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Comment by jofer | Hacker News Reader

This is super useful, but it's a bit disappointing to see map digitization called "AI".

I mean, sure, these are methods broadly in the computer vision realm and that gets referred to as "AI" sometimes. But at the end of the day, this is "find all unfilled black circles of a specified diameter on these images". It's amenable to (and has been done by) traditional computer vision methods for a long time. There are certainly a lot of cases where a CNN type approach can perform better than traditional computer vision and there are always improvements to make.

However, I think it's a bit odd to treat this type of use case as some sort of AI breakthrough that wasn't possible or wasn't frequently done in the past.

Why can't normal standard work have a press release? Why do we need to play pretend and add buzzwords just to make things sound "cool"?

...But that's just me being a bit bitter, perhaps...

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USGS have maps from over 100 years ago. They have already been digitized. These are probably projects to search through like a person would looking for things. People that collect insulators used to collect the actual maps long ago looking for old abandoned telegraph line locations (compared to today).

AI is useful for searching for targeted stuff where you can replace a person doing something that is probably pretty easy, but there is a lot of work that can be automated. Like searching for new viruses. AI has made identifying new viruses relatively easy and much quicker than a person, who typically tweaks input and data looking through what is noise to identify genome sequence of a new virus.

> Why can't normal standard work have a press release? Why do we need to play pretend and add buzzwords just to make things sound "cool"? > ...But that's just me being a bit bitter, perhaps...

Were you complaining as heavily about OCR or Markov chains ever being referenced as AI in their hay day?

The term “AI” is in an infinite treadmill and the day it stops being useable as a time sensitive reference is probably the day it surpasses humanity and becomes its own State

You can make highly accurate predictions of what contrarians will say by assuming that they define AI as "whatever computers can't do yet."

LLMs aren't truly intelligent. [No True Scotsman fallacy...] They don't really reason. [A distinction asserted without giving a falsifiable definition of reasoning...] They're just next token predictors! [Which must be mutually exclusive with intelligence, I suppose?] Etc, etc, etc. Find your favorite pretext to dismiss modern AI, ignore the holes in the argument, and satisfyingly conclude that it's all smoke and mirrors.

Consequently you see hilarious takes from skeptics, like comparing today's enormous investment in AI to when people sold blockchain cartoon monkeys. Or claiming that modern models aren't useful for anything, as if they exist in an alternative reality where hundreds of million of people don't use them daily, and there's no incessant firehose of new tools/products/results discussed in news/social media constantly.

It's not that, it's breathlessly proclaiming that techniques that have been standards for decades are "groundbreaking AI". The hyperbole makes it impossible to get at anything, and if you accurately propose a time tested solution at work these days, it gets dismissed because it's "not AI". So now standard computer vision methods that aren't AI in any way are getting proclaimed as "AI". It's quite annoying, as least from the perspective of someone who does more or less this exact thing (geospatial analysis and data processing of various types) for a living.

Folks won't let you use the right tool for the job anymore unless you make wildly hyperbolic claims about how groundbreaking it is and claim it's cutting edge AI.

The situation is bad for everyone. There's nothing wrong with using the right tool for the job and accurately describing it. I'm tired of having to inaccurately describe methods to be allowed to use them. E.g. claiming a Hough transform is "deep learning" so folks won't immediately dismiss it and demand I use some completely incorrect approach to a simple problem.

> However, I think it's a bit odd to treat this type of use case as some sort of AI breakthrough that wasn't possible or wasn't frequently done in the past.

Classic computer vision is an utter PITA - especially when dealing with multiple libraries because everyone insists on using a different bit/byte order, pixel alignment, row/col padding, "where is 0/0 coordinate located and in which directions do the axes grow" and whatnot.

The modern "AI" stuff in contrast can be done by a human in natural language, with no prior experience in coding required.

It's usually the exact opposite for this sort of thing. You can't do this with natural language. Traditional computer vision is well suited to it and works with some tweaks. "Modern" techniques for it require collecting insane amounts of training data for simple things. You can't just throw transfer learning at this because it's a lot different than standard photographs that models are trained on. The old school methods are faster and more reliable for a significant number of problems in the geospatial world. And you still need a lot of deep expertise no matter what.

Because too many people now refer to all of machine learning as AI.

All of machine learning is AI. It's a subset, by definition.

The issue is that the terms have escaped the computer science labs and the media and the public have latched on to AI, and uses it for everything.

It is true that all of this, machine learning, large language models, natural language processing and much more is AI, in the sense that it falls under the same artificial intelligence umbrella in computer science. It just feels a little like some one is using the term "construction" over and over, but what they are specifically talking about is some very specialized type of carpentry. It's not wrong, it's just not all that precise and give the wrong impression.

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