[temporalparts]

Wow, this is such a simple application of ML and it is incredibly valuable. On their website, they charge $100/month per device or $1000/year per device [0] and I bet they're making a killing.

[0] https://countthings.com/en/

[rubenbe]

Value pricing at it's finest. If the customer saves $1000/month per device, they'll happily pay the $100/month. As it still saves them $900. They don't care how complex/simple the ML implementation is.

[Aperocky]

But is there any barrier to entry though?

By the looks of it, none, the next app will charges $5 per month and it could even be generic ('round objects counter')

[TheAdamAndChe]

It may not seem like it to those of us who know how to do it, but there actually is quite a barrier to entry. You or an organization needs to have the skills to utilize machine learning algorithms, test them thoroughly, package it in an easy-to-use application, then market it and compete against competitors. Just ask your every day Joe what you should do with a learning rate if your error rate suddenly spikes or how to monitor for overfitting. Then ask them how to form an LLC or how to create and audit a liability release waiver for clients.

[TchoBeer]

I also think part of the "real skill" in this sort of business is making sure you have someone around when it breaks. If the pearl counting algo goes haywire, the pearl company wants to know that they'll have someone to contact to fix it.

[dv_dt]

The barrier to entry is that many niche markets that latch on to the original service over time are incredibly random and difficult to associate with a uniform market. You can build and price a cheaper app, but reaching the set of customers would be difficult.

[GhostVII]

A business would probably not want to take the risk of switching to an unproven app for just $100 a month in savings.

[toomuchtodo]

Until then, profit is profit.

[jjk166]

I imagine it's all in the business development. I for one never would have thought to reach out to pearl traders to service their counting needs, and odds are they're not actively seeking out computer vision expertise.

[dawnerd]

Meanwhile industrial manufacturing have been using vision systems for years to count and inspect items. This solution is waaaaay cheaper than what other alternative packages are.

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).

[keeble]

They can also count items from video https://youtu.be/EoCMUfMO2jw

[Scea91]

The difficulty of this depends on the acceptance criteria. I can imagine that the industrial requirements for FP/FN count make this quite an interesting and complicated problem.

[29athrowaway]

I would not say it's that simple, they do have many trained models with high accuracy.

https://countthings.com/en/counting-templates

[distribot]

I wish I could get a bead on how useful ML is. Hackernews makes me think it’s all hype.

[BiteCode_dev]

I don't know about others, but because a lot of IT news comes to me from American medias, I _always_ consider it hype.

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.

[freddie_mercury]

I live in Southeast Asia and am in a Facebook group where foreigners often post asking for recommendations.

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.

[WindyLakeReturn]

Perhaps it has to do with different interpretations of what is meant by 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.

[matheweis]

This is exactly right. Before I lived in Seattle, I was told (moving from the southwest), that there was no good Mexican food in Seattle.

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

[vgel]

Do you have any recs in North Seattle? Me and my wife live in Lake City and have been looking for a good (need not be the best ;) ) Mexican place.

[BiteCode_dev]

But unless you list those factors such adjective is meaningless.

[andredz]

As seen in this comment in another thread currently in the front page: https://news.ycombinator.com/item?id=27263606 (Although the person that used 'best' in that story is from the UK).

[iagovar]

But americans do get marketing though. I bet you know about a bunch of companies that were doing $thing in $country before, probably with better outcomes, but never got the recognition.

In Spain, for example, many industrial companies have a culture where marketing is nearly BS. That hurts them a lot IMO.

[ethbr0]

Both of these are interrelated.

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.

[smichel17]

The main reason I'm trying to cut down on my HN consumption a bit is the incessant pessimism. While a regular reality check is definitely useful, the "I'm so jaded by everything" vibe makes it hard to get excited about anything. I like being excited by things! It feels good and motivates me. Too much HN is a recipe for lethargy.

[rationalData]

I think the most important reason to reduce consumption is due to everything being carefully curated.

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.

[ethbr0]

Mac fans being rabid Mac apologists isn't something leaving HN is going to change.

[bonoboTP]

Depends on your baseline excitement. If you're so hyped up that you think it can classify bad and good prospective employees from a single photo, you should tone it down because that's nonsense. If you think it's all fluff, then you are also wrong. There are many great ML applications for constrained scenarios.

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).

[JackFr]

There’s also a technique called “weighing”.

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%.

[matsemann]

Cut the snark. And if you check the link, the assumption that items are identical is far off.

[JackFr]

Yeah I know. And the use cases of people on a beach, crowded theater, trees in the woods, etc. don't lend themselves to weighing.

Honestly didn't intend to come off as snarky, just clever and amusing. Tough to convey tone -- risk I take I guess. Oh well.

[carlmr]

I think you're not wrong. Even if the pearls aren't identical, if you weigh enough of them at once the central limit theorem will be on your side.

[TchoBeer]

Maybe I misunderstand, but I don't think you meant central limit theorem.

[carlmr]

I did indeed. Your pearls might be heterogenous, but they will probably have an average weight that doesn't change much over time. The central limit theorem makes it possible to estimate quite accurately how much you have of a sum quantity including having a somewhat known accuracy of that estimate, since you get a nice normal distribution with known mean and variance.

[ethbr0]

ML needs data engineering like electrical devices need electrical infrastructure (i.e. generators, transmission lines, transformers, last mile lines).

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."

[omarhaneef]

Machine Learning is not a single thing that can be useful or not.

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.

[toyg]

As usual, there are exaggerations on both sides. ML is genuinely useful for moderately complex problems like this one, and pretty rubbish for a bunch of other stuff it's been shoehorned on. There is no silver bullet, as usual.

[darksaints]

Machine learning is extremely useful, used in hundreds of industries for probably a million different things. Most of these uses are not exciting.

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.

[legulere]

Machine learning is a tool for solving certain tasks. Usefulness lies in finding new places to use those techniques. The hype is about predictions that machine learning (or AI in hype-speak) will be able to do things that it is currently not able to.

Right now machine learning seems like a useful tool for certain tasks but not as revolutionary as e.g. the invention of the car.

[rsynnott]

A reasonable summary would probably be "useful in mostly though not always modest ways in many areas, but _severely_ overhyped". It's not nothing, but it's also probably not going to lead to self-driving cars or even fully-accurate voice recognition anytime soon, say.

[typon]

Google, the world's most used search engine, uses BERT to power it's queries.

[sneak]

Sometimes I feel like the whole ML scene is just stringing me along.

[rationalData]

It's literally statistics applied to data.

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.

[alisonkisk]

There's no ML here. This is a decades old software technology being packaged as a phone app.

[alisonkisk]

There's no ML here. This is a decades old software technology being packaged as a phone app.

[bserge]

The much better cameras help a lot, I guess. I remember scanning QR codes with a 2014 and a 2018 (yeah, already old) flagship phone and being amazed at the distance the latter was able to do it from. At least 3x the distance from the QR code vs the old model, and the autofocus was near instant, too.

[carlmr]

I think you meant to say there's no AI here. Since even a linear regression is ML.