Machine Learning Is Sometimes Wrong How Deal With That Is Everything

Machine Learning Is Sometimes Wrong How Deal With That Is Everything

In my opinion, one of best implementations of visual machine learning I know of. It is simple, effective, & fun. Good job Apple, you’re good at computers!

Imagine if you were using a calculator to do some just fantastic calculations like π^π , !2, or the old standard 2+2, and every once-in-a-while, the answer would came back wrong. Better yet, you’ve implemented a microservice in your stack that occasionally just returns {[“No”]}.

This is what working with machine learning can feel like sometimes. You’re sitting there feeding it pictures of cats, and it is returning the label “cat” consistently until suddenly “guacamole” comes back.



Is this real life?

Yes, this is real life. Machine learning models, especially ones using neural networks, work in mysterious ways. We train them with lots of examples of cats, and it builds itself in a way that understands what cats look like. It is not too dissimilar to how the human brain works, but a much much much much much¹⁰ simpler version. Even so, we don’t really tell the neural networks what to do, they figure it out on their own based on your training data. There are hidden layers in deep neural networks that connect themselves in ways that make sense for your training data but have no input from us humans. We don’t tell it “this is what an eye looks like”, it just figure it out on its own.

What could possibly go wrong?


Machine learning is not about a definitive answer (yet), it is about statistics. Kind of like casinos, and in this case, you’re the house and you design systems that work in your favor.

So I shouldn’t use it then?

Fortunately, I have a definitive answer for this one; yes, you should use it! Statistics are great, people use them all the time to solve problems and complete tasks. And you should too. In truth, machine learning is saving a lot of people time and money, and is accelerating businesses all over the place. One thing they’ve all got in common is that they’ve built their user experience, workflows, processes, etc., around the fact that sometimes the models are wrong.

Perhaps, instead of doing an amazing job of explaining exactly what I mean, I’ll just give you some examples.

Example 1

Detecting nudity in video.

Video is tricky when it comes to image recognition, because what you’re dealing with is a series of frames, and as you can see from this article, sometimes something about an image just throws an image recognition model way off. See below, an example of an image recognition model correctly identifying a cat when the photo is tilted one way, but thinking it is guacamole when the photo is slightly altered.

If you build a workflow or system around needing every frame to have 100% accurate image recognition results, then you might run into trouble. However, if you think about the problem (I’m trying to send my phone an alert every time some guacamole s on my door mat), you may find that what you’re trying to solve doesn’t require frame-accurate results.

Let’s consider content moderation/nudity in video. If I’ve got a website that allows users to submit content in video, I might want to check it for nudity first. And since I get hundreds of uploads per day, I don’t really have the time to check it myself. So I turn to machine learning.

Even the machine learning models for detecting inappropriate content coming out of the massive tech giants just do okay on this problem. If I’m looking for specific incidents within a video of nudity, I’ll probably waste a lot of time hunting around looking at false positives.

BUT, what if I focused on saving myself time overall, and thought about the problem holistically?

“I don’t know, why don’t you tell me?”, you say in an incredibly irritated voice.

Okay I will. Consider your model is returning true or false for a lot of frames, and with those results comes a confidence score of some kind. Imagine if you crunched the entire video’s results to create a heat map of severity, so in a glance, you could tell just how likely 1, 200, 2000 or more videos had nudity in it that you needed to review.

deep neural networks machine learning models

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