The virtual workforce is no longer a concept of the future or a growing trend: It's a reality right now. Companies that are looking to deliver the highest level of customer care must be able to recruit the best talent without being restricted to one geographic location, which makes security a top concern. However, technology combined with data-encrypting best practices have transformed our ability to keep remote employees as secure as their brick-and-mortar counterparts. At the same time, it's worth noting that not all solutions are created equal. If you are in the market for work-at-home customer care providers, ensure these seven security measures are in place.

We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is partially observed, and the unobserved part comes after some delay. We assume that an agent, once left, cannot re-enter the system. Therefore, the job of the system is to provide an estimated score for the agent based on her instantaneous score and possibly some inference of the instantaneous score over the delayed score. In this paper, we estimate the delayed context via an online convex game between the agent and the system. We argue that the error in the score estimate accumulated over $T$ iterations is small if the regret of the online convex game is small. Further, we leverage side information about the delayed context in the form of a correlation function with the known context. We consider the settings where the delay is fixed or arbitrarily chosen by an adversary. Furthermore, we extend the formulation to the setting where the contexts are drawn from some Banach space. Overall, we show that the average penalty for not knowing the delayed context while making a decision scales with $\mathcal{O}(\frac{1}{\sqrt{T}})$, where this can be improved to $\mathcal{O}(\frac{\log T}{T})$ under special setting.

Applications of Artificial Intelligence in Business Posted: 28th June 2018 08:22 In May, Google demonstrated the ability of its artificial intelligent (AI) agent Duplex to have an actual conversation with real life people. It demonstrated it could book a hair appointment but struggled with a more nuanced conversation when attempting to make a restaurant reservation. Whilst there is a lot of hype around AI and a lot of work to be done before an agent passes the Turing Test, the impact AI is having on business should not be underestimated. Voice controlled digital assistants and facial recognition in smart phones are just the beginning. Research firm Tractica estimates that global AI enterprise software revenue will grow from $644 million in 2016 to nearly $39 billion by 2025.

Agent based simulation of social organizations, via the investigation of agents' training and learning tactics and strategies, has been inspired by the ability of humans to learn from social environments which are rich in agents, interactions and partial or hidden information. Such richness is a source of complexity that an effective learner has to be able to navigate. This paper focuses on the investigation of the impact of the environmental complexity on the game playing-and-learning behavior of synthetic agents. We demonstrate our approach using two independent turn-based zero-sum games as the basis of forming social events which are characterized both by competition and cooperation. The paper's key highlight is that as the complexity of a social environment changes, an effective player has to adapt its learning and playing profile to maintain a given performance profile

AI agents continue to rack up wins in the video game world. Last week, OpenAI's bots were playing Dota 2; this week, it's Quake III, with a team of researchers from Google's DeepMind subsidiary successfully training agents that can beat humans at a game of capture the flag. As we've seen with previous examples of AI playing video games, the challenge here is training an agent that can navigate a complex 3D environment with imperfect information. DeepMind's researchers used a method of AI training that's also becoming standard: reinforcement learning, which is basically training by trial and error at a huge scale. Agents are given no instructions on how to play the game, but simply compete against themselves until they work out the strategies needed to win. Usually this means one version of the AI agent playing against an identical clone.

Above: four of our trained agents play together on an indoor and outdoor procedurally generated Capture the Flag level. Billions of people inhabit the planet, each with their own individual goals and actions, but still capable of coming together through teams, organisations and societies in impressive displays of collective intelligence. This is a setting we call multi-agent learning: many individual agents must act independently, yet learn to interact and cooperate with other agents. This is an immensely difficult problem - because with co-adapting agents the world is constantly changing. To investigate this problem we look at 3D first-person multiplayer video games.

For Hollywood, AI is a somewhat nuanced boogie-man. Movies like Terminator and She propose a generally intelligent agent capable of being more human than human, at least in certain circumstances. The reality of AI thankfully falls far short. The mundanity of current human/AI interactions doesn't diminish the need for the engineers and designers of these systems to give some thought to human interactions. I recognize this obscures a lot of complexity but it's not really relevant here).

The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed bandit framework for matching incentives to users, whose preferences are unknown a priori and evolving dynamically in time, in a resource constrained environment. We design an algorithm that combines ideas from three distinct domains: (i) a greedy matching paradigm, (ii) the upper confidence bound algorithm (UCB) for bandits, and (iii) mixing times from the theory of Markov chains. For this algorithm, we provide theoretical bounds on the regret and demonstrate its performance via both synthetic and realistic (matching supply and demand in a bike-sharing platform) examples.

We study the problem of planning collision-free paths for a group of homogeneous robots. We propose a novel approach for turning the paths that were planned egocentrically by the robots, e.g. without taking other robots' moves into account, into collision-free trajectories and evaluate it empirically. Suggested algorithm is much faster (up to one order of magnitude) than state-of-the-art but this comes at the price of notable drop-down of the solution cost.

The team focused on a capture the flag mode, one in which the map changes from match to match. Its AI agents had to learn general strategies to be able to adapt to each new map, something humans do easily. The agents also needed to both cooperate with team members as well as compete against the opposite team, and be able to adjust to different enemy play styles. "Our agents must learn from scratch how to see, act, cooperate, and compete in unseen environments, all from a single reinforcement signal per match: whether their team won or not," wrote the researchers in a blog post. They trained a population of AI-powered agents that learn by playing the game, much like we do.