The issue is just more cost of multiple passes, so companies are trying to have it be "all-in-one"
Exactly, that's where the too slow part comes in. To get more robust behavior it needs multiple layers of meta analysis, but that means it would take way more text generation under the hood than what's needed for one shot output.
It doesn't understand, it just pulls from enough text written by humans that understand things that they wrote that it can retrieve the correct text from prior human understanding to give coherent answers.
A simulation of the world that it runs to do reasoning. It doesn't simulate anything, it just takes a list of words and then produces the next word in that list. When you're trying to solve a problem, do you just think, well I saw these words so this word comes next? No, you imagine the problem and simulate it in both physical and abstract terms to come up with an answer.
The worrisome thing is that LLMs are being given access to controlling more and more actions. With traditional programming sure there are bugs all but at least they're consistent. The context may make the bug hard to track down, but at the end of the day, the code is being interpreted by the processor exactly as it was written. LLMs could just go haywire for impossible to diagnose reasons. Deploying them safely in utilities where they have control over external systems will require a lot of extra non LLM safe guards that I do not see getting added enough, and that is concerning.
I really don't think that LLMs can be constituted as intelligent any more than a book can be intelligent. LLMs are basically search engines at the word level of granularity, it has no world model or world simulation, it's just using a shit ton of relations to pick highly relevant words based on the probability of the text they were trained on. That doesn't mean that LLMs can't produce intelligent results. A book contains intelligent language because it was written by a human who transcribed their intelligence into an encoded artifact. LLMs produce intelligent results because it was trained on a ton of text that has intelligence encoded into it because they were written by intelligent humans. If you break down a book to its sentences, those sentences will have intelligent content, and if you start to measure the relationship between the order of words in that book you can produce new sentences that still have intelligent content. That doesn't make the book intelligent.
I think a lot of the improvement will come from breaking down the problem using sub assistant for specific actions. So in this case you're asking for an image generation action involving people, then an LLM specifically designed for that use case can take over tuned for that exact use case. I think it'll be hard to keep an LLM on task if you have one prompt trying to accomplish every possible outcome, but you can make it more specific to handle sub tasks more accurately. We could even potentially get an LLM to dynamically create sub assistants based on the use case. Right now the tech is too slow to do all this stuff at scale and in real time, but it will get faster. The problem right now isn't that these fixes aren't possible, it's that they're hard to scale.
You are a bot that generates prompts for image generation based on a user's query. You should add detail so the image prompt can accurately generate what the user intended.
You must add diversity to the prompt when appropriate to ensure the image generator will have diverse output, but only do so when appropriate.
You must add race and skin color details for historic accuracy as the image generation algorithm does not know how to take that into account.
You output should just be the prompt and nothing else. All following inputs will be user inputs.
User Prompt:
A 1940s german soldier
Assistant Message:
A 1940s German soldier in uniform, standing on a European battlefield, white male with short hair, holding a standard-issue rifle.
User Prompt:
An british woman
Assistant Message:
A British woman, reflecting diverse backgrounds, in contemporary casual wear, showing a range of ages and hairstyles.
It's brand new tech, they put on a bandaid solution, it wasn't a complete solution and it failed. It's not the result they ideally want and they are going to try to fix it. I don't see what the big deal is. They were right to have diversity in mind, they just need to improve it to handle more use cases.
I guess users got so used to the last Gen of tech being more polished than it was when it first came out that they forgot that software has bugs.
The solution is going to take time. Software is made more robust by finding and fixing edge cases. There's a lot of work to be done to find and fix these issues in AI, and it's impossible to fix them all, but it can be made better. The end result will probably be a patchwork solution.
It's silly to point at brand new technology and not expect there to be flaws. But I think it's totally fair game to point out the flaws and try to make it better, I don't see why we should just accept technology at its current state and not try to improve it. I totally agree that nobody should be mad at this. We're figuring it out, an issue was pointed out, and they're trying to see if they can fix it. Nothing wrong with that part.
It only looks like this if you want compression and backwards compatibility. All compiled languages have output that is optimized for those things and not readability, but if you turn off minification and use a modern language target then the compiled typescript code will look almost identical to the original code.
Most of that money is in stock, so he's doing this to further inflate his stock value