Sarah Woodland in the U.K. runs a team of therapy ponies, bringing "joy" and "humor" to senior citizens in need of a mental health boost and a bit of company. A married couple who have long enjoyed the game of Scrabble both together and separately before they even met are never at a loss for words -- and attribute their wedded bliss in part to their love of the nostalgic game. They're still playing in tournaments built around the game decades after they began doing so. Graham Harding and his wife Helen Harding, both in their 60s, have been married for over 20 years. They met in the 1990s at Scrabble tournaments, as news agency SWNS reported.

Since Scrabble adopted an official lexicon in 1978, one thing has been constant: People have never stopped arguing about what is or isn't a word. Players have defended the game by noting that its letter strings--from AA (a kind of Hawaiian lava) to ZZZ (an interjection for sleep)--could be found in a bunch of standard North American dictionaries, books that have been used through the years to compile and revise Scrabble's tournament word list. But after an update last month introduced dozens of suspect words, riling up the community of competitive players, that's becoming harder to do. The linguistic tumult began in September, when the organization that maintains the word list used in club and tournament Scrabble, NASPA Games, published a draft of its update. The NASPA list includes all of the words in the Official Scrabble Players Dictionary, the go-to source for living-room and app players in North America, plus a lot more.

The 2023 World Scrabble Championship, held last month in Las Vegas, was an instant classic. The best-of-seven finals went the distance, with tense games, obscure words, strategic genius, and a Scrabble-record audience of 900 watching on Twitch. The winner was David Eldar, 33, of Melbourne, Australia, who defeated Harshan Lamabadusuriya, 44, a pediatrician who lives in Southmoor, England, to capture the $10,000 first prize. To reach the finals, they topped a field of 134 players from 29 countries--from Poland to Pakistan, Singapore to Sierra Leone--in a four-day, 32-game tournament. Game 6 of the finals was hailed by Scrabble experts as one of most exciting high-stakes games ever. I discussed it online with the two competitors. Our conversation has been edited and condensed for clarity. Note: The event used the international-English Scrabble dictionary, which includes substantially more words than the lexicon governing competitive play in North America. To avoid confusion, words acceptable only in the international word list are marked with a #. Stefan Fatsis: David, you trail three games to two and are up first in Game 6.

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In the mid-1970s, top players in an emerging tournament Scrabble scene persuaded the game's corporate owner to adopt a universal lexicon for competition. Players manually scraped five standard college dictionaries, recording every unique two- through eight-letter word (plus inflections) that met the game's rules. When the Official Scrabble Players Dictionary was published, in 1978, players rejoiced. "You can retire the boxing gloves and put up your swords," the Scrabble Players Newspaper wrote. "You now have an arbiter to settle all arguments."

In my job, I have seen lots of robots - all shapes and sizes - designed to clean, care for the elderly, teach, perform surgery, work as receptionists and tour guides, play Scrabble and chess, sing and dance, mix cocktails, pack shopping, deliver groceries, have sex, perform search and rescue and build cars.

Scrabble is among classic casual games getting new life on smartphones and tablets as the mobile video game audience continues to grow.

In 2015, a man named Nigel Richards won the title of French- language Scrabble World Champion. This was especially noteworthy because Richards does not speak French. What the New Zealander had done was memorize each of the 386,000 words in the entire French Scrabble dictionary, in the space of just nine weeks. Richards' impressive feat is a useful metaphor for how artificial intelligence works--real AI, not the paranoid fantasies that some self- appointed "futurists" like to warn us about. Just as Richards committed vast troves of words to memory in order to master the domain of the Scrabble board, state-of-the-art AI--or deep learning--takes in massive amounts of data from a single domain and automatically learns from the data to make specific decisions within that domain.

The current state-of-the-art Scrabble agents are not learning-based but depend on truncated Monte Carlo simulations and the quality of such agents is contingent upon the time available for running the simulations. This thesis takes steps towards building a learning-based Scrabble agent using self-play. Specifically, we try to find a better function approximation for the static evaluation function used in Scrabble which determines the move goodness at a given board configuration. In this work, we experimented with evolutionary algorithms and Bayesian Optimization to learn the weights for an approximate feature-based evaluation function. However, these optimization methods were not quite effective, which lead us to explore the given problem from an Imitation Learning point of view. We also tried to imitate the ranking of moves produced by the Quackle simulation agent using supervised learning with a neural network function approximator which takes the raw representation of the Scrabble board as the input instead of using only a fixed number of handcrafted features.

In 2015, a man named Nigel Richards won the title of French- language Scrabble World Champion. This was especially noteworthy because Richards does not speak French. What the New Zealander had done was memorize each of the 386,000 words in the entire French Scrabble dictionary, in the space of just nine weeks. Richards' impressive feat is a useful metaphor for how artificial intelligence works--real AI, not the paranoid fantasies that some self- appointed "futurists" like to warn us about. Just as Richards committed vast troves of words to memory in order to master the domain of the Scrabble board, state-of-the-art AI--or deep learning--takes in massive amounts of data from a single domain and automatically learns from the data to make specific decisions within that domain. Deep learning can automatically optimize human-given goals--called "objective functions"--with unlimited memory and superhuman accuracy.