Reverse Engineering a Neural Network's Clever Solution to Binary Addition
There's a ton of attention lately on massive neural networks with billions of parameters, and rightly so. By combining huge parameter counts with powerful architectures like transformers and diffusion, neural networks are capable of accomplishing astounding feats.
Great read. The author asks a neural network to learn a common but complex function (binary addition) and then keeps making the model smaller until it’s explainable - and then discovers the solution is quite unexpected. Great visualizations and explanation.
“Even if this particular solution was just a fluke of my network architecture or the system being modeled, it made me even more impressed by the power and versatility of gradient descent and similar optimization algorithms. The fact that these very particular patterns can be brought into existence so consistently from pure randomness is really amazing to me.”Posted on 2023-01-16T16:09:37+0000