Microsoft announced AI-focused Power Platform products at its Microsoft Ignite 2021 conference, which kicked off in earnest today. Among the highlights is Power Automate Desktop for Windows 10 users, a robotic process automation service (RPA) that automates tasks within Windows across various apps. New Power Virtual Agents features were also unveiled. RPA -- technology that automates monotonous, repetitive chores traditionally performed by human workers -- is big business. Forrester estimates that RPA and other AI subfields created jobs for 40% of companies in 2019 and that a tenth of startups now employ more digital workers than human ones.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. J.J. Watt has apparently found his team new: the Arizona Cardinals. Watt tweeted a picture of himself working out in a Cardinals shirt, signaling that he will join the team for the 2021 season. Watt agreed to a two-year deal worth $31 million, ESPN reported.
From March 1 to April 4, 2020, the Illinois Department of Employment Security received 513,173 unemployment claims -- more than the entire number of claims filed in 2019. It was impossible for IDES employees to handle this volume, resulting in many disconnected phone calls and unanswered online queries. Gov. J.B. Pritzker called for increased call center capacity, in large part through the implementation of new technologies to help employees handle the volume of queries. Gov. Pritzker wanted to minimize dropped calls and deliver a response to all online queries so citizens could receive the benefits they needed. This new technology, virtual intelligent agents, alleviated overburdened human agents from having to respond to every inquiry that came in.
Yoshua Bengio is one of the world's leading experts in artificial intelligence and deep learning. Also known as the father of deep learning, he says that for the world to change for the better with AI, a global shift in how organizations and governments share their research needs to come. In many countries, private companies, government entities, and academic institutions conduct AI research. These places must foster a global culture of open science. These research places the need to rethink how to encourage the development of impactful artificial intelligence.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russell Wilson still wants to play for the Seattle Seahawks, his agent said Thursday amid a new report detailing a potential growing fracture between the two sides. Wilson's agent Mark Rodgers told ESPN if there was a trade coming down the line Wilson would only want to play for a handful of teams. Rodgers told ESPN that Wilson's trade list would include the Dallas Cowboys, New Orleans Saints, Las Vegas Raiders and Chicago Bears.
This tutorial provides a simple introduction to using multi-agent reinforcement learning, assuming a little experience in machine learning and knowledge of Python. Reinforcement stems from using machine learning to optimally control an agent in an environment. It works by learning a policy, a function that maps an observation obtained from its environment to an action. Policy functions are typically deep neural networks, which gives rise to the name "deep reinforcement learning." The goal of reinforcement learning is to learn an optimal policy, a policy that achieves the maximum expected reward from the environment when acting.
Artificial general intelligence, the idea of an intelligent A.I. agent that's able to understand and learn any intellectual task that humans can do, has long been a component of science fiction. As A.I. gets smarter and smarter -- especially with breakthroughs in machine learning tools that are able to rewrite their code to learn from new experiences -- it's increasingly widely a part of real artificial intelligence conversations as well. But how do we measure AGI when it does arrive? Over the years, researchers have laid out a number of possibilities. The most famous remains the Turing Test, in which a human judge interacts, sight unseen, with both humans and a machine, and must try and guess which is which.
Multi-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems. The interactions between entities / components can give rise to very complex behavior patterns at the level of both individuals and the multi-agent system as a whole. Since usually only the trajectories of individual entities are observed without any knowledge of the underlying interaction patterns, and there are usually multiple possible modalities for each agent with uncertainty, it is challenging to model their dynamics and forecast their future behaviors. We introduce a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses.
For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs.