This paper introduces the Poker Hand History (PHH) file format, designed to standardize the recording of poker hands across different game variants. Despite poker's widespread popularity in the mainstream culture as a mind sport and its prominence in the field of artificial intelligence (AI) research as a benchmark for imperfect information AI agents, it lacks a consistent format that humans can use to document poker hands across different variants that can also easily be parsed by machines. To address this gap in the literature, we propose the PHH format which provides a concise human-readable machine-friendly representation of hand history that comprehensively captures various details of the hand, ranging from initial game parameters and actions to contextual parameters including but not limited to the venue, players, and time control information. In the supplementary, we provide over 10,000 hands covering 11 different variants in the PHH format. Building on our previous work on PokerKit, a premier poker hand simulation tool, we demonstrate the usages of our open-source Python implementation of the PHH parser. The source code of the parser is available on GitHub: https://github.com/uoftcprg/pokerkit

PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games. The source code is available at https://github.com/uoftcprg/pokerkit

The bacchanalia of consumerism that is Black Friday is generally focused on physical items -- you can't really market a "doorbuster" for a subscription deal to your local newspaper. But there are plenty of digital game stores that get in on the fun around this time of year, too. It's definitely worth a look to see if you can find some deals, especially if you want some new titles to show off that new PC hardware you're loading up on. While there are literally hundreds of discounts to be found on PC games across the various storefronts, the absolute best bang for your buck is probably Microsoft's Xbox Game Pass for PC, which is already the best deal in PC gaming at full price. Normally $10 a month for Netflix-style access to a few hundred PC games, a current promotion is letting players subscribe for three months for just one measly little dollar.

Securing vast and growing IoT environments may not seem to be a humanly possible task--and when the network hosts tens or hundreds of thousands of devices the task, indeed, may be unachievable. To solve this problem, vendors of security products have turned to a decidedly nonhuman alternative: artificial intelligence. "Cyberanalysts are finding it increasingly difficult to effectively monitor current levels of data volume, velocity and variety across firewalls," CapGemini noted in a survey research report, "Reinventing Cybersecurity With Artificial Intelligence." The report also noted that traditional methods may no longer be effective: "Signature-based cybersecurity solutions are unlikely to deliver the requisite performance to detect new attack vectors." In addition to conventional security software's limitations in IoT environments, CapGemini's report revealed a weakness in the human element of cybersecurity.

Graphcore, the UK-based AI chipmaker has unveiled new hardware and software innovations that push the boundaries of research and development in AI. The company has announced the second generation of its flagship Intelligence Processing Unit (IPU) chip, the GC200 or the Colossus MK2. According to Graphcore, GC200 is the most complex processor ever made. The IPU chip is at the core of every IPU-Machine M2000, a plug-and-play Machine Intelligence compute blade that has been designed for easy deployment and supports systems that can grow to massive scale. Karl Freund, Senior Analyst at Moor Insights stated, "These developments put Graphcore'first in line to challenge NVIDIA for datacenter AI'." Developed using TSMC's latest 7nm process technology, each chip contains more than 59.4 billion transistors on a single 823sqmm die.

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $a$ to perform, and receives some reward $r_{a,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{a,t}(c)$, the learner also learns the values of $r_{a,t}(c')$ for all other contexts $c'$; i.e., the rewards that would have been achieved by performing that action under different contexts. This variant arises in several strategic settings, such as learning how to bid in non-truthful repeated auctions (in this setting the context is the decision maker's private valuation for each auction). We call this problem the contextual bandits problem with cross-learning. The best algorithms for the classical contextual bandits problem achieve $\tilde{O}(\sqrt{CKT})$ regret against all stationary policies, where $C$ is the number of contexts, $K$ the number of actions, and $T$ the number of rounds. We demonstrate algorithms for the contextual bandits problem with cross-learning that remove the dependence on $C$ and achieve regret $O(\sqrt{KT})$ (when contexts are stochastic with known distribution), $\tilde{O}(K^{1/3}T^{2/3})$ (when contexts are stochastic with unknown distribution), and $\tilde{O}(\sqrt{KT})$ (when contexts are adversarial but rewards are stochastic).

Computers have gotten so good at recognizing images via machine learning, why not use that ability to teach the computer other things? That's the spirit of a new bit of research by Massachusetts Institute of Technology, which hooked up natural language processing to image recognition. MIT coordinated the activity of two machine learning systems, one for image recognition and another for speech parsing. Simultaneously, the image network learned to pick out the exact place in a picture where an object is, and the speech network picked out the exact moment in a sentence containing a word for that object in the picture. The two networks learned together, reinforcing one another until they converged on a joint answer that represents the union of the location of the object and the moment of the spoken word.

"In regular poker, to force betting, each person puts in an ante," Palansky said. "We've changed some tournaments where one person essentially pays everyone's ante at once. So, when you are in a particular spot at the table, you pay everyone's ante and the rest of the time you don't pay any ante at all. If the ante is a chip value of 100, that person may put in 900 for all nine players.

In my series of articles last March, I noted that cognitive computing and related/overlapping concepts such as artificial intelligence (AI) and machine learning had seen slow uptake in business intelligence (BI). As 2016 draws to a close, a second look is warranted, given the enormous hype and publicity the topic has drawn in the intervening months. Although there has been limited progress in AI for BI in the interim, several fascinating developments have emerged in AI research, particularly from the wide world of Google. Recent announcements suggest that the impressive advances seen in Google DeepMind AlphaGo's comprehensive defeat of Go world champion, Lee Se-dol, are only the first step on the journey. Until that moment, AI experts expected that it would still take some years before AI could outwit a world champion.

Jason Les, Dong Kim, Daniel McAulay and Jimmy Chou -- are vying for shares of a $200,000 prize purse. The ultimate goal for CMU computer scientists, as it was in the first Brains Vs. AI contest at Rivers Casino in 2015, is to set a new benchmark for artificial intelligence. 'Since the earliest days of AI research, beating top human players has been a powerful measure of progress in the field,' said Tuomas Sandholm, professor of computer science. 'That was achieved with chess in 1997, with Jeopardy! in 2009 and with the board game Go just last year.