Of course, if our universe is undecidable it must be the case that computable processes can be executed within it, and it might be the case that all of the processes that are ever executed within it are computable... but it might be that some of the processes that are executed are not computable... because the machine may.. or may not?
Computation has turned out to be a far more general concept than I think was imagined, up to the point that many computer scientists now seem to equate computation with the functioning of the universe. Recently it's been shown that there are real, physical processes which are undecidable (we cannot know if a latice of atoms has a spectral gap or not, we cannot determine if a specific particle in a fluid flow will reach a specific place or not, we cannot determine if a ray of light will reach a specific target in certain configurations of reflection).
Our world appeared computable, but it isn't, even if P=NP.
>>Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.
This is staggering bullshitp. In what way does understanding a transformer allow you to solve the core problem of LLM's that no frontier lab has managed to resolve?
>>To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.
This also is bullshit. Yes, RAG helps and reduces errors, but NOOOO! it does not fix hallucinations...
>>Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.
This is somewhat true, but really the motive is providing a soverign instance that cannot be withdrawn for arbitary reasons. Fundamentally the big providers are not going to steal your data, they may change the license to allow them to use it in the future, but then all their big customers will leave. So, they won't be able to, probably. What might well happen (and has happened) is that the USA might withdraw access with no notice leaving you high and dry.
I want to learn to build a real LLM so I looked at https://allenai.org/olmo where there are instructions and ingredients. But, unfortunately I can't afford the required compute resource so I will have to wait for a bit I guess.
I know many will draw a line between Hubris and Nemesis here, but I feel really sorry for this guy and just wish some good health for him. I personally find the whole long life influncing and health hacking movement annoying, but I recognise that many, if not most, of the folks involved are acting in good faith and just trying to feel better themselves or help others feel better.
He probably can't any more (I have no idea what the implications of his stomach disorder are, but I don't imagine great). But if he can I hope he has a beer and just relaxes and gets some happy vibes for a time.
>>Well, Viking carried an experiment that tried to detect life. Now, the consensus is that it failed, and that the experiment was incapable of creating a useful result given the chemistry of the soil. Some people argue about that, but I am in no way qualified to take part in the debate, so I would back the consensus here.
>>What is odd is that there hasn't been a single other mission that's carried any experiment that has the objective of creating that result.
>>If the objective is to find life, why isn't anyone actually looking?
>>Tell me if what I've said above is in any way factually incorrect.
So... you tell me. Why no experiment? Not one in 50 years?
In the meantime we've learned a lot about the Martian atmosphere, it's climate, it's history, it's geology, the evolution of it's surface. I would argue that if a Viking 3 had flown with a revised kit that produced a definitive signal we wouldn't have got any of that.
Well, Viking carried an experiment that tried to detect life. Now, the consensus is that it failed, and that the experiment was incapable of creating a useful result given the chemistry of the soil. Some people argue about that, but I am in no way qualified to take part in the debate, so I would back the consensus here.
What is odd is that there hasn't been a single other mission that's carried any experiment that has the objective of creating that result.
If the objective is to find life, why isn't anyone actually looking?
Tell me if what I've said above is in any way factually incorrect.
I think that median household disposable income is a much better way of looking at this than GDP.
2014 is the turn. The US gained shale, isolationism is possible, Trump 2 is created by the general (true) perception that things were good under Trump 1. The European war starts and China is left to dominate Asia.
I think the opposite - unless the discovery of life was preceeded or coincidental with the discovery of some other hyper interesting thing (for example, if Martian life has some sort of utility for medicine, maybe) then I think that would be that for Mars exploration missions. Of course there would be many announcements and excited political agreements around "continuing to explore the new frontier" but I think that no more money would appear.
I suspect that NASA knows this full well, as do Mars scientists, and I suspect that they are being very careful to make sure that definitive proof does not appear until they understand all sorts of other stuff about the planet.
Human interpretebility and human construction is going to be really important for formal specifications.
My fear is that we will get inscrutable maths that will be guarded by tiny coteries of devoties. The different inscrutable notations will be mutually incompatible, understanding one will not really help with the others. Most people will simply write english statements that cannot be verified properly.
Worse even, people will get machines to formalise their statements, and will not understand the formalism or the proofs, but will take part in the theatre of verification and act shocked when everything explodes.
I thought that for a long time the german supply chain had an advantage in terms of the precision engineering to create drive chains for ICE - but EV's don't have the same number of moving parts and hence... end of advantage?
- was the pause in model scaling a result of the benefits of RL & SFT being easier to access and quicker than scaling, or was it genuinely the result of scaling being low ROI now?
- are power densities necessary to provide high quality on device inference possible? Can the best, technically feasible, architectures accomodate T scale models and run them off batteries that fit in your hand?
- will thing slow down enough to allow edge depoloyments to realise value vs. centralised deployments.
- do edge use cases drive enough revenue to get this to happen?
- can local inference make up for model scale? Does that make sense in a latency/power race with the central infrastructure? Is there a sweet spot here?