The biggest threat to jobs might not be physical robots, but intelligent software agents that can understand our questions and speak to us, integrating seamlessly with all the other programs we use at home and at work. And call centres are particularly at risk. Last week we learned that British retail giant Marks & Spencer is moving 100 switchboard staff to other roles because chatbots are taking over their duties. "All calls to 640 M&S stores and contact centres now handled via Twilio-powered technology," boasted the California-based tech company operating the new system. M&S is now using Twilio's speech recognition software and Google's Dialogflow artificial intelligence (AI) tool to transcribe customers' verbal requests and understand their intent.

This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots. Two studies were performed to investigate whether each approach affects accomplishment of tasks and collaboration costs. Participants interacted with two equivalent financial advice chatbot systems, one composed of a single conversational adviser and the other based on a team of four experts chatbots. Results indicated that users had different forms of experiences but were equally able to achieve their goals. Contrary to the expected, there were evidences that in the teamwork situation that users were more able to predict agent behavior better and did not have an overhead to maintain common ground, indicating similar collaboration costs. The results point towards the feasibility of either of the two approaches for user collaboration with conversational agents.

We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the reward signal is the diversity of effects that each strategy produces. We test the method empirically on a simulated planar robotic arm, and establish that the method is both able discriminate between strategies of dissimilar quality, even when the differences are tenuous, and that the resulting performance is competitive with the best fixed mixture of strategies.

In October 2017, Google's Machine Perception Research organization announced the release of a large segments designed you-tube video series for AI, named AVA,"atomic visual actions" to help neural networks better train abilities to recognize and interpret human behavior [1]. In a process further described in a corresponding research paper on the project, AVA is derived from long-from video content sourced directly from films and television series featured on YouTube, chosen to reflect a wide variety of human actions as well as a diversity in the race and genders of the humans present in the clips. AVA contains the range of 210,000 actions includes videos of people walking, kicking, hugging, kissing, shaking hands. AVA database contains 57,600 clips of three-second clip short videos, for the tech giant's AI droids to watch and learn from [2] . They believe having AI agents watch thousands of videos of human behavior will help them understand "what humans are doing, what they might do next and what they are trying to achieve".

Abstract--This work proposes an engine for the Creation Of Novel Adventure Narrative (CONAN), which is a procedural quest generator. It uses a planning approach to story generation. The engine is tested on its ability to create quests, which are sets of actions that must be performed in order to achieve a certain goal, usually for a reward. The engine takes in a world description represented as a set of facts, including characters, locations, and items, and generates quests according to the state of the world and the preferences of the characters. We evaluate quests through the classification of the motivations behind the quests, based on the sequences of actions required to complete the quests. We also compare different world descriptions and analyze the difference in motivations for the quests produced by the engine. Compared against human structural quest analysis, the current engine was found to be able to replicate the quest structures found in commercial video game quests. The creation of media content has always been the domain of humans, be it for movies, music or video games. With advancement in computer technology and research, the creation of such content has seen a slight shift from the human authored to automatic computer generation. Using algorithms to procedurally create media can effectively alleviate some of the burden from artists when creating a new piece. A. Procedural Generation in Games Procedural Content Generation for Games (PCG-G) is the use of computers algorithms to generate game content, determine if it is interesting, and select the best ones on behalf of the players.[1] This type of generation becomes quite useful when trying to produce content for an industry that is more and more demanding in terms of content [1]. For instance, in the current market, game development costs are extremely high as the demand for highly complex games requires the work of many artists and many hours to be met. For instance, the Massively Multiplayer Online Role Playing Game (MMORPG) World of Warcraft has a total of 30,000 items, 5,300 creatures with which to interact and 7600 quests and has an estimated budget of twenty to one hundred and fifty million dollars for a single game [1].

It all looks strangely familiar, likely because it stood in for the planet Vulcan in Star Trek III: The Search for Spock. That feels rather apt, as I'm here to interview Professor Justin Li, who is building AI agents that will hopefully, like Spock, work alongside us humans. If that sounds too abstract, here's a real-life scenario. Imagine you've kept a digital diary for a decade. Into this repository you've dumped everything you've felt, done, achieved, and thought.

My son Max, the Director of Sport Science at Resilient Code and Chief Science Officer at Exsurgo Technologies, has turned into quite an analytics nerd (check him out on twitter at @strong_science, but he saves his good stuff for Instagram where you can follow him at "strong_by_science"). Max and Resilient Code co-founder Dr. Dustin Nabhan have been educating me on the use of Agent-based Models (ABM) as a technique to predict and prevent injuries in athletes, especially high-caliber professional athletes. Unlike the weekend warrior like myself, preventing career-ending injuries can translate into tens of millions of dollars of additional income for professional athletes[1].

This article will introduce you to a range of introductory concepts used in artificial intelligence for games (or'Game AI' for short) so that you can understand what tools are available for approaching your AI problems, how they work together, and how you might start to implement them in the language or engine of your choice. We're going to assume you have a basic knowledge of video games, and some grasp on mathematical concepts like geometry, trigonometry, etc. Most code examples will be in pseudo-code, so no specific programming language knowledge should be required. Game AI is mostly focused on which actions an entity should take, based on the current conditions. This is what the traditional AI literature refers to as controlling'intelligent agents' where the agent is usually a character in the game – but could also be a vehicle, a robot, or occasionally something more abstract such as a whole group of entities, or even a country or civilization. In each case it is a thing that ...

With advances in artificial intelligence impacting every industry from healthcare to retail, it's no wonder people are scared. After all, these pesky machines can already perform a great many tasks better than us humans and it's only going to get worse. I'm not just talking about replacing mindless busywork like sorting mail and processing tax returns – I'm talking about AI systems taking on complex jobs like forecasting financial markets, diagnosing medical patients, even making optimized hiring decisions, and doing it all better than highly trained humans. Consider the field of radiology. To become a practicing radiologist in the US, an aspiring doctor must devote 4 years to undergraduate education, another 4 years to medical school and a final 4 years to a radiology residency program.

On the surface, preventing injuries to professional-caliber athletes would seem to have little in common with preventing operational failures for a machine (i.e., autonomous vehicle, locomotive, airplane, CT Scan). However, both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness. My son Max, the Director of Sport Science at Resilient Code and Chief Science Officer at Exsurgo Technologies, has turned into quite an analytics nerd (check him out on twitter at @strong_science, but he saves his good stuff for Instagram where you can follow him at "strong_by_science"). Max and Resilient Code co-founder Dr. Dustin Nabhan have been educating me on the use of Agent-based Models (ABM) as a technique to predict and prevent injuries in athletes, especially high-caliber professional athletes. Unlike the weekend warrior like myself, preventing career-ending injuries can translate into tens of millions of dollars of additional income for professional athletes[1]. Here is the conversation with Max that got me thinking more about the similarities between ABM and Digital Twins (Max's input is in drab, boring grey and mine is in cool, hip blue): Okay, probably a boring conversation for most families, but now you know what we talk about when we go out to eat! Yea, you don't wanna sit next to us… During these conversations with Max and Dustin, I was struck by the similarities in using ABM to prevent injuries in the same way that we use Digital Twins (or what we call Asset Avatars) to prevent machine breakdowns, failures, under-performance and unplanned downtime.