Comment by auxiliarymoose
3 days ago
Super cool, thanks for sharing!
This is one of the reasons I am so skeptical of the current AI hype cycle. There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today.
You'd think that rational businesses would take the low-risk snooze-fest high-margin option any day instead of unintelligible and unreliable options that demand a lot of resources, and yet...
>This is one of the reasons I am so skeptical of the current AI hype cycle. There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today.
In 2013 my statistics professor warned that once we are in the real world, "people will come up to you trying to sell fancy machine learning models for big money, though the simple truth is that many problems can be solved better by applying straightforward statistical methods".
There has always been the ML hype, but the last couple years are a whole different level.
It does not work that way in the short term.
Say you have bet billions as a CEO, CTO, CFO. The decision has already been made. Such a steep price had to come at the cost of many groups and teams and projects in the company.
Now is not a time to water plants that offer alternatives. You will have a smoother ride choosing tools that justifies that billion dollar bet.
Decision-making in organizations is definitely a hard problem.
I think an uncomfortable reality is that a lot of decisions (technology, strategy, etc.) are not optimal or even rational, but more just an outcome of personal preferences.
Even data-driven approaches aren't immune since they depend on the analysis and interpretation of the data (which is subjective).
Data informed is good. Purely data driven is a bad idea.
After all even in Physics big advances came from thought experiments. Data is one way to reason about a decision, logic and knowledgebase is another way. Both can be very powerful if one retains the humility of fallibility.
In organizations one common failure mode is that the organisational level at which decisions are made are not the same levels where the decisions are going to have their effects felt.
It's a really difficult problem to solve. Too much decentralisation is also a bad idea. You get the mess of unplanned congested cities.
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> 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.
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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.
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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.
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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.
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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.
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>unintelligible and unreliable options that demand a lot of resources
Some options have more persuasive salesmen than others.