>You state a conclusion as fact without any supportive reasoning/evidence.
No, it's the other way around, it's a reductive view on intelligence that mistakes its own methodology for ontology.
It's obvious to see that there's no intellect in an LLM as defined above because of how they work. LLMs put one token in front of the other, they don't work towards formal ends, there's no intentionality in them. They don't synthesize the information they process into a unified experience. Thinking an LLM can apprehend what it does because it can process large amounts of text is like thinking your TI-83 understands math because it can multiply large numbers.
That's also why the failure modes of LLMs are what they are. They can churn out tens of thousands of lines of code but also just as easily go in circles like a roomba. They can process an entire encyclopedia but not solve problems a 10 year old can solve.
yeah you know, that concept we've never been able to define using language, making heavy use of the human experience which can also not be captured in language (proof: how bad LLMs are at poetry)
the question is, when comparing a human and a large language model, whether the intellect (that cannot be captured in language) is different from anything the language model can actually do (e.g. language)
the answer to this seems quite obvious to me, and I would actually posit that the onus is on the other side, to prove they are even remotely similar
maybe people think that the voice in their heads is what is doing the thinking? is that the confusion here?
Do you think the LLM is the Chain of Thought? Did you also get confused by the name? Because, much like humans, the CoT is a tool to narrativize and maintain internal coherence. The actual thinking happens invisibly, in the forward pass. Just like...
If you ballpark it as a single 3 hour downtime window per week and iid Poisson, then overlapping downtime probability of 2 providers is approximately the expected occurrence rate per 3 hours, 1/56. Not particularly surprising at all.
If it's a "thundering herd" problem where everyone's harness falls back to less popular providers that don't normally see that much demand, I'd say the probability is pretty good.
Classic cascading failure is consistent with providers failing 80 minutes apart instead of simultaneously.
"All of these" is two. OpenAI had a router issue. Anthropic had a separate issue. Anthropic uses a lot of SpaceX compute, so an Anthropic issue and a SpaceX issue can be one in the same, as was likely the case this time.
And they weren't down at precisely the same time. Anthropic's issue started ~1 hour before OpenAI's.
1. 22 "investigated" as part of a wide scope review, ie, it could mean an entire department was investigated 2. review was for "staff internet usage" so those sanctions could be for anything from regular internet porn or gambling sites or whatever else violates their employment conditions 3. OP said the plague was among "top-level police officers"
From the sysadmin PoV it's never "one guy" or even "just two"; what is common, however, is just one or two policemen, one or two soldiers, or one two judges make the news and the limelight of public prosecutions.
It's good to be seen to be tackling this kind of behaviour, rarely ever good (apparently) to fully rip the band aid off and report just how wide spread such behaviour can go.
I can't find the stat, but if memory serves me correctly, NZ over-indexes in CSAM-related crime. It's unfortunate, but it's not always good news down on our little islands.
Why not? It’s the same as everywhere else, it has men. Just knowing that places like Kiwi Farms exists is enough evidence of good number of garbage people
There's a difference between the Deepseek.com provider lunch pricing and the pricing every other provider is doing now.
Right now DS4-Pro-0813 is available from multiple providers for $1.32/million input tokens[1].
It's pretty easy to work backwards from B200 and electricity prices and see this is profitable even without the heavy serving optimization these providers are doing[1.5].
The OpenCode CEO said: "inference is very profitable and probably a good opportunity to understand some basic business math"[2] and "the inference we do is already profitable and that's with some middlemen involved"[3]
If at this point people don't believe inference can be profitable, and providers can turn the prices up and down to choose exactly how profitable they make it I don't know what to say.
You heard of JEPA? LLM's have all sorts of garbage they have memorized. Reasoning in latent space instead of in text significantly reduces the number of needed parameters.
You should look at some literature around it. I don't have time to pull it up now but it's been shown that much smaller small million parameters JEPA model outperforms much bigger LLMS in some applications. Keep in mind JEPA is area of active research.
Have been using it as my primary harness for personal work for I'd say 6 months. I recommend everyone create their own harness at least to learn. There are a lot of practical benefits.
Casual consumers are using American models because their usage is low. As usage scales, the economics heavily favor open weight models. The API pricing from American companies is absurd. This is particularly true in an enterprise setting.
Open weight model hosts don't have the compute to meet enterprise demand. A large part of why these models are so cheap is because overall demand for them is incredibly low. Back in May, Gemini alone was doing about a month's worth of Openrouter tokens every day.
I disagree totally. DeepSeek raised prices because they couldn’t serve the demand. But there are tons of American vendors ready to fulfill it. Many enterprises, including the one I work for, are swapping to open weights.
Prove that human intellect is different and that we solve problems using fundamentally different processes. I’m waiting.
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