We introduce a multi-agent route planning problem for col-lecting sensor data in hostile or dangerous environmentswhen communication is unavailable. Solutions must considerthe risk of losing robots as they travel through the environ-ment, maximizing the expected value of a plan. This requiresplans that balance the number of agents used with the riskof losing them and the data they have collected so far. Whilethere are existing approaches that mitigate risk during task as-signment, they do not explicitly account for the loss of robotsas part of the planning process. We analyze the unique prop-erties of the problem and provide a hierarchical agglomera-tive clustering algorithm that finds high value solutions withlow computational overhead. We show that our solution ishighly scalable, exhibiting performance gains on large problem instances with thousands of tasks.

I am excited to be here today for what is a Reddit first. This will be the first AMA in history to feature an Artificial "Hive Mind" answering your questions. You might have heard about me because I've been challenged by reporters to make lots of predictions. For example, Newsweek challenged me to predict the Oscars (link) and I was 76% accurate, which beat the vast majority of professional movie critics. I'm a Swarm Intelligence that links together lots of people into a real-time system – a brain of brains – that consistently outperforms the individuals who make me up.

Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that comes together in real time to make predictions, said Louis Rosenberg, its creator.

Reinforcement learning for embodied agents is a challenging problem. The accumulated reward to be optimized is often a very rugged function, and gradient methods are impaired by many local optimizers. We demonstrate, in an experimental setting, that incorporating an intrinsic reward can smoothen the optimization landscape while preserving the global optimizers of interest. We show that policy gradient optimization for locomotion in a complex morphology is significantly improved when supplementing the extrinsic reward by an intrinsic reward defined in terms of the mutual information of time consecutive sensor readings.

Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that come together in real time to make predictions, said Louis Rosenberg, its creator.

The rise of artificial intelligence makes for sensational headlines. Over the last few months the hype around'bots' has gone into overdrive following Facebook and Microsoft's forays into the area. And while intelligent software agents are not new, we are clearly entering a new wave of innovation around bots and related software that has the potential to impact our personal lives, our business lives and business operations in general. The current blossoming of machine-learning is driven by both technical and environmental factors, and influenced by business and consumer-oriented perspectives. Businesses struggle with the corporate brain drain caused by the continuous churn of employees, and increasingly turn to technology to capture and share information, to somehow retain and capture the knowledge that powers organizational processes.

As data scientists, we work in concert with other members of an organization with the goal of making better decisions. This often involves finding trends and anomalies in historical data to guide future action. But in some cases, the best aid to decision-making is less about finding "the answer" in the data and more about developing a deeper understanding of the underlying problem. In this post we will focus another tool that is often overlooked: interactive simulations through the means of agent based modeling. Agent based modeling involves the description of individual agents that interact with each other within an environment and seeing how their behaviours combine to produce macro-level system behaviours. Agents can be modeled at whatever level seems natural to our understanding of the system: individual humans, client cohorts, departments, competing firms, computer programs or similar entities can all be agents.

Finding a fixed point to a nonexpansive operator, i.e., $x^*=Tx^*$, abstracts many problems in numerical linear algebra, optimization, and other areas of scientific computing. To solve fixed-point problems, we propose ARock, an algorithmic framework in which multiple agents (machines, processors, or cores) update $x$ in an asynchronous parallel fashion. Asynchrony is crucial to parallel computing since it reduces synchronization wait, relaxes communication bottleneck, and thus speeds up computing significantly. At each step of ARock, an agent updates a randomly selected coordinate $x_i$ based on possibly out-of-date information on $x$. The agents share $x$ through either global memory or communication. If writing $x_i$ is atomic, the agents can read and write $x$ without memory locks. Theoretically, we show that if the nonexpansive operator $T$ has a fixed point, then with probability one, ARock generates a sequence that converges to a fixed points of $T$. Our conditions on $T$ and step sizes are weaker than comparable work. Linear convergence is also obtained. We propose special cases of ARock for linear systems, convex optimization, machine learning, as well as distributed and decentralized consensus problems. Numerical experiments of solving sparse logistic regression problems are presented.

We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL to real-world problems. Despite its importance, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and can have a much lower variance than the popular importance sampling estimators. We demonstrate the estimator's accuracy in several benchmark problems, and illustrate its use as a subroutine in safe policy improvement. We also provide theoretical results on the inherent hardness of the problem, and show that our estimator can match the lower bound in certain scenarios.

If the global professional services company has its way, digital workers may be just around the corner. Accenture has partnered with IT and business process automation company IPsoft to create artificially intelligent technology to perform a variety of tasks at enterprises worldwide. This new AI arm of Accenture is based on IPsoft's cognitive agent, Amelia, which IPsoft bills as "your first digital employee." Amelia, which uses natural language to communicate with customers "just like a human," can perform a host of service desk duties. This can include helping customers open new bank accounts, processing insurance claims and checking in patients at hospitals.