By the looks of it, none, the next app will charges $5 per month and it could even be generic ('round objects counter')
They could probably get away with charging a lot more if they build a lite assembly line style counter that takes in a video feed. (They might already have this, I didn’t look too much into it).
The reason is cultural: american can't seem to do without superlatives. They didn't eat good carrots yesterday, there were AMAZING carrots. They don't introduce me to their friends, but to their VERY BEST friends. They are always EXCITED to do X and SO something about Y.
The result is that none of those words have any value anymore. Louis C.K had a very good bit on that.
And then you add agenda, ads, and geek bias toward the new, and you got a recipe for over hype for pretty much everything (NoSQL all the things, Microservice all the things, PWA all the things, SPA all the things, OOP all the things, FP all the things, rust all the things, typescript all the things, etc).
So yes, my default assumption is that stuff on HN are over hyped (although interesting), until proven otherwise.
Americans always phrase their questions as, "What's the BEST pizza?" or "What's the BEST sushi?"
As if they would die (or could even tell the difference) if they had the fourth or tenth best option in the city instead.
And if you recommend a place too expensive or too far away it becomes immediately clear they don't actually want the best.
All they actually want is a decent place to eat at a moderate price level.
But as Americans they can't say that. They have to pretend they deserve nothing but the BEST.
If I told you the best Thai place around where I live, I would tell you a place that is best not just because of its food, but also its price, parking, distance to travel. It is the best in that it is the most optimal given my own value judgment of those factors.
Of course after I moved there I found quite a number of excellent Mexican food restaurants, and for a few years I wondered what strange definition they were using.
I eventually figured out that they meant that there was no good Mexican food within walking distance of downtown
Casa Del Mariachi (off 125th) has great breakfast burritos. Haven’t tried the rest of their food, only came across them recently. Should be good though.
Azteca by Northgate is only okay if you don’t want to go far, you could do a lot better.
Plaza Garcia is your classic southwest Mexican food and one of our favorites. It’s in Kirkland but worth the drive.
Acapulco Fresh Mexican Grill in Kenmore is pretty good.
Taqueria El Sabor in Shoreline is also pretty good.
Anita’s Food Truck on Aurora also has good burritos. Wouldn’t classify them as typical southwest fare though.
And if you’re up for driving, the list wouldn’t be complete without mentioning Jumbo Burrito in Oak Harbor. Take the Ferry to Whidbey, do a day trip across Whidbey, stop at Jumbo and try the carnitas before you cross Deception Pass on the way back. You’re welcome.
In Spain, for example, many industrial companies have a culture where marketing is nearly BS. That hurts them a lot IMO.
I think one of the reasons Americans overuse superlatives is because they're constantly bathed in expensive marketing.
Consequently, everyone is used to being told everything is good / great / the best. Which means anyone actually expressing that in a human capacity has to reach for "the very best" or similar.
Actual issues from a particular fruit company like the butterfly keyboard was cause of getting flagged, you weren't allowed to talk about it. Similar problems happen to this day.
But this pearl counting does not require modern ML at all. It can be done with decades old image processing algorithms like Canny edge detection, Hough transform, thresholding, Hu moments etc. How reliably is another question. This kind of stuff is/used to be called "Machine Vision" (related to computer vision, but in hard industry they like to say machine vision).
It’s a transformation which, by assuming the items are identical, turns quantity from a discrete value to a continuous one with some loss of precision. In many cases measurement times can be reduced my more than 99%.
Honestly didn't intend to come off as snarky, just clever and amusing. Tough to convey tone -- risk I take I guess. Oh well.
It turns out companies have wildly different maturities and proficiencies with these precursors, in addition to simple company ages. Many from having under-invested in actual (not consulting BS) technology transformation and skilling for decades.
Consequently, ML is ridiculous to consider and doomed to failure for company A. While company B can toss a simple model at their well-architected data systems and get immediate ROI.
This is underappreciated, because VPs and consultants are not typically in the business of saying "Our systems are out of date and have poor hygiene, and we can't do this new thing because of that."
It is more like the scientific method. It depends on the data available, and whether something in the arsenal can create something useful.
Most experiments yield absolutely nothing, and some achieve delightfully counter-intuitive useful results.
The criticism on HN -- perhaps correct(1) -- is not that it is hype but that it is not new. Is statistics great? of course, but its been around for decades, perhaps centuries, so it seems strange to proclaim how it will solve problems now.
(1) ML can be distinguished from statistics if you want to. I am not interested in this particular debate myself.
The latest trendy forms of machine learning, which are all some form of deep learning neural network, are pushing beyond the boundaries of human capability, but for a fairly narrow set of usecases. Some people get excited cause they can exceed human capabilities for some object recognition type of task, and then end up thinking that SkyNet is around the corner and they rightfully get called out for it.
You should know though that it is rarely the experts that are guilty of overhyping. It's usually VCs, or product managers, or marketers, or regular software engineers that took an intro class on Coursera where they were told exactly how to solve a problem but haven't yet been exposed to how hard it is to generalize.
With deep neural networks in particular, all of the new innovations have come from novel neural connection topologies. Most of the successful new topologies are the result of attempts to model biological function of some sorts, but that is just the tip of the iceberg. With neuron counts technically unbounded, and the topological search space essentially being the factorial of the neuron count, we will never fully explore the capabilities of neural networks, and only an infinitessimally tiny fraction of those would ever be useful in any circumstance. So deep learning is still extremely exciting because the opportunities are so boundless, yet still extremely disappointing because of how hard it is to find anything useful.
Right now machine learning seems like a useful tool for certain tasks but not as revolutionary as e.g. the invention of the car.
If your data is good and you can fit it to a model it will work.
The problem is that data is often poor and the idealized models don't fit real world conditions so you can have problems like overfitting.
Btw, we are not an outsourcing company. Everything is done in Romania: development, sales, management.
[0] https://countthings.com/en/