In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.

Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree boosting. We further combine these learned classifiers into an ensemble model and compare its performance to a modified pediatric early warning score (PEWS) baseline that relies on expert defined guidelines. To gauge model generalizability, we perform an inter-facility evaluation where we train our algorithm on data from one facility and perform evaluation on a hidden test dataset from a separate facility. We show that improvements are witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80 vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).

In this paper we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the number of observations are fewer than the number of parameters in the model. We show that with quadratic activations the optimization landscape of training such shallow neural networks has certain favorable characteristics that allow globally optimal models to be found efficiently using a variety of local search heuristics. This result holds for an arbitrary training data of input/output pairs. For differentiable activation functions we also show that gradient descent, when suitably initialized, converges at a linear rate to a globally optimal model. This result focuses on a realizable model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted weight coefficients.

Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent study respectively. This paper establishes formal connections between deep generative modeling approaches through a new formulation of GANs and VAEs. We show that GANs and VAEs are essentially minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to exchange ideas across research lines in a principled way. For example, we transfer the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism for leveraging generated samples. Quantitative experiments show generality and effectiveness of the imported extensions.

The manuscript titled "AlphaGo, deep learning, and the future of the human microscopist" in this month's issue of the Archives of Pathology & Laboratory Medicine1 describes the triumph of Google's (Mountain View, California) artificial intelligence (AI) program, AlphaGo, which beat the 18-time world champion of Go, an ancient Chinese board game far more complex than chess. The authors have hypothesized that the development of intuition and creativity combined with the raw computing of AI heralds an age where well-designed and well-executed AI algorithms can solve complex medical problems, including the interpretation of diagnostic images, thereby replacing the microscopist. Of note, in a prior work, the microscope was predicted to have a 75% chance of remaining in use for another 144 years.2 To support their hypothesis, the authors presented recent studies that compared the performance of nontraditional interpreters to those of experienced pathologists, in making accurate diagnoses (note: 1 author disclosed a significant financial interest in an AI company). One study examined the potential of using pigeons (yes, pigeons) for medical image studies,3 wherein the pigeons engaged in a matching game of completely benign and unambiguously malignant breast histology images.

In March of last year, Google's (Menlo Park, California) artificial intelligence (AI) computer program AlphaGo beat the best Go player in the world, 18-time champion Lee Se-dol, in a tournament, winning 4 of 5 games.1 At first glance this news would seem of little interest to a pathologist, or to anyone else for that matter. After all, many will remember that IBM's (Armonk, New York) computer program Deep Blue beat Garry Kasparov--at the time the greatest chess player in the world--and that was 19 years ago. The rules of the several-thousand-year-old game of Go are extremely simple. The board consists of 19 horizontal and 19 vertical black lines. Players take turns placing either black or white stones on vacant intersections of the grid with the goal of surrounding the largest area and capturing their opponent's stones.

Scientists in the US have developed a computer program called JAABA that uses machine learning to map groups of neurons responsible for the different behaviors observed in tiny fruit flies. The brain is a tangled mess of neurons that continues to mystify neurologists. Creating a detailed map allows scientists to learn the anatomy of the brain in the hopes that they can understand how behavior manifests neurologically. Drosophila melanogaster, or fruit flies, are a good place to start. The insects have a poppy seed-sized brain with 100,000 neurons compared to the 100 billion in human brains.

Infosys, which reported a consolidated net profit of Rs. 3,483 crore for the first quarter of fiscal 2017-18, has said it continues to help clients drive automation and innovation into the core of their businesses. The company's own artificial intelligence platform Nia, which it launched this year, is among the cutting-edge technologies it's focusing on. "We are training our existing employees for these new skills. Last quarter, we finished training 3,000 people on AI technologies," Chief Executive Officer Vishal Sikka said. "There is absolutely no slowdown in hiring in India," he said, when asked about the impact in the country due to the recruitment drive in global markets like the U.S. Mr. Sikka arrived for the media briefing in a driverless golf cart, a vehicle that has been indigenously developed at its Mysuru campus.

Automation, digital platforms, and other innovations are changing the fundamental nature of work. Understanding these shifts can help policy makers, business leaders, and workers move forward. The world of work is in a state of flux, which is causing considerable anxiety--and with good reason. There is growing polarization of labor-market opportunities between high- and low-skill jobs, unemployment and underemployment especially among young people, stagnating incomes for a large proportion of households, and income inequality. Migration and its effects on jobs has become a sensitive political issue in many advanced economies. And from Mumbai to Manchester, public debate rages about the future of work and whether there will be enough jobs to gainfully employ everyone.

IBM's Watson unit is receiving heat today in the form of a scathing equity research report from Jefferies' James Kisner. The group believes that IBM's investment into Watson will struggle to return value to shareholders. In recent years, IBM has increasingly leaned on Watson as one of its core growth units -- a unit that sits as a proxy for projecting IBM's future value. In the early days, IBM's competitive advantage was its longstanding relationships with Fortune 500 companies. IBM Watson effectively operates as a consultancy where the company engages in high-value contracts with corporates to implement Watson technology for specific business cases.