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Comment by punnerud | Hacker News Reader

Most of the article is about the first of Google’s 43 rules about ML: “Don’t be afraid to launch a product without machine learning.”

and this is the first part of the description:

“ Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics. If you think that machine learning will give you a 100% boost, then a heuristic will get you 50% of the way there.

(..)”

https://developers.google.com/machine-learning/guides/rules-...

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That makes it sound like the problem is lack of data, which isn't true.

The problem is that the kind of ML that involves downloading a framework from github and tweaking features until the percent goes up is actually built on certain statistical models under the hood that people don't understand and that don't fit the process they're trying to model.

When the statistical model is correct you don't need loads of data. E.g., you don't need more than a thousand respondents to make valid inferences about millions of people in a sociological survey.

For real.

People are downloading ready-made models from repositories to try and solve minor problems.

Guess what, your problem might be a simple linear regression. Yes you can solve it with a DNN (one level, one neuron - but hey, don't keep that from putting it into your CV) but you don't need to.

At university, I generated Markov chains of the solution space from a single neuron that was being used as a binary classifier. You take n samples, average them out and look at the decision boundary. The decision boundary itself is linear but the margin of error is not.

It was really cool. Attempting to implent Hamiltonian MCMC on a single neuron really forced you to learn what a gradient is in regards to NN.

But the problem really is lack of data in many cases. Not necessarily lack of it in quantity, but lack of it in quantity in any usable/trustworthy form.

There point of that statement is that you need data to train a valid model for your usecase. And you may need a lot of it if you are trying to train a neural network.

Using a pre-trained model only works if the usecase it was trained for matches you're closely enough.

Huge amounts of data was necessary in the early models and is still when you want to win DL competition.

I fine tuned YOLOv5 with a few dozens hand-labelled images to make an object detector in a semi-controlled environment.

The idea that you need a million images to train a detector or a classifier is now totally wrong. Fine-tuning can be done on a very small dataset.

Yes, this is the third paragraph of the article.

Meta: I feel like a lot of people (including me) just come to HN for the comments, which are often (subjectively) better than the article itself.

Basically the heading becomes the random discussion topic that gets thrown in the room.

Maybe there is an experimental social platform in that:

(Re-)create a HN or reddit look-alike, but instead of user submitted links just pick random headings from news sites. Every ten minutes, post a new one without any context or link to be discussed and voted by the audience.

No idea where this would take us.

Isn’t that what «Ask HN:» is for? You can also just post a title without any link.

So you are basically asking for a subset of HN? To avoid echo chamber I think the links is a good thing.

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