> 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?
I know some examples but not too many. Care to share more examples?
- 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!
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.
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)
If you're not convinced, I suggest you to search for the law firms, hospitals, and laboratories ... all of which are using AI models as of today to do both the research and boiler-plate work. Creative industries are being literally erased by the generative AI as we are speaking. What will happen with the Photoshop and other similar tools when I can create whatever I want using the free AI model in literally 2 seconds without prior knowledge? What will happen with majority of movie effect makers when single guy will be able to do the work of 5 people at the same time? Or interior designers? The heck, what will happen with the Google search - I anticipate nobody will be using it in a year or two. I already don't because it's a massive sink of time compared to what I can do with perplexity for example.
There's many many examples. You just need to have your mind open to see it.
* Show me a discrete manufacturing company using AI models for statistical process control or quality reporting
* Show me a pharmaceutical company using AI models for safety data analysis
* Show me an engineering company using AI models for structural design
The list goes on and on. There are precious few industries or companies that have replaced traditional analysis & prediction with AI. Why? Because one of two things are true: 1) their data is already in highly structured relational stores that have long legacies of SQL-based extraction and analysis, 2) they're in regulated industries and have to have audit-proof, explainable reporting, or 3) they need evidence-based design and analysis that has a key component coming from real people observing real processes in action.
For all the hyped "AI Automation" you read about, there are 100 other things that aren't, or where firms don't believe they can be, or where they'll struggle to for [reasons].
For my work, it's important to form strong and correct mental models of complex systems so I can reason about them well. It's more about thinking and writing clearly than anything else. LLMs tend to include subtle mistakes or even completely incorrect information (and reasoning!) which disrupts this process.
On the creative industry side...well, you can produce some results that look fine by themselves, but producing large-scale cohesive artwork (games, movies, etc.)? It's mostly human elbow-grease for the foreseeable future.
You see, so many humans do that as well but yet we make it believe that LLMs are somehow special here. Yes, they make mistakes, we make mistakes, I make mistakes, your colleague makes mistakes, but that's not the point of this discussion at all and it's a form of confirmation bias.
Why this reflex gag happens, I think, is because people are biased to catch somebody in making the mistake so that they can feel superior and irreplaceable. We do that to keep our position strong (in society, work), and this is completely natural - it's called the survival instinct, and it is present in our species regardless of LLMs. LLMs are just one of the ways we can obviously trigger this condition.
So, your response is no more special than other people combating the AI but take into account that "The problems I work on are not well-represented in training data" or "correctness matters far more than speed" or "it's important to form strong and correct mental models of complex systems so I can reason about them well" makes a great deal of strong assumptions. Almost any domain can literally copy-paste this into their defense, and this is also something interesting I observed through the course of years - every domain I worked in, and there were plenty, each domain thought that it is their domain which is the "hardest". Vanity.
LLMs outputs are, at best, first drafts that have not been reviewed or revised, and they certainly do not have analytical reasoning behind their content.
I am not claiming that my domain is uniquely challenging, but the statements I made are factual for my work (and likely many adjacent fields, too).
Would you trust your life to a pacemaker or aircraft control system designed and manufactured quickly by people without a correct understanding of what they are developing, working purely off of information from the Internet and other semi-public sources? I wouldn't. But maybe you are braver than me!
While we are at writing documentation, did you hear about the latest layoffs from MySQL which, among others, impacted heavily the documentation team as well cutting their size from 8 to 3 people?
I hadn't heard about the MySQL layoffs. I hope the affected people find good new opportunities.
I imagine more of the benefit of AI will come from companies prioritizing infrastructure and effective organization than from the technology itself.
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.
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.
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.
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.