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Comment by deepGem | Hacker News Reader

My greatest takeaway from fiddling around with machine learning for a past few years is this:

Most of the business use cases are deterministic in nature. It is really hard to shoe in a probabilistic response in such use cases, as illustrated in this article. It's even harder to convert this probabilistic response to a deterministic response (humans in the loop), at a cost comparable to just maintaining the deterministic system.

There are a few business cases that naturally are probabilistic - ad bidding, stock price prediction, recommendations. The giants have taken over this already.

I am still scratching my head to find a use case for a startup to jump in, use machine learning and solve a growing business use case. Probably there are some healthcare related use cases but I have no idea about that domain.

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To me, the more interesting question with ML isn't "How can I build an entire business around a model," but rather "How can I exponentially increase a product's value with machine learning?"

For example, I don't theoretically need Netflix/Spotify/YouTube to have a great recommendation system. All I need them to do is stream media to my device. However, their recommendation systems add immense value to their product for me.

A similar example would be navigation services (Uber/Google Map's ETA prediction, for example). I don't necessarily need those apps to give me predictions about traffic patterns—their core functionality is just giving me directions—but it makes the product much more valuable to me.

For those businesses, this increase in value leads to an increase in usage and therefore revenue—it's not simply an abstract "nice to have."

I think oftentimes the argument about the value of ML comes down to a binary decision of whether or not ML can 100% replace humans in a given domain, and that this is a flawed model. ML is capable of improving most products, in my opinion, and we're already seeing it happen.

I would take it a step further. What you're talking about is incremental improvements to traditional services or products.

What the poster above you is getting at, whether they realize it or not, is that they are trying to identify markets where ML could exist solely as the service or product.

I don't think the latter is ever going to be possible for ML much like that same comment alludes too.

Maybe makes ML a bit less sexy, but also is probably true of any innovative technology.

Imagine QA of manufacturing production items. You can place cameras next to produced items and observe if they satisfy some quality requirements or sort them into buckets of certain quality levels. Now extend this to many other areas. It's cognitive automation basically, i.e. replacing humans with machines for tasks where humans need to think but which are repetitive.

Computer vision is commonly used already in industry for quality control. Most of the parameters can easily be controlled with deterministic algorithms e.g. my crisps (chips) aren't too burnt or too raw based on simple colour ranging, my biscuits are the correct shape etc.

ML potentially lets you do that without explicitly listing which failure states you are trying to detect.

I'm not convinced that is a good thing, because it just gives you an excuse to not understand your own manufacturing process and how it can go wrong.

I think this is a good observation. One thing I’ve been thinking about is probabilistically evaluating traditional software. Every un-caught exception, for example, is usually a false negative when evaluating “did the code correctly handle the input?”

Not that ML code is bug-free. But it seems like most traditional software is probabilistically correct, despite not being thought of that way.

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