Saudi Arabia oil facility attack launched from Iranian soil, US officials say

FOX News

President Trump says the U.S. is'locked and loaded' against the attackers of a key Saudi oil facility'depending on verification'; Mark Meredith reports. Cruise missiles and drones used in the weekend assault on Saudi Arabia's oil installations were launched from Iranian soil, U.S. officials told Fox News on Monday. The early morning strikes Saturday that hit Aramco's main crude processing facility knocked out 5.7 million barrels of daily oil production for Saudi Arabia -- or more than 5 percent of the world's daily crude production. Defense Secretary Mark Esper attended an emergency National Security Council meeting on Sunday at the White House along with Vice President Pence, where military options were discussed, officials told Fox News. Earlier Monday in Austria, Energy Secretary Rick Perry placed the blame for the attack squarely on Iran.

r/MachineLearning - [R] Neural Oblivious Decision Ensembles


TL;DR: authors propose a DenseNet-like ensemble of decision trees, trained end-to-end by backpropagation and beats both xgboost and neural networks on heterogeneous ("tabular") data. Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.

MXNet vs PyTorch: Comparison of the Deep Learning Frameworks


Deep learning rapidly grew in popularity as a subset of machine learning that learns through Artificial Neural Networks. Using the vast data, it educates its deep neural networks to attain better accuracy and results without a human programmer. Deep learning frameworks such as Caffe, Deeplearning4j, Keras, MXNet, PyTorch, and Tensorflow rely upon cuDNN, NCCL, DALI or other types of libraries for a high-performance multi-GPU accelerated training. NGC is a GPU-Optimized software hub that simplifies high-performance computing, deep learning, and machine learning structure and workflows. This is becoming a tremendous help to developers, researchers, and data scientists by eliminating the need to manage or build DL frameworks from the source.

Detecting Fraud in Real-Time using Unsupervised Machine Learning


With over 15 years of experience in Fraud and AML, and over 25 years of experience in financial services, Dan has helped countless customers solve their financial crime challenges in banking, insurance and government. Dan started out his career in IT, then moved to the business side to help business units implement technology. He then moved into Financial Crimes when consulting at TD Bank on their AML implementation in 2003. From there, Dan joined SAS and was the Fraud lead for Canada. Dan departed SAS for 5 years and gained further experience with TransUnion as the Director of Fraud and ID management.

One Step Closer to Human Intelligence - MIT CSAIL Combine Sight And Touch in AI


In more industrial situations, an AI system that can recognize different materials and grasp things more effectively without having to repeatedly try to pick up an object could bring new capabilities to a wide range of different processes and sectors. Handling extremely hazardous materials such as nuclear waste, for example, could be made far safer if a human were not required to control a robotic arm and a system could use image inputs to learn how best to pick up a container or even raw radioactive waste with a significantly reduced chance of dropping and spilling toxic material. In construction, autonomous lifting arms or those attached to vehicles could calculate the weight of an object based on its material and 3D images of, say, a steel girder. When digging or drilling to lay foundations, prepare a site, or laying underwater pipelines, ultrasonic images could be fed into the system and paired with tactile probe data to determine exactly where to drill in real-time without damaging existing infrastructure or delicate ecosystems.

Today we would like to introduce one of our virtual employees to you: Our data quality rule number 1297 - in short "DQR-1297".


Data quality rules are the hidden champions of data cleansing tools – these help improve the quality of the master data records in data quality tools. Based on different data quality dimensions, data stewards establish different quality rules to determine if a data set is of good or poor quality. Creating data quality rules can be very time consuming. An easy approach could be to adopt proven algorithms, which already work very well in the context of customer and vendor data within our data sharing community. Today, we would like to introduce one of our virtual employees to you: Our data quality rule number 1297;"DQR-1297", for short.

Extreme Networks Makes Investment to Develop Next-Generation Cloud and AI-based Network Solutions in Ireland


READING, England and SHANNON, Ireland, September 16, 2019 ― Extreme Networks, a software-driven networking company, announced today that it will invest 3 million Euro to expand its R&D program in Shannon, Ireland. The investment will create 20 new jobs in engineering, data science, and software engineering over the next two years, with the Irish government providing additional funding in the form of research, development, and innovation grant worth over 500,000 Euro. The newly established R&D team will focus on developing a next-generation cloud portal for network applications and services, as well as a state-of-the-art AI-based security system for the Internet of Things (IoT). The Shannon R&D base is part of a long-term strategy by Extreme Networks that will result in the creation of a substantial number of highly specialized engineering jobs in the region over the next five years. With the network industry currently experiencing clear shifts in spending patterns as enterprises focus more on applications and security over physical hardware, this support for the program in Shannon is the next step in building a new'Cloud and AI Centre of Excellence' for Extreme Networks.

The 10 most important moments in AI (so far)


This article is part of Fast Company's editorial series The New Rules of AI. More than 60 years into the era of artificial intelligence, the world's largest technology companies are just beginning to crack open what's possible with AI--and grapple with how it might change our future. Click here to read all the stories in the series. Artificial intelligence is still in its youth. But some very big things have already happened.