▲ 135 ▼ AI’s recursive self-improvement might not come so quickly after all (www.technologyreview.com) submitted 1 month ago by inari@piefed.zip to c/technology@lemmy.world 34 comments fedilink hide all child comments
[+] MangoCats@feddit.it 6 points 1 month ago [removed by mod] permalink fedilink source parent replies: [–] FaceDeer@fedia.io 10 points 1 month ago (-1 children) Indeed, throughout nature it's almost all sigmoids. The trick is that sigmoids look exponential before the inflection point and it's hard to predict when that inflection point is going to come. permalink fedilink source parent hideshow -1 child comments load more comments (-1 replies) [–] melfie@lemmy.zip 1 point 1 month ago (-1 children) Looking at LLM benchmarks over time, there was still doubling of benchmark scores as of 2024 to mid-2025, then it inflected to more modest, incremental improvements, so sigmoid seems about right. Now we are seeing open models rapidly catching up now that the frontier has significantly slowed. permalink fedilink source parent hideshow -1 child comments replies: [+] MangoCats@feddit.it 1 point 1 month ago [removed by mod] permalink fedilink source parent replies: [–] melfie@lemmy.zip 2 points 1 month ago (-1 children) I saw this article today summarizing a Mozilla report that open weight models are about 4 months behind the frontier: https://arstechnica.com/ai/2026/09/exclusive-open-chinese-models-close-gap-with-silicon-valleys-frontier-ai-models/ Progress has slowed at the frontier and open weight is right on their heels. The general advice is that Fable and the like are best for niche use cases and to make an open weight model your daily driver because they’re almost as good and are a lot cheaper. In my case for personal use, I’m perfectly happy with Qwen 3.8 27B running locally and don’t care to deal with data collection or huge bills to get a bit stronger of a model. permalink fedilink source parent hideshow -1 child comments load more comments (-1 replies) load more comments (-2 replies)
[–] FaceDeer@fedia.io 10 points 1 month ago (-1 children) Indeed, throughout nature it's almost all sigmoids. The trick is that sigmoids look exponential before the inflection point and it's hard to predict when that inflection point is going to come. permalink fedilink source parent hideshow -1 child comments load more comments (-1 replies)
[–] melfie@lemmy.zip 1 point 1 month ago (-1 children) Looking at LLM benchmarks over time, there was still doubling of benchmark scores as of 2024 to mid-2025, then it inflected to more modest, incremental improvements, so sigmoid seems about right. Now we are seeing open models rapidly catching up now that the frontier has significantly slowed. permalink fedilink source parent hideshow -1 child comments replies: [+] MangoCats@feddit.it 1 point 1 month ago [removed by mod] permalink fedilink source parent replies: [–] melfie@lemmy.zip 2 points 1 month ago (-1 children) I saw this article today summarizing a Mozilla report that open weight models are about 4 months behind the frontier: https://arstechnica.com/ai/2026/09/exclusive-open-chinese-models-close-gap-with-silicon-valleys-frontier-ai-models/ Progress has slowed at the frontier and open weight is right on their heels. The general advice is that Fable and the like are best for niche use cases and to make an open weight model your daily driver because they’re almost as good and are a lot cheaper. In my case for personal use, I’m perfectly happy with Qwen 3.8 27B running locally and don’t care to deal with data collection or huge bills to get a bit stronger of a model. permalink fedilink source parent hideshow -1 child comments load more comments (-1 replies) load more comments (-2 replies)
[+] MangoCats@feddit.it 1 point 1 month ago [removed by mod] permalink fedilink source parent replies: [–] melfie@lemmy.zip 2 points 1 month ago (-1 children) I saw this article today summarizing a Mozilla report that open weight models are about 4 months behind the frontier: https://arstechnica.com/ai/2026/09/exclusive-open-chinese-models-close-gap-with-silicon-valleys-frontier-ai-models/ Progress has slowed at the frontier and open weight is right on their heels. The general advice is that Fable and the like are best for niche use cases and to make an open weight model your daily driver because they’re almost as good and are a lot cheaper. In my case for personal use, I’m perfectly happy with Qwen 3.8 27B running locally and don’t care to deal with data collection or huge bills to get a bit stronger of a model. permalink fedilink source parent hideshow -1 child comments load more comments (-1 replies)
[–] melfie@lemmy.zip 2 points 1 month ago (-1 children) I saw this article today summarizing a Mozilla report that open weight models are about 4 months behind the frontier: https://arstechnica.com/ai/2026/09/exclusive-open-chinese-models-close-gap-with-silicon-valleys-frontier-ai-models/ Progress has slowed at the frontier and open weight is right on their heels. The general advice is that Fable and the like are best for niche use cases and to make an open weight model your daily driver because they’re almost as good and are a lot cheaper. In my case for personal use, I’m perfectly happy with Qwen 3.8 27B running locally and don’t care to deal with data collection or huge bills to get a bit stronger of a model. permalink fedilink source parent hideshow -1 child comments load more comments (-1 replies)