But scientists are concerned about the consequences of robots being able to identify and kill people without human oversight. Demis Hassabis at Google DeepMind and founder of SpaceX, Elon Musk, are among 2,400 people who signed the pledge which aims to discourage governments from constructing killer AI. President of the Future of Life Institute, Max Tegmark, said in a statement: "I'm excited to see AI leaders shifting from talk to action, implementing a policy that politicians have thus far failed to put into effect.

A distributed file system, a MapReduce programming framework, and an extended family of tools for processing huge data sets on large clusters of commodity hardware, Hadoop has been synonymous with "big data" for more than a decade. But no technology can hold the spotlight forever. While Hadoop remains an essential part of the big data platforms, and the major Hadoop vendors--namely Cloudera, Hortonworks, and MapR--have changed their platforms dramatically. Once-peripheral projects like Apache Spark and Apache Kafka have become the new stars, and the focus has turned to other ways to drill into data and extract insight. Let's take a brief tour of the three leading big data platforms, what each adds to the mix of Hadoop technologies to set it apart, and how they are evolving to embrace a new era of containers, Kubernetes, machine learning, and deep learning.

Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as well as their relationships (i.e., edges) with each other. Many useful insights can be derived from graph-structured data as demonstrated by an ever-growing body of work focused on graph mining. However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to incorporate "attention" into graph mining solutions. An attention mechanism allows a method to focus on task-relevant parts of the graph, helping it to make better decisions. In this work, we conduct a comprehensive and focused survey of the literature on the emerging field of graph attention models. We introduce three intuitive taxonomies to group existing work. These are based on problem setting (type of input and output), the type of attention mechanism used, and the task (e.g., graph classification, link prediction, etc.). We motivate our taxonomies through detailed examples and use each to survey competing approaches from a unique standpoint. Finally, we highlight several challenges in the area and discuss promising directions for future work.

Machine comprehension question answering, which finds an answer to the question given a passage, involves high-level reasoning processes of understanding and tracking the relevant contents across various semantic units such as words, phrases, and sentences in a document. This paper proposes the novel question-aware sentence gating networks that directly incorporate the sentence-level information into word-level encoding processes. To this end, our model first learns question-aware sentence representations and then dynamically combines them with word-level representations, resulting in semantically meaningful word representations for QA tasks. Experimental results demonstrate that our approach consistently improves the accuracy over existing baseline approaches on various QA datasets and bears the wide applicability to other neural network-based QA models.

A vexing problem in artificial intelligence is reasoning about events that occur in complex, changing visual stimuli such as in video analysis or game play. Inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience, we developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures. We additionally propose a deep learning architecture that performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as easy settings of the COG dataset. However, several settings of COG result in datasets that are progressively more challenging to learn. After training, the network can zero-shot generalize to many new tasks. Preliminary analyses of the network architectures trained on COG demonstrate that the network accomplishes the task in a manner interpretable to humans.

ABSTRACT This paper addresses the problem of stylized text generation in a multilingual setup. A version of a language model based on a long short-term memory (LSTM) artificial neural network with extended phonetic and semantic embeddings is used for stylized poetry generation. Phonetics is shown to have comparable importance for the task of stylized poetry generation as the information on the target author. The quality of the resulting poems generated by the network is estimated through bilingual evaluation understudy (BLEU), a survey and a new cross-entropy based metric that is suggested for the problems of such type. The experiments show that the proposed model consistently outperforms random sample and vanilla-LSTM baselines, humans also tend to attribute machine generated texts to the target author. Index Terms-- stylized text generation, poetry generation, artificial neural networks, multilingual models 1. INTRODUCTION The problem of making machine-generated text feel more authentic has a number of industrial and scientific applications, see, for example, [1] or [2]. Most modern generative models are trained on huge corpora of texts which include different contributions from various authors.

In recent weeks DeepMind and OpenAI have each shared that they developed agents which can learn to complete the first level of the Atari 2600 game Montezuma's Revenge. These claims are important because Montezuma's Revenge is important. Unlike the vast majority of the games in the Arcade Learning Environment (ALE), which are now easily solved at superhuman level by learned agents, Montezuma's Revenge has been hitherto unsolved by Deep Reinforcement Learning methods and was thought by some to be unsolvable for years to come. What distinguishes Montezuma's Revenge from other games in the ALE is its relatively sparse rewards. For those unfamiliar, that means that the agent only receives reward signals after completing specific series of actions over extended periods of time.

Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2. While today we play with restrictions, we aim to beat a team of top professionals at The International in August subject only to a limited set of heroes. We may not succeed: Dota 2 is one of the most popular and complex esports games in the world, with creative and motivated professionals who train year-round to earn part of Dota's annual $40M prize pool (the largest of any esports game). OpenAI Five plays 180 years worth of games against itself every day, learning via self-play. It trains using a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores -- a larger-scale version of the system we built to play the much-simpler solo variant of the game last year. Using a separate LSTM for each hero and no human data, it learns recognizable strategies.

The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning. This blog is however not addressing the absolute beginner.

As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated--at least in the short term. The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM's Watson, Rethink Robotics' Baxter, DeepMind, or Google's driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines' ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.