There's nothing new about worrying that superintelligent machines may endanger humanity, but the idea has lately become hard to avoid. A spurt of progress in artificial intelligence as well as comments by figures such as Bill Gates--who declared himself "in the camp that is concerned about superintelligence"--have given new traction to nightmare scenarios featuring supersmart software. Now two leading centers in the current AI boom are trying to bring discussion about the dangers of smart machines down to Earth. Google's DeepMind, the unit behind the company's artificial Go champion, and OpenAI, the nonprofit lab funded in part by Tesla's Elon Musk, have teamed up to make practical progress on a problem they argue has attracted too many headlines and too few practical ideas: How do you make smart software that doesn't go rogue? "If you're worried about bad things happening, the best thing we can do is study the relatively mundane things that go wrong in AI systems today," says Dario Amodei, a curly-haired researcher on OpenAI's small team working on AI safety.

AI is Confusing for many people. Centiment is making it easy to use deep learning for marketing, Join the Centiment team to check out the tool and get some much-needed insights on what's going on in the fields of deep learning and how it affects marketing. Yidi will be talking about some of the NLP work we do and how it relates to machine vision and the monetization of social content. Daron will be talking about the role of AI in Film.

There's nothing new about worrying that superintelligent machines may endanger humanity, but the idea has lately become hard to avoid. A spurt of progress in artificial intelligence as well as comments by figures such as Bill Gates--who declared himself "in the camp that is concerned about superintelligence"--have given new traction to nightmare scenarios featuring supersmart software. Now two leading centers in the current AI boom are trying to bring discussion about the dangers of smart machines down to Earth. Google DeepMind, the unit behind the company's artificial Go champion, and OpenAI, the nonprofit lab funded in part by Tesla's Elon Musk, have teamed up to make practical progress on a problem they argue has attracted too many headlines and too few practical ideas: How do you make smart software that doesn't go rogue? "If you're worried about bad things happening, the best thing we can do is study the relatively mundane things that go wrong in AI systems today," says Dario Amodei, a curly-haired researcher on OpenAI's small team working on AI safety.

Jason Yosinski sits in a small glass box at Uber's San Francisco, California, headquarters, pondering the mind of an artificial intelligence. An Uber research scientist, Yosinski is performing a kind of brain surgery on the AI running on his laptop. Like many of the AIs that will soon be powering so much of modern life, including self-driving Uber cars, Yosinski's program is a deep neural network, with an architecture loosely inspired by the brain. And like the brain, the program is hard to understand from the outside: It's a black box. This particular AI has been trained, using a vast sum of labeled images, to recognize objects as random as zebras, fire trucks, and seat belts. Could it recognize Yosinski and the reporter hovering in front of the webcam? Yosinski zooms in on one of the AI's individual computational nodes--the neurons, so to speak--to see what is prompting its response.

Concepts of Artificial Intelligence, Deep Learning and Artificial Neural Networks form the basis of many Machine Learning algorithms which can be used to simplify many real-world problems. Machine Learning aims at breaking the necessity to formulate long and complex programs and focuses on training the machine to learn from the training data sets. Though human-alike thinking and decision making is still very far, ML algorithms can be used in various scenarios to predict human behaviour which would otherwise had been impossible. Typical examples of the use of ML algorithms are, shopping recommendation in Amazon or Flipkart based on your previous buying decisions, suggestions from Netflix or Amazon prime depending on the genres of movies you have watched earlier, etc. New fields such as Predictive Analytics, Data Science etc are emerging from the transition to machine-based thinking and learning instead of human intervention. Of course, there are various algorithms to learn and measure and various ways to optimise them, but they come later.

Audio super-resolution aims to reconstruct a high-resolution audio waveform given a lower-resolution waveform as input. There are several potential applications for this type of upsampling in such areas as streaming audio and audio restoration. One traditional solution is to use a database of audio clips to fill in the missing frequencies in the downsampled waveform using a similarity metric (see this and this paper). Inspired by the successful applications of deep learning to image super-resolution, there is recent interest in using deep neural networks to accomplish this upsampling on raw audio waveforms. After prototyping several methods, I focused on implementing and customizing recently published research from the 2017 International Conference on Learning Representations (ICLR).

Today NVIDIA and Baidu today announced a broad partnership to bring the world's leading artificial intelligence technology to cloud computing, self-driving vehicles and AI home assistants. NVIDIA and Baidu have pioneered significant advances in deep learning and AI," said Ian Buck, NVIDIA vice president and general manager of accelerated computing. "We believe AI is the most powerful technology force of our time, with the potential to revolutionize every industry. Our collaboration aligns our exceptional technical resources to create AI computing platforms for all developers – from academic research, startups creating breakthrough AI applications, and autonomous vehicles." Speaking at Baidu's AI developer conference in Beijing, Baidu president and COO Lu Qi described his company's plans to work with NVIDIA to: Today, we are very excited to announce a broader and deeper strategic partnership with NVIDIA," said Lu Qi, Baidu president and COO, at Baidu Create 2017 in Beijing.

Recurrent Neural Networks (RNNs) are gaining a lot of attention in recent years because it has shown great promise in many natural language processing tasks. Despite their popularity, there are a limited number of tutorials which explain how to implement a simple and interesting application using the state-of-art tools. In this series, we will use a recurrent neural network to train an AI programmer, which can write Java code like a real programmer. This post shows the steps to construct an LSTM neural network and use it to generate Java code like a programmer. If you follow the post, running the code is just one click away.

This document comes from Keras Documentation. You have just found Keras. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This is inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specially, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.