There’s an alternate timeline where the non-profit side of the company won, Altman the Conman was booted and exposed, and OpenAI kept developing machine learning in a way that actually benefits actual use cases.
Cancer screenings approved by a doctor could be accurate enough to save so many lives and so much suffering through early detection.
Instead, Altman turned a promising technology into a meme stock with a product released too early to ever fix properly.
No, there isn’t really any such alternate timeline. Good honest causes are not profitable enough to survive against the startup scams. Even if the non-profit side won internally, OpenAI would just be left behind, funding would go to its competitors, and OpenAI would shut down. Unless you mean a radically different alternate timeline where our economic system is fundamentally different.
I mean wikipedia managed to do it. It just requires honest people to retain control long enough. I think it was allowed to happen in wikipedia’s case because the wealthiest/greediest people hadn’t caught on to the potential yet.
There’s probably an alternate timeline where wikipedia is a social network with paid verification by corporate interests who write articles about their own companies and state-funded accounts spreading conspiracy theories.
AI models can outmatch most oncologists and radiologists in recognition of early tumor stages in MRI and CT scans.
Further developing this strength could lead to earlier diagnosis with less-invasive methods saving not only countless live and prolonging the remaining quality life time for the individual but also save a shit ton of money.
I’m fully aware that those are different machine learning models but instead of focussing on LLMs with only limited use for mankind, advancing on Image Recognition models would have been much better.
Wasn’t it proven that AI was having amazing results, because it noticed the cancer screens had doctors signature at the bottom? Or did they make another run with signatures hidden?
There were more than one system proven to “cheat” through biased training materials.
One model used to tell duck and chicken apart because it was trained with pictures of ducks in the water and chicken on a sandy ground, if I remember correctly.
Since multiple medical image recognition systems are in development, I can’t imagine they’re all this faulty trained with unsuitable materials.
They are not ‘faulty’, they have been fed wrong training data.
This is the most important aspect of any AI - it’s only as good as the training dataset is. If you don’t know the dataset, you know nothing about the AI.
That’s why every claim of ‘super efficient AI’ need to be investigated deeper. But that goes against line-goes-up principle. So don’t expect that to happen a lot.
There’s an alternate timeline where the non-profit side of the company won, Altman the Conman was booted and exposed, and OpenAI kept developing machine learning in a way that actually benefits actual use cases.
Cancer screenings approved by a doctor could be accurate enough to save so many lives and so much suffering through early detection.
Instead, Altman turned a promising technology into a meme stock with a product released too early to ever fix properly.
No, there isn’t really any such alternate timeline. Good honest causes are not profitable enough to survive against the startup scams. Even if the non-profit side won internally, OpenAI would just be left behind, funding would go to its competitors, and OpenAI would shut down. Unless you mean a radically different alternate timeline where our economic system is fundamentally different.
I mean wikipedia managed to do it. It just requires honest people to retain control long enough. I think it was allowed to happen in wikipedia’s case because the wealthiest/greediest people hadn’t caught on to the potential yet.
There’s probably an alternate timeline where wikipedia is a social network with paid verification by corporate interests who write articles about their own companies and state-funded accounts spreading conspiracy theories.
There are infinite timelines, so, it has to exist some(wehere/when/[insert w word for additional dimension]).
Or we get to a time where we send a reprogrammed terminator back in time to kill altman 🤓
What is OpenAI doing with cancer screening?
AI models can outmatch most oncologists and radiologists in recognition of early tumor stages in MRI and CT scans.
Further developing this strength could lead to earlier diagnosis with less-invasive methods saving not only countless live and prolonging the remaining quality life time for the individual but also save a shit ton of money.
That is a different kind of machine learning model, though.
You can’t just plug in your pathology images into their multimodal generative models, and expect it to pop out something usable.
And those image recognition models aren’t something OpenAI is currently working on, iirc.
I’m fully aware that those are different machine learning models but instead of focussing on LLMs with only limited use for mankind, advancing on Image Recognition models would have been much better.
I agree but I also like to point out that the AI craze started with LLMs and those MLs have been around before OpenAI.
So if openAI never released chat GPT, it wouldn’t have become synonymous with crypto in terms of false promises.
Don’t know about image recognition but they released DALL-E , which is image generating and in painting model.
Wasn’t it proven that AI was having amazing results, because it noticed the cancer screens had doctors signature at the bottom? Or did they make another run with signatures hidden?
There were more than one system proven to “cheat” through biased training materials. One model used to tell duck and chicken apart because it was trained with pictures of ducks in the water and chicken on a sandy ground, if I remember correctly.
Since multiple medical image recognition systems are in development, I can’t imagine they’re all
this faultytrained with unsuitable materials.They are not ‘faulty’, they have been fed wrong training data.
This is the most important aspect of any AI - it’s only as good as the training dataset is. If you don’t know the dataset, you know nothing about the AI.
That’s why every claim of ‘super efficient AI’ need to be investigated deeper. But that goes against line-goes-up principle. So don’t expect that to happen a lot.