Machine translation data can be the potential equivalent to ImageNet in NLP. The authors try to prove this hypothesis by adopting the attentional sequence-to-sequence model trained for machine translation. These models usually contain an LSTM-based encoder. They make use of this encoder to obtain, what they call, the contextual vectors. This process involves two steps.

It aims to promote and develop friendly AI in a way to benefit humanity as a whole. It aims to "freely collaborate" by making its patents and research open to the public.

In this blog series, I've focused on how NetApp can help you streamline your artificial intelligence projects. With technologies and services for managing data everywhere, NetApp is well positioned to solve your AI data challenges. The post Top Five Things to Know About Data Storage for AI and Deep Learning appeared first on NetApp Blog.

To put that into perspective, experts at OpenAI recently developed Dactyl, a robotic hand that could handle objects. This is a task that any human child learns to perform subconsciously at an early age. But it took Dactyl 6,144 CPUs and 8 GPUs and about one hundred years' worth of experience to develop the same skills. While it is a fascinating achievement, it also highlights the stark differences between narrow AI and the way the human brain works.

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it's no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI. These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.

"With a total processing and interpretation time of 1.2 seconds, such a triage system can alert physicians to a critical finding that may otherwise remain in a queue for minutes to hours," says senior author Eric Oermann, MD, Instructor in the Department of Neurosurgery at the Icahn School of Medicine at Mount Sinai. "We're executing on the vision to develop artificial intelligence in medicine that will solve clinical problems and improve patient care." This is the first study to utilize artificial intelligence for detecting a wide range of acute neurologic events and to demonstrate a direct clinical application. Researchers used 37,236 head CT scans to train a deep neural network to identify whether an image contained critical or non-critical findings. The platform was then tested in a blinded, randomized controlled trial in a simulated clinical environment where it triaged head CT scans based on severity.

This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Links to individual lecture videos for the course: Lecture 1: Introduction to Deep Learning and Self-Driving Cars https://youtu.be/1L0TKZQcUtA Lecture 2: Deep Reinforcement Learning for Motion Planning https://youtu.be/QDzM8r3WgBw Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task https://youtu.be/U1toUkZw6VI Lecture 4: Recurrent Neural Networks for Steering through Time https://youtu.be/nFTQ7kHQWtc Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles https://youtu.be/ByZF8_-OJNI

The understanding of geographical reality is a process of data representation and pattern discovery. Former studies mainly adopted continuous-field models to represent spatial variables and to investigate the underlying spatial continuity/heterogeneity in the regular spatial domain. In this article, we introduce a more generalized model based on graph convolutional neural networks (GCNs) that can capture the complex parameters of spatial patterns underlying graph-structured spatial data, which generally contain both Euclidean spatial information and non-Euclidean feature information. A trainable semi-supervised prediction framework is proposed to model the spatial distribution patterns of intra-urban points of interest(POI) check-ins. This work demonstrates the feasibility of GCNs in complex geographic decision problems and provides a promising tool to analyze irregular spatial data.

From self-driving vehicles and back-flipping robots to virtual assistants who book our next appointment at the hair salon or at that restaurant for dinner - machine learning systems are becoming increasingly ubiquitous. The main reason for this is that these methods boast remarkable predictive capabilities. However, most of these models remain black boxes, meaning that it is very challenging for humans to follow and understand their intricate inner workings. Consequently, interpretability has suffered under this ever-increasing complexity of machine learning models. Especially with regards to new regulations, such as the General Data Protection Regulation (GDPR), the necessity for plausibility and verifiability of predictions made by these black boxes is indispensable. Driven by the needs of industry and practice, the research community has recognised this interpretability problem and focussed on developing a growing number of so-called explanation methods over the past few years. These methods explain individual predictions made by black box machine learning models and help to recover some of the lost interpretability. With the proliferation of these explanation methods, it is, however, often unclear, which explanation method offers a higher explanation quality, or is generally better-suited for the situation at hand. In this thesis, we thus propose an axiomatic framework, which allows comparing the quality of different explanation methods amongst each other. Through experimental validation, we find that the developed framework is useful to assess the explanation quality of different explanation methods and reach conclusions that are consistent with independent research.

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.