I’ve heard this sentiment a fair bit in recent years and I’ve never been able to find a good answer online.
In: Technology
So some AI terms to explain first. Nodes are basically just equations that take inputs and give outputs, Weights are numbers that you multiply the inouts by, Biases are numbers that you add to the equation at a node. To simplify more, a node is y = m1x1 + m2x2 + b with as many mx pairs as it needs, weights are the ms and biases are the bs.
Now to explain, lets build some simple AIs. 2 inputs to the system, a and c 1 layer of nodes combining them and then a layer to output them. So this system basically is y = (md)((ma)a+(mc)c+b1)+b2. In this systen we have 3 weights, md ma and mc and 2 biases b1 and b2. This AI we can very easily understand exactly how its working.
Now lets add an input. So 3 inputs now a,c, and d lets say. We are still going to combine them all so now we have 3 nodes in our first layer for ac, ad, cd but still one node in our output layer for combining those three. Im going to skip writing this out because it would be long but now simply by adding 1 input we now have 9 weights and 4 biases. This is still understandable but now much harder to follow.
Now lets add another layer between our initial combination and our output so now instead of ac, ad, and cd going to output we combine them to be (ac)(ad), (ad)(cd), and (ac)(dc) and then those go to our output. Now we have 15 weights and 7 biases. This is even harder to trace back to see how much a c or d are contributing.
Real AI and especially the fancier ones like the ones youre talking about are going to be using millions to billions of inputs and dozens to hundreds of layers. You theoretically can still see all the weights and biases they are using but actually following them back to see how any individual input effected the output is functionally impossible
Latest Answers