PERFORMANCE


This Algorithm Can Detect Pneumonia More Accurately Than a Radiologist

#artificialintelligence

An algorithm developed by researchers at Stanford University proved more effective than human radiologists in diagnosing cases of pneumonia. Much research has been shared on the potential of Artificial Intelligence applied to medicine, and in some cases, can reach a level of accuracy that exceeds the performance of professionals. Following this line, Stanford researchers published a document on CheXNet, the convolutional neuronal network, which they developed with the ability to detect pneumonia symptoms. To do this, he uses the traditional method, chest radiographs. It works with 112,120 images of chest X-rays referring to 14 types of diseases.


A global collaboration to create "artificial organisms" just went live

#artificialintelligence

Mindfire, a new foundation with the goal of "decoding the mind" to help develop true artificial intelligence (AI) is launching November 17th in Zurich, Switzerland. Futurism spoke with the founder of Starmind and president of the foundation, Pascal Kaufmann to learn more about its goals and the path to reach them. "We cannot achieve True AI until we understand actual intelligence. Intelligence has evolved as a means of nature to successfully guide us through an ever-changing environment. This gave rise to behavior, emotions, and consciousness.


Machine Learning: A Complete and Detailed Overview

#artificialintelligence

Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. This is a summary (with links) to an article series that's intended to be a comprehensive, in-depth guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques in the field. The first chapter of the series starts with both a formal and informal definition of machine learning. This is followed by a discussion of the machine learning process end-to-end, the different types of machine learning, potential goals and outputs, and a categorized overview of the most widely used machine learning algorithms.


Causal Inference With pandas.DataFrames – adam kelleher – Medium

@machinelearnbot

I've been working on a causality package in Python with the aim of making causal inference really easy for data analysts and scientists. This weekend, I added a new feature (currently unreleased, but you can find it on master) that I think really achieves the goal of making causal inference based on conditioning easy for users at all skill levels. I wanted to share the feature, but I thought I should say a few words first about what I'm trying to do by making causal inference more accessible in the first place. Is this just adding fuel to the fire? If you'd like to skip the hand-waving and just see the new features, feel free to jump along to the next section.


Australia just landed its first high performance centre for esports

Mashable

Oceania's esports industry just took a huge step forward. Australia has opened its very first Esports High Performance Centre in Sydney, a new home base for Oceania's leading League of Legends team, the LG Dire Wolves. Established in Sydney's city sporting precinct, sitting in the side of Allianz Stadium looking towards the Sydney Cricket Ground, the facility aims to drive growth and development in Australia's esports industry. The facility will be stocked with new technology in eye-tracking and performance analysis, as part of a partnership with the University of Technology Sydney. The Dire Wolves, alongside Australia's leading mixed-gender Counter-Strike team, Supa-Stellar, will train and develop surrounded by some of Sydney's traditional sports teams, also residents of the precinct, including the Sydney Swans, Sydney Sixers, Sydney Roosters, Sydney FC, Cricket NSW, and the NSW Waratahs.


Which algorithm takes the crown: Light GBM vs XGBOOST?

@machinelearnbot

If you are an active member of the Machine Learning community, you must be aware of Boosting Machines and their capabilities. The development of Boosting Machines started from ADABOOST to today's favourite XGBOOST. XGBOOST has become a de-facto algorithm for winning competitions at Analytics Vidhya and Kaggle, simply because it is extremely powerful. But given lots and lots of data, even XGBOOST takes a long time to train. Many of you might not be familiar with the Light Gradient Boosting, but you will be after reading this article.


Komatsu Helps Improve Mining Performance with Industrial Internet of Things (IIoT) Platform Powered by Cloudera

#artificialintelligence

Cloudera, Inc. (NYSE: CLDR), the modern platform for machine learning and analytics, optimized for the cloud, announced that Komatsu, a leading global heavy equipment manufacturer, has implemented a cloud-based Industrial Internet of Things (IIoT) analytics platform powered by Cloudera Enterprise and Microsoft Azure. The platform enables Komatsu teams to help mining customers around the world continuously monitor the performance of some of the largest equipment used in surface and underground mining, increase asset utilization and productivity, and deliver essential resources including energy and industrial minerals for the global economy. Komatsu's JoySmart Solutions is an IIoT-based service that helps customers optimize machine performance using machine data and analytics. The JoySmart platform ingests, stores and processes a wide variety of data collected from mining equipment operating around the globe, often at very remote locations in harsh conditions. Types of equipment monitored includes longwall mining systems, electric rope shovels, continuous miners and wheel loaders.


IBM Introduces New Software to Ease Adoption of AI, Machine Learning and Deep Learning - insideBIGDATA

#artificialintelligence

IBM announced new software to deliver faster time to insight for high performance data analytics (HPDA) workloads, such as Spark, Tensor Flow and Caffé, for AI, Machine Learning and Deep Learning. Based on the same software, which will be deployed for the Department of Energy's CORAL Supercomputer Project at both Oak Ridge and Lawrence Livermore, IBM will enable new solutions for any enterprise running HPDA workloads. New to this launch is Deep Learning Impact (DLI), a set of software tools to help users develop AI models with the leading open source deep learning frameworks, like TensorFlow and Caffe. The DLI tools are complementary to the PowerAI deep learning enterprise software distribution already available from IBM. Also new is web access and simplified user interfaces for IBM Spectrum LSF Suites, combining a powerful workload management platform with the flexibility of remote access.


AIOps tools portend automated infrastructure management

#artificialintelligence

Automated infrastructure management took a step forward with the emergence of AIOps monitoring tools that use machine learning to proactively identify infrastructure problems. Orchestration tools are becoming increasingly popular as part of the DevOps process as they allow admins to focus on more critical tasks, rather than the routine steps it takes to move a workflow along. Our experts analyze the top solutions in the market, namely: Automic, Ayehu, BMC Control-M, CA, Cisco, IBM, Micro Focus, Microsoft, ServiceNow, and VMware. You forgot to provide an Email Address. This email address doesn't appear to be valid.


Lecture 14 Deep Reinforcement Learning

#artificialintelligence

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to maximize its reward. We discuss different algorithms for reinforcement learning including Q-Learning, policy gradients, and Actor-Critic. We show how deep reinforcement learning has been used to play Atari games and to achieve super-human Go performance in AlphaGo. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems.