Comment by ricksharp | Hacker News Reader

Comment by ricksharp | Hacker News Reader

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Right, I’ve been reading about machine learning for about 5 years, have read hundreds of articles about different techniques, and have often tried to explore ways it could be used.

However, I’ve never found a practical use in software engineering.

Every time I think I discover something that could use machine learning, I usually don’t have any data to work with or don’t have a clear definition of what the inputs and outputs would be.

In the end, I find a way to develop a solution that doesn’t need AI and often makes me realize that AI would not have been able to provide the required reliability.

I even participated in a self-driving RC car competition (running on raspi 3). I ended up scrapping the ML solution because it could only run at 10 hertz which was not even close to what was needed.

I ended up writing a custom algorithm in C that ran at 200fps and could do “advanced” moves like backing up and course correcting.

I’m not saying that ML doesn’t have its place. I’m saying that right now it’s all about images and nlp and has huge costs.

In any practical usage, the ML parts that I would need are already available as a service that I can use almost free (like azure cognitive services).

> In the end, I find a way to develop a solution that doesn’t need AI and often makes me realize that AI would not have been able to provide the required reliability.

This was brought home to me back in the 1990s at a place where we were trying to popularise expert system technology (a positively prehistoric form of AI). It was one of my first jobs in industry after leaving academia. The goal was to create a system that could predict the speed at which a unit of military vehicles could move so that a contact report expert system could decide whether two groups of vehicles could have feasibly moved between two points in a given time. The system could use this to help decide whether two reports referred to two different enemy units or a single unit that had moved from point A to point B.

After quite a lot of time cranking out and debugging rules to describe the movement behaviours, I realised that some simple convoy arithmetic would do just as good a job - units are often constrained by the speed of the slowest vehicle, and respect inter-vehicle distances. For most purposes this simple arithmetic was just as good (and orders of magnitude faster) than the complex rule engine.

I think there is a lot of more practical deep learning that are coming out, but it often times working with the people who know the field intimately.

For example, in my PhD thesis project, I am making a series of deep learning models to make a cardiac MRI autopilot. I've build a series of deep learning networks to localize the cardiac landmarks that define the cardiac imaging planes. And our group has even made it into a clinical prototype that works within our imaging workflow.

I think the field is shifting in that ML technologies are increasingly requiring domain level knowledge in order to make a practical endpoint.

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