> There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today.

I know some examples but not too many. Care to share more examples?

auxiliarymoose 3 hours ago | parent | next [-]

Some off the top of my head...

- Instead of trying to get LLMs to answer user questions, write better FAQs informed by reviewing tickets submitted by customers

- Instead of RAG for anything involving business data, have some DBA write a bunch of reports that answer specific business questions

- Instead of putting some copilot chat into tools and telling users to ask it to e.g. "explain recent sales trends", make task-focused wizards and visualizations so users can answer these with hard numbers

- Instead of generating code with LLMs, write more expressive frameworks and libraries that don't require so much plumbing and boilerplate

Of course, maybe there is something I am missing, but these are just my personal observations!

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menaerus 3 hours ago | parent [-]

With all due respect, all of those examples are the examples of "yesterday" ... that's how we have been bringing money to businesses for decades, no? Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned.

Businesses are incentivized to be more productive and cost-effective since they are solely profit-driven so they naturally see this as an opportunity to make more money by hiring less people while keeping the amount of work done roughly the same or even more.

So "classical" approach to many of the problems is I think the thing of a past already.

	▲	auxiliarymoose 3 hours ago \| parent [-]
		> Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned. We really don't. There are demos that look cool onstage, but there is a big difference between "in store good" and "at home good" in the sense that products aren't living up to their marketing during actual use. IMO there is a lot of room to grow within the traditional approaches of "yesterday" - The problem is that large orgs get bogged down in legacy + bureaucracy, and most startups don't understand the business problems well enough to make a better solution. And I don't think that there is any technical silver bullet that can solve either of these problems (AI or otherwise)

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srean 3 hours ago | parent | prev [-]

In the realm of data science, Linear models and SAT solvers used cleverly will get you a surprisingly long way.

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menaerus 3 hours ago | parent | next [-]

I thought the OCR was one of the obvious examples where we have a classical technology that is already working very well but in the long-run I don't see it surviving. _Generic_ AI models already can do the OCR kinda good but they are not even trained for that purpose, it's almost incidental - they've never been trained to extract the, let's say name/surname from some sort of a document with a completely unfamiliar structure, but the crazy thing is that it does work somehow! I think that once somebody finetunes the AI model only for this purpose I think there's a good chance it will outperform classical approach in terms of precision and scalability.

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srean 3 hours ago | parent [-]

In general I agree. For OCR I agree vehemently. Part of the reason is the structure of the solution (convolutions) match the space so well.

The failure cases are those where AI solutions have to stay in a continuous debug, train, update mode. Then you have to think about the resources you need, both in terms of people as well as compute to maintain such a solution.

Because of the way the world works, it's endemic nonstationarity, the debug-retrain-update is a common state of affairs even in traditional stats and ML.

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menaerus 2 hours ago | parent [-]

I see. Let's take another example here, I hope I understood you - imagine you have an AI model which is connected to all of your company's in-house data generation sources such as wiki, chat, jira, emails, merge requests, excel sheets, etc. Basically everything that can be deemed useful to query or to create business inteligence on top of. These data sources are continously generating more and more data every day, and given their nature they are more or less unstructured.

Yet, we have such systems in place where we don't have to retrain the model with ever-growing data. This is one example I could think of but it kinda suggests that models, at least for some purposes, don't have to be retrained continuously to keep them running well.

I also use a technique of explaining something to the AI model he has not seen before (according to the wrong answer I got from it previously), and it manages to evolve the steps, whatever they are, so that it gives me the correct answer in the end. This also suggests that capacity of the models is larger than what they have been trained on.

	▲	srean 2 hours ago \| parent [-]
		Data science solutions are different in the sense they rarely ever get done and dusted in a sense a sorting library might. There's almost always something or the other breaking. Did the nature of data change. Did my upstream data feed change. Why are these small set of examples not working for this high paying customer. You would need resources to understand and fix these problems quarter after quarter. A rich network of data dependencies can be a double edged sword. Rarely are upstream code and data changes benign to the output of the layer you own. There are two cases where AI solutions are perfect. They are so good that they are fire and forget. The second is that your customer is a farmer not a gardener. Individual failing saplings mean little to him. If a single misbehaving plant can cause commercially significant damage then when choosing opaque tools you must consider the maintenance cost you may be signing up for. Say I have a ton of historical data that is being continuously added to. It's a real temptation to replace the raw data with a model that uses less number of parameters than the raw data. In a sense lossy compression. Can be a very bad idea. Data instances where the model does not fit well may be the most important pieces of art information. Model paints with a broad brush stroke. If you are hunting faults, you have been aware that a lossy compression can paper them away. You are also potentially harming a future model that could have been trained but you have thrown away a decade of useful data because storage costs were running so high. No easy solution. General recommendation would be to compress but losslessly simply because you know not what may be valuable in the future. If it's impossible, then so be it, you have to eat that opportunity cost in the future, but you did your best.

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jmalicki an hour ago | parent | prev [-]

I've seen a lot of uses for SAT solvers, but what do you use them for in data science? I can't find many references to people using them in that context.

	▲	srean an hour ago \| parent [-]
		Root causing from symptoms is one case where SAT or their ML analogue -- graphical models are quite useful.