While artificial intelligence (AI) agents have become increasingly skilled at communicating with humans, they still struggle with several aspects of language, including complex semantics. The term semantics refers to the area of linguistics that relates to the meaning associated with specific words or logical connections between different concepts. A few years ago, researchers at Allen Institute for AI developed a game called Iconary, which is designed to improve the ability of AI techniques to communicate and make connections between different objects. In a recent paper pre-published on arXiv and presented at last year's ENMLP conference, the researchers introduced a more advanced version of the game and trained machine learning algorithms to play against each other or with humans. "Our paper is based on a project at AI2 aimed at training models to play Iconary, a Pictionary-based game we created, where a player has to guess what another player is drawing," Christopher Clark, one of the researchers who carried out the study, told TechXplore.

Communicating with humans is challenging for AIs because it requires a shared understanding of the world, complex semantics (e.g., metaphors or analogies), and at times multi-modal gestures (e.g., pointing with a finger, or an arrow in a diagram). We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community. In Iconary, a Guesser tries to identify a phrase that a Drawer is drawing by composing icons, and the Drawer iteratively revises the drawing to help the Guesser in response. This back-and-forth often uses canonical scenes, visual metaphor, or icon compositions to express challenging words, making it an ideal test for mixing language and visual/symbolic communication in AI. We propose models to play Iconary and train them on over 55,000 games between human players. Our models are skillful players and are able to employ world knowledge in language models to play with words unseen during training. Elite human players outperform our models, particularly at the drawing task, leaving an important gap for future research to address. We release our dataset, code, and evaluation setup as a challenge to the community at http://www.github.com/allenai/iconary.

This special series explores the evolving relationship between humans and machines, examining the ways that robots, artificial intelligence and automation are impacting our work and lives. For decades, the games that put artificial intelligence to the test have been played human vs. machine – whether it's checkers, chess, Go, poker, StarCraft or "Jeopardy." Why isn't there a game where the AI and the human are on the same side? Now there is, and you can play, too. Researchers at Seattle's Allen Institute for Artificial Intelligence are taking the wraps off Iconary, a Pictionary-type puzzle game in which an AI and human players take turns putting together pictures and guessing what phrases the pictures signify.

It might be a frivolous after-dinner game to you, but Pictionary could perhaps give AI programs a deeper understanding of the world. AI's lack of common sense is one of the main obstacles to the development of chatbots and voice assistants that are genuinely useful. What's more, while AI programs can trounce the best human players of many games, including chess, Go, and (more recently) StarCraft, mastering them offers only a narrow measure of artificial intelligence. Learning to play chess, for instance, does nothing to help a computer play Sudoku. Researchers at the Allen Institute for AI (Ai2) believe that Pictionary could push machine intelligence beyond its current limits.

What do the games of chess, Jeopardy!, Go, Texas Hold'em, and StarCraft have in common? In each of these competitive arenas, an AI has resoundingly beat the best human players in the world. These victories are astounding feats of artificial intelligence--yet they've become almost humdrum. At the Allen Institute for Artificial Intelligence (AI2), in Seattle, researchers set out to do something different. Their AllenAI collaborates with a human player in a Pictionary-style drawing and guessing game, which is won through human-AI cooperation.

Scientists are using the popular drawing game Pictionary to teach artificial intelligence common sense. AI researchers at the Allen Institute for Artificial Intelligence (AI2), a non-profit lab in Seattle, developed a version of the game called Iconary in order to teach its AllenAI artificial intelligence abstract concepts from pictures alone. Iconary was made public on 5 February in order to encourage people to play the game with AllenAI. By learning from humans, the researchers hope AllenAI will continue to develop common sense reasoning. "Iconary is one of the first times an AI system is paired in a collaborative game with a human player instead of antagonistically working against them," the Iconary website states.

When the arrow appeared next to the birdcage, I finally understood what my partner was trying to say. The game was a clone of Pictionary--I had to guess the phrase based on a drawing. My partner had initially depicted a duck next to a cage, plus a hand, and a pond. Only after I asked for another drawing and the arrow was added did I realize the hand was "releasing" the duck, not feeding it. "You win!!!" I was told, after typing in the full answer.

It might be a frivolous after-dinner game to you, but Pictionary could perhaps give AI programs a deeper understanding of the world. AI's lack of common sense is one of the main obstacles to the development of chatbots and voice assistants that are genuinely useful. What's more, while AI programs can trounce the best human players of many games including chess, Go, and more recently, StarCraft, mastering such games offer only a narrow measure of artificial intelligence. Learning to play chess, for instance, does nothing to help a computer play Sudoku. Researchers at the Allen Institute for AI (Ai2) believe that Pictionary could push machine intelligence beyond its current limits.