Nvidia's previous denials are a significant issue from a board of directors position. He either lied to the board about it, directed the company's PR people to make a false statement about it, or both. And that lie turned out to be material (in a SEC sense of the word material).
It would be very difficult for the board to regain trust in his ability to make choices that are correct for Nvidia, rather than himself.
> Why is an affair with a customer grounds for announcing a resignation that caused an instant 10% stock price drop?
OP said
>A CEO admitting to an affair with a customer *after previously denying it*, seems like a sufficient reason for removal
It's not the crime. It's the cover up.
In this case Jensen either 1) lied to the board about the affair, 2) directed the Nvidia PR to publish a materially false statement about it, or 3) both.
From a company governance standpoint it's incredibly hard to trust Jensen when he's either lying to you, using the company to protect his personal life, or (again) both.
Admitting to the affair with a customer is a nothingburger. Directing the company to release a false statement about it, and that customer turns out to be a Chinese asset if a BIG fucking deal.
Lying about it served no one's interest but Jensen's, and ended up hurting the company significantly.
>Permission to make digital or hard copies of portions of this work for personal use is granted;
permission to make digital or hard copies of portions of this work for classroom use is also granted,
but seems ill-advised
This is an example of why I'm a little stumped about why people are excited about Jev-style decision models. For any application at scale it'd be way more accurate to have a dedicated classifier using the right architecture (neural net, bayesian, etc.) LLMs drive the cost of making those models down to near zero.
Just use the LLM to make a thing that's actually good at what you need it to do.
Jev's launch was the best AI era marketing campaign I've seen so far. It was executed and timed so perfectly that Jev became synonymous with decision models overnight.
I think it reveals how many people were only familiar with LLMs as the generic AI tool for everything. It only took one ambitious marketing campaign for someone to come along and present a simpler technology as a new breakthrough to audiences that hadn't considered anything else.
The largest LLMs are really amazing for how much they can accomplish on unseen tasks. That is undeniably remarkable. The mistake that gets made everywhere is thinking that they're the best tool for every task, because LLMs are the only AI tool in most developer's AI toolbelt right now.
>many people were only familiar with LLMs as the generic AI tool for everything
That’s exactly how the biggest vendors have been marketing them, it’s no wonder non-experts think of them that way. I’m not an expert myself but have a passing familiarity with theory as a SWE and grad student who did some research for machine translation and currently am in a computing-adjacent field. I use LLMs heavily for work and have never come across any coder-oriented tutorial, guide, etc. encouraging use of LLMs as decision models specifically, even if they are being used that way “beneath the hood.” Jev’s announcement seemed sort of new functionality to me but even in the first post about it on HN (where I heard about it), iirc people were pointing out that LLMs do the same thing, at least to some extent.
I think you have a very narrow and specific experience with people claiming to be "devs".
I don't know of any devs in real life who would loudly and publicly get excited about anything unless it was their own completed project to show off.
The tools are a footnote. It's precisely because it's so hard to get a project that far along that they would be familiar with many more tools than they would ever speak about. I suppose some people have a low bar. I wouldn't really call them "devs", especially now in this "AI era".
I think you're coming at this from a very narrow and specific experience with a few people you know.
Jev really went from the announcement to being discussed all over overnight. We're discussing how popular things are in general, not among your narrow and specific group of people you work with.
Jev's marketing can't be that good because as a dev I was thoroughly unimpressed by it. Isn't it just structured output? If you embed LLMs in your applications, you were almost certainly using structured outputs already. Maybe they did additional training on top of some open Chinese model, but almost always this makes things worse not better. There's no way Jev is better than a 5-5 series Claude model at any kind of classification task.
Like that old adage "that's a feature not a product" - Jev is just a system prompt, not a product.
I think Jev tapped into a real desire of many people. A dream of (a kind of) Oracle. Where you ask the question as messily as you like, but you get as an output a decision that a plank of wood (or a piece of code) could follow literally.
Doesn't matter it's wrong significant fraction of the time and you have no clue what went wrong because the output is yes/no.
Classifier models actually make it think about how the input should be formatted and pre-processed and that kinda breaks the dream.
it's also the unfortunate practice of trying to use the new shiny thing everywhere and anywhere to signal that you're keeping with the times. It's funny how i've seen newer devs brush off things as 'traditional ml'
Which is nuts because one thing LLMs are actually pretty good up is generating "traditional ml" harnesses. You just have to describe the problem you are trying to solve and ask the LLM for algorithm suggestions.
It's also nuts because "traditional ml" generally requires a fraction of the resources of an LLM. If you need to auto label, tag, categorize, it's never been easier to setup that sort of system in any project.
Hardly a surprise when the curriculum is vastly different between institutions with cohort going through an accelerated boot camp and others being told low level computing is not useful. There are resources for people that are curious and want to broaden their knowledge, but it has to come from the individual.
> Just use the LLM to make a thing that's actually good at what you need it to do.
This is the end state of most imagined use cases for “agentic” software IMO. Use the AI to build software, with a classifier or LLM as the expansion joint in awkward spots.
The frustrating part about an LLM is at a fundamental level it's designed to do the wrong thing, it's designed to provide an answer for things it can't do, e.g. one argument I've been in recently at work is someone using it to summarise numerical data. They've a raw data spreadsheet they're putting into ChatGPT and asking it to produce a report in a particular style. So the LLM is doing its job, predicting the tokens to emit, and not actually doing mathematical analysis of the underlying data.
I had a team proudly show me how they were feeding their dashboard data into an LLM to predict growth needs, instead of doing basic linear regression or any number of pre-existing basic statistical analysis. The part that depressed me the most on that was it was a bunch of engineers that did it, the people you'd most hope would understand what an LLM is/does.
For numerical work, there’s no reason to assume the computation is happening in the LLM. All frontier models could handily write a Python script to do linear regression then report the result, so the quality of the responses here depends entirely on the prompt + harness.
+1. I noticed ChatGPT now pulls out a calculator or writes a script instead of hallucinating numbers. Initially it was terrible, but recently it seems pretty good... that said, if you're doing serious work with this you should probably double check. And if you're doing repeated work, obviously get it to write a script or formula instead of doing 1-offs.
It makes sense to use an LLM to write the queries when exploring data, but if you are building a dashboard that’s going to be used repeatedly, you should save the queries so they can be validated and improved over time. The LLM shouldn’t be rewriting a new query every time you open the dashboard.
Definitely. I’ve found Cursor + Grok to be the best budget models for this type of tool use, although I’ve yet to try out the latest Anthropic ones.
You can go “here’s a spreadsheet of some data, do a thing to it” and it’ll go through, have it plan out the type of calculations you want to do, and it’ll write you a program to parse your sheet, calculate what you want, then output an result sheet.
The key to good results with minimal effort seems to be the planning modes to me, combined with the /goal command. A little bit of validation before implementation goes a long way.
They’re very much trained to do this, in fact. Even Step 3.5 Flash, a medium sized open model from way earlier this year is trained to use python to calculate answers on the fly :)
I'm building a little side business with LLMs doing 90%+ of the work, and this includes numerical analysis of revenue to figure out where to direct effort.
Today the problem you're mentioning is easily avoided if you are aware by ensuring you direct the agents to build tools for anything deterministic.
Every week "we" (myself and Claude) start by analysing revenue from the previous week. Claude will do some manual proposals but they will be based on analysis done by a deterministic script. It will then run workflows that are increasingly calling deterministic scripts - every week another few decision points the agent used to do (and that I had to then manually vet) gets replaced by deterministic scripts.
It is what allows what is a very low margin opportunity to work - it is scaling with the total amount of manual labour and total token use remaining near constant. It's an opportunity that I won't go into much detail about because the margins are thin and the moat narrow and it's basically only viable because of this approach of setting a tiny budget where ~1/3 of it or so is dedicated to systematically eliminating manual and LLM work from the process.
Even so, I expect the opportunity to actually extract profit from it is going to be short-lived - the moat will eventually evaporate if LLMs keep getting cheaper and better in favour of people "just" asking an LLM to do the whole thing end to end.
So on one hand I agree with you - there are lots of things that'd be better automated with scripts, and where there might well always continue to be efficiencies you can extract by turning things into deterministic scripts instead. On the other hand, LLMs are already so cheap for a lot of things that it increasingly doesn't matter, and good enough that where it does matter they will increasingly also just write tools themselves if you ask them and sometimes - and likely increasingly so over time - without even being asked.
Yeah like the other say it depends on the harness. "into chatgpt" could mean many things. It's likely not predicting the next tokens to emit but rather thinking "okay the data is in format x i need to map it to y, and also this is a good fit for statistical model z" and then writing and executing a python script that runs whatever statistical model on the inputs and then produces some other output file that it then uses to produce a report.
That being said, the correct way would be to just use the LLM to produce the script, validate it themselves (often times there's something very minor wrong), and then use that script next time instead of an LLM to rewrite the same script over and over every time you need to produce a report.
These days I wouldn't be surprised if the models are smart enough to get the analysis correct.
However sometimes I think that AI use shows a revealed preference around things. In this case, the actual data just ... maybe never mattered to them at all? People wanted a shiny chart and they got a shiny chart.
For example, a client want a classifier to decide to make a go/no go decision on roofing jobs based on weather. The end result doesn’t use LLMs at all, but does use a lot of other techniques including human feedback when it makes the wrong call.
(Now the work crew gets the decision at 5 AM instead of 5:30 and the guy making the decision doesn’t have to wake up at all.)
I think it means the person making the decision does not have to get up early anymore in order to make a decision. The people receiving the decision still receive the decision, and a bit earlier.
I think there are a lot of practical applications where something that works few-shot and doesn't require training is quite appealing if the cost of inference is low. That's the sweet spot for Jev and similar models. If you have time and an eval set to train it, clearly a "real" special-purpose classifier should have much better performance (and for most applications cheaper cost/faster inference), but Jev and friends are way better than using a full-fat transformer LLM for a classification task.
As an example, I've been using winnow running locally as a decision model in openchamber to select between llms for doing various tasks. I don't have an eval set of different coding and other tasks that I could use to train a proper classifier. This I just download off huggingface and it works out of the box and has cut my token spend to a fraction of what it was. So it's good enough to be beneficial.
I do agree with you, but I do think you're also making the classic "curse of knowledge" mistake.
You have to know the right words to ask the LLM to make and train a classifier. You and I already know the words. But they are not actually that trivial to learn for an average joe, and people are lazy.
The tooling around LLMs is still nascent and, in my opinion, quite poor. You may be able to shotgun easily-falsifiable mathematical proofs with raw compute, but get into the messy world of real data, uncertainty quantification, and a host of other things that go into building real world models, and I think that an LLM plus tooling dedicated to the task plus a human in the loop will open up capabilities not available to an LLM in a chat or development centric software environment.
I don't believe this is true with current frontier coding agents. Or at least, it's becoming increasingly less true, such that I think it will effectively be nullified in ~6 months.
«We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.» [0]
It is not that access to all information was hidden to anyone before, it was harder to find it, sure. Now it can be found easily and aggregated, however, it is not as simple as saying "Do X" and believe that X is correct. Recall the infamous Swing & Product Management meme? [1] The problem is the absence of information you provide, the LLM will have to make a guess of what you mean, much like the human. Likelihoods of this guess being correct depends on the domain and background.
So yes, it'll likely build whatever you ask it (I do think we're there already), but if that matches what you intended to actually get is vastly different. You'll be settling with "Good enough" though and declare it working.
Others already pointed out that 6 months seem to be the constant of distance, irrespective of the date.
They /can/ be driven anywhere a car can be driven. We're seeing a staged, safe rollout for highways and snowy areas Right Now. This isn't cold fusion or quantum computers...
It's funny you mention this, because I've been thinking lately that all the focus on code quality benchmarks for LLMs seems to miss something. In my experience, LLMs aren't actually all that good at prompting other LLMs, and that's exactly the skill that matters for proper subagent usage.
The corpus of code available to train LLMs on is rich in both volume and quality. The same can't be said for LLM prompts. Prompts only came into existence alongside LLMs themselves, so most of the prompts out there are actually LLM generated, and training on them causes every model to overfit to the same patterns.
LLMs can give clear and concise directives to other LLMs, but only for basic tasks. Beyond that, they frequently overstuff their prompts with too much prose and too much direction, and worse, they often fill them with subtle contradictions.
Of course, there's a spectrum here. Some models turn out to be better at this than others, and I suspect this is what differentiates Fable-type models from the rest, and why vendors have started keeping their models' reasoning confidential.
Quick tip here: Where possible try to trick the model into thinking whatever task it is writing will / could be read and performed by one of your coworkers rather than another agent. It will quickly fall into line and stop writing prompts meant for dumb historic low intelligence LLMs.
The "agent factories" available to employees at the labs and hyperscalers clearly prove that agent delegation does work. Today, maybe that capacity lies with Fable class models, as you pointed out, but there is no doubt with opus 5.5 that this capability will be available to cheaper models as they get better.
That's what I've been doing in areas I lack knowledge tbh. Instead of a half assed attempt at describing what I need, I ask an llm to take my base idea and write it up using industry standard jargon as if I were commissioning a team to do the work. Has been working pretty well so far!
Okay so I just asked it "What question should I be asking you but I don't because I'm not smart enough to think of it?" Response was kind of interesting I have to say.
Ten years ago, in France, two doctors (one in a professional setting and the other during a private consultation) asked me about a wound I had on my shoulder; obviously I couldn't identify what it was, and they offered no suggestions, so I supposed it was not something important.
It took a visit to a cardiologist—for an entirely different reason—for him to suggest that I rapidly consult a dermatologist.
It was a carcinoma, and surgery removed it.
If ChatGPT had existed back then, this carcinoma would have been removed several years earlier.
They are supposed to, but often times they don't and there are no consequences for it.
Current diagnostic setup relies on humans having vast expert knowledge, when in fact it's almost always impossible.
A well trained neural net/LLM will always be better at visual detection because of sheer number of examples it sees during training, compared to a dermatologist which, if lucky will observe no more than 20 patients/day x 270 days a year x 40 years of practice i.e. around 220k samples throughout his lifetime.
MD with other specialties will see significantly less and we are talking about top of the top specialists.
An average MD knows more amount medical diagnostics than than the average Joe, but in principle much less than a well trained AI.
Do we have those kinds of models already? Maybe not yet but, ceteris paribus, we will definitely have them in the future.
IMHO we do have those models already but they aren't available to the public, though their availability would certainly lower cost for healthcare providers.
Is your "low but real" risk comparable to the risk of driving a motorcycle, of riding in a car, or of using the bathroom?
Reasonable treatments for small, non-aggressive skin cancers (or suspected cancers) are extremely low risk. And reasonable practitioners will often suggest that you merely keep an eye on an abnormal spot - because it doesn't look worth the bother of any sort of treatment for now.
That's not how risk management works, you don't disregard one because another is higher, because risk compose. At the end of the day you'll be in a car accident coming from a surgery you didn't need.
Not sure why this is getting downvoted. In its specific case that's quite obviously not a good example because removing benign spots isn't harmful, but overlooking malignant ones is, so there is no downside.
But if we think a little further and generalize then there is great risk (and by extension, harm) to delegate diagnosis and treatment (of health issues in general, including potentially or supposedly fatal ones) to ChatGPT and similar tech. That we'd end up with a net positive is far from obvious.
Your point doesn’t really make a lot of sense. Even with ChatGPT existing and making 90% bad calls, a doctor isn’t likely to just “blindly” be cutting out anomalies.
Obviously, error rate of ChatGPT should be measured by comparing to human diagnoses and when it plateaus, we should investigate a possibility that the number is driven mostly by human errors.
One thing that is exciting about Jev-style decision models is that it makes it cheap for things where they can do zero-shot of things where it's not worth your time building a curated dataset.
Bonus: after using them for a while, you get the beginnings of a training set if you change your mind.
You can do that with LLMs too - it's just a cost and latency question.
E.g. I use an LLM to label my e-mail. I do it because it's low risk enough that I don't care about it being incredibly precise - it does not automatically act on anything, it just labels - and it means I don't have to curate a dataset of what e.g. should get the "receipts" label.
An LLM zero-shots it well enough and cheaply enough to remove my incentive to train a model for my individual e-mail.
If I wanted to scale it up, Jev or similar would probably be better.
A dedicated classifier would almost certainly provide even better precision if I invested the time to curate a dataset.
But the threshold where that is worth it for me given the precision I get from a cheap LLM is pretty high, and it's also less flexible.
E.g. one of the nice things about using a generic model for this is that whenever I add a new tag, it starts using it immediately and odds are good that at most I need to add a line to a file with one-line descriptions of what the folder name means to get decent results.
So there's a space for both LLMs/Jev-likes and custom classifiers.
Use Jev-like models (or LLMs) when the effort to curate a dataset to train a dedicated classifier is too high, and switch to a dedicated classifier when/if you need to scale something to a point where the economics justifies the effort or when you already have the dataset anyway (EDIT: and if you start with an LLM or Jev, collect the data - it will provide a good starting point for your training set if/when you decide to train a dedicated classifier)
Prompted LLM classifiers are good for when training data is expensive and/or you don’t expect a high inference volume. It’s basically choosing lower model development costs in exchange for higher inference costs, which can be good for TCO, time to market, or both. Heck, I’ve worked on some applications that weren’t even feasible before LLM-prompted classification because of the cost to acquire enough data to train a supervised model.
Cost/benefit tips in favor of supervised machine learning when training data is cheap and/or plentiful, or when you expect to be able to amortize model development costs across a whole lot of inference.
I don't know, a lot of the things we used to take for granted as "hard" just aren't anymore. Writing a parser for a compiler? It used to be an endeavor that took several days, now an LLM will do it in 20 mins.
Same for annoying training jobs, with sufficient data you can probably let an LLM loose on it in autotrain mode and it will come back with a decent classifier.
Training classifiers is shockingly easy nowadays with LLMs. This is probably the biggest single piece of bespoke software I ship that I didn’t used to.
We have in the works a classifier for if a prospective customer will be long term profitable or not. This changes everything.
> We have in the works a classifier for if a prospective customer will be long term profitable or not
I'm curious, why do you use a classifier for long-term profitability? Wouldn't predicting expected lifetime profit with a forecasting model be more meaningful than just predicting whether a customer will be profitable or not?
That's awful for the misclassified customers who will not get the attention they need. Not to mention that people talk and if you treat some customers badly, the others (the ones from the top of your pile) might reconsider working with you. That doesn't automatically make this a bad idea, but I would tread very carefully.
It seems to me that hype cycle of jev made it harder to understand rather than easier.
Universal classifier just seems nonsensical. It seems clear that it can't have accurate probability distributions on any possible question you might ask it. Moreover, giving only "maybe" answers to everything adds a layer of equivocation that makes it harder to catch it in a clear mistake.
That said, I've encountered plenty of fuzzy judgment questions now (ie is this case more like example X or example Y?) that reasoning models struggle with. I could see how, in lieu of a dedicated classifier, a model like jev, ostensibly trained to guesstimate the relative likelihood of natural language hypotheses, may do well.
But it still feels wrong to call it a classifier in the traditional sense, let alone a universal classifier.
What we've been trying Jev for is interpreting things like short product descriptions provided by customers, and determine which of the natural language categories fits best.
The list of categories are provided by the government and can change several times a year, and the product descriptions are almost random (think the various titles of AliExpress listings of essentially the same product).
What kind of dedicated classifier would be better? Genuinely curious.
> For any application at scale it'd be way more accurate to have a dedicated classifier using the right architecture
Agreed, and that’s the premise behind Jeffy, open source, bundles pretrained task-specific models that run (and train) locally on CPU: https://jeffyclassify.com/
At the same time, I’ve been working with some people implementing it, and there’s definitely value in having a more general model like Jev, or an LLM, for initial exploration and data labeling, to then use that data and train the task-specific model. It’s a whole workflow
Also, the market/pain insight of Typesafe is super interesting. They realized that a big chunk of the market was using LLMs as classifiers, and they built a solution around that (very much to your point, a task-specific model, rather than a more general LLM), and it worked! (Also they have the ability to generate a lot of attention, given the founder being ex-OpenAI and having raised quite a bit of money too), so it’s not just a technical/engineering thing, it’s a business/market/need/discoverability issue as well
I just ran an experiment, I took the banking77 dataset, took the labels out, and got an LLM (Opus 5) to label them, then trained a logistic regression classifier (Jeffy http://github.com/nicobrenner/jeffy) on the resulting set
Got the same accuracy and f1 from the Opus-label dataset than the original one (I realize that Opus might have banking77 in its training data, so might need to account for that somehow)
But the point is: it's way cheaper to run the LLM model over your data to label once, and then use a cheap classifier, than using the LLM directly for classification
For this particular case, it's probably worth it because a dermatologist visit is $200. If I were the NHS or Kaiser, I'd partner with other integrated insurance and service providers to collect as much training data as possible and fund classifier development.
> For any application at scale it'd be way more accurate to have a dedicated classifier
"at scale" or the above use case of "specialized domain"
Jev being cheaper than an a full LLM call creates places where other tools might work better later - when the volume or the available data warrants it.
System optimal, code optimal and cost optimal aren't always aligned. Pick the right tool for the job - and dont get married to it. On occasion worse is better, and moreso when your testing or it is a one off/disposable.
Agreed. I did do image processing on skin and have to say it is just very very difficult to get 100% correct. In my opinion, even if some systems do outperform some doctors, it can only work as diagnostic support.
Anomalies are very hard to detect on certain skin tones and creating data sets from image material is quite a lot of work, even if you have access to large clinics specialised on dermatology with diagnosed and undiagnosed material.
Not seeing LLM to perform very well here and hallucination would be a major issue (not at all a solved problem, even frontier models do hallucinate/match the most probable path. Perhaps a problem that cannot be solved without arbitrary rules.)
And I guess some SVM still outperform CNN, just that CNN provide a pretty good learning environment that can improve upon ingesting more data. Although there is a risk associated with that as well.
Still, if we had well documented data material, many algorithms and AIs would perform well. The work is getting the data in a dependable state.
People like me who are excited about Jev-style models aren't going to use them to diagnose cancer. We are going to use them to make fairly straightforward decisions very quickly (300 ms or less) and very cheaply ($0.042 per M input tokens - output tokens aren't billed). Even the lower end frontier models will take much longer and be much more expensive, so these decision models are perfect for certain things.
If you know the exact domain you're working in, then a trained model could potentially do better, but its not a guarantee. For example, I fine-tuned Muse Glimmer on a specific domain and then tested it against Mercury Decide - which I've found does better than Jev - and they were even (and no fine-tuning was done on Mercury). But Mercury Decide was was faster and cheaper. The P95 for latency was about 5.6x lower. Decision models are very, very fast.
A classifier requires a ton of labeled data to be processed for it to be good. Jev and likes are zero shot classifiers where none of the data is required.
Imo both are useful for different tasks. I am not going to use jev based models for compliance or any tasks where being wrong can bankrupt my business. For something trivial like sentiment analysis it’s a god sent.
I've found that while this technically true, if you're building a classifier of any importance, you're going to need to monitor it and grade the efficacy of any changes, which will require labeled data anyways.
This kind of approach works for one-shot "let's just see if it works" type stuff, but anything that I would stand behind needs at least some level of labeled data and curated dataset.
> Just use the LLM to make a thing that's actually good at what you need it to do.
More generally, continue to build systems. More complex, more robust, systems.
An LLM is just one of many tools available to integrate into a system, but it’s not going to replace every system: trivially, any system that requires physical manipulation of reality can’t be replaced by an LLM. I’m not going to eat my morning cereal using an LLM.
There are tradeoffs (cost, accuracy, all sorts of metrics) that play into which tool you use for which part of the system. There will be choices that are always more cost effective:
The appeal is that you can incorporate more context in a decision problem without knowing in advance what that context might look like.
Take the problem of determining if two people in a genealogy database represent the same person. It's a decision problem. There will be some data in structured formats you can count on, but which may be missing, or may even be wrong. There will be lots of fuzzy data (born in Newtown yes, but which Newtown?) and unstructured data (notes, etc.)
Now, do you want to try to assemble a dataset to train your own classifier on that, or do you want to throw a general purpose LLM-rooted decision model on it?
There's a great bit of shortform video content floating around that shows a stammering younger guy in a welding mask:
> Do you have two pieces of metal that need to become one?
> But if it became two pieces again it wouldn't be a big safety concern?
> If that's you call Bob's OK welding!
That's Jev, and ~all zero-shot classification-by-LLM. It's universally OK. And that's great, for the tranche of problems where OK is good enough. But anywhere OK is not good enough, one would be advised to use or build something somewhat more robust.
Classifiers can require a lot of examples to train correctly. You can take a decision model's outputs and apply platt scaling to arrive at a good decision boundary a lot faster.
Eventually, yes, it makes sense to move to a pure classifier model as it's probably a lot cheaper. But then you need to learn how to serve your own classifier model correctly. Compute costs are being driven up by demand to serve models and unless you're careful it's easy to use your GPUs less effectively than a company specializing in serving a model.
>LLMs drive the cost of making those models down to near zero.
The last place I worked and my current job have a tremendously bureaucratic process for standing up new internally trained models. You could easily spend ten times as much mental energy and effort doing the paperwork and revisiting it annually as you do in the care and feeding of the model itself.
If a canned product meets the requirement, there's a lot to be said for using it. Even if it costs a bit more per decision, the all-in costs are WAY lower.
Part of the excitement to me is when tech simply becomes part of the zeitgeist. It can be simultaneously true that there's nothing new under the sun and that simply getting the knowledge of some useful tech out to the broader population helps everyone by getting many more productive minds involved in improving the processes.
I think there is a parallel between engineering systems (specialized) and commodity cloud hardware (general purpose).
While no doubt, you can get better performance using a specialized hardware … there becomes a breaking point when the commodity / general purpose is good enough while allowing you lots more flexibility as well as typically being cheaper that is become hard to justify the specialized solution.
> Just use the LLM to make a thing that's actually good at what you need it to do.
Thinking about this every time someone shares their setup and there's LLMs doing simple periodical things directly instead of having the LLM write a deterministic Python script that will never try to hack your sandbox lol
> why people are excited about Jev-style decision models.
I think its a combination of being new to classification models and not using in anger.
LLM Decision models are still relatively new and suffer from a number of issues. Order of choices impacts probability, it starts to break down over 20 classifications and even how you word things impacts probability.
My understanding of the statement is trying to use it in production for the first time. Once you start to actually use decision models beyond something simple you start to see the limitations.
I still fail to see what jev does. ALL llms produce a probability for all tokens. All jev did was stop there. Its function could be replicating by adding one "break;" statement in the right place in any other LLM.
The LLM is like the swiss army knife in these situations. It can KINDA do it all, but not very well in a lot of cases. Better to have specialized tools in most cases. And you're right, the LLM is pretty good for figuring out or building out the right tool for the job.
> For any application at scale it'd be way more accurate to have a dedicated classifier using the right architecture
There’s lots of applications where it’s not being deployed at scale. Also lots of cases where someone who knows they need something classified does not know what a classifier is.
Sure, but for some (if not many) business applications Jev style classification is probably good enough that people don't really see the value in spinning up their own model right now.
I have been incredibly impressed by LLMs for coding and data analysis and was expecting the LLMs to perform better so I was surprised by the results especially since this is an old model and nowhere near state of the art for skin cancer diagnosis.
A transformer trained only on cancer images could probably do better but the old model is trained on a well curated data set to do one thing. A decision model can decide on a much broader universe of things, only a sliver of which is cancer (and probably there's a smaller cancer dataset too)
For some types of applications, Jev is also useful as a stepping stone, you use it for the cases it's most confident so you already get gains from using it, and then collect all the usage to train your specialized model.
Don’t those decision models mainly route to other models? My understanding is that they wouldn’t handle a request but forward it along with some kind of semantic capability.
The only real usecase I can see for jev style like models are processing long lists of items. i.e server logs or financial transactions or sensor data in robotics.
it's precisely because you can tune them to do that off a strong base
and because they start off generalized, you can train them against multiple of your workloads (they'll start with some generalized performance/world knowledge about them)
Which allows you to embed proprietary processes/data (world knowledge only you may have) across multiple domains into a model cheaply
Yes, of course, with time, attention, higher maintenance costs, and slower time to respond to requirements changes you can build a specialized system that performs better than an off the shelf general purpose solution. But you cost money. And you can quit and go work somewhere else, stranding the project. And the data might be garbage you can’t train on without substantial effort. And training takes time, which has opportunity costs that are often substantial. And the requirements may change tomorrow with no notice, invalidating all of your work.
I don’t want to pay someone to maintain a first rate classifier when I can buy a pretty good classifier as a service. I don’t want to pay someone to maintain and build server infrastructure when I can buy that as a service.
I feel like I’m being gaslit on HN when a solution seems obviously valuable to me because it eliminates or modifies constraints, and people dismiss that as if managing constraints wasn’t most of high level engineering work.
I tuned out of HN for a few years, came back in the middle of the AI renaissance, and I am constantly raising my eyebrows and hearing this Upton Sinclair quote in my head:
“It is difficult to get a man to understand something when his salary depends on his not understanding it.”
I mean, it's the pareto principle, right? 80% of the applications require 20% of the intelligence. If I can drop in something without needing to create high quality training data that's good enough, that's great! Skin cancer probably isn't one of those applications.
Both trims have only cold gas thrusters. The differences are whether they're just for forward acceleration or also stabilization (4 in the back, or 4 in the back, 4 down, and one on each side). The "Rocket" trim also only has one seat.
It’s people who graduated law school. First year was brutal. Second and third were a breeze and kind of boring.
The first year is really teaching you the lingo and how to think like a lawyer. After that it’s almost entirely electives and rarely requires hard work (Fed Courts being the exception).
I've also heard of successful lawyers who did correspondence classes and night school for 18 months or something and just went and took the bar exam. I guess those guys mostly make their money by putting up billboards and farming out referrals?
>I guess those guys mostly make their money by putting up billboards and farming out referrals?
I don't know why the manner in which someone studied law would have any impact on how or what they practice now. Most of the law and skill learned is by actually practicing, not what you learn in law school. Law school is about how to think like a lawyer, not how to practice law.
Pretty much any even mid-sized legal market is going to have really well-respected attorneys who graduated from some fancy top 5 law school, and some really well-respected attorneys who graduated from a local law school where they took night classes.
The manner in which a lawyer studied obviously impacts their practice. If someone passes the bar at the age of 35 without a degree from anywhere, they're not going to be hired as an associate. If they want to make money, they're going to be a sole practitioner, and need to spend their time attracting clients. The economics of that naturally lead to a situation where a lawyer would arbitrage between paying for ads and doling out referrals. That's not to say they can't think like a lawyer or be a great lawyer, just that the starting point and the ladder to making money are very different from both that of a 26-year-old grad entering a firm and that of someone who leaves a firm to start their own practice.
So many employers over-index on name brand credentials though. In Law and otherwise. In tech we’ve all witnessed a brilliant, hard working, experienced developer candidate who went to a no name school get overlooked in favor of some doofus who happened to go to Stanford.
It would be very difficult for the board to regain trust in his ability to make choices that are correct for Nvidia, rather than himself.
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