Written language is very powerful apparently. After all LLM can generate SVG, python code to use Blender etc.
One demo i saw with LLM and code use: "Generate a small snake game" and because the author still had the Blender MCP tool connection, the LLM decided to generate 3D assets through Blender for that game.
> ... but it's not like the discovery of the steam engine or electricity.
completly disagree. People might have googled before but the human<>computer interface was never in any way as accessable as it is now for a normal human being. Can i use Photoshop? yes but i learned it. My sisters played around with Dall-E and are now able to do simiiliar things.
It might feel boring to you that technology accessability drips down like this, but this changes a lot for a lot of people. The entry barrier to everything got a lot lower. It makes a huge difference to you as a human being if you have rich parents and good teachers or not. You had never the chance to just get help like this. Millions of kids struggle because they don't have parents they can ask certain questions required for understanding topics in school.
Steam Engine = fundamental for our scaling economy
electricity = fundamental for liberating all of us from day time
internet = interconnecting all of us
LLM/ML/AI = liberating knowledge through accessability
> 'There hasn’t been a real breakthrough in over two years.'
DeepSeek alone was a real breakthrough.
But let me ask an LLM about this:
- Mixture of Experts (MoE) scaling
- Long-context handling
- Multimodal capabilities
- Tool use & agentic reasoning
Funny enough your comment comes before claude 4.0 release (again increase in performance, etc.) and the Google IO.
We don't know if we found all 'low hanging fruits'. The meta paper about thinking in latent space came out in February. I would definitly call this a low hanging fruit.
We are limited, very hard, on infrastructure. Every experiement you want to try consumes a lot of it. If you look at the top x GPU AI clusters, we don't have that many on the planet. We have Google, Microsoft, Azure, Nvidia, Baidu, Tesla and xAI, Cerebras. Not that many researcher are able to just work on this.
Google has now its first Diffusion based Model active. 2025! We are so far away from testing out more and more approaches, architectures etc. And we are optimizing on every front. Cost, speed, precision etc.
Not sure what promises you heard. For me a lot of them came true.
I created images and music which was enjoyable. I use it to add more progress to an indie side project I'm playing around with (i added more functionality to it with ai stuff like claude code and now jules.google than i did myself in the last 3 years).
It helps my juniors to become better in their jobs.
Everything related to sound / talking to a computer is now solved. I talked to gemini yesterday and i interruptted it.
Image segmentation became a solved problem and that was really hard before.
I can continue my list of things AI/ML made things possible in the last few years which were impossible before that.
Of course its my opinion, its my comment after all.
Nonetheless, survival can't be the life goal after all the moon will drift away from earth in the future, the sun will explode and if we survive that as a species, all bonds between elements will disolve.
It also can't be about giving your dna away because your dna has very little to no impact over just a handful of generations.
And no the goal of our society has to be to have as much energy available as possible to us. So much energy, that energy doesn't matter. There is enough ways of generating energy without a real issue at all. Fusion, renewable energy directly from the sun.
There is also no inherant issue right now preventing us all having clean stable energy besides capitalsm. We have the technology, we have the resources, we have the manufacturing capacity.
To finish my comment: Its not about energy, its about entropy. You need energy to create entropy. We don't even consume the energy of the sun, we use it for entropy and dissipate it back to space after.
Im pretty sure kid/child ai porn already exist somewhere. But i'm quite lucky despite knowing rotten.com and plenty of other sides, never having seen real so i doubt i will see fake child porn.
Whats the elephant in the room now? Nothing changed. Whoever consumes real will consume fake too. FBI/CIA will still try to destroy cp rings.
We could even think it might make this situation somehow better because they might consume purely virtual cp?
We already have seen that Opensource can compete which is a lot more than people expected. After all opensource and running huge models?
But what it means, that with time, Opensource will be as good as what commercial offerings now have. Hardware will get cheaper, research is open or delayed open.
How is that dismantling my argument or relating to the point i made?
Just because some people on reddit 'laugh' at these discussions, in one of the PRs a contributor/maintainer actually said that they enabled it on purpose, are not forced and are happy to test it out.
And someone somewhere has and want to test stuff. Whats the issue? Test it out, play around with it, keep it or disable it.
And i think .net as a repository is a very good example. The people on github copilot side are probably very happy about this experiement. For me its also great, it seems like github copilot is still struggling a bit.
And copilot is called copilot because they do not advertice it as replacement.
I do understand it but i also think that the current LLMs are the first step to it.
GPT-3 started proper investment into this topic, there was not enough research done in this direction and now it is. People like Yann LeCun already analyse different approaches/architecture but they still use the infrastructure of LLMs (ML/GPUs) and potentially the data.
I never said that LLM is the breaktrhough in consesnes.
But you can also ask LLM strategies for thinking. It can tell you a lot of things. We will see if a LLM will be a fundamental part of AGI or not but GPU/ML will probably be.
I also think that the compression mechanism through LLM lead to concepts through optimization. You can see from the antropic paper, that an LLM doesn't work in normal language space but in a high dimensional one and then 'expresses' the output in a language you like.
We also see that real multi modal models are better in a lot of tasks due to a lot more context available through them. Estimating what someone said due to context.
The necessary infrastructure and power requirement is something i accept too. We can assume, i do, that further progress in a lot of topics will require this type of compute and it also solves our data bottleneck: normal CPU architecture is limited by memory databus.
Also in comparision to a lot of other companies, if the richest companies in the world invest in nuclear, i think this is a lot better than any other companies. They have a lot higher margins and knowledge. co2 is a market separator for them too.
I also expect this amount of compute to be the base for fixing real issues we all face like cancer or optimizing cancer or any other sickness detection. We need to make medicin a lot cheaper and if someone in africa can do a cheap x ray and send it to the cloud to get any feedback, that would / could help a lot of people.
Doing complex and massive protein analysis or mRna research in virtual space, also requires GPUs.
All of this happened in a timespan of only a few years. I have not seen anything progressing as fast as AI/ML currenly does and as unfortunate it is, this needs compute.
Even my small inhouse image recognition fine tuning explodes when you do a handful parameter optimizations but the quality is a lot better than what we had before.
And enabling people to have real natural language UI is HUGE. It makes so much more accessable. Not just for people with a disability.
Things like 'do a eli5 on topic x'. "explain to me this concept" etc. I would have loved that when i tried to be successful in the university math curiculum.
All of that is already crazy and still is. But in parallel what Nvidia and others currently do with ML and Robotics is also something which requires all of that compute. And the progress is again breath taking. The current flood of basic robots standing and walking around is due to ML.
No clue why you would say its a major source of danger. We have plenty of mechanism in place to prevent issues and due to the nature of IaC and how we handle state, we could literlay tear down everything and are back up running in around 2h with a complex system with 10 componentes based on k8s.
If this was 'a simple user error' or 'not using the right tool for the job' than this was an error from smart people and it still got fixed by using AI/ML in an instant.
With this, my argument still stands even if it would be for a different reason which i personally doubt.
Hey look you see a stone falling