> Neural networks also often prove unstable, with slight alterations in the data they receive leading to wild changes in outcomes. For instance, previous resear

> Neural networks also often prove unstable, with slight alterations in the data they receive leading to wild changes in outcomes. For instance, previous research found that changing a single pixel on an image can make an AI think a horse is a frog. Referencing a 7 year old paper. > To overcome these challenges, the Thousand Brains Project aims to develop a new AI platform by reverse engineering the neocortex Oh just going to reverse-engineer the brain, why didn't anyone think of that before > Today’s neural networks are based on basic neuroscience from 80 years ago. We’ve learned a lot about neuroscience since then, and we want to use that knowledge to advance AI Huh? I wasn't aware any neural networks were based on biology at all other than the loose activate and fire idea. > “Once we learn how to build one cortical column, we can build as many as we want,” Hawkins says. Easy, just have to learn how to do it > When you look at deep networks, they don’t fundamentally understand the world, which is why if you just change one tiny feature of an image, they often don’t recognize it Not really true unless you develop something specifically to do this. Also, "understand" needs to be defined Fluff piece that reads as an ad hoping for funding. Sounds like a money pit to me.