When James's father died, he did what any of us would do in the throes of grief: he sought comfort. He went looking for it in the expected places – friends, family – but he found it somewhere unexpected: in the video game The Legend of Zelda: Majora's Mask. "Dad had always loved games. He gave me his NES when he got the SNES, and my formative memories were playing Mario Kart 64 with him, my uncle, and my little sister. Shortly after my father passed, the Wii added some N64 games to its catalogue that I had loved to play growing up, and that started the journey I needed to take to forgive him," says James. "I had felt abandoned by him – when I was right at the shifting point of puberty, about to learn how to drive, he just wasn't there. "Majora's Mask was always one of my favourites; I had the strategy guide and would read it to my dad in the truck when I would go with him to work during the summer.

If the wave of new technologies in the food industry make you feel like a lion searching for courage, you're not alone. "The food industry is kind of technology averse," Dr. David Acheson, CEO of The Acheson Group, told Quality Assurance & Food Safety magazine in December 2020. "They don't embrace new technology." But leveraging these tools, such as using blockchain for tracebacks or Internet of Things (IoT) devices to monitor a facility in real time, can have implications for several parts of the food industry. It's also important to understand that tech isn't coming to replace you or all of your processes.

This article was co-written by Chris Davis and Brandon Metzger. From detailed homework review to back office automation, progress in artificial intelligence will continue to explode in the year ahead. In 2018, Metis Strategy interviewed nearly 40 CIOs, CDOs and CTOs of companies with over $1 billion in revenue as part of our Technovation podcast and column. When asked to identify the emerging technologies that are of growing interest or are making their way onto their 2019 roadmap, 75 percent of the technology leaders highlighted artificial intelligence, while 40 percent said blockchain and 13 percent cited the Internet of Things. AI, an umbrella term for technologies that enable machines to accomplish tasks that previously required human intelligence, could rapidly upend the competitive landscape across industries.

THE world reeled when Lee Sedol – one of the great modern players of the ancient board game Go – was beaten by Google's DeepMind artificial intelligence (AI) program, AlphaGo. The AI managed to outmaneuver Lee at his own game, one which rewards players' strategic judgment and creative analyses. To achieve this, DeepMind provided AlphaGo with the basic framework of the game, recordings of previous games and made it play itself continuously. The software mimics the processes of human learning – and as it went along, AlphaGo learned to be a better player over time. The day of the face-off, AlphaGo beat Lee four games to one and was awarded the highest Go game-master ranking.

However, as is well known, the but-for test is not always sufficient to determine causality. Consider the following The original Halpern-Pearl definition of causality well-known example, taken from [Paul and Hall, 2013]: [Halpern and Pearl, 2001] was updated in the journal Suzy and Billy both pick up rocks and throw them version of the paper [Halpern and Pearl, 2005] at a bottle. Suzy's rock gets there first, shattering to deal with some problems pointed out by Hopkins the bottle. Since both throws are perfectly accurate, and Pearl [2003]. Here the definition is modified Billy's would have shattered the bottle had it not yet again, in a way that (a) leads to a simpler definition, been preempted by Suzy's throw.

One of Judea Pearl's many, many important contributions to the study of causality was the first attempt to use the mathematical tools of causal modeling to give an account of "actual causation", a notion that has been of considerable interest among philosophers and legal theorists (Pearl, 2000, Chapter 10). Pearl later revised his account of actual causation in joint work with Halpern (Halpern & Pearl, 2005). A number of authors (Hall, 2007; Halpern, 2008; Hitchcock, 2007; Menzies, 2004) have suggested that an account of actual causation must be sensitive to considerations of normality, as well as to causal structure. In (Halpern & Hitchcock, 2011), we suggest a way of incorporating considerations of normality into the Halpern-Pearl theory, and show how to extend the account to illuminate features of the psychology of causal judgment, as well as features of causal reasoning in the law. Our account of actual causation makes use of "extended causal models", which include both structural equations among a set of variables, and a partial preorder on possible worlds, which represents the relative "normality" of those worlds. We actually want to think of people as working with the structural equations and normality order to evaluate actual causation. However, consideration of even simple examples immediately suggests a problem. A direct representation of the equations and normality order is too cumbersome for cognitively limited agents to use effectively. If our account of actual causation is to be at all realistic as a model of human causal judgment, some form of compact representation will be needed.

Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.