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Yes, and we've also decided that they deserve the title "engineering" more than software engineering does. Most engineering disciplines have to deal with toler
by simonw 1y ago
Yes, and we've also decided that they deserve the title "engineering" more than software engineering does.
Most engineering disciplines have to deal with tolerances and uncertainty - the real world is non-deterministic.
Software engineering is easy in comparison because computers always do exactly what you tell them to do.
The ways LLMs fail (and the techniques you have to use to account for that) have more in common than physical engineering disciplines than software engineering does!
- timr 1y agolol. who is “we”? I honestly can’t tell if you’re being serious. I’m going to start a second career in lottery “engineering”, since that’s a stochastic process too.
- scuff3d 1y agoLol. This has to be a troll. No way someone seriously wrote this and meant it.
- cadamsdotcom 1y agoYep. Consider woodworking - the wood you use might warp over time, or maybe part of it ends up in the sun or the thing you’ll make gets partly exposed to water. Can you make a thing that’ll serve its purpose and look good for years under those constraints? A professional carpenter can. We have it easy in software.
- dingnuts 1y agoWoodworking is to civil engineering as being an IT help desk rep is to being a software engineer. Woodworking isn't engineering either. If you build a system with aspects you can measure and predictably tune, you're engineering. If you're making skilled alterations to an existing structure or system without applied math or science, you're partaking in a craft. Software engineering blurs the lines, sure, but woodworking isn't engineering ever.
- mpalmer 1y agoPhysical engineers might scoff good-naturedly at an attempt by project managers to refer to work scheduling as "logistics engineering". But they really shouldn't because obviously scheduling and logistics is difficult, involving a lot of uncertainty and tolerances.
- timr 1y agoUncertainty and tolerance implies that you have a predictable distribution in the first place. Engineers are not just dealing with a world of total chaos, observing the output of the chaos, and cargo culting incantations that seem to work for right now [1]…oh wait nevermind we’re doing a different thing today! Have you tried paying for a different tool, because all of the real engineers are using Qwghlm v5 Dystopic now? There’s actually real engineering going on in the training and refining of these models, but I personally wouldn’t include the prompting fad of the week to fall under that umbrella. [1] I hesitate to write that sentence because there was a period where, say, bridges and buildings were constructed in this manner. They fell down a lot, and eventually we made predictable, consistent theoretical models that guide actual engineering, as it is practiced today. Will LLM stuff eventually get there? Maybe! But right now we’re still plainly in the phase of trying random shit and seeing what falls down.
- bdangubic 1y agoexactly why calling this engineering is downright criminal
- voidhorse 1y agoI completely agree that much of software engineering is not engineering, and building systems around LLMs is no better in this sense. When the central component of your system is a black box that you cannot reason about, have no theory around, and have essentially no control over (a model update can completely change your system behavior) engineering is basically impossible from the start. Practices like using autoscorers to try and constrain behaviors helps, but this doesn't make the enterprise any more engineering because of the black box problem. Traditional engineering disciplines are able to call themselves engineering only because they are built on sophisticated physical theories that give them a precise understanding of the behaviors of materials under specified conditions. No such precision is possible with LLMs, as far as I have seen. The determinism of traditional computing isn't really relevant here and targets the wrong logical level. We engineer systems, not programs.
- empath75 1y agoThis is completely backwards. Engineers built steam engines first through trial and error and then eventually the laws of thermodynamics were invented to explain how steam engines work. Trial and error and fumbling around and creating rules of thumbs for systems you don’t entirely understand is the purest form of engineering.
- voidhorse 1y agoI would argue it's more correct to call that phase experimentation. I doubt the early manufacturers of steam machines would even call themselves engineers in a serious or precise sense. They were engineers in the sense of "builder of engine" as a specific object, but the term's meaning has evolved from that basic initial usage. A discipline becomes engineering when we achieve a level of understanding. such that we can be mathematically precise about it. Of course experimentation and trial and error are a fundamental part of that process, but there's a reason we have a word to distinguish processes which become more certain and precise thereafter and why we don't just call anything and everything engineering of some form.
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- aeve890 1y ago>The ways LLMs fail (and the techniques you have to use to account for that) have more in common than physical engineering disciplines than software engineering does! Ah yes, the God given free parameters in the Standard Model, including obviously the random seed of a transformer. What if just put 0 in the inference temperature? The randomness in llms is a technical choice to generate variations in the selection of the next token. Physical engineering? Come on.
- andai 1y ago>just set temp to 0 to make LLMs deterministic Does that really work? And is it affected by the almost continuous silent model updates? And gpt-5 has a "hidden" system prompt, even thru the API, which seemed to undergo several changes since launch...
- simonw 1y agoIt famously does not: https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
- Scipio_Afri 1y agoHey Simon, do you have any posts diving into how one might be able to deal with evaluating LLMs or Machine Learning models in general when reproducibility is so difficult given non-determinism? Pytorch has an article on it https://docs.pytorch.org/docs/stable/notes/randomness.html https://docs.pytorch.org/docs/stable/notes/randomness.html but then doesn't really go into how one would then take this deterministic result, and evaluate a model that is in production (which would very likely need for performance reasons the non-determinism features enabled). While this affects all models it seems, I think the case gets worse for in particular LLMs because I would imagine all backends, including proprietary ones, are batching users prompts. Other concurrent requests seem to change the output of your request, and then if there is even a one token change to the input or output token, especially on large inputs or outputs, the divergence can compound. Also vLLM's documentation mentions this: https://docs.vllm.ai/en/latest/usage/faq.html https://docs.vllm.ai/en/latest/usage/faq.html So how does one do benchmarking of AI/ML models and LLMs reliably (lets ignore arguing over the flaws of the metrics themselves, and just the fact that the output for any particular input can diverge given the above). You'd also want to redo evals as soon as any hardware or software stack changes are made to the production environment. Seems like one needs to setup a highly deterministic backend, by forcing non-deterministic behavior in pytorch and using a backend which doesn't do batching for an initial eval that would allow for troubleshooting and non-variation in output to get a better sense of how consistent the model without the noise of batching and non-deterministic GPU calculations/kernels etc. However then, for production, when determinism isn't guaranteed because you'd need batching and non-determism for performance, I would think that one would want to do multiple runs in various real-world situations (such as multiple users doing all sorts of different queries at the same time) and do some sort of averaging of the results. But I'm not entirely sure, because I would imagine the types of queries other users are making would then change the results fairly significantly. I'm not sure how much the batching that vLLM does would change the results of the output; but vLLM does say that batching does influence changes in the outputs.
- dingnuts 1y agoThe tools mechanical and civil engineers use are predictable. You're confusing the things these engineers design, which have tolerances and things like that, with the tools themselves. If an engineer built an internal combustion engine that misfired 60% of the time, it simply wouldn't work. If an engineer measured things with a ruler that only measured correctly 40% of the time, that would be the apt analogy. The tool isn't what makes engineering a practice, it's the rigor and the ability to measure and then use the measurements to predict outcomes to make things useful. Can you predict the outcome from an LLM with an "engineered" prompt? No, and you aren't qualified to even comment on it since your only claim to fame is a fucking web app
- skylurk 1y agoCivil engineers deal with contractors who would misfire 100% of the time if they could get away with it.
- simonw 1y ago> No, and you aren't qualified to even comment on it since your only claim to fame is a fucking web app Whoa, where did that come from?
- graemefawcett 1y agoIf you're claiming those to be the success ratios you're having with AI assisted engineering, perhaps the phrase context in, tokens out might help. The relationship is symmetrical I have found. In general, the more constraints you apply on the solution space via context, the more likely the correct solution is to stabilize. It also helps to engineer the solution in such a way that the correct solution is also the easiest and this the most likely. It takes time, but like most skills can be learned.
- quequon 1y agoClassic shilling behavior of the insufferably embarrassing: redefining words to the benefit of those who pay your bills to the confusion of everyone else. The definition of engineering, according to people outside the pocket of the llm industry: > The application of scientific and mathematical principles to practical ends such as the design, manufacture, and operation of efficient and economical structures, machines, processes, and systems. How do these techniques apply scientific and mathematical principals? I would argue to do either of those requires reproducibility, and yet somehow you are arguing the less reproducible something is that the more like "physical engineering" it becomes.
- deleted 1y ago[deleted]
- simonw 1y agoBeing accused of shilling for saying that context engineering is closer to traditional engineering than software engineering is a new one for me.