An issue in industry was that companies would not allow training on their data across customers. Understandably so, as SVM dimensions are readily interpretable given some thought. General purpose datasets were ill-suited too depending on the task.
So you'd start from scratch every time, annotate features like crazy, and often didn't have enough training data overall.
LLMs "solved" that by just ignoring the previous rules and training on everything, which lets the necessary world knowledge seep back in.
I'm sure we'll see many combinations of LLMs and regular machine intelligence in the near future.
If you want to make a ticket classification model, you don't need to train across many customers doing ticket classification, who might not want you to train on their data and whose data is impossible to obtain unless you already are a large company. Instead, you can train on a completely different task which forces the model to learn the syntactic and semantic structure of the English language — say predicting missing words in Wikipedia articles, which you can't do successfully without a good understanding of English — and then fine-tune that model on your specific task with much less data.
Because the initial task is so general, you can have one company do the expensive pretraining, share the base model, and then have everybody else just do the finetuning on their (small) proprietary dataset.
LLMs and models like Jev go one step further, letting you use them as classifiers with no training at all, with nothing but rules in the prompt. And with a few tricks, you can indeed use them to get pretty high-quality labels, which you can then distill into a fine-tuning dataset for a much smaller model.
> say predicting missing words in Wikipedia articles, which you can't do successfully without a good understanding of English
Word2vec is a two layer perceptron.
I feel like people in the field forgot everything.
True, but could be taken the wrong way. LLMs were also not able to train on that private customer data. They trained on so much general public data that they became capable enough to operate generally even on private data they had not been trained on.
That was the big breakthrough. I know that’s what you were trying to say, but on sites like this there are a lot of people who will see a comment like that and assume that LLMs like Sonnet were fed all of the private customer data that was withheld from training other models.
[1] I went down the rabbit hole of using sequence kernels quite a bit back then, see https://arxiv.org/abs/1910.13800, but the timing was terrible. RNNs and variants like LSTMs were beginning to become popular, and everyone (including me) switched.
You can also do stuff like setup active learning but have the labeller be an llm as well to make things more efficient.
Definitely worth it for PoC level things if not full ones.
Edit - also reviewing, so it can run training, test, review failure cases and investigate itself. You can leave them to plough through a few iterations where the early part is the classic time you’d have a few obviously bad sets of results.
Building an SVM isn't a tricky job, but you probably need a data scientist involved, and to think carefully about a labeled dataset.
I think we're seeing the modelling equivalent of something like electron apps. Where they waste a tonne of resources, but make an easier developement experience.
Will be interesting to see how much or little this changes with LLM costs.
So between llm and pseudo experts, at this stage, I prefer to go with a LLM.
Well not anymore... You can just get AI to build it.
Just like, LLMs can build web back ends, but I'd want a security professional to give it some kind of thumbs up. Especially if a lot of money rides on it.
Your model only took minutes to train if you don't count the data collection and labelling work
Saying that the field got reduced to only LLMs is like saying that the field of combustion engines got reduced to cars. The trucks, combine harvesters and peaker plants are still here, they're just in the background, invisible to people who are not part of the industry.
Banks still use random forests and SVMs, medicine and image processing still use CNNs (and people who take photos with their phone don't even realize how much image processing is being done), TTS, ASR and predictive typing models are in wide use, and there's probably still plenty of BERT in spam classification and such.
In software engineering, we learned a long time ago that giant monoliths are not the answer. You want smaller, but specialized units that work together. They perform better at their specific task, are more maintainable, you can just upgrade one part of the system without having to impact the others, ....
I don't know where the focus on building a single model that can do everything comes from.
That depends on the question. If the question is "How do I utilize 10,000 software engineers?" then yes, a monolith is not the answer. However, this isn't a problem most companies face. Starting out with microservices is rarely a good idea either as you'll have a hard time getting the boundaries right.
So if that’s a part of the future, what’s the purpose of spending billions of dollars on large AI models? Is this just a continuation of the story that there is no moat? For the big model makers?
Why do you need a master of everything? when you can just use a much smaller specialist AI model.
If you want a model that can do X but don't care about Y And Z, intuition says you only have to train the model on data relating to X. However, early LLM research showed that doing this results in a far less X-capable model than one trained on data for Y and Z as well.
I would assume a smaller model trained specifically to produce code based on natural language input would perform at least just as well, which would already be better given the smaller size / probably lower training resource requirements. This is of course, as already said, an assumption. But generalizing typically comes at a cost, there is no reason why this wouldn't apply to LLMs, it even applies to humans.
Coding an exercise app will benefit from a model that can code and understands what exercise is. A car showroom website will benefit from a model that understands what relevant filters would be.
Nah, let’s stand firm here and not give in so easily. Parent suggests a coding model needs exercise info in case you want to code an exercise model? Absurd, that leads to the know everything monolith to do anything.
Language, reasoning, tool use, and domain knowledge.. in this case the domain is coding. Someone wants an exercise app? Great, so we look that shit up, we don’t bake it in. We don’t want to bake anything we don’t need in.
We don’t even need to explain this completely reasonable and totally normal engineering preference that small is beautiful. it’s the other side that needs to justify wtf exercise has to do with coding or why everything from twitter posts to a working knowledge of 15th century French poetry just might be helpful for disconnected domains, like say serious work in contemporary astrophysics. Back to basics, No one asked for that, No one should accept as given that this is the way anything needs to work
No, the point is having broader knowledge than just "can code" is important for building software for humans to use.
> We don’t even need to explain this completely reasonable and totally normal engineering preference that small is beautiful.
It is, of course it is, as long as that small thing is useful. My argument is not "let's put literally everything in this thing just in case" it's "broader world knowledge is important for building many things".
Also, we don't look at CVs or histories of people and say "hmm, this person could be good at coding for us but I really wish they didn't have such a broad and expansive history".
> Great, so we look that shit up
But what do you have it look up? What is exercise? What is a muscle and what is a human? How is it going to understand those references? Does it have the broad enough understanding to see where the gaps are?
At some level unless all you want is "loop over the array and sum the results into a variable called sales_total" converted into code, you have to have some broader knowledge about things.
And once you start considering that maybe it needs more than simply code, we're not disagreeing we're just discussing where the line should be. Which I've also not placed, because there's no simple statement to make on that.
Damn right we don't say that. And I don't look at a carpenter and wonder if his head is hard enough to drive nails like a hammer either. People aren't tools, and tools aren't people. You've conflated them because they are both capable of doing work. And that's exactly the type of confusion and category error that the perspective I'm arguing against will lead to.
Wanting a multipurpose tool or system is occasionally reasonable. Much more often it is very, very to close misunderstanding the nature of a problem, what a tool is, or what a system should be. The particulars of purpose and mechanism of action matter, which is why a blowtorch is sometimes a wrench but only if you're kind of desperate. It is always a very scandalous thing to speak ill of someone's best new electric banana-phone / dishwasher-piano that they want to sell you. But that just sounds messy to me, I'm not sure it will improve the sound quality or taste of anything involved.
That might mean that spending all that money on that giant master of none model might lose out to the much smaller specialist model, uh-oh…
The Bitter Lesson is that human-written rule models will always lose out to trained models in the end.
Does it? All of the latest frontier models that have made technical details publicly available are using MoE; smaller specialized units that work together.
LLMs are the powerful general-purpose CPU of the ML world. Using an LLM as a classifier is woefully inefficient compared to using a dedicated classifier. On the other hand, that LLM exists right now and you can hook it up in ten seconds. You could ask: why use an LLM when a dedicated classifier would be so much more efficient? You could also ask: why build a dedicated classifier when a sufficiently capable LLM is available off the shelf? Neither one is the right answer every time.
"AGI"
When I asked why we don't build some NLP tagging which would be cheaper and run in a fraction of time, the lead told me, "What if we need to use more unstructured data? GPT can handle that without any extra work."
The team has yet to make any other changes to the pipeline, but in the two-ish years since then, they've spent a ton of money and wasted countless hours on delayed processing because of it. Some day, maybe, they'll learn that traditional ML techniques are superior in virtually every way to just throwing data at an LLM to see what sticks.
With the LLM you don't even need a data scientist to train the model, you just need someone who can connect to APIs. And the LLM is fine with adding and removing categories. No retraining.
It makes the initial faces incredibly fast. Then it is slow and expensive to run, so once the system settles, it becomes the target for model optimization, e.g, a dedicated, or semi-dedicated model.
I see coworkers using AI for tasks like "visit an API endpoint, download a file, put it in S3". This is so stupid. Okay if you want to use AI, but use it to write python code to do that, why pay in tokens every time you want to run that task, it makes no sense
Probably under the hood Claude creates some temporary python code or something.
But it bothers me, I don't know why. It's like paying a subscription service for the most stupid tasks
That is the reason.
Once you then have a system that is used enough, it starts making sense to look to replace that with a leaner classifier, but LLMs have a place in vast number of places where we'd previously never use a classifier because it was too much effort.
Absolutely! But that doesn't rule out that there are AIbros that will tell you to use an LLM, even if the task could be done with a static analyzer with a few rules. Use an LLM because it's AI!