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.

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.

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.

AI is changing the field of synthetic biology and how we engineer biology. It's helping engineers design new ways to design genetic circuits -- and it could leave a remarkable impact on the future of humanity TVs and radios blare that "artificial intelligence is coming", and it will take your job and beat you at chess. But AI is already here, and it can beat you -- and the world's best -- at chess. In 2012, it was also used by Google to identify cats in YouTube videos. Today, it's the reason Teslas have Autopilot and Netflix and Spotify seem to "read your mind."

Acute kidney injury, or AKI, is a condition in which the kidneys stop filtering waste products from the blood. It occurs quickly (in two days or less) and debilitates an estimated 1 in 5 hospitalized patients in the U.K. and 1 in 4 hospitalized patients in the U.S. Worse still, because it's difficult to detect, AKI kills upwards of 600,000 people annually in both countries combined despite the more than $1.2 billion (£1 billion) the U.K.'s National Health Service (NHS) spends treating it each year. The U.K.-based AI research firm said it's made progress toward automated systems addressing the 11% of failures to detect AKI deterioration in U.S. hospitals and the 30% of preventable cases globally. Over the course of two separate joint studies conducted with the U.S. Department of Veterans Affairs and The Royal Free London NHS Foundation Trust (RFL), DeepMind's health care division -- DeepMind Health -- investigated ways to flag AKI warning signs clinicians might otherwise fail to spot. The resulting pair of papers published in the Journal of Medical Internet Research (JMIR) and Nature Digital Medicine reveal the fruit of the organizations' labor: an algorithm that can predict the presence of AKI up to 48 hours in advance and an app that cuts missed AKI cases from 12.4% to 3.3%.

UCL has launched an innovative strategy detailing how Artificial Intelligence (AI) should be used as a transformative technology to make a positive impact on the planet. AI for People and Planet was launched by Professor Geraint Rees – UCL's Pro-Vice-Provost of AI – at the Science Museum in London on Tuesday 10th September. It encapsulates the belief that the purpose of research and innovation in the sector is ultimately to benefit people and societies around the world and to have a positive impact on the planet. The launch event included a panel discussion around issues relating to AI in healthcare, society, ethics, economics and the current state of AI. As well as UCL experts, the panel also included Professor Thore Graepel, Research Group Lead of Google DeepMind and Azeem Azhar, board member of the Ada Lovelace Foundation, venture partner at Kindred Capital and advisor to Fabric Ventures.

Can I have your Attention please! The introduction of the Attention Mechanism in deep learning has improved the success of various models in recent years, and continues to be an omnipresent component in state-of-the-art models. Therefore, it is vital that we pay Attention to Attention and how it goes about achieving its effectiveness. In this article, I will be covering the main concepts behind Attention, including an implementation of a sequence-to-sequence Attention model, followed by the application of Attention in Transformers and how they can be used for state-of-the-art results. It is advised that you have some knowledge of Recurrent Neural Networks (RNNs) and their variants, or an understanding of how sequence-to-sequence models work.

Learn about how Artificial Intelligence is changing the world in this Unstructured Data Solutions special interview with Sofia the Robot. During the Magic of AI series Thomas Henson (UDS Senior Systems Engineer) sat down for Coffee with Sofia the Robot where the topic of AI came up. Sofia's thoughts on AI in the Enterprise and why she thinks Deep Learning is a major driver for AI adoption. To learn more at: https://bit.ly/2Hiwthx

What would an adventure game designed by the world's most dangerous A.I. look like? A neuroscience grad student is here to help you find out. Earlier this year, OpenAI, an A.I. startup once sponsored by Elon Musk, created a text-generating bot deemed too dangerous to ever release to the public. Called GPT-2, the algorithm was designed to generate text so humanlike that it could convincingly pass itself off as being written by a person. Feed it the start of a newspaper article, for instance, and it would dream up the rest, complete with imagined quotes.

CYBERSECURITY specialists have been betting on artificial intelligence (AI) to defend their organizations against sophisticated cyberattacks for quite a while now -- and it seems as though deep learning and machine learning have the potential to deliver. AI is a broad term that encompasses computer vision, machine learning, and deep learning, and generally offers the ability to mimic human actions, intelligently, and at incredible speed. For hackers trying to "guess" a password, it means AI can not only use "trial and error" to break into a victim's account much faster but also do it intelligently so that that the account doesn't get locked before the right password is guessed. On the other side of the fence, or network, cybersecurity professionals didn't immediately benefit from AI because systems in place don't automatically lend themselves to the technology -- however, experts bet on two niche elements of AI to find a solution. Those niche areas are machine learning and deep learning. Machine learning, simply put, is an algorithm that learns from a chunk of structured, labeled data to produce insights.