Created by researchers at the Massachusetts Institute of Technology (MIT) Media Lab, the AI author is powered by deep-learning algorithms that allow it to collaborate with budding human horror writers.

Over the past year, I've spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time. However, there doesn't seem to be a solid tutorial online on how to actually write unit tests for neural network code. Even places like OpenAI only found bugs by staring at every line of their code and try to think why it would cause a bug.

Back in June, an image generator that could turn even the crudest doodle of a face into a more realistic looking image made the rounds online. That system used a fairly new type of algorithm called a generative adversarial network (GAN) for its AI-created faces, and now, chipmaker NVIDIA has developed a system that employs a GAN to create far most realistic-looking images of people. Artificial neural networks are systems developed to mimic the activity of neurons in the human brain. In a GAN, two neural networks are essentially pitted against one another. One of the networks functions as a generative algorithm, while the other challenges the results of the first, playing an adversarial role. As part of their expanded applications for artificial intelligence, NVIDIA created a GAN that used CelebA-HQ's database of photos of famous people to generate images of people who don't actually exist.

MIT's new A.I. is another illustration of how spookily useful artificial intelligence can be in the creative process. If you want something really spooky to get you in the mood for Halloween, how about the prospect of machines which don't just carry out regular routinized work, but can actually be creative -- thereby performing a function we typically view as being quintessentially human? That's (kind of) what researchers at Massachusetts Institute of Technology (MIT) have developed with a new October 31-themed artificial intelligence project: The world's first collaborative A.I. horror writer. Anyone is welcome to reply to the tweet with the next installment of the story, thereby prompting Shelley to reply again with the next part. "Shelley is a deep learning-based A.I. that took her name [from] horror story writer, Mary Shelley," Pinar Yanardhag, one of the researchers on the project, told Digital Trends.

In recent artificial intelligence (AI) research, convolutional neural networks (CNNs) can create artificial agents capable of self-learning. Self-learning autonomous moving objects utilize machine vision techniques based on processing and recognizing objects in digital images. Afterwards, deep reinforcement learning (Deep-RL) is applied to understand and learn intelligent actions and controls. The objective of our research is to study methods and designs on how machine vision and deep machine learning algorithms can be implemented in a virtual world (e.g., a computer game) for moving objects (e.g., vehicles or aircrafts) to improve their navigation and detection of threats in real life. In this paper, we create a framework for generating and using data from computer games to be used in CNNs and Deep-RL to perform intelligent actions. We show the initial results of applying the framework and identify various military applications that may benefit from this research.

When working with robots it is very important that the robot understands the user. This is more difficult when the user is only able to speak to it. You do not want a robot to call for milk when the user said call for help. It is possible for a robot to get a clear understanding of the user in a lab environment where there is no noise or reverberation to distort the instructions. However, in a normal setting this is not always the case. We concentrate on speaker separation to improve speech recognition. To do this we use non-negative matrix factorisation (NMF) and deep learning techniques. For training and testing these techniques, we introduce a new corpus that is recorded with a microphone array. In this paper, we use different NMF and deep learning techniques for the speaker separation. We found that adding directional information improves the separation when there is no noise or reverberation. However, when reverberation is present we saw that the NMF technique with the Itakura-Saito cost function out performs the other techniques. With deep learning we found that a recurrent neural networks is able to perform the separation of the speakers.

Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is the key to close the gap, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph and geometric convolution baselines on benchmark datasets in multiple domains.

One of the main challenges of deep learning methods is the choice of an appropriate training strategy. In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures. In this article, we propose an extra training step, called post-training, which only optimizes the last layer of the network. We show that this procedure can be analyzed in the context of kernel theory, with the first layers computing an embedding of the data and the last layer a statistical model to solve the task based on this embedding. This step makes sure that the embedding, or representation, of the data is used in the best possible way for the considered task. This idea is then tested on multiple architectures with various data sets, showing that it consistently provides a boost in performance. One of the main challenges of the deep learning methods is to efficiently solve the highly complex and non-convex optimization problem involved in the training step.

NVIDIA just announced the NVIDIA GPU Cloud (NGC) -- a GPU-accelerated cloud platform that makes it easy to get started with the top deep learning frameworks on-premises or on Amazon Web Services. The cloud-based service is available immediately to users of the just-announced Amazon Elastic Compute Cloud (Amazon EC2) P3 instances featuring NVIDIA Tesla V100 GPUs. NVIDIA plans to expand support to other cloud platforms soon. "The NVIDIA GPU Cloud democratizes AI for a rapidly expanding global base of users," said Jim McHugh, vice president and general manager of Enterprise Systems at NVIDIA. "NGC frees developers from the complexity of integration, allowing them to move quickly to create sophisticated neural networks that deliver the transformative powers of AI."

Research[R] LSTM as a Dynamically Computed Element-wise Weighted Sum - reinterprets gating in LSTMs as self-attention over time (a weighted sum over the candidate states c _t); shows that dependence on h_t-1 for computing c _t is not necessary; thus gating does the heavy lifting in LSTMs, not h h mappings (openreview.net)