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While we're on the topic, OpenAI just announced their 4o image model, though it's not rolled out to 100% of users yet (AKA not me yet) https://openai.com/index/introducing-4o-image-generation/ If it performs nearly as well as their cherry-picked examples, it knocks the socks off of gemini flash image gen.
Now we have Gemini, Grok, and ChatGPT all releasing image editing, a previously unheard of feature, within two weeks of each other. Interesting how that happens.
When it comes to text generation models, I can't bring myself to care anymore. They all do the basics proficiently, which is generate code snippets and console commands and trivia answers to questions that I can ask in a few sentences. And they all sort of fail on really complex stuff like doing my work for me in making changes to a 1M line codebase.
The text generation in those example images is phenomenal.
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Damn, if it’s better than Gemini then it looks like you were right and they were sitting on that capability. It reaffirms my opinion that OpenAI is the most insidious AI company out there.
Why insidious?
Some unordered reasons:
They call themselves OpenAI and yet nothing is open. They don’t publish research or release open source or open weight models.
They positioned themselves as a non-profit to get clout and talent and later reneges.
They charge an insane amount compared to other companies to price anchor because they want the SOTA models to only be accessible by elites.
They hide the thinking traces from their thinking models and ban people who try to figure out their methods with prompt engineering.
And then tack on “they hold back revolutionary features until their competitor releases their own”
Based on the talent banking Altman when the board tried to remove him, the talent is more than happy with the reneging.
The investors were clearly not happy. Also his chief talent, Ilya, did leave him with many other OG founders.
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Thanks. I didn't realize they'd finally released 4o image capabilities.
LLMs have been "good enough" for my use cases for a while now. I haven't seen anything like the jump from GPT 3.5 to 4, where prompts that previously had been borderline useless magically became useful. Not that I mind incremental improvement, I'd say that reasoning models were a decent change in terms of QOL and reliability when it came to complex reasoning.
If I had to use an LLM for my day job, then pretty much any recent model would work fine. While medical records can be massive, dense and a PITA to work with, they're not as complex as a large programming project. The whole field was built around humans needing to keep the gist of things in working memory. I'm more constrained by things like data protection laws, access points idiot-proofed by IT, and the lack of tooling to make it easy to transfer records or information back out. Ah, the wonders of the NHS..
I remain quite confident that LLMs will continue to improve, and suspect a lot of the recent sense of being underwhelmed that I've experienced is because the rapid rate of iteration makes changes less stark. They're beating agentic behavior into them, more by the minute.
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