The argument is that scams based on exploiting goodness causes a lot more harm compared to the ones based on exploiting greed. Because it trains people that doing good deeds is not worth it (they might be scammed.) And even if the rate of such scams are low, just reading about them makes people afraid of potential consequences of doing good deeds. So I absolutely agree that such scams should have very harsh punishments, because they do not only have immediate consequences, but they degrade trust in our society.
I strongly disagree and I am having trouble understanding what kind of world you envision, what will it look like?
The problem as I see it is not robots coming for my job and taking away my ability to earn a salary. That can be solved by societal structures like you are saying, even though I am somewhat pessimistic of our ability to do so in our current political climate.
The problem I see is robots coming for my mind and taking away any stakes and my ability to do anything that matters. If the robot is an expert in all fields why would you bother to learn anything? The fact that it takes time and energy to learn new skills and knowledge is what makes the world interesting. And this is exactly what happened before when machines took over a lot of human labour, luckily there were still plenty of things they couldn't do and thus ways to keep the world interesting. But if the machines start to think for us, what then is left for us to do?
Absolutely, I am sceptical of AI omin many ways, but primarily it is about the AI companies and my lack of trust in them. I find it unfortunate that all of the clearly brilliant engineers working at these companies are to preoccupied with always chasing newer and better model trying to reach the dream of AGI do not stop and ask themselves: who are they working for? What happens if they eventually manage to create a model that can replace most or even all of human computer work?
Why whould anyone think that these companies will contribute to the good of humanity when they are even bigger and more powerful, when they seem to care so little now?
"I find it unfortunate that all of the clearly brilliant engineers working at these companies are to preoccupied with always chasing newer and better model trying to reach the dream of AGI do not stop and ask themselves: who are they working for?"
Have you seen the people who do OpenAI demos? It becomes pretty apparent upon inspection, what is driving said people.
They apparently managed gold in the IOI as well. A result that was extremely surprising for me and causes me to rethink a lot of assumptions I have about current LLMs. Unfortunately there was very little transparency on how they managed those results and the only source was a Twitter post. I want to know if there was any third party oversight, what kind of compute they used, how much power what kind of models and how they were set up? In this case I see that DeepMind at least has a blog post, but as far as I can see it does not answer any of my questions.
I think this is huge news, and I cannot imagine anything other than models with this capability having a massive impact all over the world. It causes me to be more worried than excited, it is very hard to tell what this will lead which is probably what makes it scary for me.
However with so little transparency from these companies and extreme financial pressure to perform well in these contests, I have to be quite sceptical of how truthful these results are. If true I think it is really remarkable, but I really want some more solid proof before I change my worldview.
So outside of human intervention, I don't think the specifics really matter. What this means is that it is possible and that this capability will in time be commoditized.
This is helpful in framing the conversation, especially with "skeptics" of what these models are capable of.
To a certain extent I agree. But as far as I know I cannot go to chatgpt.com and paste the newest ICPC problems and get full solutions. And there is no information about what they do differently. For a competition like the ICPC, which is academic in its nature, I think it is very unfortunate to setup a seperate AI track like this without publishing clear public information about what that actually entails. And have clear requirements for these AI companies to publish their methology. I know it is a nice source of sponsorships for them, but the ICPC should afford to stand up a bit for academic integrity.
Without any of this I can't even know for sure if there was any human intervention. I don't really think so, but as I mentioned the financial pressure to perform well is extreme so I can totally see that happening. Maybe ICPC did have some oversight, but please write a bit about it then.
If you assume no human intervention then all of this is of course irrelevant if you only care about the capabilities that exist. But still the implications of a general model performing at this level vs something more like a chess model trained specifically on competitive programming are of course different, even if the gap may close in the future. And how much compute/power was used, are we talking hundreds of kWhs? And does that just means larger models than normally or intelligent bruteforcing through a huge solutionspace? If so, then it is not clear how much they will be able to scale down the compute usage while keeping the performance at the same level
If you assume the brain is a computer (why wouldn't it be is my stance), we have a long ways to go in the optimization department, both in hardware and in software. If it's possible to do at all using hundreds of kilowatt-hours of electricity, no reason it shouldn't be possible within a few hundred Wh (which is a scary prospect, I agree, with consequences hard to imagine when realized.)
Thanks for the link! I have been thinking a lot about topics related to this lately and this looks like an interesting read. Skimming through it I might not agree with all the ideas of the author I very much welcome new ideas about how to organize our society and our legal system. I have especially been thinking about how the legal system is mostly fiction and what makes it work is not so much what the laws say and what the punishments are for breaking them, but rather our shared belief in the system. And I think this is problematic because it takes away our agency to imagine other ways of doing things, we think our current legal system is the only possible system because it has to be. Thoughts of anything else would cause a collapse.
I have also been thinking about how basing morality on following laws is a bad idea. You should not be afraid of breaking laws or rules if they are stupid. All of modern workers rights, women's rights, etc are based on brave people of the past that were not afraid of breaking stupid laws of their time, why should today be any different.
I was wondering if anyone here has experimented with running a cluster of SBC for LLM inference? Ex. the Radxa ROCK 5C has 32GB of memory and also a NPU and only costs about 300 euros. I'm not super up to date on the architecture on modern LLMs, but as far as I understand you should be able to split the layers between multiple nodes? It is not that much data the needs to be sent between them, right? I guess you won't get quite the same performance as a modern mac or nvidia GPU, but it could be quite acceptable and possibly a cheap way of getting a lot of memory.
On the other hand I am wondering about what is the state of the art in CPU + GPU inference. Prompt processing is both compute and memory constrained, but I think token generation afterwards is mostly memory bound. Are there any tools that support loading a few layers at a time into a GPU for initial prompt processing and then switches to CPU inference for token generation? Last time I experimented it was possible to run some layers on the GPU and some on the CPU, but to me it seems more efficient to run everything on the GPU initially (but a few layers at a time so they fit in VRAM) and then switch to the CPU when doing the memory bound token generation.
> I was wondering if anyone here has experimented with running a cluster of SBC for LLM inference? Ex. the Radxa ROCK 5C has 32GB of memory and also a NPU and only costs about 300 euros.
> Last time I experimented it was possible to run some layers on the GPU and some on the CPU, but to me it seems more efficient to run everything on the GPU initially (but a few layers at a time so they fit in VRAM) and then switch to the CPU when doing the memory bound token generation.
Moving layers over the PCIe bus to do this is going to be slow, which seems to be the issue with that strategy. I think it the key is to use MoE and be smart about which layers go where. This project seems to be doing that with great results:
Related to this Applied Science on YouTube has a pretty cool video where he demonstrates how a single laser diode can be used to measure miniscule vibrations. I wonder if this would be sufficient for a laser microphone?
