Comment by menaerus
3 days ago
> 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?
3 days ago
> 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?
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!
I agree, however, I've seen first hand how the AI fever and mandate from the top has finally busted enough information silos that previously 'have some DBA write a bunch of reports that answer specific business questions' just wasn't feasible in the first place, and now is.
Yeah, I will say it provides a good excuse to leadership & external stakeholders for fixing technical debt and working cross-functionally! Making infrastructure "AI-ready" and all that.
I imagine more of the benefit of AI will come from companies prioritizing infrastructure and effective organization than from the technology itself.
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.
> 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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In my domain, I see lots of people reaching immediately for "AI" techniques to solve sensor fusion and state estimation problems where a traditional Kalman filter type solution would be faster and much more interpretable.
Incidentally, I worked on the exact same thing - Kalman filtering for tracking objects in hard real-time systems. And it is not quite as simple as one would think - developing mathematical models for all kinds of different objects that one might wanna track is far from trivial, and it was difficult to model the real-world with more or less simplistic discrete equations. And it didn't work completely reliably so we needed an extra layer of confidence - I don't remember what we used back then but it was yet another algorithm with yet another source of data.
There's a lot of situations where you genuinely need more sophisticated algorithms. I'm talking about people reaching for those kinds of things before they determine the traditional algorithms won't work.
Sigma point filters ? The escalation ladder is usually KF, EKF, unscented KF, sigma point ...
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In the realm of data science, Linear models and SAT solvers used cleverly will get you a surprisingly long way.
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.
Yes, that's correct about OCR. I work as a machine vision engineer in the semiconductor industry, where each wafer usually has both OCR text and machine-readable codes such as barcodes, QR codes, or data matrix codes. The OCR typically uses the SEMI font standard.
To achieve accurate OCR results, I need to preprocess the image by isolating each character, sorting them from left to right, and using regular expressions (regex) to verify the output. However, I prefer machine-readable codes because they are simpler to use, feature built-in error detection, and are much more reliable. While deep-learning OCR solutions often perform well, they cannot guarantee the 100 percent accuracy required in our applications.
This approach is similar to how e-wallet payments use cameras to scan QR codes instead of OCR text, as QR codes provide greater reliability and accuracy.
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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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.
Root causing from symptoms is one case where SAT or their ML analogue -- graphical models are quite useful.