Comment by seasily
5 years ago
The author is clueless. There's a lot of hype and a lot of people / products that aren't actually any good at ML (or exist as an academic venture without getting their hands dirty).
But when ML works--Uber forecasting demand, Instacart optimizing shopping paths, delivery service route dispatching, and an infinite number of other real-world use cases with double-digit process improvements--it's extraordinary.
Sales forecasting, fraud prediction, modern search, and more are all powered by ML, and throwing a few stones at bad business plans, bad marketing, or over-marketing doesn't change the fact that those things are bad precisely because they're not that good at actual machine learning.
So when ML seems like a scam that means it's just not done right. And there are some successes that show that it does actually work, really. When it's done right. As determined by its ultimate success.
We are an industry as notorious for snake oil as we are for our lack of standards. And both have standard apologetics. The parent's comment is reminiscent of the refrains "Agile, you're doing it wrong" and "that's not the way to write microservices," among many similar others.
I don't think the problem with any of this is ML as such. ML is just software. But software has problems.
The examples you give seem to fit into the "marginal process improvement" mentioned, I should think? I mean, forecasting demand, optimizing shopping paths, route dispatching, etc, don't require ML to do well. ML -might- make it a bit better still, but is it enough to offset the work that went into it? Couldn't say.
Certainly, the number of times I've seen business stakeholders treat ML as magic, where they say "It's like (standard business process) but we'll use ML!" to try and create a business case is appalling. And I think that's more what he's referring to; in many companies, ML is a solution in search of a problem, one that the business is quite happy to pay for to say they're doing (it pleases stockholders), and data scientists are happy to accept money for (it's a job, after all).
If a "marginal" process improvement increases your conversion rate by a double digit percent, then it's not all that marginal, and it's not a scam.
It's kind of boring to read "our revenue jumped 4% after adding multi-objective optimisation to our existing model", but if you stick a few 4% improvements together and apply them to a big revenue stream, you get a big number.
What you're missing is the scale at which ML allows you to do those things.
Classical algorithms for path-finding for example might work really well in narrow cases that have firm constraints. ML allows you to expand the scale of optimizations arbitrarily.
I agree. To use an example from a talk I attended last year:
A govt. department contracted out the development of an ML model to identify an invasive tree species from sat and aerial imagery. New Zealand is sparsely populated and mountainous, so crews are deployed for a week at a time by helicopter to remove these trees. It is very expensive. By being able to scan large amounts of the country for these trees, they can optimise the removal. The model appears to work very well and can identify the trees when they are young.
These kind of deep models are hard to do with traditional computer vision.
How can we look at what openai is doing and think that ml is a scam. GPT-3 probably could write a better blog post!
If AI/ML is real, why do so many obvious Russian hooker bots message me on IG?
Your annoyance is not a cost high enough for Mark to pay
because you like them. they see you do but you don't accept it yet. trust the ai