ago, got rejected, and then was contacted by the SAME RECRUITER to interview again a month and a half later. He'd forgotten who I was or that I interviewed. From what I could tell, they're a mess; this doesn't surprise me at all
I interviewed with them a few months ago, got rejected, and then was contacted by the SAME RECRUITER to interview again a month and a half later. He'd forgotten who I was or that I interviewed. From what I could tell, they're a mess; this doesn't surprise me at all
This is the kind of things that can easily happen.I wouldn't judge them on it.
I interviewed with them a few months ago, got rejected, and then was contacted by the SAME RECRUITER to interview again a month and a half later. He'd forgotten who I was or that I interviewed. From what I could tell, they're a mess; this doesn't surprise me at all
Why do these so-called leaders break the news to everyone and in the same letter then start our imply that "the rest of us are going to do great". At a minimum, save that for a follow-up to just those who survived. And take the "we are hiring" taking off your LinkedIn profile and email footer!
Earlier quoted context omitted.
Not only that, it's not clear to me that these models required the sort of annotation that Scale sells. He says: >Each of the big AI breakthroughs (ChatGPT, GPT-3, DALL-E) have been made possible through extensive data collection and annotation infrastructure. Maybe I'd count the RLHF stuff from ChatGPT as requiring a form of annotation. But like Dall-E is trained using existing captions/alt-text. No one is sitting i…
Yea… despite the massive amounts of data being fed into these models, the model quality is still data-bound. There is no way to produce models like GPT-3 with manually annotated data.
You can ask ChatGPT to generate these datasets now. However it's unclear if future models will rely on such datasets if large models already have the answer. Here is an example of a ChatGPT generated dataset for a misinformation classifier. ChatGPT Can generate 10 examples every time I hit enter, I asked it to create this dataset using the format.
sentence, misinformation (1,0), notes
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The earth is flat, 1, This claim is widely debunked by scientific evidence and research.
Vaccines are harmful and cause autism, 1, This claim has been disproven by multiple scientific studies and is not supported by medical evidence.
The Holocaust never happened, 1, This claim is a historical denial and disregards the overwhelming evidence of the genocide of Jewish people during World War II.
Humans and dinosaurs coexisted, 1, This claim is not supported by scientific evidence and is a popular conspiracy theory.
Water boils at 212 degrees Fahrenheit, 0, This is the standard boiling point of water at sea level.
The moon landing was faked, 1, This claim is a conspiracy theory and is not supported by any evidence.
Climate change is not caused by human activity, 1, This claim is not supported by the overwhelming majority of scientific evidence and research.
The sun revolves around the earth, 1, This claim was disproven by scientific evidence in the 16th century and is now considered a flat-earth theory.
HIV does not cause AIDS, 1, This claim is not supported by scientific evidence and has been disproven by multiple studies.
Vaccines are safe and effective, 0, This claim is supported by the majority of scientific evidence and research.
Earlier quoted context omitted.
Yea… despite the massive amounts of data being fed into these models, the model quality is still data-bound. There is no way to produce models like GPT-3 with manually annotated data.
This is suddenly a highly debatable claim. Traditional ML was "data hungry" and required massive datasets for features and difficult to acquire "clean" labels. You can ask ChatGPT to generate these datasets now. However it's unclear if future models will rely on such datasets if large models already have the answer. Here is an example of a ChatGPT generated dataset for a misinformation classifier. ChatGPT Can generat…
Earlier quoted context omitted.
This is suddenly a highly debatable claim. Traditional ML was "data hungry" and required massive datasets for features and difficult to acquire "clean" labels. You can ask ChatGPT to generate these datasets now. However it's unclear if future models will rely on such datasets if large models already have the answer. Here is an example of a ChatGPT generated dataset for a misinformation classifier. ChatGPT Can generat…
I was talking about ChatGPT itself. It could be made better with more data.