A hybrid of deep learning and symbolic AI took the prize at a major puzzle competition.
What’s new: A system called Dr. Fill outscored nearly 1,300 human contestants at April’s annual American Crossword Puzzle Tournament, Slate reported.
How it works: Oregon polymath Matt Ginsberg debuted a logic-based system at the tournament in 2012, taking 11th place. This year, Ginsberg paired his model with a neural crossword solver developed by at UC Berkeley.
- Trained on a database of 6 million paired clues and answers, the Berkeley system reads puzzle clues and generates candidate words.
- It serves candidates to the symbolic system, which calculates the probability that each one is the correct answer based on factors like the number of letters and whether its spelling conflicts with intersecting words.
- At the late-April tournament, Dr. Fill made only three errors and solved the final puzzle in 49 seconds — over two minutes ahead of the fastest human.
Behind the news: Founded in 1978, the American Crossword Puzzle Tournament requires competitors to complete eight puzzles in two days. The three fastest and most accurate competitors face off on a final puzzle to vie for the $3,000 grand prize.
Why it matters: Neural networks and symbolic systems are often seen as competing approaches. Together, they can help solve previously elusive problems.
We’re thinking: What’s a 12-letter catchphrase that describes a persistent attitude toward gaining knowledge and skills?