UK went through industrialization leading to its empire, and the US was the industrial power during its ascent. Same thing with Japan before WWII.

Many imoeralistic powers seem to go through big industrial growth before expansion.

I’m not sure how good a source it is, but Wikipedia says it was multimodal and came out about two years ago - https://en.m.wikipedia.org/wiki/GPT-4. That being said.

The comparisons though are comparing the LLM benchmarks against gpt4o, so maybe a valid arguement for the LLM capabilites.

However, I think a lot of the more recent models are pursing architectures with the ability to act on their own like Claude’s computer use - https://docs.anthropic.com/en/docs/build-with-claude/computer-use, which DeepSeek R1 is not attempting.

Edit: and I think the real money will be in the more complex models focused on workflows automation.

My main point is that gpt4o and other models it’s being compared to are multimodal, R1 is only a LLM from what I can find.

Something trained on audio/pictures/videos/text is probably going to cost more than just text.

But maybe I’m missing something.

My understanding is it’s just an LLM (not multimodal) and the train time/cost looks the same for most of these.

• DeepSeek ~$6million https://www.theregister.com/2025/01/26/deepseek_r1_ai_cot/?td=rt-3a
• Llama 2 estimated ~$4-5 million https://www.visualcapitalist.com/training-costs-of-ai-models-over-time/

I feel like the world’s gone crazy, but OpenAI (and others) is pursing more complex model designs with multimodal. Those are going to be more expensive due to image/video/audio processing. Unless I’m missing something that would probably account for the cost difference in current vs previous iterations.

I like your assumption is cheese covered corn rather than it being Mac and cheese.

Just some quick Google searches so not sure how reputable, but didn’t feel like copying random links.

But yeah, that’s why I called them out as estimates as I suspect there is a lot of room for error in those numbers.

I had to looks this one up, but missed the “galaxy” vs “universe”. There are an estimated 3 trillion trees, 100-400 billion stars in the milky way galaxy, but potentially 1 septilliom stars in the universe.

However all three of these are estimates, so who actually knows.

I’m actually not sure how you’d label the axis here. The info being conveyed is the relationship between two separate things.

Yeah in the modern age internet access should be considered a necessity. There are a lot of things you can’t do without the internet (like get a job or pay bills).

So LLMs can trace their origin back to the 2017 paper “Attention is all you need”, they with diffusion models have enabled prompt based image generation at an impressive quality.

However, looking at just image generation you have GANs as far back as 2014 with style GANs (ones that you could more easily influence) dating back to 2018. While diffusion models also date back to 2015, I don’t see any mention of use in images until early 2020’s.

Thats also ignoring that all of these technologies go back further to lstms and CNNs, which go back further into other NLP/CV technologies. So there has been a lot of progress here, but progress isn’t also always linear.

I feel like one of those isn’t like the others

You may have used women in the prompt, but what it created definitely looks like a little girl.

My understanding is that it’s a difficult feature to support and they can’t guarantee it works well. That’s the only explanation I’ve ever seen, cause to me it’s almost critical for working on a laptop.

I dont get why hibernate isn’t a more popular feature, I use it extensively as I hate having to set everything back up on each restart.

Its also one of my biggest issues with using Linux as it’s usually broken there.

Yeah that’s right, seems my link didn’t populate right.

https://en.wikipedia.org/wiki/Grok

For those who aren’t familiar with the word, it comes from the 1961 scifi novel “Stranger in a Strange Land”.

I think you’re missing the point. No LLM can do math, most humans can. No LLM can learn new information, all humans can and do (maybe to varying degrees, but still).

AMD just to clarify by not able to do math. I mean that there is a lack of understanding in how numbers work where combining numbers or values outside of the training data can easily trip them up. Since it’s prediction based, exponents/tri functions/etc. will quickly produce errors when using large values.

Here’s an easy way we’re different, we can learn new things. LLMs are static models, it’s why they mention the cut off dates for learning for OpenAI models.

Another is that LLMs can’t do math. Deep Learning models are limited to their input domain. When asking an LLM to do math outside of its training data, it’s almost guaranteed to fail.

Yes, they are very impressive models, but they’re a long way from AGI.

Just read up more about the systems and always thought they charged you more, didn’t realize that for the time being they are zero interest loans.

Seems unsustainable, but sounds like they’re using the credit card technique of charing the storefront. It’ll be interesting to see where the bnpl industry goes.

Why be the bad guy when you can just enable them.