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Nobody has any idea what AI is going to look like five years from now. Five years ago we had GPT-2; AI couldn't code at all. Five years from now AI might stil

by kragen 1y ago

Nobody has any idea what AI is going to look like five years from now. Five years ago we had GPT-2; AI couldn't code at all. Five years from now AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. Anyone who tells you they know what the future looks like five years from now is lying.

tmn 1y ago
There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like
- kragen 1y ago
  No, there isn't. When speaking of logically consistent possibilities, the two problems are precisely isomorphic under Boolean negation.
  - bryanrasmussen 1y ago
    good point, someone recently said > Five years from now AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. do all these seem logically consistent possibilities to you?
    - kragen 1y ago
      Yes, obviously. You presumably don't know what "consistent" means in logic, and your untutored intuition is misleading you into guessing that possibilities like those could conceivably be inconsistent. https://en.m.wikipedia.org/wiki/Consistency https://en.m.wikipedia.org/wiki/Consistency
      - bryanrasmussen 1y ago
        or I just wanted to make sure that you were adamant that the list of those three possibilities were equally probable, to reiterate > AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. that each of these things, being logically consistent, have equal chances of being the case 5 years from now?
        kragen 1y ago
        No. Fuck off. There's no uniform probability distribution over the reals, so stop trying to put bullshit in my mouth.
        bryanrasmussen 1y ago
        OK well you obviously seem to be having some bad time about something in your life right now so I won't continue, other than to note the comment that started this said >There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like which I took to mean there are probability distributions around what things will happen, and it seemed to be your assertion that there wasn't, that a number of things only one of which seemed especially probable, were equally probable. I'm glad to learn you don't think this as it seems totally crazy, especially for someone praising LLMs which after all spend their time making millions of little choices based on probability.
noosphr 1y ago
Unless we have another breakthrough like attention we do know that AI will keep struggling with context and costs will grow quadratically with context. On a codebase of 10,000 lines any action will cost 100,000,000 AI units. One with 1,000,000 it will cost 1,000,000,000,000 AI units. I work on these things for a living and no one else seems to ever think two steps ahead on what the mathematical limitations of the transformer architecture mean for transformer based applications.
- kragen 1y ago
  It's only been 8 years since the attention breakthrough. Since then we've had "sparsely-gated MoE", RLHF, BERT, "Scaling Laws", Dall-E, LoRA, CoT, AlphaFold 2, "Parameter-Efficient Fine-Tuning", and DeepSeek's training cost breakthrough. AI researchers rather than physicists or chemists won the Nobel Prizes in physics and (for AlphaFold) chemistry last year. Agentic software development, MCP, and video generation are more or less new this year. Humans also keep struggling with context, so while large contexts may limit AI performance, they won't necessarily prevent them from being strongly superhuman.
  - lossolo 1y ago
    > Since then we've had "sparsely-gated MoE", RLHF, BERT, "Scaling Laws", Dall-E, LoRA, CoT, AlphaFold 2, "Parameter-Efficient Fine-Tuning", and DeepSeek's training cost breakthrough. OK, I will bite. So "Sparsely-gated MoE" isn’t some new intelligence, it's a sharding trick. You trade parameter count for FLOPs/latency with a router. And MoE predates transformers anyway. RLHF is packaging. Supervised finetune on instructions, learn a reward model, then nudge the policy. That’s a training objective swap plus preference data. It's useful, but not breakthrough. CoT is a prompting hack to force the same model to externalize intermediate tokens. The capability was there, you’re just sampling a longer trajectory. It’s UX for sampling. Scaling laws are an empirical fit telling you "buy more compute and data" That’s a budgeting guideline, not new math or architecture. https://www.reddit.com/r/ProgrammerHumor/comments/8c1i45/stack_more_layers/ https://www.reddit.com/r/ProgrammerHumor/comments/8c1i45/sta... LoRA is linear algebra 101, low rank adapters to cut training cost and avoid touching the full weights. The base capability still comes from the giant pretrained transformer. AlphaFold 2’s magic is mostly attention + A LOT of domain data/priors (MSAs, structures, evolutionary signal). Again attention core + data engineering. "DeepSeek’s cost breakthrough" is systems engineering. Agentic software dev/MCP is orchestration, that’s middleware and protocols, it helps use the model, it doesn’t make the model smarter. Video generation? Diffusion with temporal conditioning and better consistency losses. It’s DALL-E style tech stretched across time with tons of data curation and filtering. Most headline "wins" are compiler and kernel wins: FlashAttention, paged KV-cache, speculative decoding, distillation, quantization (8/4 bit), ZeRO/FSDP/TP/PP... These only move the cost curve, not the intelligence. The biggest single driver the last few years has been the data so de dup, document quality scores, aggressive filtration, mixture balancing (web/code/math), synthetic bootstrapping, eval driven rewrites etc etc. You can swap half a dozen training "tricks" and get similar results if your data mix and scale are right. For me a real post attention "breakthrough", would be something like: training that learns abstractions with sample efficiency far beyond scaling laws, reliable formal reasoning, causal/world-model learning that transfers out of distribution. None of the things you listed do that. Almost everything since attention is optimization, ops, and data curation. I mean give me exact pretrain mix, filtering heuristics, and finetuning datasets for Claude/GPT-5 and without peeking at the secret sauce architecture I can get close just by matching tokens, quality filters and training schedule. The "breakthroughs" are mostly better ways to spend compute and clean data, not new ways to think.

by kragen 1y ago

tmn 1y ago

There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like

kragen 1y ago
No, there isn't. When speaking of logically consistent possibilities, the two problems are precisely isomorphic under Boolean negation.
- bryanrasmussen 1y ago
  good point, someone recently said > Five years from now AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. do all these seem logically consistent possibilities to you?
  - kragen 1y ago
    Yes, obviously. You presumably don't know what "consistent" means in logic, and your untutored intuition is misleading you into guessing that possibilities like those could conceivably be inconsistent. https://en.m.wikipedia.org/wiki/Consistency https://en.m.wikipedia.org/wiki/Consistency
    - bryanrasmussen 1y ago
      or I just wanted to make sure that you were adamant that the list of those three possibilities were equally probable, to reiterate > AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos. that each of these things, being logically consistent, have equal chances of being the case 5 years from now?
      - kragen 1y ago
        No. Fuck off. There's no uniform probability distribution over the reals, so stop trying to put bullshit in my mouth.
        bryanrasmussen 1y ago
        OK well you obviously seem to be having some bad time about something in your life right now so I won't continue, other than to note the comment that started this said >There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like which I took to mean there are probability distributions around what things will happen, and it seemed to be your assertion that there wasn't, that a number of things only one of which seemed especially probable, were equally probable. I'm glad to learn you don't think this as it seems totally crazy, especially for someone praising LLMs which after all spend their time making millions of little choices based on probability.

noosphr 1y ago

Unless we have another breakthrough like attention we do know that AI will keep struggling with context and costs will grow quadratically with context. On a codebase of 10,000 lines any action will cost 100,000,000 AI units. One with 1,000,000 it will cost 1,000,000,000,000 AI units. I work on these things for a living and no one else seems to ever think two steps ahead on what the mathematical limitations of the transformer architecture mean for transformer based applications.

kragen 1y ago
It's only been 8 years since the attention breakthrough. Since then we've had "sparsely-gated MoE", RLHF, BERT, "Scaling Laws", Dall-E, LoRA, CoT, AlphaFold 2, "Parameter-Efficient Fine-Tuning", and DeepSeek's training cost breakthrough. AI researchers rather than physicists or chemists won the Nobel Prizes in physics and (for AlphaFold) chemistry last year. Agentic software development, MCP, and video generation are more or less new this year. Humans also keep struggling with context, so while large contexts may limit AI performance, they won't necessarily prevent them from being strongly superhuman.
- lossolo 1y ago
  > Since then we've had "sparsely-gated MoE", RLHF, BERT, "Scaling Laws", Dall-E, LoRA, CoT, AlphaFold 2, "Parameter-Efficient Fine-Tuning", and DeepSeek's training cost breakthrough. OK, I will bite. So "Sparsely-gated MoE" isn’t some new intelligence, it's a sharding trick. You trade parameter count for FLOPs/latency with a router. And MoE predates transformers anyway. RLHF is packaging. Supervised finetune on instructions, learn a reward model, then nudge the policy. That’s a training objective swap plus preference data. It's useful, but not breakthrough. CoT is a prompting hack to force the same model to externalize intermediate tokens. The capability was there, you’re just sampling a longer trajectory. It’s UX for sampling. Scaling laws are an empirical fit telling you "buy more compute and data" That’s a budgeting guideline, not new math or architecture. https://www.reddit.com/r/ProgrammerHumor/comments/8c1i45/stack_more_layers/ https://www.reddit.com/r/ProgrammerHumor/comments/8c1i45/sta... LoRA is linear algebra 101, low rank adapters to cut training cost and avoid touching the full weights. The base capability still comes from the giant pretrained transformer. AlphaFold 2’s magic is mostly attention + A LOT of domain data/priors (MSAs, structures, evolutionary signal). Again attention core + data engineering. "DeepSeek’s cost breakthrough" is systems engineering. Agentic software dev/MCP is orchestration, that’s middleware and protocols, it helps use the model, it doesn’t make the model smarter. Video generation? Diffusion with temporal conditioning and better consistency losses. It’s DALL-E style tech stretched across time with tons of data curation and filtering. Most headline "wins" are compiler and kernel wins: FlashAttention, paged KV-cache, speculative decoding, distillation, quantization (8/4 bit), ZeRO/FSDP/TP/PP... These only move the cost curve, not the intelligence. The biggest single driver the last few years has been the data so de dup, document quality scores, aggressive filtration, mixture balancing (web/code/math), synthetic bootstrapping, eval driven rewrites etc etc. You can swap half a dozen training "tricks" and get similar results if your data mix and scale are right. For me a real post attention "breakthrough", would be something like: training that learns abstractions with sample efficiency far beyond scaling laws, reliable formal reasoning, causal/world-model learning that transfers out of distribution. None of the things you listed do that. Almost everything since attention is optimization, ops, and data curation. I mean give me exact pretrain mix, filtering heuristics, and finetuning datasets for Claude/GPT-5 and without peeking at the secret sauce architecture I can get close just by matching tokens, quality filters and training schedule. The "breakthroughs" are mostly better ways to spend compute and clean data, not new ways to think.