I hadn’t realized that sudokus could be witty until earlier this year, when I did one that literally made me laugh, because the solutions were so clever and quirky. Foolishly, I neglected to make a note of the sudoku so I could reproduce it. But I haven’t made that mistake with this futoshiki:

Using more-than and less-than signs to deduce values, fill each line and column with the numbers 1 to 5 so that no number occurs twice in the same row or column

It’s not witty like that lost sudoku, but I think futoshikis are even more beautiful and enjoyable than sudokus, because they’re even more elemental. They’re also rooted in the magic of binary, thanks to the more-than / less-than clues. And when there’s only one number on the original grid, completing them feels like growing a flower from a seed.

Alpha Beat a Gamer

Abstract: The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case. — “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”, 5/XII/2017.