Could AWS Be the Right Choice for IoT and Machine Learning? 'Hark' the herald robots sing H5N6 Bird Flu Strain Spreads to Be S. Korea's Worst AI Outbreak Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

The rules of the game called psychological science.

Louis Rosenberg thinks he has found a way to make us all a lot smarter. Rosenberg runs a Silicon Valley startup called Unanimous AI, which has built a tool to support human decision-making by crowdsourcing opinions online. It lets hundreds of participants respond to a question all at once, pooling their collective insight, biases and varying expertise into a single answer. Since launching in June, Unanimous AI has registered around 50,000 users and answered 230,000 questions. Rosenberg thinks this hybrid human-computer decision-making machine – once dubbed an'artificial' artificial intelligence – could help us tackle some of the world's toughest questions.

Survival: "Our entire existence is based on having the best detection mechanism possible," says Valerie Bannert-Thurner at Nasdaq. Certain things make Valerie Bannert-Thurner raise an eyebrow when looking for signs of bad behavior on the Nasdaq exchange. "I like the example of excessive cheering because the guys just can't help themselves but cheer," said Bannert-Thurner, who is senior vice president and head of risk and surveillance at Nasdaq. Another worrisome indicator is seemingly too-good-to-be-true trading profits. "If people are excessively profitable given how they trade and in comparison to everybody else trading the same instruments with similar styles, then we ask, is this luck or something else?" Bannert-Thurner said.

Our society went from an agrarian economy to mass-producing affordable goods using steam power, electricity and, eventually, computers and automation. We've gone from the horse and buggy to the Model T, and now we're on to self-driving cars! The smart factories of the Industry 4.0 era will be powered by the internet of things, cloud computing and cyber-physical systems (CPS) technologies. Cyber-physical systems are powered by enabling cloud technologies which allow intelligent objects and cloud-based programmatic modules to communicate and interact with each other. These new cyber-physical manufacturing facilities use robotics, sensors, big data, automation, artificial intelligence, virtual reality, augmented reality, additive manufacturing, cybersecurity systems and other cutting-edge technologies to deliver unprecedented flexibility, precision and efficiency to the manufacturing process.

UK-based VC David Kelnar wrote an excellent primer on Artificial Intelligence that is a relatively quick read and helps explain the technology and its advancement over the past sixty years since the term was coined in the mid 1950s. I like this chart which explains the relationship between AI, machine learning, and deep learning. But my favorite part of David's post is his explanation of why AI has taken off in the past five years, as this chart shows: Like most non-linear curves, it is not one thing, but a number of things happening simultaneously, that is causing this explosion of interest. Research is constantly coming up with better ways to train models and machines. The same chips that make graphics come alive on your screen are used to train models, and these chips are improving rapidly.

The groundbreaking machine is currently under development from Italian researchers who hope to mimic the human learning experience. The hopes of the scientists are that the robot will develop and learn from nothing – much like a new-born child would. The project is being led by Goal Robots in Italy, who hope to have the first learnable robot within four years. Gianluca Baldassarre, from the Institute of Cognitive Sciences and Technologies of the National Research Council (CNR-Istc), who is co-ordinating the project, said: "The goal is to make robots that are able to learn independently, experimenting and making experiences exactly like young children, between one and two years old, and then apply their new acquired skills."

The Perona-Malik model has been very successful at restoring images from noisy input. In this paper, we reinterpret the Perona-Malik model in the language of Gaussian scale mixtures and derive some extensions of the model. Specifically, we show that the expectation-maximization (EM) algorithm applied to Gaussian scale mixtures leads to the lagged-diffusivity algorithm for computing stationary points of the Perona-Malik diffusion equations. Moreover, we show how mean field approximations to these Gaussian scale mixtures lead to a modification of the lagged-diffusivity algorithm that better captures the uncertainties in the restoration. Since this modification can be hard to compute in practice we propose relaxations to the mean field objective to make the algorithm computationally feasible. Our numerical experiments show that this modified lagged-diffusivity algorithm often performs better at restoring textured areas and fuzzy edges than the unmodified algorithm. As a second application of the Gaussian scale mixture framework, we show how an efficient sampling procedure can be obtained for the probabilistic model, making the computation of the conditional mean and other expectations algorithmically feasible. Again, the resulting algorithm has a strong resemblance to the lagged-diffusivity algorithm. Finally, we show that a probabilistic version of the Mumford-Shah segementation model can be obtained in the same framework with a discrete edge-prior.

Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1. Here we consider the case that the weight determining the acceptance probability itself is fluctuating. This situation is common in many numerical studies. We show that it is possible to construct a rigorous Monte Carlo algorithm that visits points in state space with a probability proportional to their average weight. The same approach has the potential to transform the methodology of a certain class of high-throughput experiments or the analysis of noisy datasets.

Hierarchy Of Multi-label classifiers (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and secondly employing a given base multi-label classifier (MLC) to the resulting sub-problems. The primary goal is to effectively address class imbalance and scalability issues that often arise in real-world multi-label classification problems. In this work, we present the general setup for a HOMER model and a simple extension of the algorithm that is suited for MLCs that output rankings. Furthermore, we provide a detailed analysis of the properties of the algorithm, both from an aspect of effectiveness and computational complexity. A secondary contribution involves the presentation of a balanced variant of the k means algorithm, which serves in the first step of the label hierarchy construction. We conduct extensive experiments on six real-world datasets, studying empirically HOMER's parameters and providing examples of instantiations of the algorithm with different clustering approaches and MLCs, The empirical results demonstrate a significant improvement over the given base MLC.