Earlier quoted context omitted.
Where exactly did you look?
Google, Wikipedia, kagi. Now, please tell me your source.
The Pope.
AI 2027
561–570 of 641 posts
Re: AI 2027
#562Earlier quoted context omitted.
Isn't this just a form of next token prediction? i.e. you'll keep your options open for a potential rhyme if you select words that have many associated rhyming pairs, and you'll further keep your options open if you focus on broad topics over niche
Assuming the task remains just generating tokens, what sort of reasoning or planning would say is the threshold, before it's no longer "just a form of next token prediction?"
This is an interesting question, but it seems at least possible that as long as the fundamental operation is simply "generate tokens", that it can't go beyond being just a form of next-token prediction. I don't think people were thinking of human thought as a stream of tokens until LLMs came along. This isn't a very well-formed idea, but we may require an AI for which "generating tokens" is just one subsystem of a larger system, rather than the only form of output and interaction.
Re: AI 2027
#563Earlier quoted context omitted.
its vague, and could have meant anything. everyone knew parameters would grow and its reasonable to expect that things that grow have diminishing returns at some point. this happened in late 2023 and throughout 2024 as well.
That quote almost perfectly describes o1, which was the first major model to explicitly build in compute time as a part of its scaling. (And despite claims of vagueness, I can't think of a single model release it describes better). The idea of a scratchpad was obvious, but no major chatbot had integrated it until then, because they were all focused on parameter scaling. o1 was released at the very end of 2024.
"bureaucracies of various designs" does not objectively describe what o1 does.
It describes almost anything.
Re: AI 2027
#564Sam Altman is Ryan holiday
Re: AI 2027
#565The story is entertaining, but it has a big fallacy - progress is not a function of compute or model size alone. This kind of mistake is almost magical thinking. What matters most is the training set. During the GPT-3 era there was plenty of organic text to scale into, and compute seemed to be the bottleneck. But we quickly exhausted it, and now we try other ideas - synthetic reasoning chains, or just plain synthetic…
This is what I think as well. Unfortunately for the AI proponents they already made an example of the software industry. Its on news reports in the US and globally; most people are no longer recommending to get into the industry, etc. Software for better or worse has made an example for other industries as to what "not to do" both w.r.t data (online and option), and culture (e.g. open source, open tests, etc).
Anecdotally most people I know are against AI - they see more negatives from it than positives. Reading things like this just reinforces that belief.
The question of why are we even doing this? Why did we invent this? etc. Most people aren't interested in creating a "worthy successor" at best that eliminates them and potentially their children seeing that goal as nothing but naive and dare I say it wrong. All these thoughts will come from reading the above for most people.
Re: AI 2027
#566Earlier quoted context omitted.
Best reply in this entire thread, and I align with your thinking entirely. I also absolutely hate this idea amongst tech-oriented communities that because an AI can do some algebra and program an 8-bit video game quickly and without any mistakes, it's already overtaking humanity. Extrapolating from that idea to some future version of these models, they may be capable of solving grad school level physics problems and…
> that's not what _humanity_ is about I've not spent too long thinking on the following, so I'm prepared for someone to say I'm totally wrong, but: I feel like the services economy can be broadly broken down into: pleasure, progress and chores. Pleasure being poetry/literature, movies, hospitality, etc; progress being the examples you gave like science/engineering, mathematics; and chore being things humans need to c…
I'm not sure where construction and physical work goes into your categories. Process and chores maybe. But I think AI will struggle in the physical domain - validation is difficult and repeated experiments to train on are either too risky, too costly or potentially too damaging (i.e. in the real world failure is often not an option unlike software where test benches can allow controlled failure in a simulated env).
Re: AI 2027
#567Claude summarize
The summary at https://ai-2027.com outlines a predictive scenario for the impact of superhuman AI by 2027. It involves two possible endings: a "slowdown" and a "race." The scenario is informed by trend extrapolations, expert feedback, and previous forecasting successes. Key points include:
- *Mid-2025*: AI agents begin to transform industries, though they are unreliable and expensive. - *Late 2025*: Companies like OpenBrain invest heavily in AI research, focusing on models that can accelerate AI development. - *Early 2026*: AI significantly speeds up AI research, leading to faster algorithmic progress. - *Mid-2026*: China intensifies its AI efforts through nationalization and resource centralization, aiming to catch up with Western advancements.
The scenario aims to spark conversation about AI's future and how to steer it positively[1].
Sources [1] ai-2027.com https://ai-2027.com [2] AI 2027 https://ai-2027.com
Re: AI 2027
#568Earlier quoted context omitted.
> even if this doesn’t lead to AGI, at the very least it’s likely the final “warning shot” we’ll get before it’s suddenly and irreversibly here. I agree that it's good science fiction, but this is still taking it too seriously. All of these "projections" are generalizing from fictional evidence - to borrow a term that's popular in communities that push these ideas. Long before we had deep learning there were people l…
> there are exciting applications which will end up underfunded after the current AI bubble bursts Could you provide examples? I am genuinely interested.
I'm personally very excited about the progress in interactive theorem proving. Before the current crop of deep learning heuristics there was no generally useful higher-order automated theorem proving system. Automated theorem proving could prove individual statements based on existing lemmas, but that only works in extremely restricted settings (propositional or first-order logic). The problem is that in order to apply a statement of the form "for all functions with this list of properties, ..." you need to come up with a function that's related to what you're trying to prove. This is equivalent to coming up with new lemmas and definitions, which is the actually challenging part of doing mathematics or verification.
There has finally been progress here, which is why you see high-profile publications from, e.g., Deepmind about solving IMO problems in Lean. This is exciting, because if you're working in a system like Coq or Lean your progress is monotone. Everything you prove actually follows from the definitions you put in. This is in stark contrast to, e.g., using LLMs for programming, where you end up with a tower of bugs and half-working code if you don't constantly supervise the output.
---
But well, the degree of excitement is my own bias. From other people I spoke to recently: - Risk-assessment diagnostics in medicine. There are a bunch of tests that are expensive and complex to run and need a specialist to evaluate. Deep learning is increasingly used to make it possible to do risk assessments with cheaper automated tests for a large population and have specialists focus on actual high-risk cases. Progress is slow for various reasons, but it has a lot of potential. - Weather forecasting uses a sparse set of inputs: atmospheric data from planes, weather baloons, measurements at ground stations, etc. This data is then aggregated with relatively stupid models to get the initial conditions to run a weather simulation. Deep learning is improving this part, but while there has been some encouraging initial progress this needs to be better integrated with existing simulations (purely deep learning based approaches are apparently a lot worse at predicting extreme weather events). Those simulations are expensive, they're running on some of the largest supercomputers in the world, which is why progress is slow.
Re: AI 2027
#569Earlier quoted context omitted.
Best reply in this entire thread, and I align with your thinking entirely. I also absolutely hate this idea amongst tech-oriented communities that because an AI can do some algebra and program an 8-bit video game quickly and without any mistakes, it's already overtaking humanity. Extrapolating from that idea to some future version of these models, they may be capable of solving grad school level physics problems and…
OK but getting good at science/engineering is what matters because that's what gives AI and people who wield it power. Once AI is able to build chips and datacenters autonomously, that's when singularity starts. AI doesn't need to understand humans or act human-like to do those things.
I think what they mean is that the fundamental question is IF any intelligence can really break out of its confined area of expertise and control a substantial amount of the world just by excelling in highly verifiable domains. Because a lot of what humans need to do is decisions based on expertise and judgement that in systems follows no transparent rules.
I guess it’s the age old question if we really know what we are doing („experience“) or we just tumble through life and it works out because the overall system of humans interacting with each other is big enough. The current state of world politics makes be think it’s the latter.
Re: AI 2027
#570Earlier quoted context omitted.
https://slatestarcodex.com/2014/07/30/meditations-on-moloch/
Thanks for the read. One could think that the answer is to simply stop being a part of it, but then again you're from the genus that outcompeted everyone else in staying alive. Nature is such a shitty joke by design, not sure how one is supposed to look at the hypothetical designer with warmth in their heart.
Natur is actually designed really well, it’s a just a shitty joke for our race and the ones who have to give way for nature to run its course.