Hacker News new | past | comments | ask | show | jobs | submit login

It is typical for ML systems to surpass human performance while having very different characteristics in what it got right/wrong. For example in ImageNet, DCNN got a lot of points from distinguishing different breeds of dogs with subtle visual differences which are hard for human without training. I think AlphaGo is also demonstrating some of these non-human characteristics as a consequence of the Monte Carlo Tree Search and optimization objective such as the brilliant move as well as the obvious slack moves/mistakes mentioned by the commentators. I suspect that we are not close to the perfect game, as proving a perfect play requires expanding the enormous search tree and we do not have any analytical solutions nor brute force solutions.



Guidelines | FAQ | Support | API | Security | Lists | Bookmarklet | Legal | Apply to YC | Contact

Search: