exploration and search and, you know, trying different things. And that whole process of, of test time scaling, Inference , is really about thinking. And it's about reasoning, it's about planning, it's about search, it's about... And so how could thatpossibly be compute light? And we were absolutely right about that. You know, so test time scaling is intensely compute intensive.
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software inference , on the reasoning, even on the pre-training?
natural as breathing they are rung on the ladder of inference okay now the term the next term once you recognize that is calledan assumpt now the assumpt is a a term I actually I made up you can use any any
structures a lot of other things just to name a few and then of course with Statistics methods and surveys experimental design statistical inference computation graphics and software to do a lot of this just to name some um this particular this entirebody of work and this book in particular red state blue state has been blogged about quite a bit on places like
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software With inference scaling, you don't spend money during training, you spend money later per query, and then it's also like math. How long is my model gonna be
Supertasks. Trial and error predicates. Inductive inference machines. Evolutionary computers. Fuzzy computation. Trans recursive operators.
animals do how they behave or physiology but the the the sort of the the logic of the inference is as always very weak there's nothing in what we measure that guarantees that it's accompanied by experience I had an interesting personal reminder of this when my son who was four at the time had a hernia operation
whom fat and sweet wasn't that important and who took little pleasure in sexual of inference For What Might Have Been or what might be it allows s for
Most paintings typically includes some mixture of details that are faithful to the subject, details that are distorted or embellished, and inferences and interpretations that are neither absolutely true nor entirely false, but rather, a reflection of what the artist's perspective. The same is true of memory." So this quote truly transformed my way of thinking about memory
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software was during inference scaling to achieve peak performance in certain tasks.
have is experience. You know, the rest we infer. So matter is an inference from experience and all of the laws of physics are an inference from experience. I would be more sympathetic with that view than to say, you know, actually all that there is is the molecules and and and the feelings somehow, you know, I don't need to explain how they just somehow coexist
the here and now. So I really go into great lengths of explaining you know if that causal inference helps you by fine by all means keep it and you I have no problem with that. I see it as a narrative that can really help and there is really a reason why you should
I've got three patterns here. We draw some causative inference based on that pattern that we've seen, and this is a phenomenon called apophenia.
The data we're having right now is very unstructured. And the inference we can make from those data sets is at a 95% confident level.
This is slightly technical. Your chart on the inference labor share and the markup, I've actually been working on that problem. This is a paper by Eeckhout and De Loecker that you're referring to.
would stick with the same one on the second go. process with inference of data.
damage to this part of the brain, what can the person no longer do? Therefore by inference , what does that part of the brain do? And he's really one of the seminal thinkers in neuropsychology and neuroscience that started to map the brain many, many years ago.
I've got three patterns here. We draw these inferences based on these random patterns, effectively.
kinds of inferences they draw about how the machine works. Um, and let me give
But we all make inferences about people based on how they look and how they dress.
So people will make inferences about you based on your appearance.
You can make inferences .
- We give you two out of three rights. Agentic systems can access sensitive information, it can execute code, and it can communicate we're going to need 'em for inference ." The market for inference is, you know,
known for the open weights, not their platform yet. - Many companies will sell you open-model inference at a very low cost. With OpenRouter, it's easy to look at multi-model things.
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software OpenAI's o1 was famous for introducing inference time scaling. And I think less famously for also showing that you can scale reinforcement learning training
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software Is a lot of that inference ? Is a lot of that training?
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software It's all about scaling inference , scaling post-training, scaling context, continual learning, scaling data, synthetic data?
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software The knobs are the training and the inference scaling where you can get gains. In a world where we had, let's say, infinite
Even if you don't consider the inference benefits, which are also big.
We have nothing but inductive inference to tell us that the next two years are gonna be like the last 10 years.
Some are faster and slower at inference , some have to be more expensive, some have to be less expensive.
If you're trying to make an inference about the general population, and you take a slice out of the population that's not a representative sample,
constantly trying to solve a reverse inference problem, because it has to determine the causes of sensations when
You're making a correct inference regarding the names.
management gurus he a professor at Harvard he's no longer with us but he created what is called this mind map it's a ladder of inference now there are many many different explanations for the process for thinking but this is this one is actually quite popular and and I think it it it it makes sense so we have rungs on a ladder and we start with what
And then you could make an inference with regards to the particular root cause.
psychological cause that's my inference for this kind of event and when it's happening in many different countries
set that we're making inferences of but the point of this experiment is to tell
So the point is we all make inferences about each other.
Some of us make different inferences than others.
Would you be more sure of your inferences or less sure?
is test time, and I still remember people telling me that, "Inference ? Oh, yeah, that's easy. Pre-training, that's hard." These are giant systems that people are talking about. Inference must be easy. And so inference chips are gonna be little tiny chips, and ... you know, they're not, they're not like NVIDIA's chips. Oh, those are gonna be complicated and expensive, and, you know, we could make... And this is- in the future, inference is gonna be the biggest market, and it's gonna be easy, and
And so this entire rack system is completely different than the previous one, and it's got all these new components in it. And the reason for that is because the last one was designed to run MoE large language models, inference . And this one is to run agents and agents bang on tools, and- - Obviously, the design of the system had to have been done before Claude Code, Codex, OpenClaw. So you were anticipating the future, essentially.
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software on making the attention mechanism scale linearly with inference token prediction. So there was Qwen2-VL, for example,
But Kimi K2 Thinking and Kimi K2 is a model that is very popular. People say that has very good creative writing and also in doing some software post-training, inference , context size, data, and synthetic data?
And this dramatically reduces both your training and inference cost.
This is all about reducing memory usage during inference .
And then, even at inference time, for the standard models, they expend the same amount of computation for each input.
The extreme relies on Helmholtz and thinking about a perception as inference .
There are other ways where you need to draw inference , where you can give speech-to-text.