141–150 of 194 posts
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I've seen this claim thrown around a few times but haven't really seen any evidence that it's true, beyond a few unconvincing anecdotes.
What kind of evidence would this involve? Would you have agreed this was the case at Twitter for a while?
Stop attacking other people and mind your own business, especially if you're making stuff up.
Kinda scratching my head at the purpose of the prompt understanding examples they show off. From previous papers I've seen in the space, shouldn't they be trying various compositional things like "A blue cube next to a red sphere" and variations thereof? Instead they use >The robin flew from his swinging spray of ivy on to the top of the wall and he opened his beak and sang a loud, lovely trill, merely to show off. N…
"A flying squirrel gliding between trees": It won't be able to do it. Just telling it "flying squirrel" will often generate squirrels with bat wings coming off their backs.
Ahh, but that's just a tiny, specific thing missing from the data set! Surely that'll get fixed eventually as they add more training data...
"A fox girl hugging a bunny girl hugging a cat girl": The only way to make this work is with fancy stuff like Segment Anything (SAM) working with Stable Diffusion. Alternative prompts of the same thing:
"A fox girl and a bunny girl and a cat girl all hugging each other"
It's such a simple thing; generative AI can make three people hugging each other no problem. However, trying to get it to generate three different types of people in the same scene is really, really hard and largely dependent on luck.
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According to Fiona Cicconi, Google’s chief people officer, Google employed 30,000 managers before the recent layoffs. The hard truth is Google needs a Twitter style culling. Take all those billions you're burning and give it to people with a builder mentality, not career sheeple. Unfortunately the same executives who would oversee this are the ones who need to be culled first.
From what I understand, Google has a unusually large number of engineers who are happy to coast, and would actively avoid taking on anything important. That seems more of an issue to me compared to middle management bloat.
One low-level issue is how long everything has to take because of tooling. Engineers have way too much patience for overcomplicated garbage and tend to obsess over pointless details. Kind of in the opposite direction of coasting, but still a real problem.
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I've seen this claim thrown around a few times but haven't really seen any evidence that it's true, beyond a few unconvincing anecdotes.
How come you're readily willing to accept that managers will coast, but not that engineers will coast?
I've met many from the managerial class without these traits that seem to have no problem coasting and trancending actual meticulous work because their game is all about personal career management, not the hyperfocus a lot of us here engage in daily.
This post has more information: https://cloud.google.com/blog/products/ai-machine-learning/i... I can't figure out how to try this thing. The closest I got was this sentence: "To get started with Imagen 2 on Vertex AI, find our documentation or reach out to your Google Cloud account representative to join the Trusted Tester Program."
I think the process is 1. Go to console.cloud.google.com 2. Go to model garden 3. Search imagegeneration 4. End up at https://console.cloud.google.com/vertex-ai/publishers/google... And for whatever reason that is where the documentation is. Sample request curl -X POST \ -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json; charset=utf-8" \ -d @request.json \ "https://us-c…
What a shitshow.
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According to Fiona Cicconi, Google’s chief people officer, Google employed 30,000 managers before the recent layoffs. The hard truth is Google needs a Twitter style culling. Take all those billions you're burning and give it to people with a builder mentality, not career sheeple. Unfortunately the same executives who would oversee this are the ones who need to be culled first.
> Google employed 30,000 managers before the recent layoffs. I'm guessing that number included product/program managers, not just "people managers".
I think the competition for text to image services is over and open source, stable diffusion won. It doesn't matter how detailed (or whatever counts as "better") corporate text-to-image products get, stable diffusion is good enough which really is good enough. Unlike the corporate offerings, open source txt2img doesn't have random restrictions (no its not just porn at this point) and actually allows for additional sc…
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I've seen this claim thrown around a few times but haven't really seen any evidence that it's true, beyond a few unconvincing anecdotes.
It's true, was there through 2016-2023. People just have different definitions of what coasting means. In general don't think "doing nothing" or "avoiding work" think "add certainty to process + decision making like everyone else does", and much more importantly "avoiding friction because as soon as there's even a little bit, people leverage it" More detail on what causes this: - processes become elongated through wh…
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I think the process is 1. Go to console.cloud.google.com 2. Go to model garden 3. Search imagegeneration 4. End up at https://console.cloud.google.com/vertex-ai/publishers/google... And for whatever reason that is where the documentation is. Sample request curl -X POST \ -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json; charset=utf-8" \ -d @request.json \ "https://us-c…
Once I finally got mostly set up for that, with billing and everything, it said it's only available for a limited number of customers, with a "request access" link to a google form with further links (to enable https://aiplatform.googleapis.com/ ) which 404. What a shitshow.
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How are two completely different models from different groups, converging on what looks like the exact same person? Number 1 and 3 are eerily similar. I don't understand.
It's because the only thing these models can do is rip off existing images, and the prompt is very specific. "Generative AI" is a learned, lossy compression codec. You should not be surprised that the range of outputs for a given input seems limited.