This is important because it can make our computer programs run way faster and use a lot less energy, which is great for things like smartphones or laptops where battery life matters.

Here’s an example: let’s say we have a model that uses floating point numbers to predict whether someone will buy a product based on their age, income, and other factors. This model might use something called “deep learning” which is basically just a fancy way of saying it can learn from lots of data and make predictions really accurately. But the problem with deep learning models is they’re really big and take up a lot of memory to store all those numbers in.

So what we do instead is quantize the model, which means turning all those floating point numbers into simpler ones called “integers”. This makes the model smaller and faster to run on our devices, but it also means that some accuracy might be lost because there are fewer possible values for each number. But in most cases, this loss of accuracy isn’t a big deal because we can still get pretty good results with quantized models.

Here’s an example: let’s say the original model used floating point numbers to predict whether someone would buy a product based on their age (which was stored as a number between 0 and 100) and their income (which was also stored as a number between 0 and 100). But with quantization, we might turn those numbers into integers that can only take values of -5 to +5. This means that if someone’s age is actually 32, it would be rounded down to -4 because the nearest integer in our new system is -5 (since 32-30=2 and 2 divided by 10 = 0.2 which is less than half of 1). Similarly, if someone’s income is $67,000, it would be rounded down to +4 because the nearest integer in our new system is +5 (since 67,000-65,000=2 and 2 divided by 10 = 0.2 which is less than half of 1).

So while we might lose some accuracy with quantization, it’s still a pretty cool trick that can make our computer programs run faster and use less energy. And in most cases, the loss of accuracy isn’t really noticeable anyway!