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I switched from chatgpt to Perplexity; and now to Kimi K2, after reading an article here explaining that all the fear around some of the Chinese models spying and so on.. is simply not true. I have to say that in my experience Kimi K2 is way better than perplexity. I hope we can get our act together. Seems that building this Ai's requires a level of collaboration that is in opposition to greed.
Earlier quoted context omitted.
It solely is improving on efficiency. While it is extremely valuable given the disproportionate (to value) costs of these things, your statement almost sounds like it has improved an even more challenging aspect, pushing performance.
It's a generic comment that I don't think is even specifically about Kimi Linear or this submission, you could leave the same comment on almost any AI/ML submission and it'd say the same amount and be as relevant/irrelevant.
I switched from chatgpt to Perplexity; and now to Kimi K2, after reading an article here explaining that all the fear around some of the Chinese models spying and so on.. is simply not true. I have to say that in my experience Kimi K2 is way better than perplexity. I hope we can get our act together. Seems that building this Ai's requires a level of collaboration that is in opposition to greed.
Why do you think either of Perplexity or Kimi are better than GPT-5?
Each model, the tooling you used and even what prompts you use for what model, impacts a lot of the quality of responses you get from the models.
How likely are we to NOT see the AI data center apocalypse through better algorithms?
Everyone is worried about AI data centers destroying the planet with their extreme energy needs. Though it seems we have a big learning curve still to make AI inference and training more efficient. How likely are we to NOT see the AI data center apocalypse through better algorithms?
Everyone is worried about AI data centers destroying the planet with their extreme energy needs. Though it seems we have a big learning curve still to make AI inference and training more efficient. How likely are we to NOT see the AI data center apocalypse through better algorithms?
But so far this has just lead to more induced demand. There are a lot of things we would use LLMs for if it was just cheap enough, and every increase in efficiency makes more of those use cases viable
I switched from chatgpt to Perplexity; and now to Kimi K2, after reading an article here explaining that all the fear around some of the Chinese models spying and so on.. is simply not true. I have to say that in my experience Kimi K2 is way better than perplexity. I hope we can get our act together. Seems that building this Ai's requires a level of collaboration that is in opposition to greed.
Of course with Kimi there is fear because the Chinese government can easily pressure Moonshot AI into sharing the data, and other countries have to work to stealthily siphon data off without being caught by Chinese counterintelligence. As opposed to GPT5 where the American government can easily pressure OpenAI and every other country has to stealthily siphon data off without being caught by American counterintelligence. The only way to be reasonably certain that you aren't spied on is to run your own models or rent GPU time to run models.
The bigger worry imho is whether the models are booby-trapped to give poisoned answers when they detect certain queries or when they detect that you work for a competitor or enemy of China. But that has to be reasonably stealthy to work
Everyone is worried about AI data centers destroying the planet with their extreme energy needs. Though it seems we have a big learning curve still to make AI inference and training more efficient. How likely are we to NOT see the AI data center apocalypse through better algorithms?
Near certain IMO. Algorithmic improvements have outpaced hardware improvements for decades. We're already seeing the rise of small models and how simple tweaks can make small models very capable problem solvers, better even than state of the art large models. Data center scaling is nearing its peak IMO as we're hitting data limits which cap model size anyway.
Everyone is worried about AI data centers destroying the planet with their extreme energy needs. Though it seems we have a big learning curve still to make AI inference and training more efficient. How likely are we to NOT see the AI data center apocalypse through better algorithms?
We have already seen huge efficiency increases over the last two years. Small models have become increasingly capable, the minimum viable model size for simple tasks keeps shrinking, and proprietary model providers have long stopped talking about new milestones in model sizes and instead achieved massive price cuts through methods they largely keep quiet about (but that almost certainly include smaller models and int…