The world as we know it is moving towards machines -- big time. But we cannot fully utilize the working of any machine without a lot of human interaction. In order to do that, we need some kind of intelligence for the machines. This is where artificial intelligence comes in. It is the concept of machines being smart enough to carry out numerous tasks without any human intervention.

Several Texas A&M researchers are working on various innovative applications for Artificial Intelligence (AI), intelligent systems, machine learning and natural language processing technologies. For instance, computer engineering assistant professor Zhangyang "Atlas" Wang said his group works broadly in the general fields of computer vision, machine learning and multimedia signal processing. "My research is mainly focused on Artificial Intelligence -- machine learning or deep learning -- for disease diagnostics, to be more specific, use computational method to predict Alzheimer's disease," computer science Ph.D. candidate Ye Yuan, who works with Wang, said. "Other projects in our group include action recognition & privacy preserving, pixelated image abstraction, optimization algorithms for neural network design, etc." The end goal of the research, according to Yuan, is to provide a better AI algorithm for detecting and classifying data such as medical scans.

Recent methods in artificial intelligence enable AI software to produce rich and creative digital artifacts such as text and images painted from scratch. One technique used in creating these artifacts are generative adversarial networks (GANs). Generative adversarial networks are a recent breakthrough in machine learning. Initially proposed by Ian Goodfellow and colleagues at the University of Montreal at NIPS 2014, the GAN approach enables the specification and training of rich probabilistic deep learning models using standard deep learning technology. Allowing for flexible probabilistic models is important in order to capture rich phenomena present in complex data.

Artificial Intelligence (AI) has certainly been one of the hero buzz terms in digital advertising in 2017, with plenty jumping on the bandwagon, but also plenty more investing significant money and resource into what is tipped to shape the future of the industry. In a series of features reflecting on the past year and looking ahead to what we can expect in 2018, ExchangeWire invite over 100 thought leaders from across the industry to share their views. In the latest installment of the series, experts predict what we should expect from AI in 2018. "Marketers will have to start thinking seriously about deep learning. AI was the buzzword of 2017, but next year everyone will be focusing on what AI really can do. Marketers are now digitally-savvy enough to ask tough questions about what deep learning is and what it can do on a high level, and will be informed enough to see the potential for their industry. As more advanced forms of AI become market-ready, deep learning will be at the forefront of any of those conversations, and marketers will begin to understand the value of using deep learning to buy media and create audiences to advertise against."

A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online December 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, MD, PhD, from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.

Deep Learning ultimately is about finding a minimum that generalizes well -- with bonus points for finding one fast and reliably. Our workhorse, stochastic gradient descent (SGD), is a 60-year old algorithm (Robbins and Monro, 1951) [1], that is as essential to the current generation of Deep Learning algorithms as back-propagation. Different optimization algorithms have been proposed in recent years, which use different equations to update a model's parameters. Adam (Kingma and Ba, 2015) [18] was introduced in 2015 and is arguably today still the most commonly used one of these algorithms. This indicates that from the Machine Learning practitioner's perspective, best practices for optimization for Deep Learning have largely remained the same.

You don't have to agree with Elon Musk's apocalyptic fears of artificial intelligence to be concerned that, in the rush to apply the technology in the real world, some algorithms could inadvertently cause harm. This type of self-learning software powers Uber's self-driving cars, helps Facebook identify people in social-media posts, and let's Amazon's Alexa understand your questions. Now DeepMind, the London-based AI company owned by Alphabet Inc., has developed a simple test to check if these new algorithms are safe. Researchers put AI software into a series of simple, two-dimensional video games composed of blocks of pixels, like a chess board, called a gridworld. It assesses nine safety features, including whether AI systems can modify themselves and learn to cheat.

The Christmas season holds a special place in my heart. Not because I enjoy cold weather. And certainly not because I relish the taste of eggnog (the consistency alone makes my stomach turn). Instead, Christmas means a lot to me because of my dad. As I mentioned in a post a few weeks ago, I had a particularly rough childhood. There was a lot of mental illness in my family.

Bike sharing is a vital piece in a modern multi-modal transportation system. However, it suffers from the bike unbalancing problem due to fluctuating spatial and temporal demands. Accurate bike sharing demand predictions can help operators to make optimal routes and schedules for bike redistributions, and therefore enhance the system efficiency. In this study, we propose a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model to predict station-level hourly demands in a large-scale bike-sharing network. With each station as a vertex in the network, the new proposed GCNN-DDGF model is able to automatically learn the hidden correlations between stations, and thus overcomes a common issue reported in the previous studies, i.e., the quality and performance of GCNN models rely on the predefinition of the adjacency matrix. To show the performance of the proposed model, this study compares the GCNN-DDGF model with four GCNNs models, whose adjacency matrices are from different bike sharing system matrices including the Spatial Distance matrix (SD), the Demand matrix (DE), the Average Trip Duration matrix (ATD) and the Demand Correlation matrix (DC), respectively. The five types of GCNN models and the classic Support Vector Regression model are built on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNN-DDGF model has the lowest Root Mean Square Error, followed by the GCNN-DC model, and the GCNN-ATD model has the worst performance. Through a further examination, we find the learned DDGF captures some similar information embedded in the SD, DE and DC matrices, and it also uncovers more hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.

We develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities to form an objective function that isolates the heterogeneous treatment effects, and then optimize the learned objective. This approach has several advantages over existing methods. From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice, e.g., penalized regression, a deep net, or boosting; moreover, these methods can be fine-tuned by cross-validating on the learned objective. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property, whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these nuisance components. We implement variants of our method based on both penalized regression and convolutional neural networks, and find promising performance relative to existing baselines.