American consumers believe that artificial intelligence (AI) will provide solutions to complex societal problems such as climate change, according to a new survey. Sixty-three percent of U.S. consumers believe AI will help solve complex problems, and 59 percent say it will contribute to a more fulfilling life, according to a PwC survey released Tuesday. Majorities of consumers believe AI will help tackle major issues like climate change, global health, economic growth and cancer. Nearly seven in 10 see it producing solutions for cybersecurity and privacy, and six in 10 say the same for personal financial security and fraud. Thirty-one percent also believe AI will help with gender equality issues.

Companies from Singapore to Finland are racing to improve artificial intelligence so software can automatically spot and block videos of grisly murders and mayhem before they go viral on social media. None, so far, claim to have cracked the problem completely. A Thai man who broadcast himself killing his 11-month-old daughter in a live video on Facebook this week, was the latest in a string of violent crimes shown live on the social media company. The incidents have prompted questions about how Facebook's reporting system works and how violent content can be flagged faster. A dozen or more companies are wrestling with the problem, those in the industry say.

The "Learn SQL the Hard Way" and "SQL Problems & Solutions" are definitely worth looking in to. If you're looking for something slightly more fun and interactive, try GalaXQL. GalaXQL is a visual platform, offering lessons on SQL in a database of fictional galaxies. The galaxy rendering reflects the changes you make in the database.

Leading provider of aerial imagery and data analytics expands data extraction capabilities for local government, insurance and infrastructure sectors. Bothell, WA (April 26, 2017) – EagleView, the leading provider of aerial imagery and data analytics for government and commercial industries, is proud to announce the acquisition of OmniEarth, developer of machine learning technologies and decision-making tools for the water resource management, energy and insurance markets. With this acquisition, EagleView gains OmniEarth's machine learning capabilities, resulting in higher accuracy and precision of existing automated datasets. OmniEarth's ability to extract data from geospatial imagery will enhance EagleView's property reports and Pictometry imagery classification of land areas such as impervious surfaces or irrigated farmland. It will also better identify roof shape and condition, tree overhang, decks, pools and other notable property features.

Information contained on this page is provided by an independent third-party content provider. If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com Joint customer Insight implements Mintigo's Predictive Analytics and Artificial Intelligence to deliver actionable sales intelligence into SAP C4C CRM Mintigo, the leader of predictive marketing and sales technology, today announced its new solution for infusing actionable sales intelligence into SAP's C4C. Mintigo's innovation is relevant to a potential demographic of businesses, who have never before been able to harness the true potential of predictive marketing for sales to increase their revenue. This unique integration, will allow SAP C4C and SAP Hybris Marketing users, such as Insight, to empower their sales organization with intelligent client profile information that facilitates more meaningful conversations with prospects and customers. Historically, enterprises using SAP have had a difficult time integrating relevant client information provided by machine learning tools.

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using dont cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

This paper explores the Coevolutionary Optional Prisoner's Dilemma (COPD) game, which is a simple model to coevolve game strategy and link weights of agents playing the Optional Prisoner's Dilemma game. We consider a population of agents placed in a lattice grid with boundary conditions. A number of Monte Carlo simulations are performed to investigate the impacts of the COPD game on the emergence of cooperation. Results show that the coevolutionary rules enable cooperators to survive and even dominate, with the presence of abstainers in the population playing a key role in the protection of cooperators against exploitation from defectors. We observe that in adverse conditions such as when the initial population of abstainers is too scarce/abundant, or when the temptation to defect is very high, cooperation has no chance of emerging. However, when the simple coevolutionary rules are applied, cooperators flourish.

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.

Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard Metropolis-Hastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the Distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, the localization problem in a wireless sensor network and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided.

We address the problem of parameter estimation in models of systems biology from noisy observations. The models we consider are characterized by simultaneous deterministic nonlinear differential equations whose parameters are either taken from in vitro experiments, or are hand-tuned during the model development process to reproduces observations from the system. We consider the family of algorithms coming under the Bayesian formulation of Approximate Bayesian Computation (ABC), and show that sensitivity analysis could be deployed to quantify the relative roles of different parameters in the system. Parameters to which a system is relatively less sensitive (known as sloppy parameters) need not be estimated to high precision, while the values of parameters that are more critical (stiff parameters) need to be determined with care. A tradeoff between computational complexity and the accuracy with which the posterior distribution may be probed is an important characteristic of this class of algorithms.