Agents


The future of border security is a Artificial Intelligence powered Lie- Detector Kiosks - IncubateIND Media

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The U.S Department of Homeland Security had funded a research approximately 6 years ago of the virtual border agent technology, better known as AVATAR (Automated Virtual Agent for Truth Assessments in Real Time) and had tested it at the U.S Mexico border on travelers voluntarily. Canada and EU has also tested the robot like kiosk that is asking travelers a series of questions. If the trend continues, International Travelers could be speaking with kiosk to determine if they are lying on any aspect at an airport or border crossings. The technology can also be used to screen the refugees and unwanted travelers travelling to any country. It can also be used to screen the citizenship applications, processing visas and many other such inter-related services.


Emergence of coexisting ordered states in active matter systems

Science

Active systems can produce a far greater variety of ordered patterns than conventional equilibrium systems. In particular, transitions between disorder and either polar- or nematically ordered phases have been predicted and observed in two-dimensional active systems. However, coexistence between phases of different types of order has not been reported. We demonstrate the emergence of dynamic coexistence of ordered states with fluctuating nematic and polar symmetry in an actomyosin motility assay. Combining experiments with agent-based simulations, we identify sufficiently weak interactions that lack a clear alignment symmetry as a prerequisite for coexistence.


Agilox Robots Rely on Swarm Intelligence

Forbes Technology

I talked to Dirk Erlacher, the CEO of Agilox, on this topic. Austrian headquartered Agilox designs and manufactures mobile logistics robots that use "swarm intelligence" to intelligently navigate through warehouses and factories, delivering pallets and totes where they are needed. A mobile logistics robot (MLR) is a more advanced form of an automatic guided vehicle (AGV); AGVs are used to reduce labor by taking over tasks that were traditionally performed with fork lifts. More complex AGVs have fleet management software. This software makes sure that not too many AGVs are in the same aisles, decides which AGV has the right of way at crossings, and in more complex scenarios, decides which unit will be used to complete a particular task and how it will navigate through the facility.


Modern Game Theory and Multi-Agent Reinforcement Learning Systems

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Most artificial intelligence(AI) systems nowadays are based on a single agent tackling a task or, in the case of adversarial models, a couple of agents that compete against each other to improve the overall behavior of a system. However, many cognition problems in the real world are the result of knowledge built by large groups of people. Take for example a self-driving car scenario, the decisions of any agent are the result of the behavior of many other agents in the scenario. Many scenarios in financial markets or economics are also the result of coordinated actions between large groups of entities. How can we mimic that behavior in artificial intelligence(AI) agents?


Predicting A Better Future With Swarm Intelligence Big Cloud Recruitment

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Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI – algorithms modelled on swarms in nature that amplifies human intelligence.


Seven Things To Look For In A Secure Work-At-Home Customer Care Provider

Forbes Technology

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.


Online Scoring with Delayed Information: A Convex Optimization Viewpoint

arXiv.org Machine Learning

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.


DeepMind's AI agents exceed 'human-level' gameplay in Quake III

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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.


Capture the Flag: the emergence of complex cooperative agents DeepMind

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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.


UX Design in the age of Machine Learning – Data Driven Investor – Medium

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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).