You can apply that same argument for the of Python in the ML world. It results in all sorts of pain points that everyone has to deal with all the time.
All large-scale projects are made of mediocre parts. At some point you run out of brilliance and have to structure it so that mediocre can still be a positive contribution.
Queen Victoria was direct ruler of India from 1858, and Empress of India from 1876 until 1901, so the "leakage" may not be from the future so much as the contemporaneously recent past. Same reason models get confused about what features work in what versions of software.
(Also, Queen Elizabeth I is the one who granted a royal charter to the East India Company, in 1600 - and that company eventually handed rule of India over to Queen Victoria. So British queens were a major presence in India.)
Your bar is too low. With the coffee cup, you at least have access to all the pieces - in theory, although not in engineering practice. With Aristotle, you don't have anything close to that.
Recreating Aristotle in any meaningful way, other than a model trained on his surviving writing of a million or so words, is simply not possible even in principle.
That's easy! All you have to do is simulate the whole universe on a computer, and then go the point when Aristotle is lecturing. Record all his works, then ctrl-c out of that and then feed those recordings into the LLM's training data. For the coffee, you just rewind the simulation and ctrl-c and ctrl-v it at the point you want.
OK I'll raise the bar--make sure when you reassemble the coffee cup and put the coffee back into it, the coffee is the exact same temperature as when you threw the whole shooting match onto the floor ;)
EDIT: and you don't get to re-heat it.
EDIT AGAIN: to be clear, in my post above (and this one) by "put the coffee back in" I meant more precisely "put every molecule of coffee that splashed/sloshed/flowed/whatever out when the cup smashed back into the re-assembled cup" i.e. "restore the system back to the initial state". Not "refill the glued-together pieces of your shattered coffee cup with new coffee".
Was it? My memory is that there were GPS watches (e.g. Garmin) before GPS became common in phones. Wasn't the miniaturization already there by the time phones started integrating GPS?
It's all about power -- the computation required for processing the signal in the presence of noise, multi path fading etc. The RF part is not the limitation.
I worked with a guy who fits this description. If there’s something he can reinvent poorly, he’d do it. A big part of what I did while working on the same team as him was slowly chipping away at his weird domain by introducing standard tools. I always had to advocate for them in such a way that they were solving a different problem, and then slowly work towards a point where some of his homegrown stuff became unnecessary.
As to the job security idea: the only people who do this are people who aren’t good at creating real value, so they have to try to create niches where they’re needed.
My personal theory is that Helm may be ok for distributing a pre-packaged solution to other people. Then people mistook it for a tool that should be used in-house to deploy a company’s own systems, where it makes much less sense.
It makes absolute sense. You can use no variables and still deploy helm chart. It is a directory of plain old yaml objects. And add customization when you need as you evolve. Good luck doing that with kustomize.
Why do you believe that humans have access to an “internal thought process”? I.e. what do you think is different about an agent’s narration of a thought process vs. a human’s?
I suspect you’re making assumptions that don’t hold up to scrutiny.
I made no such claim and I don't understand what direct relevance you believe the human thought process has to the issue at hand.
You appear to be defaulting to the assumption that LLMs and humans have comparable thought processes. I don't think it's on me to provide evidence to the contrary but rather on you to provide evidence for such a seemingly extraordinary position.
For an example of a difference, consider that inserting arbitrary placeholder tokens into the output stream improves the quality of the final result. I don't know about you but if I simply repeat "banana banana banana" to myself my output quality doesn't magically increase.
> I don't understand what direct relevance you believe the human thought process has to the issue at hand.
You're the one who raised it. Perhaps you should clarify what you mean by "isn't real" - do you believe a human narrating their thought process is saying something that's more real?
Someone else replied to your comment asking essentially the same question, perhaps better phrased:
> What would be different if it was "real"? What makes you think that when humans "narrate" "their" "internal thought process", it's any more "real"?
No, I did not raise it. I said that X is false. You responded with "why do you think Y is true" and now you ask "do you believe that Y is true" neither of which is relevant to X being true or false. Humans and LLMs are not the same thing. The colloquial term for this is whataboutism.
What do I mean by isn't real? Exactly what I said originally. It's a roleplay of something that sounds plausible as opposed to what actually happened. There is obviously some process that is producing the output. The thinking trace is not a representation of that underlying process. Rather the thinking trace is an adjacent output of that same process.
Given that LLMs can speak basically any language and answer almost any arbitrary question much like a human would, the claim that LLMs have comparable (not identical) thought processes to humans does not seem extraordinary at all.
What does that mean though, to “have access to our underlying thoughts”? Humans can obviously mentally do things that are impossible for a language model to do, because it’s trivial to show that humans do not need language to do mental tasks, and this includes things related to thought, so I don’t really get what is being argued in the first place.
> it’s trivial to show that humans do not need language to do mental tasks
LLMs don't need language to do mental tasks, either. Their input and output is language - like humans - but in between, the high-dimensional vector representations (often loosely called latent space) are not language in any meaningful sense.
LLMs can benefit from "thinking out loud" much as humans can. The issue is not whether the supposed "thoughts" are actually representative on any "internal" thoughts, but rather that explicating the problem in more detail can help reach better conclusions.
One point I was making is that the idea that humans are doing something "special" (or in the OP comment's terms, "real") in this area isn't well-supported, in fact there's plenty of evidence against it.
> LLMs can benefit from "thinking out loud" much as humans can.
The two processes aren't equivalent. An LLM that fills the thinking trace with a meaningless placeholder token will still exhibit improved performance. There are also regularly things in the thinking trace that don't match the final output if you look closely but on the surface they appear convincing.
It's largely a trained performance. If you go in with the erroneous expectation that it accurately reflects the underlying thought process then you're likely to come away with faulty conclusions.
My point is that language is not a requirement for humans to perform mental tasks absolutely. It is a fundamental requirement of a large language model.
That's a meaningless argument of definition. Replace the language input and output with something else and it's no longer termed an LLM. It's like saying that a "human who writes with right hand" fundamentally requires his right hand in order to write anything because without it he is no longer a "human who writes with right hand" despite that he is still writing (now with his left hand).
Kubernetes, in the form of k3s, was a critical success factor for us with the onprem deployment of our SaaS product.
What's the problem with a single-node cluster? We use that for e.g. dev environments, as well as some small onprem deployments.
> Even when cloud deployed, K8s mostly functions as a batteries-not-included wrapper around the underlying cloud provider services and APIs.
Which batteries are not included? The "wrapper around the underlying cloud provider services and APIs" is enormously important. Why would you prefer to use a less well-designed, more vendor-specific set of APIs?
I seriously don't get these criticisms of k8s. K8s abstracts away, and standardizes, an enormous amount of system complexity. The people who object to it just don't have the requirements where it starts making sense, that's all.
> Kubernetes, in the form of k3s, was a critical success factor for us with the onprem deployment of our SaaS product.
What surprises and gotchas did you have to deal with using k3s as a Kubernetes implementation?
Did you use an LB? Which one? I'm assuming all your onprem nodes were just linux servers with very basic equipment (the fanciest networking equipment you used were 10GbE PCIe cards, nothing more special than that?)
We sell to enterprise customers. All of them deploy our solution on internal cloud-style VM clusters. We use the Traefik ingress controller by default.
There really weren't any particular surprises or gotchas at that level.
In this context, I've never had to deal with anything at the level of the type of Ethernet card. That's kind of the point: platforms like k8s abstract away from that.
You can apply that same argument for the of Python in the ML world. It results in all sorts of pain points that everyone has to deal with all the time.
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