Trying to understand Deep Learning but all the resources I’m finding are like: “and inside this black box is where the magic little goblin twists his dials and out comes your probability!”
Ugh.
In: 1
Basically, it just means that humans can’t figure out the neural network’s algorithm.
Consider an AI that is being trained to distinguish between cats and dogs. Humans give the computer a training set: hundreds of pictures of cats and dogs rectangles of all different colors, breeds, and sizes. Each of these pictures is labeled by people, so the computer can see the correct answer.
There are several different components of each picture that the computer can consider. Fur length, tail length, nose width, size, eyes, color, ear shape, etc… Some characteristics, like fur length and color, are less important than others. At first, the AI looks at each trait equally to guess if the image is a cat or dog.
When the computer guesses wrong, it’ll try different weights (basically a number that says whether the characteristic is important or not). Maybe eye shape and nose shape are more important (and need higher weights) while fur color is less important (needing a lower weight). The computer cycles through many different weight combinations until it finally guesses right.
Then the AI applies this combination of weights to the next picture. Once again, it makes adjustments until it guesses correctly. Over time, the computer eventually reaches a combination of weights that allows it to guess every picture correctly.
Now, imagine this but way more complex. The AI might look for certain characteristics first, and then use different combinations of weights depending on that first analysis. This can happen dozens of times, with each characteristic influencing the weighting to distinguish the next characteristic.
For example, maybe the AI determines that the animal is small. Then it considers all of the characteristics that might be present in a small dog or cat. Does it have pointed ears? If yes, then it further adjusts the weights to consider all the characteristics it has seen associated with small, pointed-ear dogs and cats.
It is difficult to overstate how complex this decision tree can become. Maybe if the animal is just a little bigger, the AI considers tail length to be more important than ear shape. Since people didn’t program these weights, the exact algorithms the AI uses can be impossible to figure out.
There are so many combinations that can lead to the same outcome…if you take the same AI and train them on the same image set but with the images in a different order, they might eventually arrive at the same answers in very different ways.
This makes sense: people do the same thing. You might look at an animal, notice a long nose, floppy ears, and long tail, and determine it’s a dog. I might look at the same animal, see a bone in its mouth and hear it panting and determine its a dog based on that. For each of us, the specific set of characteristics we notice first trigger various memories in our heads that tell our brain: yep, it’s a dog. In reality, there are millions of combinations of characteristics and corresponding memories that can lead a person to recognize a dog.
The same goes for a deep learning network.
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